Assessment of Problem-Based Learning in the Undergraduate Statistics Course

ERIC Educational Resources Information Center

Karpiak, Christie P.

2011-01-01

Undergraduate psychology majors (N = 51) at a mid-sized private university took a statistics examination on the first day of the research methods course, a course for which a grade of "C" or higher in statistics is a prerequisite. Students who had taken a problem-based learning (PBL) section of the statistics course (n = 15) were compared to those…

Comparisons between physics-based, engineering, and statistical learning models for outdoor sound propagation.

PubMed

Hart, Carl R; Reznicek, Nathan J; Wilson, D Keith; Pettit, Chris L; Nykaza, Edward T

2016-05-01

Many outdoor sound propagation models exist, ranging from highly complex physics-based simulations to simplified engineering calculations, and more recently, highly flexible statistical learning methods. Several engineering and statistical learning models are evaluated by using a particular physics-based model, namely, a Crank-Nicholson parabolic equation (CNPE), as a benchmark. Narrowband transmission loss values predicted with the CNPE, based upon a simulated data set of meteorological, boundary, and source conditions, act as simulated observations. In the simulated data set sound propagation conditions span from downward refracting to upward refracting, for acoustically hard and soft boundaries, and low frequencies. Engineering models used in the comparisons include the ISO 9613-2 method, Harmonoise, and Nord2000 propagation models. Statistical learning methods used in the comparisons include bagged decision tree regression, random forest regression, boosting regression, and artificial neural network models. Computed skill scores are relative to sound propagation in a homogeneous atmosphere over a rigid ground. Overall skill scores for the engineering noise models are 0.6%, -7.1%, and 83.8% for the ISO 9613-2, Harmonoise, and Nord2000 models, respectively. Overall skill scores for the statistical learning models are 99.5%, 99.5%, 99.6%, and 99.6% for bagged decision tree, random forest, boosting, and artificial neural network regression models, respectively.

Application of pedagogy reflective in statistical methods course and practicum statistical methods

NASA Astrophysics Data System (ADS)

Julie, Hongki

2017-08-01

Subject Elementary Statistics, Statistical Methods and Statistical Methods Practicum aimed to equip students of Mathematics Education about descriptive statistics and inferential statistics. The students' understanding about descriptive and inferential statistics were important for students on Mathematics Education Department, especially for those who took the final task associated with quantitative research. In quantitative research, students were required to be able to present and describe the quantitative data in an appropriate manner, to make conclusions from their quantitative data, and to create relationships between independent and dependent variables were defined in their research. In fact, when students made their final project associated with quantitative research, it was not been rare still met the students making mistakes in the steps of making conclusions and error in choosing the hypothetical testing process. As a result, they got incorrect conclusions. This is a very fatal mistake for those who did the quantitative research. There were some things gained from the implementation of reflective pedagogy on teaching learning process in Statistical Methods and Statistical Methods Practicum courses, namely: 1. Twenty two students passed in this course and and one student did not pass in this course. 2. The value of the most accomplished student was A that was achieved by 18 students. 3. According all students, their critical stance could be developed by them, and they could build a caring for each other through a learning process in this course. 4. All students agreed that through a learning process that they undergo in the course, they can build a caring for each other.

A Meta-Analytic Review of Studies of the Effectiveness of Small-Group Learning Methods on Statistics Achievement

ERIC Educational Resources Information Center

Kalaian, Sema A.; Kasim, Rafa M.

2014-01-01

This meta-analytic study focused on the quantitative integration and synthesis of the accumulated pedagogical research in undergraduate statistics education literature. These accumulated research studies compared the academic achievement of students who had been instructed using one of the various forms of small-group learning methods to those who…

Teaching Introductory Business Statistics Using the DCOVA Framework

ERIC Educational Resources Information Center

Levine, David M.; Stephan, David F.

2011-01-01

Introductory business statistics students often receive little guidance on how to apply the methods they learn to further business objectives they may one day face. And those students may fail to see the continuity among the topics taught in an introductory course if they learn those methods outside a context that provides a unifying framework.…

A novel data-driven learning method for radar target detection in nonstationary environments

DOE PAGES

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

2016-04-12

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

An Empirical Consideration of a Balanced Amalgamation of Learning Strategies in Graduate Introductory Statistics Classes

ERIC Educational Resources Information Center

Vaughn, Brandon K.

2009-01-01

This study considers the effectiveness of a "balanced amalgamated" approach to teaching graduate level introductory statistics. Although some research stresses replacing traditional lectures with more active learning methods, the approach of this study is to combine effective lecturing with active learning and team projects. The results of this…

The Necessity of the Hippocampus for Statistical Learning

PubMed Central

Covington, Natalie V.; Brown-Schmidt, Sarah; Duff, Melissa C.

2018-01-01

Converging evidence points to a role for the hippocampus in statistical learning, but open questions about its necessity remain. Evidence for necessity comes from Schapiro and colleagues who report that a single patient with damage to hippocampus and broader medial temporal lobe cortex was unable to discriminate new from old sequences in several statistical learning tasks. The aim of the current study was to replicate these methods in a larger group of patients who have either damage localized to hippocampus or a broader medial temporal lobe damage, to ascertain the necessity of the hippocampus in statistical learning. Patients with hippocampal damage consistently showed less learning overall compared with healthy comparison participants, consistent with an emerging consensus for hippocampal contributions to statistical learning. Interestingly, lesion size did not reliably predict performance. However, patients with hippocampal damage were not uniformly at chance and demonstrated above-chance performance in some task variants. These results suggest that hippocampus is necessary for statistical learning levels achieved by most healthy comparison participants but significant hippocampal pathology alone does not abolish such learning. PMID:29308986

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

Effect of Internet-Based Cognitive Apprenticeship Model (i-CAM) on Statistics Learning among Postgraduate Students.

PubMed

Saadati, Farzaneh; Ahmad Tarmizi, Rohani; Mohd Ayub, Ahmad Fauzi; Abu Bakar, Kamariah

2015-01-01

Because students' ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is 'value added' because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students' problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students.

Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

PubMed

Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

2012-03-15

To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

Active learning methods for interactive image retrieval.

PubMed

Gosselin, Philippe Henri; Cord, Matthieu

2008-07-01

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

The Impact of Team-Based Learning on Nervous System Examination Knowledge of Nursing Students.

PubMed

Hemmati Maslakpak, Masomeh; Parizad, Naser; Zareie, Farzad

2015-12-01

Team-based learning is one of the active learning approaches in which independent learning is combined with small group discussion in the class. This study aimed to determine the impact of team-based learning in nervous system examination knowledge of nursing students. This quasi-experimental study was conducted on 3(rd) grade nursing students, including 5th semester (intervention group) and 6(th) semester (control group). The traditional lecture method and the team-based learning method were used for educating the examination of the nervous system for intervention and control groups, respectively. The data were collected by a test covering 40-questions (multiple choice, matching, gap-filling and descriptive questions) before and after intervention in both groups. Individual Readiness Assurance Test (RAT) and Group Readiness Assurance Test (GRAT) used to collect data in the intervention group. In the end, the collected data were analyzed by SPSS ver. 13 using descriptive and inferential statistical tests. In team-based learning group, mean and standard deviation was 13.39 (4.52) before the intervention, which had been increased to 31.07 (3.20) after the intervention and this increase was statistically significant. Also, there was a statistically significant difference between the scores of RAT and GRAT in team-based learning group. Using team-based learning approach resulted in much better improvement and stability in the nervous system examination knowledge of nursing students compared to traditional lecture method; therefore, this method could be efficiently used as an effective educational approach in nursing education.

Adult Learners' Preferred Methods of Learning Preventative Heart Disease Care

ERIC Educational Resources Information Center

Alavi, Nasim

2016-01-01

The purpose of this study was to investigate the preferred method of learning about heart disease by adult learners. This research study also investigated if there was a statistically significant difference between race/ethnicity, age, and gender of adult learners and their preferred method of learning preventative heart disease care. This…

Online Statistics Labs in MSW Research Methods Courses: Reducing Reluctance toward Statistics

ERIC Educational Resources Information Center

Elliott, William; Choi, Eunhee; Friedline, Terri

2013-01-01

This article presents results from an evaluation of an online statistics lab as part of a foundations research methods course for master's-level social work students. The article discusses factors that contribute to an environment in social work that fosters attitudes of reluctance toward learning and teaching statistics in research methods…

Finnish upper secondary students' collaborative processes in learning statistics in a CSCL environment

NASA Astrophysics Data System (ADS)

Kaleva Oikarinen, Juho; Järvelä, Sanna; Kaasila, Raimo

2014-04-01

This design-based research project focuses on documenting statistical learning among 16-17-year-old Finnish upper secondary school students (N = 78) in a computer-supported collaborative learning (CSCL) environment. One novel value of this study is in reporting the shift from teacher-led mathematical teaching to autonomous small-group learning in statistics. The main aim of this study is to examine how student collaboration occurs in learning statistics in a CSCL environment. The data include material from videotaped classroom observations and the researcher's notes. In this paper, the inter-subjective phenomena of students' interactions in a CSCL environment are analysed by using a contact summary sheet (CSS). The development of the multi-dimensional coding procedure of the CSS instrument is presented. Aptly selected video episodes were transcribed and coded in terms of conversational acts, which were divided into non-task-related and task-related categories to depict students' levels of collaboration. The results show that collaborative learning (CL) can facilitate cohesion and responsibility and reduce students' feelings of detachment in our classless, periodic school system. The interactive .pdf material and collaboration in small groups enable statistical learning. It is concluded that CSCL is one possible method of promoting statistical teaching. CL using interactive materials seems to foster and facilitate statistical learning processes.

The Effect of Project Based Learning on the Statistical Literacy Levels of Student 8th Grade

ERIC Educational Resources Information Center

Koparan, Timur; Güven, Bülent

2014-01-01

This study examines the effect of project based learning on 8th grade students' statistical literacy levels. A performance test was developed for this aim. Quasi-experimental research model was used in this article. In this context, the statistics were taught with traditional method in the control group and it was taught using project based…

The Effect on the 8th Grade Students' Attitude towards Statistics of Project Based Learning

ERIC Educational Resources Information Center

Koparan, Timur; Güven, Bülent

2014-01-01

This study investigates the effect of the project based learning approach on 8th grade students' attitude towards statistics. With this aim, an attitude scale towards statistics was developed. Quasi-experimental research model was used in this study. Following this model in the control group the traditional method was applied to teach statistics…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Akcakaya, Murat; Nehorai, Arye; Sen, Satyabrata

Most existing radar algorithms are developed under the assumption that the environment (clutter) is stationary. However, in practice, the characteristics of the clutter can vary enormously depending on the radar-operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. Therefore, to overcome such shortcomings, we develop a data-driven method for target detection in nonstationary environments. In this method, the radar dynamically detects changes in the environment and adapts to these changes by learning the new statistical characteristics of the environment and by intelligibly updating its statistical detection algorithm. Specifically, we employ drift detection algorithms to detectmore » changes in the environment; incremental learning, particularly learning under concept drift algorithms, to learn the new statistical characteristics of the environment from the new radar data that become available in batches over a period of time. The newly learned environment characteristics are then integrated in the detection algorithm. Furthermore, we use Monte Carlo simulations to demonstrate that the developed method provides a significant improvement in the detection performance compared with detection techniques that are not aware of the environmental changes.« less

"Flipping" the introductory clerkship in radiology: impact on medical student performance and perceptions.

PubMed

Belfi, Lily M; Bartolotta, Roger J; Giambrone, Ashley E; Davi, Caryn; Min, Robert J

2015-06-01

Among methods of "blended learning" (ie, combining online modules with in-class instruction), the "flipped classroom" involves student preclass review of material while reserving class time for interactive knowledge application. We integrated blended learning methodology in a "flipped" introductory clerkship in radiology, and assessed the impact of this approach on the student educational experience (performance and perception). In preparation for the "flipped clerkship," radiology faculty and residents created e-learning modules that were uploaded to an open-source website. The clerkship's 101 rising third-year medical students were exposed to different teaching methods during the course, such as blended learning, traditional lecture learning, and independent learning. Students completed precourse and postcourse knowledge assessments and surveys. Student knowledge improved overall as a result of taking the course. Blended learning achieved greater pretest to post-test improvement of high statistical significance (P value, .0060) compared to lecture learning alone. Blended learning also achieved greater pretest to post-test improvement of borderline statistical significance (P value, .0855) in comparison to independent learning alone. The difference in effectiveness of independent learning versus lecture learning was not statistically significant (P value, .2730). Student perceptions of the online modules used in blended learning portions of the course were very positive. They specifically enjoyed the self-paced interactivity and the ability to return to the modules in the future. Blended learning can be successfully applied to the introductory clerkship in radiology. This teaching method offers educators an innovative and efficient approach to medical student education in radiology. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Real-time Mainshock Forecast by Statistical Discrimination of Foreshock Clusters

NASA Astrophysics Data System (ADS)

Nomura, S.; Ogata, Y.

2016-12-01

Foreshock discremination is one of the most effective ways for short-time forecast of large main shocks. Though many large earthquakes accompany their foreshocks, discreminating them from enormous small earthquakes is difficult and only probabilistic evaluation from their spatio-temporal features and magnitude evolution may be available. Logistic regression is the statistical learning method best suited to such binary pattern recognition problems where estimates of a-posteriori probability of class membership are required. Statistical learning methods can keep learning discreminating features from updating catalog and give probabilistic recognition of forecast in real time. We estimated a non-linear function of foreshock proportion by smooth spline bases and evaluate the possibility of foreshocks by the logit function. In this study, we classified foreshocks from earthquake catalog by the Japan Meteorological Agency by single-link clustering methods and learned spatial and temporal features of foreshocks by the probability density ratio estimation. We use the epicentral locations, time spans and difference in magnitudes for learning and forecasting. Magnitudes of main shocks are also predicted our method by incorporating b-values into our method. We discuss the spatial pattern of foreshocks from the classifier composed by our model. We also implement a back test to validate predictive performance of the model by this catalog.

Diagnosis of students' ability in a statistical course based on Rasch probabilistic outcome

NASA Astrophysics Data System (ADS)

Mahmud, Zamalia; Ramli, Wan Syahira Wan; Sapri, Shamsiah; Ahmad, Sanizah

2017-06-01

Measuring students' ability and performance are important in assessing how well students have learned and mastered the statistical courses. Any improvement in learning will depend on the student's approaches to learning, which are relevant to some factors of learning, namely assessment methods carrying out tasks consisting of quizzes, tests, assignment and final examination. This study has attempted an alternative approach to measure students' ability in an undergraduate statistical course based on the Rasch probabilistic model. Firstly, this study aims to explore the learning outcome patterns of students in a statistics course (Applied Probability and Statistics) based on an Entrance-Exit survey. This is followed by investigating students' perceived learning ability based on four Course Learning Outcomes (CLOs) and students' actual learning ability based on their final examination scores. Rasch analysis revealed that students perceived themselves as lacking the ability to understand about 95% of the statistics concepts at the beginning of the class but eventually they had a good understanding at the end of the 14 weeks class. In terms of students' performance in their final examination, their ability in understanding the topics varies at different probability values given the ability of the students and difficulty of the questions. Majority found the probability and counting rules topic to be the most difficult to learn.

Effect of Internet-Based Cognitive Apprenticeship Model (i-CAM) on Statistics Learning among Postgraduate Students

PubMed Central

Saadati, Farzaneh; Ahmad Tarmizi, Rohani

2015-01-01

Because students’ ability to use statistics, which is mathematical in nature, is one of the concerns of educators, embedding within an e-learning system the pedagogical characteristics of learning is ‘value added’ because it facilitates the conventional method of learning mathematics. Many researchers emphasize the effectiveness of cognitive apprenticeship in learning and problem solving in the workplace. In a cognitive apprenticeship learning model, skills are learned within a community of practitioners through observation of modelling and then practice plus coaching. This study utilized an internet-based Cognitive Apprenticeship Model (i-CAM) in three phases and evaluated its effectiveness for improving statistics problem-solving performance among postgraduate students. The results showed that, when compared to the conventional mathematics learning model, the i-CAM could significantly promote students’ problem-solving performance at the end of each phase. In addition, the combination of the differences in students' test scores were considered to be statistically significant after controlling for the pre-test scores. The findings conveyed in this paper confirmed the considerable value of i-CAM in the improvement of statistics learning for non-specialized postgraduate students. PMID:26132553

A comparative study on effect of e-learning and instructor-led methods on nurses’ documentation competency

PubMed Central

Abbaszadeh, Abbas; Sabeghi, Hakimeh; Borhani, Fariba; Heydari, Abbas

2011-01-01

BACKGROUND: Accurate recording of the nursing care indicates the care performance and its quality, so that, any failure in documentation can be a reason for inadequate patient care. Therefore, improving nurses’ skills in this field using effective educational methods is of high importance. Since traditional teaching methods are not suitable for communities with rapid knowledge expansion and constant changes, e-learning methods can be a viable alternative. To show the importance of e-learning methods on nurses’ care reporting skills, this study was performed to compare the e-learning methods with the traditional instructor-led methods. METHODS: This was a quasi-experimental study aimed to compare the effect of two teaching methods (e-learning and lecture) on nursing documentation and examine the differences in acquiring competency on documentation between nurses who participated in the e-learning (n = 30) and nurses in a lecture group (n = 31). RESULTS: The results of the present study indicated that statistically there was no significant difference between the two groups. The findings also revealed that statistically there was no significant correlation between the two groups toward demographic variables. However, we believe that due to benefits of e-learning against traditional instructor-led method, and according to their equal effect on nurses’ documentation competency, it can be a qualified substitute for traditional instructor-led method. CONCLUSIONS: E-learning as a student-centered method as well as lecture method equally promote competency of the nurses on documentation. Therefore, e-learning can be used to facilitate the implementation of nursing educational programs. PMID:22224113

Improving Education in Medical Statistics: Implementing a Blended Learning Model in the Existing Curriculum.

PubMed

Milic, Natasa M; Trajkovic, Goran Z; Bukumiric, Zoran M; Cirkovic, Andja; Nikolic, Ivan M; Milin, Jelena S; Milic, Nikola V; Savic, Marko D; Corac, Aleksandar M; Marinkovic, Jelena M; Stanisavljevic, Dejana M

2016-01-01

Although recent studies report on the benefits of blended learning in improving medical student education, there is still no empirical evidence on the relative effectiveness of blended over traditional learning approaches in medical statistics. We implemented blended along with on-site (i.e. face-to-face) learning to further assess the potential value of web-based learning in medical statistics. This was a prospective study conducted with third year medical undergraduate students attending the Faculty of Medicine, University of Belgrade, who passed (440 of 545) the final exam of the obligatory introductory statistics course during 2013-14. Student statistics achievements were stratified based on the two methods of education delivery: blended learning and on-site learning. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Mean exam scores for the blended learning student group were higher than for the on-site student group for both final statistics score (89.36±6.60 vs. 86.06±8.48; p = 0.001) and knowledge test score (7.88±1.30 vs. 7.51±1.36; p = 0.023) with a medium effect size. There were no differences in sex or study duration between the groups. Current grade point average (GPA) was higher in the blended group. In a multivariable regression model, current GPA and knowledge test scores were associated with the final statistics score after adjusting for study duration and learning modality (p<0.001). This study provides empirical evidence to support educator decisions to implement different learning environments for teaching medical statistics to undergraduate medical students. Blended and on-site training formats led to similar knowledge acquisition; however, students with higher GPA preferred the technology assisted learning format. Implementation of blended learning approaches can be considered an attractive, cost-effective, and efficient alternative to traditional classroom training in medical statistics.

A comparative study on effect of e-learning and instructor-led methods on nurses' documentation competency.

PubMed

Abbaszadeh, Abbas; Sabeghi, Hakimeh; Borhani, Fariba; Heydari, Abbas

2011-01-01

Accurate recording of the nursing care indicates the care performance and its quality, so that, any failure in documentation can be a reason for inadequate patient care. Therefore, improving nurses' skills in this field using effective educational methods is of high importance. Since traditional teaching methods are not suitable for communities with rapid knowledge expansion and constant changes, e-learning methods can be a viable alternative. To show the importance of e-learning methods on nurses' care reporting skills, this study was performed to compare the e-learning methods with the traditional instructor-led methods. This was a quasi-experimental study aimed to compare the effect of two teaching methods (e-learning and lecture) on nursing documentation and examine the differences in acquiring competency on documentation between nurses who participated in the e-learning (n = 30) and nurses in a lecture group (n = 31). The results of the present study indicated that statistically there was no significant difference between the two groups. The findings also revealed that statistically there was no significant correlation between the two groups toward demographic variables. However, we believe that due to benefits of e-learning against traditional instructor-led method, and according to their equal effect on nurses' documentation competency, it can be a qualified substitute for traditional instructor-led method. E-learning as a student-centered method as well as lecture method equally promote competency of the nurses on documentation. Therefore, e-learning can be used to facilitate the implementation of nursing educational programs.

Multi-fidelity machine learning models for accurate bandgap predictions of solids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

Multi-fidelity machine learning models for accurate bandgap predictions of solids

DOE PAGES

Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

2016-12-28

Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

The impact of the learning contract on self-directed learning and satisfaction in nursing students in a clinical setting.

PubMed

Sajadi, Mahboobeh; Fayazi, Neda; Fournier, Andrew; Abedi, Ahmad Reza

2017-01-01

Background: The most important responsibilities of an education system are to create self-directed learning opportunities and develop the required skills for taking the responsibility for change. The present study aimed at determining the impact of a learning contract on self-directed learning and satisfaction of nursing students. Methods: A total of 59 nursing students participated in this experimental study. They were divided into six 10-member groups. To control the communications among the groups, the first 3 groups were trained using conventional learning methods and the second 3 groups using learning contract method. In the first session, a pretest was performed based on educational objectives. At the end of the training, the students in each group completed the questionnaires of self-directed learning and satisfaction. The results of descriptive and inferential statistical methods (dependent and independent t tests) were presented using SPSS. Results: There were no significant differences between the 2 groups in gender, grade point average of previous years, and interest toward nursing. However, the results revealed a significant difference between the 2 groups in the total score of self-directed learning (p= 0.019). Although the mean satisfaction score was higher in the intervention group, the difference was not statistically significant. Conclusion: This study suggested that the use of learning contract method in clinical settings enhances self-directed learning among nursing students. Because this model focuses on individual differences, the researcher highly recommends the application of this new method to educators.

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Counting Better? An Examination of the Impact of Quantitative Method Teaching on Statistical Anxiety and Confidence

ERIC Educational Resources Information Center

Chamberlain, John Martyn; Hillier, John; Signoretta, Paola

2015-01-01

This article reports the results of research concerned with students' statistical anxiety and confidence to both complete and learn to complete statistical tasks. Data were collected at the beginning and end of a quantitative method statistics module. Students recognised the value of numeracy skills but felt they were not necessarily relevant for…

Machine Learning Approaches for Clinical Psychology and Psychiatry.

PubMed

Dwyer, Dominic B; Falkai, Peter; Koutsouleris, Nikolaos

2018-05-07

Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice given its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.

A Web-Based Learning Tool Improves Student Performance in Statistics: A Randomized Masked Trial

ERIC Educational Resources Information Center

Gonzalez, Jose A.; Jover, Lluis; Cobo, Erik; Munoz, Pilar

2010-01-01

Background: e-status is a web-based tool able to generate different statistical exercises and to provide immediate feedback to students' answers. Although the use of Information and Communication Technologies (ICTs) is becoming widespread in undergraduate education, there are few experimental studies evaluating its effects on learning. Method: All…

Difficulties in Learning and Teaching Statistics: Teacher Views

ERIC Educational Resources Information Center

Koparan, Timur

2015-01-01

The purpose of this study is to define teacher views about the difficulties in learning and teaching middle school statistics subjects. To serve this aim, a number of interviews were conducted with 10 middle school maths teachers in 2011-2012 school year in the province of Trabzon. Of the qualitative descriptive research methods, the…

Integrating the ACR Appropriateness Criteria Into the Radiology Clerkship: Comparison of Didactic Format and Group-Based Learning.

PubMed

Stein, Marjorie W; Frank, Susan J; Roberts, Jeffrey H; Finkelstein, Malka; Heo, Moonseong

2016-05-01

The aim of this study was to determine whether group-based or didactic teaching is more effective to teach ACR Appropriateness Criteria to medical students. An identical pretest, posttest, and delayed multiple-choice test was used to evaluate the efficacy of the two teaching methods. Descriptive statistics comparing test scores were obtained. On the posttest, the didactic group gained 12.5 points (P < .0001), and the group-based learning students gained 16.3 points (P < .0001). On the delayed test, the didactic group gained 14.4 points (P < .0001), and the group-based learning students gained 11.8 points (P < .001). The gains in scores on both tests were statistically significant for both groups. However, the differences in scores were not statistically significant comparing the two educational methods. Compared with didactic lectures, group-based learning is more enjoyable, time efficient, and equally efficacious. The choice of educational method can be individualized for each institution on the basis of group size, time constraints, and faculty availability. Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Learning Opportunities for Group Learning

ERIC Educational Resources Information Center

Gil, Alfonso J.; Mataveli, Mara

2017-01-01

Purpose: This paper aims to analyse the impact of organizational learning culture and learning facilitators in group learning. Design/methodology/approach: This study was conducted using a survey method applied to a statistically representative sample of employees from Rioja wine companies in Spain. A model was tested using a structural equation…

Statistical and Machine Learning forecasting methods: Concerns and ways forward

PubMed Central

Makridakis, Spyros; Assimakopoulos, Vassilios

2018-01-01

Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions. PMID:29584784

Students' Perceptions of Statistics: An Exploration of Attitudes, Conceptualizations, and Content Knowledge of Statistics

ERIC Educational Resources Information Center

Bond, Marjorie E.; Perkins, Susan N.; Ramirez, Caroline

2012-01-01

Although statistics education research has focused on students' learning and conceptual understanding of statistics, researchers have only recently begun investigating students' perceptions of statistics. The term perception describes the overlap between cognitive and non-cognitive factors. In this mixed-methods study, undergraduate students…

Evidence for social learning in wild lemurs (Lemur catta).

PubMed

Kendal, Rachel L; Custance, Deborah M; Kendal, Jeremy R; Vale, Gillian; Stoinski, Tara S; Rakotomalala, Nirina Lalaina; Rasamimanana, Hantanirina

2010-08-01

Interest in social learning has been fueled by claims of culture in wild animals. These remain controversial because alternative explanations to social learning, such as asocial learning or ecological differences, remain difficult to refute. Compared with laboratory-based research, the study of social learning in natural contexts is in its infancy. Here, for the first time, we apply two new statistical methods, option-bias analysis and network-based diffusion analysis, to data from the wild, complemented by standard inferential statistics. Contrary to common thought regarding the cognitive abilities of prosimian primates, our evidence is consistent with social learning within subgroups in the ring-tailed lemur (Lemur catta), supporting the theory of directed social learning (Coussi-Korbel & Fragaszy, 1995). We also caution that, as the toolbox for capturing social learning in natural contexts grows, care is required in ensuring that the methods employed are appropriate-in particular, regarding social dynamics among study subjects. Supplemental materials for this article may be downloaded from http://lb.psychonomic-journals.org/content/supplemental.

Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

NASA Astrophysics Data System (ADS)

Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

2017-12-01

Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

Regularized learning of linear ordered-statistic constant false alarm rate filters (Conference Presentation)

NASA Astrophysics Data System (ADS)

Havens, Timothy C.; Cummings, Ian; Botts, Jonathan; Summers, Jason E.

2017-05-01

The linear ordered statistic (LOS) is a parameterized ordered statistic (OS) that is a weighted average of a rank-ordered sample. LOS operators are useful generalizations of aggregation as they can represent any linear aggregation, from minimum to maximum, including conventional aggregations, such as mean and median. In the fuzzy logic field, these aggregations are called ordered weighted averages (OWAs). Here, we present a method for learning LOS operators from training data, viz., data for which you know the output of the desired LOS. We then extend the learning process with regularization, such that a lower complexity or sparse LOS can be learned. Hence, we discuss what 'lower complexity' means in this context and how to represent that in the optimization procedure. Finally, we apply our learning methods to the well-known constant-false-alarm-rate (CFAR) detection problem, specifically for the case of background levels modeled by long-tailed distributions, such as the K-distribution. These backgrounds arise in several pertinent imaging problems, including the modeling of clutter in synthetic aperture radar and sonar (SAR and SAS) and in wireless communications.

Safe semi-supervised learning based on weighted likelihood.

PubMed

Kawakita, Masanori; Takeuchi, Jun'ichi

2014-05-01

We are interested in developing a safe semi-supervised learning that works in any situation. Semi-supervised learning postulates that n(') unlabeled data are available in addition to n labeled data. However, almost all of the previous semi-supervised methods require additional assumptions (not only unlabeled data) to make improvements on supervised learning. If such assumptions are not met, then the methods possibly perform worse than supervised learning. Sokolovska, Cappé, and Yvon (2008) proposed a semi-supervised method based on a weighted likelihood approach. They proved that this method asymptotically never performs worse than supervised learning (i.e., it is safe) without any assumption. Their method is attractive because it is easy to implement and is potentially general. Moreover, it is deeply related to a certain statistical paradox. However, the method of Sokolovska et al. (2008) assumes a very limited situation, i.e., classification, discrete covariates, n(')→∞ and a maximum likelihood estimator. In this paper, we extend their method by modifying the weight. We prove that our proposal is safe in a significantly wide range of situations as long as n≤n('). Further, we give a geometrical interpretation of the proof of safety through the relationship with the above-mentioned statistical paradox. Finally, we show that the above proposal is asymptotically safe even when n(')

Effects of Learning Style and Training Method on Computer Attitude and Performance in World Wide Web Page Design Training.

ERIC Educational Resources Information Center

Chou, Huey-Wen; Wang, Yu-Fang

1999-01-01

Compares the effects of two training methods on computer attitude and performance in a World Wide Web page design program in a field experiment with high school students in Taiwan. Discusses individual differences, Kolb's Experiential Learning Theory and Learning Style Inventory, Computer Attitude Scale, and results of statistical analyses.…

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

PubMed

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-06-17

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

PubMed Central

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-01-01

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

Comparison of the effect of lecture and blended teaching methods on students’ learning and satisfaction

PubMed Central

SADEGHI, ROYA; SEDAGHAT, MOHAMMAD MEHDI; SHA AHMADI, FARAMARZ

2014-01-01

Introduction: Blended learning, a new approach in educational planning, is defined as an applying more than one method, strategy, technique or media in education. Todays, due to the development of infrastructure of Internet networks and the access of most of the students, the Internet can be utilized along with traditional and conventional methods of training. The aim of this study was to compare the students’ learning and satisfaction in combination of lecture and e-learning with conventional lecture methods. Methods: This quasi-experimental study is conducted among the sophomore students of Public Health School, Tehran University of Medical Science in 2012-2013. Four classes of the school are randomly selected and are divided into two groups. Education in two classes (45 students) was in the form of lecture method and in the other two classes (48 students) was blended method with e-Learning and lecture methods. The students’ knowledge about tuberculosis in two groups was collected and measured by using pre and post-test. This step has been done by sending self-reported electronic questionnaires to the students' email addresses through Google Document software. At the end of educational programs, students' satisfaction and comments about two methods were also collected by questionnaires. Statistical tests such as descriptive methods, paired t-test, independent t-test and ANOVA were done through the SPSS 14 software, and p≤0.05 was considered as significant difference. Results: The mean scores of the lecture and blended groups were 13.18±1.37 and 13.35±1.36, respectively; the difference between the pre-test scores of the two groups was not statistically significant (p=0.535). Knowledge scores increased in both groups after training, and the mean and standard deviation of knowledge scores of the lectures and combined groups were 16.51±0.69 and 16.18±1.06, respectively. The difference between the post-test scores of the two groups was not statistically significant (p=0.112). Students’ satisfaction in blended learning method was higher than lecture method. Conclusion: The results revealed that the blended method is effective in increasing the students' learning rate. E-learning can be used to teach some courses and might be considered as economic aspects. Since in universities of medical sciences in the country, the majority of students have access to the Internet and email address, using e-learning could be used as a supplement to traditional teaching methods or sometimes as educational alternative method because this method of teaching increases the students’ knowledge, satisfaction and attention. PMID:25512938

Machine learning for neuroimaging with scikit-learn.

PubMed

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Machine learning for neuroimaging with scikit-learn

PubMed Central

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain. PMID:24600388

Predicting Acquisition of Learning Outcomes: A Comparison of Traditional and Activity-Based Instruction in an Introductory Statistics Course.

ERIC Educational Resources Information Center

Geske, Jenenne A.; Mickelson, William T.; Bandalos, Deborah L.; Jonson, Jessica; Smith, Russell W.

The bulk of experimental research related to reforms in the teaching of statistics concentrates on the effects of alternative teaching methods on statistics achievement. This study expands on that research by including an examination of the effects of instructor and the interaction between instructor and method on achievement as well as attitudes,…

Comparison of the effect of lecture and blended teaching methods on students' learning and satisfaction.

PubMed

Sadeghi, Roya; Sedaghat, Mohammad Mehdi; Sha Ahmadi, Faramarz

2014-10-01

Blended learning, a new approach in educational planning, is defined as an applying more than one method, strategy, technique or media in education. Todays, due to the development of infrastructure of Internet networks and the access of most of the students, the Internet can be utilized along with traditional and conventional methods of training. The aim of this study was to compare the students' learning and satisfaction in combination of lecture and e-learning with conventional lecture methods. This quasi-experimental study is conducted among the sophomore students of Public Health School, Tehran University of Medical Science in 2012-2013. Four classes of the school are randomly selected and are divided into two groups. Education in two classes (45 students) was in the form of lecture method and in the other two classes (48 students) was blended method with e-Learning and lecture methods. The students' knowledge about tuberculosis in two groups was collected and measured by using pre and post-test. This step has been done by sending self-reported electronic questionnaires to the students' email addresses through Google Document software. At the end of educational programs, students' satisfaction and comments about two methods were also collected by questionnaires. Statistical tests such as descriptive methods, paired t-test, independent t-test and ANOVA were done through the SPSS 14 software, and p≤0.05 was considered as significant difference. The mean scores of the lecture and blended groups were 13.18±1.37 and 13.35±1.36, respectively; the difference between the pre-test scores of the two groups was not statistically significant (p=0.535). Knowledge scores increased in both groups after training, and the mean and standard deviation of knowledge scores of the lectures and combined groups were 16.51±0.69 and 16.18±1.06, respectively. The difference between the post-test scores of the two groups was not statistically significant (p=0.112). Students' satisfaction in blended learning method was higher than lecture method. The results revealed that the blended method is effective in increasing the students' learning rate. E-learning can be used to teach some courses and might be considered as economic aspects. Since in universities of medical sciences in the country, the majority of students have access to the Internet and email address, using e-learning could be used as a supplement to traditional teaching methods or sometimes as educational alternative method because this method of teaching increases the students' knowledge, satisfaction and attention.

The Novel Quantitative Technique for Assessment of Gait Symmetry Using Advanced Statistical Learning Algorithm

PubMed Central

Wu, Jianning; Wu, Bin

2015-01-01

The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis. PMID:25705672

The novel quantitative technique for assessment of gait symmetry using advanced statistical learning algorithm.

PubMed

Wu, Jianning; Wu, Bin

2015-01-01

The accurate identification of gait asymmetry is very beneficial to the assessment of at-risk gait in the clinical applications. This paper investigated the application of classification method based on statistical learning algorithm to quantify gait symmetry based on the assumption that the degree of intrinsic change in dynamical system of gait is associated with the different statistical distributions between gait variables from left-right side of lower limbs; that is, the discrimination of small difference of similarity between lower limbs is considered the reorganization of their different probability distribution. The kinetic gait data of 60 participants were recorded using a strain gauge force platform during normal walking. The classification method is designed based on advanced statistical learning algorithm such as support vector machine algorithm for binary classification and is adopted to quantitatively evaluate gait symmetry. The experiment results showed that the proposed method could capture more intrinsic dynamic information hidden in gait variables and recognize the right-left gait patterns with superior generalization performance. Moreover, our proposed techniques could identify the small significant difference between lower limbs when compared to the traditional symmetry index method for gait. The proposed algorithm would become an effective tool for early identification of the elderly gait asymmetry in the clinical diagnosis.

Students perception on the usage of PowerPoint in learning calculus

NASA Astrophysics Data System (ADS)

Othman, Zarith Sofiah; Tarmuji, Nor Habibah; Hilmi, Zulkifli Ab Ghani

2017-04-01

Mathematics is a core subject in most of the science and technology courses and in some social sciences programs. However, the low achievement of students in the subject especially in topics such as Differentiation and Integration is always an issue. Many factors contribute to the low performance such as motivation, environment, method of learning, academic background and others. The purpose of this paper is to determine the perception of learning mathematics using PowerPoint on Integration concepts at the undergraduate level with respect to mathematics anxiety, learning enjoyment, mobility and learning satisfaction. The main content of the PowerPoint presentation focused on the integration method with historical elements as an added value. The study was conducted on 48 students randomly selected from students in computer and applied sciences program as experimental group. Questionnaires were distributed to students to explore their learning experiences. Another 51 students who were taught using the traditional chalkboard method were used as the control group. Both groups were given a test on Integration. The statistical methods used were descriptive statistics and independent sample t-test between the experimental and the control group. The finding showed that most students perceived positively to the PowerPoint presentations with respect to mobility and learning satisfaction. The experimental group performed better than the control group.

Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis

PubMed Central

Tabelow, Karsten; König, Reinhard; Polzehl, Jörg

2016-01-01

Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning. PMID:27303809

Medical students preference of problem-based learning or traditional lectures in King Abdulaziz University, Jeddah, Saudi Arabia.

PubMed

Ibrahim, Nahla Khamis; Banjar, Shorooq; Al-Ghamdi, Amal; Al-Darmasi, Moroj; Khoja, Abeer; Turkistani, Jamela; Arif, Rwan; Al-Sebyani, Awatif; Musawa, Al-Anoud; Basfar, Wijdan

2014-01-01

Problem-based learning (PBL) is the most important educational innovations in the past 4 decades. The objective of the study was to compare between the preference of medical students for PBL and the preference for traditional lectures regarding learning outcomes (e.g., knowledge, attitude, and skills) gained from both methods. A cross-sectional study was conducted among medical students who studied the hybrid curriculum (PBL and traditional lectures) in King Abdulaziz University, Jeddah, in 2011. Data was collected through a pre-constructed, validated, confidentially anonymous, and self-administered questionnaire. Students' perceptions toward PBL and traditional lectures were assessed through their response to 20 statements inquired about both methods of learning using a five-point Likert scale. Descriptive and analytic statistics were performed using SPSS, version 21 (SPSS Inc, Chicago, Ill., USA). Learners preferred PBL more to traditional lectures for better linking the knowledge of basic and clinical sciences (t test=10.15, P < .001). However, no statistical significant difference (P > .05) was observed regarding the amount of basic knowledge recalled from both methods. Students preferred PBL more to lectures for better learning attitudes, skills, future outcomes, and learning satisfaction (P < .05). PBL motivates students to learn better than lecturing (P < .05). From students' opinion, the mean total skill gained from PBL (47.2 [10.6]) was much higher than that of lectures (33.0 [9.9]), and a highly statistical significant difference was observed (t test=20.9, P < .001). Students preferred PBL more to traditional lectures for improving most of learning outcome domains, especially, learning attitudes and skills. Introducing hybrid-PBL curriculum in all Saudi universities is highly recommended.

Identifying Social Learning in Animal Populations: A New ‘Option-Bias’ Method

PubMed Central

Kendal, Rachel L.; Kendal, Jeremy R.; Hoppitt, Will; Laland, Kevin N.

2009-01-01

Background Studies of natural animal populations reveal widespread evidence for the diffusion of novel behaviour patterns, and for intra- and inter-population variation in behaviour. However, claims that these are manifestations of animal ‘culture’ remain controversial because alternative explanations to social learning remain difficult to refute. This inability to identify social learning in social settings has also contributed to the failure to test evolutionary hypotheses concerning the social learning strategies that animals deploy. Methodology/Principal Findings We present a solution to this problem, in the form of a new means of identifying social learning in animal populations. The method is based on the well-established premise of social learning research, that - when ecological and genetic differences are accounted for - social learning will generate greater homogeneity in behaviour between animals than expected in its absence. Our procedure compares the observed level of homogeneity to a sampling distribution generated utilizing randomization and other procedures, allowing claims of social learning to be evaluated according to consensual standards. We illustrate the method on data from groups of monkeys provided with novel two-option extractive foraging tasks, demonstrating that social learning can indeed be distinguished from unlearned processes and asocial learning, and revealing that the monkeys only employed social learning for the more difficult tasks. The method is further validated against published datasets and through simulation, and exhibits higher statistical power than conventional inferential statistics. Conclusions/Significance The method is potentially a significant technological development, which could prove of considerable value in assessing the validity of claims for culturally transmitted behaviour in animal groups. It will also be of value in enabling investigation of the social learning strategies deployed in captive and natural animal populations. PMID:19657389

Changing Attitudes and Facilitating Understanding in the Undergraduate Statistics Classroom: A Collaborative Learning Approach

ERIC Educational Resources Information Center

Curran, Erin; Carlson, Kerri; Celotta, Dayius Turvold

2013-01-01

Collaborative and problem-based learning strategies are theorized to be effective methods for strengthening undergraduate science, technology, engineering, and mathematics education. Peer-Led Team Learning (PLTL) is a collaborative learning technique that engages students in problem solving and discussion under the guidance of a trained peer…

Student Attitudes to Learning Business Statistics: Comparison of Online and Traditional Methods

ERIC Educational Resources Information Center

Suanpang, Pannee; Petocz, Peter; Kalceff, Walter

2004-01-01

Worldwide, electronic learning (E-learning) has become an important part of the education agenda in the last decade. The Suan Dusit Rajabhat University (SDRU), Thailand has made significant efforts recently to use Internet technologies to enhance learning opportunities. The results reported here are part of a pioneering study to determine the…

Evolving Learning Paradigms: Re-Setting Baselines and Collection Methods of Information and Communication Technology in Education Statistics

ERIC Educational Resources Information Center

Gibson, David; Broadley, Tania; Downie, Jill; Wallet, Peter

2018-01-01

The UNESCO Institute for Statistics (UIS) has been measuring ICT in education since 2009, but with such rapid change in technology and its use in education, it is important now to revise the collection mechanisms to focus on how technology is being used to enhance learning and teaching. Sustainable development goal (SDG) 4, for example, moves…

Methods of learning in statistical education: Design and analysis of a randomized trial

NASA Astrophysics Data System (ADS)

Boyd, Felicity Turner

Background. Recent psychological and technological advances suggest that active learning may enhance understanding and retention of statistical principles. A randomized trial was designed to evaluate the addition of innovative instructional methods within didactic biostatistics courses for public health professionals. Aims. The primary objectives were to evaluate and compare the addition of two active learning methods (cooperative and internet) on students' performance; assess their impact on performance after adjusting for differences in students' learning style; and examine the influence of learning style on trial participation. Methods. Consenting students enrolled in a graduate introductory biostatistics course were randomized to cooperative learning, internet learning, or control after completing a pretest survey. The cooperative learning group participated in eight small group active learning sessions on key statistical concepts, while the internet learning group accessed interactive mini-applications on the same concepts. Controls received no intervention. Students completed evaluations after each session and a post-test survey. Study outcome was performance quantified by examination scores. Intervention effects were analyzed by generalized linear models using intent-to-treat analysis and marginal structural models accounting for reported participation. Results. Of 376 enrolled students, 265 (70%) consented to randomization; 69, 100, and 96 students were randomized to the cooperative, internet, and control groups, respectively. Intent-to-treat analysis showed no differences between study groups; however, 51% of students in the intervention groups had dropped out after the second session. After accounting for reported participation, expected examination scores were 2.6 points higher (of 100 points) after completing one cooperative learning session (95% CI: 0.3, 4.9) and 2.4 points higher after one internet learning session (95% CI: 0.0, 4.7), versus nonparticipants or controls, adjusting for other performance predictors. Students who preferred learning by reflective observation and active experimentation experienced improved performance through internet learning (5.9 points, 95% CI: 1.2, 10.6) and cooperative learning (2.9 points, 95% CI: 0.6, 5.2), respectively. Learning style did not influence study participation. Conclusions. No performance differences by group were observed by intent-to-treat analysis. Participation in active learning appears to improve student performance in an introductory biostatistics course and provides opportunities for enhancing understanding beyond that attained in traditional didactic classrooms.

Academic Staff Perspectives Towards Adoption of E-learning at Melaka Manipal Medical College: Has E-learning Redefined our Teaching Model?

PubMed

Bhardwaj, A; Nagandla, K; Swe, K Mm; Abas, A Bl

2015-01-01

E-learning is the use of Information and Communication Technology (ICT) to provide online education and learning. E- Learning has now been integrated into the traditional teaching as the concept of 'blended learning' that combines digital learning with the existing traditional teaching methods to address the various challenges in the field of medical education. Structured e-learning activities were started in Melaka Manipal Medical College in 2009 via e-learning platform (MOODLE-Modular Object-Oriented Dynamic Learning Environment). The objective of the present study is to investigate the faculty opinions toward the existing e-learning activities, and to analyse the extent of adopting and integration of e-learning into their traditional teaching methods. A cross sectional study was conducted among faculties of Medicine and Dentistry using pre-tested questionnaires. The data was analyzed by using the statistical package for social science, SPSS, version 16.0. The result of our survey indicates that majority of our faculty (65.4%) held positive opinion towards e-learning. Among the few, who demonstrated reservations, it is attributed to their average level of skills and aptitude in the use of computers that was statistically significant (p<0.05). Our study brings to light the need for formal training as perquisite to support e-learning that enables smooth transition of the faculty from their traditional teaching methods into blended approach. Our results are anticipated to strengthen the existing e-learning activities of our college and other universities and convincingly adopt e-learning as a viable teaching and learning strategy.

Impact of e-learning on nurses' and student nurses knowledge, skills, and satisfaction: a systematic review and meta-analysis.

PubMed

Lahti, Mari; Hätönen, Heli; Välimäki, Maritta

2014-01-01

To review the impact of e-learning on nurses' and nursing student's knowledge, skills and satisfaction related to e-learning. We conducted a systematic review and meta-analysis of randomized controlled trials (RCT) to assess the impact of e-learning on nurses' and nursing student's knowledge, skills and satisfaction. Electronic databases including MEDLINE (1948-2010), CINAHL (1981-2010), Psychinfo (1967-2010) and Eric (1966-2010) were searched in May 2010 and again in December 2010. All RCT studies evaluating the effectiveness of e-learning and differentiating between traditional learning methods among nurses were included. Data was extracted related to the purpose of the trial, sample, measurements used, index test results and reference standard. An extraction tool developed for Cochrane reviews was used. Methodological quality of eligible trials was assessed. 11 trials were eligible for inclusion in the analysis. We identified 11 randomized controlled trials including a total of 2491 nurses and student nurses'. First, the random effect size for four studies showed some improvement associated with e-learning compared to traditional techniques on knowledge. However, the difference was not statistically significant (p=0.39, MD 0.44, 95% CI -0.57 to 1.46). Second, one study reported a slight impact on e-learning on skills, but the difference was not statistically significant, either (p=0.13, MD 0.03, 95% CI -0.09 to 0.69). And third, no results on nurses or student nurses' satisfaction could be reported as the statistical data from three possible studies were not available. Overall, there was no statistical difference between groups in e-learning and traditional learning relating to nurses' or student nurses' knowledge, skills and satisfaction. E-learning can, however, offer an alternative method of education. In future, more studies following the CONSORT and QUOROM statements are needed to evaluate the effects of these interventions. Copyright © 2013 Elsevier Ltd. All rights reserved.

Impaired Statistical Learning in Developmental Dyslexia

PubMed Central

Thiessen, Erik D.; Holt, Lori L.

2015-01-01

Purpose Developmental dyslexia (DD) is commonly thought to arise from phonological impairments. However, an emerging perspective is that a more general procedural learning deficit, not specific to phonological processing, may underlie DD. The current study examined if individuals with DD are capable of extracting statistical regularities across sequences of passively experienced speech and nonspeech sounds. Such statistical learning is believed to be domain-general, to draw upon procedural learning systems, and to relate to language outcomes. Method DD and control groups were familiarized with a continuous stream of syllables or sine-wave tones, the ordering of which was defined by high or low transitional probabilities across adjacent stimulus pairs. Participants subsequently judged two 3-stimulus test items with either high or low statistical coherence as being the most similar to the sounds heard during familiarization. Results As with control participants, the DD group was sensitive to the transitional probability structure of the familiarization materials as evidenced by above-chance performance. However, the performance of participants with DD was significantly poorer than controls across linguistic and nonlinguistic stimuli. In addition, reading-related measures were significantly correlated with statistical learning performance of both speech and nonspeech material. Conclusion Results are discussed in light of procedural learning impairments among participants with DD. PMID:25860795

Multiple Kernel Learning with Random Effects for Predicting Longitudinal Outcomes and Data Integration

PubMed Central

Chen, Tianle; Zeng, Donglin

2015-01-01

Summary Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although kernel-based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data but not for longitudinal data. It is thus important to develop time-sensitive prediction rules that make use of the longitudinal nature of the data. In this paper, we develop a novel statistical learning method for longitudinal data by introducing subject-specific short-term and long-term latent effects through a designed kernel to account for within-subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of the distinctive feature of each data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzheimer's Disease (Alzheimer's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to study prediction of mild cognitive impairment, and show a substantial gain in performance while accounting for the longitudinal aspect of the data. PMID:26177419

Control chart pattern recognition using RBF neural network with new training algorithm and practical features.

PubMed

Addeh, Abdoljalil; Khormali, Aminollah; Golilarz, Noorbakhsh Amiri

2018-05-04

The control chart patterns are the most commonly used statistical process control (SPC) tools to monitor process changes. When a control chart produces an out-of-control signal, this means that the process has been changed. In this study, a new method based on optimized radial basis function neural network (RBFNN) is proposed for control chart patterns (CCPs) recognition. The proposed method consists of four main modules: feature extraction, feature selection, classification and learning algorithm. In the feature extraction module, shape and statistical features are used. Recently, various shape and statistical features have been presented for the CCPs recognition. In the feature selection module, the association rules (AR) method has been employed to select the best set of the shape and statistical features. In the classifier section, RBFNN is used and finally, in RBFNN, learning algorithm has a high impact on the network performance. Therefore, a new learning algorithm based on the bees algorithm has been used in the learning module. Most studies have considered only six patterns: Normal, Cyclic, Increasing Trend, Decreasing Trend, Upward Shift and Downward Shift. Since three patterns namely Normal, Stratification, and Systematic are very similar to each other and distinguishing them is very difficult, in most studies Stratification and Systematic have not been considered. Regarding to the continuous monitoring and control over the production process and the exact type detection of the problem encountered during the production process, eight patterns have been investigated in this study. The proposed method is tested on a dataset containing 1600 samples (200 samples from each pattern) and the results showed that the proposed method has a very good performance. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A Meta-Analysis of Suggestopedia, Suggestology, Suggestive-accelerative Learning and Teaching (SALT), and Super-learning.

ERIC Educational Resources Information Center

Moon, Charles E.; And Others

Forty studies using one or more components of Lozanov's method of suggestive-accelerative learning and teaching were identified from a search of all issues of the "Journal of Suggestive-Accelerative Learning and Teaching." Fourteen studies contained sufficient statistics to compute effect sizes. The studies were coded according to substantive and…

Statistical Mechanics of the Delayed Reward-Based Learning with Node Perturbation

NASA Astrophysics Data System (ADS)

Hiroshi Saito,; Kentaro Katahira,; Kazuo Okanoya,; Masato Okada,

2010-06-01

In reward-based learning, reward is typically given with some delay after a behavior that causes the reward. In machine learning literature, the framework of the eligibility trace has been used as one of the solutions to handle the delayed reward in reinforcement learning. In recent studies, the eligibility trace is implied to be important for difficult neuroscience problem known as the “distal reward problem”. Node perturbation is one of the stochastic gradient methods from among many kinds of reinforcement learning implementations, and it searches the approximate gradient by introducing perturbation to a network. Since the stochastic gradient method does not require a objective function differential, it is expected to be able to account for the learning mechanism of a complex system, like a brain. We study the node perturbation with the eligibility trace as a specific example of delayed reward-based learning, and analyzed it using a statistical mechanics approach. As a result, we show the optimal time constant of the eligibility trace respect to the reward delay and the existence of unlearnable parameter configurations.

Exploring Task- and Student-Related Factors in the Method of Propositional Manipulation (MPM)

ERIC Educational Resources Information Center

Leppink, Jimmie; Broers, Nick J.; Imbos, Tjaart; van der Vleuten, Cees P. M.; Berger, Martijn P. F.

2011-01-01

The method of propositional manipulation (MPM) aims to help students develop conceptual understanding of statistics by guiding them into self-explaining propositions. To explore task- and student-related factors influencing students' ability to learn from MPM, twenty undergraduate students performed six learning tasks while thinking aloud. The…

Inquiring the Most Critical Teacher's Technology Education Competences in the Highest Efficient Technology Education Learning Organization

ERIC Educational Resources Information Center

Yung-Kuan, Chan; Hsieh, Ming-Yuan; Lee, Chin-Feng; Huang, Chih-Cheng; Ho, Li-Chih

2017-01-01

Under the hyper-dynamic education situation, this research, in order to comprehensively explore the interplays between Teacher Competence Demands (TCD) and Learning Organization Requests (LOR), cross-employs the data refined method of Descriptive Statistics (DS) method and Analysis of Variance (ANOVA) and Principal Components Analysis (PCA)…

Using an Instructional LAN to Teach a Statistics Course.

ERIC Educational Resources Information Center

Barnes, J. Wesley; And Others

1988-01-01

Discusses a computer assisted learning system for engineering statistics based on personalized system of instruction methods. Describes the system's network, development, course structure, programing, and security. Lists the benefits of the system. (MVL)

Perceptron ensemble of graph-based positive-unlabeled learning for disease gene identification.

PubMed

Jowkar, Gholam-Hossein; Mansoori, Eghbal G

2016-10-01

Identification of disease genes, using computational methods, is an important issue in biomedical and bioinformatics research. According to observations that diseases with the same or similar phenotype have the same biological characteristics, researchers have tried to identify genes by using machine learning tools. In recent attempts, some semi-supervised learning methods, called positive-unlabeled learning, is used for disease gene identification. In this paper, we present a Perceptron ensemble of graph-based positive-unlabeled learning (PEGPUL) on three types of biological attributes: gene ontologies, protein domains and protein-protein interaction networks. In our method, a reliable set of positive and negative genes are extracted using co-training schema. Then, the similarity graph of genes is built using metric learning by concentrating on multi-rank-walk method to perform inference from labeled genes. At last, a Perceptron ensemble is learned from three weighted classifiers: multilevel support vector machine, k-nearest neighbor and decision tree. The main contributions of this paper are: (i) incorporating the statistical properties of gene data through choosing proper metrics, (ii) statistical evaluation of biological features, and (iii) noise robustness characteristic of PEGPUL via using multilevel schema. In order to assess PEGPUL, we have applied it on 12950 disease genes with 949 positive genes from six class of diseases and 12001 unlabeled genes. Compared with some popular disease gene identification methods, the experimental results show that PEGPUL has reasonable performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Can blended learning and the flipped classroom improve student learning and satisfaction in Saudi Arabia?

PubMed Central

Sajid, Muhammad R.; Abothenain, Fayha; Salam, Yezan; AlJayar, Dina; Obeidat, Akef

2016-01-01

Objectives To evaluate student academic performance and perception towards blended learning and flipped classrooms in comparison to traditional teaching. Methods This study was conducted during the hematology block on year three students. Five lectures were delivered online only. Asynchronous discussion boards were created where students could interact with colleagues and instructors. A flipped classroom was introduced with application exercises. Summative assessment results were compared with previous year results as a historical control for statistical significance. Student feedback regarding their blended learning experience was collected. Results A total of 127 responses were obtained. Approximately 22.8% students felt all lectures should be delivered through didactic lecturing, while almost 35% felt that 20% of total lectures should be given online. Students expressed satisfaction with blended learning as a new and effective learning approach. The majority of students reported blended learning was helpful for exam preparation and concept clarification. However, a comparison of grades did not show a statistically significant increase in the academic performance of students taught via the blended learning method. Conclusions Learning experiences can be enriched by adopting a blended method of instruction at various stages of undergraduate and postgraduate education. Our results suggest that blended learning, a relatively new concept in Saudi Arabia, shows promising results with higher student satisfaction. Flipped classrooms replace passive lecturing with active student-centered learning that enhances critical thinking and application, including information retention.  PMID:27591930

Cognitive Transfer Outcomes for a Simulation-Based Introductory Statistics Curriculum

ERIC Educational Resources Information Center

Backman, Matthew D.; Delmas, Robert C.; Garfield, Joan

2017-01-01

Cognitive transfer is the ability to apply learned skills and knowledge to new applications and contexts. This investigation evaluates cognitive transfer outcomes for a tertiary-level introductory statistics course using the CATALST curriculum, which exclusively used simulation-based methods to develop foundations of statistical inference. A…

Mutual interference between statistical summary perception and statistical learning.

PubMed

Zhao, Jiaying; Ngo, Nhi; McKendrick, Ryan; Turk-Browne, Nicholas B

2011-09-01

The visual system is an efficient statistician, extracting statistical summaries over sets of objects (statistical summary perception) and statistical regularities among individual objects (statistical learning). Although these two kinds of statistical processing have been studied extensively in isolation, their relationship is not yet understood. We first examined how statistical summary perception influences statistical learning by manipulating the task that participants performed over sets of objects containing statistical regularities (Experiment 1). Participants who performed a summary task showed no statistical learning of the regularities, whereas those who performed control tasks showed robust learning. We then examined how statistical learning influences statistical summary perception by manipulating whether the sets being summarized contained regularities (Experiment 2) and whether such regularities had already been learned (Experiment 3). The accuracy of summary judgments improved when regularities were removed and when learning had occurred in advance. In sum, calculating summary statistics impeded statistical learning, and extracting statistical regularities impeded statistical summary perception. This mutual interference suggests that statistical summary perception and statistical learning are fundamentally related.

Decision Process to Identify Lessons for Transition to a Distributed (or Blended) Learning Instructional Format

DTIC Science & Technology

2009-09-01

instructional format. Using a mixed- method coding and analysis approach, the sample of POIs were categorized, coded, statistically analyzed, and a... Method SECURITY CLASSIFICATION OF 19. LIMITATION OF 20. NUMBER 21. RESPONSIBLE PERSON 16. REPORT Unclassified 17. ABSTRACT...transition to a distributed (or blended) learning format. Procedure: A mixed- methods approach, combining qualitative coding procedures with basic

Does Reviewing Lead to Better Learning and Decision Making? Answers from a Randomized Stock Market Experiment

PubMed Central

Wessa, Patrick; Holliday, Ian E.

2012-01-01

Background The literature is not univocal about the effects of Peer Review (PR) within the context of constructivist learning. Due to the predominant focus on using PR as an assessment tool, rather than a constructivist learning activity, and because most studies implicitly assume that the benefits of PR are limited to the reviewee, little is known about the effects upon students who are required to review their peers. Much of the theoretical debate in the literature is focused on explaining how and why constructivist learning is beneficial. At the same time these discussions are marked by an underlying presupposition of a causal relationship between reviewing and deep learning. Objectives The purpose of the study is to investigate whether the writing of PR feedback causes students to benefit in terms of: perceived utility about statistics, actual use of statistics, better understanding of statistical concepts and associated methods, changed attitudes towards market risks, and outcomes of decisions that were made. Methods We conducted a randomized experiment, assigning students randomly to receive PR or non–PR treatments and used two cohorts with a different time span. The paper discusses the experimental design and all the software components that we used to support the learning process: Reproducible Computing technology which allows students to reproduce or re–use statistical results from peers, Collaborative PR, and an AI–enhanced Stock Market Engine. Results The results establish that the writing of PR feedback messages causes students to experience benefits in terms of Behavior, Non–Rote Learning, and Attitudes, provided the sequence of PR activities are maintained for a period that is sufficiently long. PMID:22666385

A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network.

PubMed

Gharehbaghi, Arash; Linden, Maria

2017-10-12

This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk.

The Relationship Between Procrastination, Learning Strategies and Statistics Anxiety Among Iranian College Students: A Canonical Correlation Analysis

PubMed Central

Vahedi, Shahrum; Farrokhi, Farahman; Gahramani, Farahnaz; Issazadegan, Ali

2012-01-01

Objective: Approximately 66-80%of graduate students experience statistics anxiety and some researchers propose that many students identify statistics courses as the most anxiety-inducing courses in their academic curriculums. As such, it is likely that statistics anxiety is, in part, responsible for many students delaying enrollment in these courses for as long as possible. This paper proposes a canonical model by treating academic procrastination (AP), learning strategies (LS) as predictor variables and statistics anxiety (SA) as explained variables. Methods: A questionnaire survey was used for data collection and 246-college female student participated in this study. To examine the mutually independent relations between procrastination, learning strategies and statistics anxiety variables, a canonical correlation analysis was computed. Results: Findings show that two canonical functions were statistically significant. The set of variables (metacognitive self-regulation, source management, preparing homework, preparing for test and preparing term papers) helped predict changes of statistics anxiety with respect to fearful behavior, Attitude towards math and class, Performance, but not Anxiety. Conclusion: These findings could be used in educational and psychological interventions in the context of statistics anxiety reduction. PMID:24644468

Traditional learning and problem-based learning: self-perception of preparedness for internship.

PubMed

Millan, Laís Pereira Bueno; Semer, Beatriz; Rodrigues, José Mauro da Silva; Gianini, Reinaldo José

2012-01-01

This study aims to evaluate Pontificia Universidade Católica de São Paulo (PUC-SP) medical students' perception of their preparedness to attend the internship course by comparing students who entered the internship in 2009, who were taught according to the traditional learning method, and those who entered the internship in 2010, who were taught according to the new method, i.e. problem-based learning (PBL). 50 traditional learning method students answered a standard Lickert scale questionnaire upon entering internship in 2009. In 2010, the process was repeated with PBL students. The questionnaire was based upon the Preparation for Hospital Practice Questionnaire. This questionnaire was evaluated by professors from three medical schools in Brazil regarding its applicability. The original questions were classified according to the importance these professors attributed to them, and less important questions were removed. Scores obtained from the Student's t-test were considered significant with p < 0.05. A significant statistical difference was observed in 16 questions, and the traditional learning method students reported higher average scores. When questions were divided into dimensions, a significant statistical difference appeared in the dimensions " social aspects of health", "medical skills", and "ethical concepts"; traditional learning method students again reported higher scores (p < 0.001 for all dimensions). Higher scores were also reported when the average of the answers to the whole questionnaire was calculated. Traditional learning method students consider themselves to be better prepared for internship activities than PBL students, according to the following three comparative means: by analyzing the answers to each question, by grouping these answers into dimensions, and by calculating the means of answers to the whole questionnaire.

Free Energy Computations by Minimization of Kullback-Leibler Divergence: An Efficient Adaptive Biasing Potential Method for Sparse Representations

DTIC Science & Technology

2011-10-14

landscapes. It is motivated by statistical learning arguments and unifies the tasks of biasing the molecular dynamics to escape free energy wells and...statistical learning arguments and unifies the tasks of biasing the molecular dynamics to escape free energy wells and estimating the free energy...experimentally, to characterize global changes as well as investigate relative stabilities. In most applications, a brute- force computation based on

Needs of the Learning Effect on Instructional Website for Vocational High School Students

ERIC Educational Resources Information Center

Lo, Hung-Jen; Fu, Gwo-Liang; Chuang, Kuei-Chih

2013-01-01

The purpose of study was to understand the correlation between the needs of the learning effect on instructional website for the vocational high school students. Our research applied the statistic methods of product-moment correlation, stepwise regression, and structural equation method to analyze the questionnaire with the sample size of 377…

Student Learning Outcomes and Pedagogy in Online and Face-to-Face College English Composition: A Mixed Methods Study

ERIC Educational Resources Information Center

Montagne, Lisa

2012-01-01

This mixed methods study combined quantitative statistics and qualitative inquiry to determine if any differences exist between how students in face-to-face and online college English composition courses performed on and demonstrated knowledge of the California state curriculum standards, and to explore the online learning environment in this…

Using Real-Life Data When Teaching Statistics: Student Perceptions of this Strategy in an Introductory Statistics Course

ERIC Educational Resources Information Center

Neumann, David L.; Hood, Michelle; Neumann, Michelle M.

2013-01-01

Many teachers of statistics recommend using real-life data during class lessons. However, there has been little systematic study of what effect this teaching method has on student engagement and learning. The present study examined this question in a first-year university statistics course. Students (n = 38) were interviewed and their reflections…

Demonstrating the Effectiveness of an Integrated and Intensive Research Methods and Statistics Course Sequence

ERIC Educational Resources Information Center

Pliske, Rebecca M.; Caldwell, Tracy L.; Calin-Jageman, Robert J.; Taylor-Ritzler, Tina

2015-01-01

We developed a two-semester series of intensive (six-contact hours per week) behavioral research methods courses with an integrated statistics curriculum. Our approach includes the use of team-based learning, authentic projects, and Excel and SPSS. We assessed the effectiveness of our approach by examining our students' content area scores on the…

Peer-Assisted Learning in Research Methods and Statistics

ERIC Educational Resources Information Center

Stone, Anna; Meade, Claire; Watling, Rosamond

2012-01-01

Feedback from students on a Level 1 Research Methods and Statistics module, studied as a core part of a BSc Psychology programme, highlighted demand for additional tutorials to help them to understand basic concepts. Students in their final year of study commonly request work experience to enhance their employability. All students on the Level 1…

Learning during a Collaborative Final Exam

ERIC Educational Resources Information Center

Dahlstrom, Orjan

2012-01-01

Collaborative testing has been suggested to serve as a good learning activity, for example, compared to individual testing. The aim of the present study was to measure learning at different levels of knowledge during a collaborative final exam in a course in basic methods and statistical procedures. Results on pre- and post-tests taken…

The Development of a Decision Support System for Mobile Learning: A Case Study in Taiwan

ERIC Educational Resources Information Center

Chiu, Po-Sheng; Huang, Yueh-Min

2016-01-01

While mobile learning (m-learning) has considerable potential, most of previous strategies for developing this new approach to education were analysed using the knowledge, experience and judgement of individuals, with the support of statistical software. Although these methods provide systematic steps for the implementation of m-learning…

Methods of Learning in Statistical Education: A Randomized Trial of Public Health Graduate Students

ERIC Educational Resources Information Center

Enders, Felicity Boyd; Diener-West, Marie

2006-01-01

A randomized trial of 265 consenting students was conducted within an introductory biostatistics course: 69 received eight small group cooperative learning sessions; 97 accessed internet learning sessions; 96 received no intervention. Effect on examination score (95% CI) was assessed by intent-to-treat analysis and by incorporating reported…

Using statistical and machine learning to help institutions detect suspicious access to electronic health records

PubMed Central

Kim, Jihoon; Grillo, Janice M; Ohno-Machado, Lucila

2011-01-01

Objective To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs. Methods From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures. Results The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM. Limitations The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction. Conclusion The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs. PMID:21672912

Active learning for ontological event extraction incorporating named entity recognition and unknown word handling.

PubMed

Han, Xu; Kim, Jung-jae; Kwoh, Chee Keong

2016-01-01

Biomedical text mining may target various kinds of valuable information embedded in the literature, but a critical obstacle to the extension of the mining targets is the cost of manual construction of labeled data, which are required for state-of-the-art supervised learning systems. Active learning is to choose the most informative documents for the supervised learning in order to reduce the amount of required manual annotations. Previous works of active learning, however, focused on the tasks of entity recognition and protein-protein interactions, but not on event extraction tasks for multiple event types. They also did not consider the evidence of event participants, which might be a clue for the presence of events in unlabeled documents. Moreover, the confidence scores of events produced by event extraction systems are not reliable for ranking documents in terms of informativity for supervised learning. We here propose a novel committee-based active learning method that supports multi-event extraction tasks and employs a new statistical method for informativity estimation instead of using the confidence scores from event extraction systems. Our method is based on a committee of two systems as follows: We first employ an event extraction system to filter potential false negatives among unlabeled documents, from which the system does not extract any event. We then develop a statistical method to rank the potential false negatives of unlabeled documents 1) by using a language model that measures the probabilities of the expression of multiple events in documents and 2) by using a named entity recognition system that locates the named entities that can be event arguments (e.g. proteins). The proposed method further deals with unknown words in test data by using word similarity measures. We also apply our active learning method for the task of named entity recognition. We evaluate the proposed method against the BioNLP Shared Tasks datasets, and show that our method can achieve better performance than such previous methods as entropy and Gibbs error based methods and a conventional committee-based method. We also show that the incorporation of named entity recognition into the active learning for event extraction and the unknown word handling further improve the active learning method. In addition, the adaptation of the active learning method into named entity recognition tasks also improves the document selection for manual annotation of named entities.

Attitudes towards statistics of graduate entry medical students: the role of prior learning experiences

PubMed Central

2014-01-01

Background While statistics is increasingly taught as part of the medical curriculum, it can be an unpopular subject and feedback from students indicates that some find it more difficult than other subjects. Understanding attitudes towards statistics on entry to graduate entry medical programmes is particularly important, given that many students may have been exposed to quantitative courses in their previous degree and hence bring preconceptions of their ability and interest to their medical education programme. The aim of this study therefore is to explore, for the first time, attitudes towards statistics of graduate entry medical students from a variety of backgrounds and focus on understanding the role of prior learning experiences. Methods 121 first year graduate entry medical students completed the Survey of Attitudes toward Statistics instrument together with information on demographics and prior learning experiences. Results Students tended to appreciate the relevance of statistics in their professional life and be prepared to put effort into learning statistics. They had neutral to positive attitudes about their interest in statistics and their intellectual knowledge and skills when applied to it. Their feelings towards statistics were slightly less positive e.g. feelings of insecurity, stress, fear and frustration and they tended to view statistics as difficult. Even though 85% of students had taken a quantitative course in the past, only 24% of students described it as likely that they would take any course in statistics if the choice was theirs. How well students felt they had performed in mathematics in the past was a strong predictor of many of the components of attitudes. Conclusion The teaching of statistics to medical students should start with addressing the association between students’ past experiences in mathematics and their attitudes towards statistics and encouraging students to recognise the difference between the two disciplines. Addressing these issues may reduce students’ anxiety and perception of difficulty at the start of their learning experience and encourage students to engage with statistics in their future careers. PMID:24708762

Deconstructing Statistical Analysis

ERIC Educational Resources Information Center

Snell, Joel

2014-01-01

Using a very complex statistical analysis and research method for the sake of enhancing the prestige of an article or making a new product or service legitimate needs to be monitored and questioned for accuracy. 1) The more complicated the statistical analysis, and research the fewer the number of learned readers can understand it. This adds a…

Comparing problem-based learning and lecture as methods to teach whole-systems design to engineering students

NASA Astrophysics Data System (ADS)

Dukes, Michael Dickey

The objective of this research is to compare problem-based learning and lecture as methods to teach whole-systems design to engineering students. A case study, Appendix A, exemplifying successful whole-systems design was developed and written by the author in partnership with the Rocky Mountain Institute. Concepts to be tested were then determined, and a questionnaire was developed to test students' preconceptions. A control group of students was taught using traditional lecture methods, and a sample group of students was taught using problem-based learning methods. After several weeks, the students were given the same questionnaire as prior to the instruction, and the data was analyzed to determine if the teaching methods were effective in correcting misconceptions. A statistically significant change in the students' preconceptions was observed in both groups on the topic of cost related to the design process. There was no statistically significant change in the students' preconceptions concerning the design process, technical ability within five years, and the possibility of drastic efficiency gains with current technologies. However, the results were inconclusive in determining that problem-based learning is more effective than lecture as a method for teaching the concept of whole-systems design, or vice versa.

A Guide to the Literature on Learning Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Friedland, Peter (Technical Monitor)

1994-01-01

This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and more generally, learning probabilistic graphical models. Because many problems in artificial intelligence, statistics and neural networks can be represented as a probabilistic graphical model, this area provides a unifying perspective on learning. This paper organizes the research in this area along methodological lines of increasing complexity.

Comparing the Effectiveness of Traditional and Active Learning Methods in Business Statistics: Convergence to the Mean

ERIC Educational Resources Information Center

Weltman, David; Whiteside, Mary

2010-01-01

This research shows that active learning is not universally effective and, in fact, may inhibit learning for certain types of students. The results of this study show that as increased levels of active learning are utilized, student test scores decrease for those with a high grade point average. In contrast, test scores increase as active learning…

Statistical Learning Theory for High Dimensional Prediction: Application to Criterion-Keyed Scale Development

PubMed Central

Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul

2016-01-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257

Generational diversity in associate degree nursing students: Teaching styles and preferences in Pennsylvania

NASA Astrophysics Data System (ADS)

Kitko, Jennifer V.

2011-12-01

Nursing educators face the challenge of meeting the needs of a multi-generational classroom. The reality of having members from the Veteran and Baby Boomer generations in a classroom with Generation X and Y students provides an immediate need for faculty to examine students' teaching method preferences as well as their own use of teaching methods. Most importantly, faculty must facilitate an effective multi-generational learning environment. Research has shown that the generation to which a person belongs is likely to affect the ways in which he/she learns (Hammill, 2005). Characterized by its own attitudes, behaviors, beliefs, and motivational needs, each generation also has distinct educational expectations. It is imperative, therefore, that nurse educators be aware of these differences and develop skills through which to communicate with the different generations, thereby reducing teaching/learning problems in the classroom. This is a quantitative, descriptive study that compared the teaching methods preferred by different generations of associate degree nursing students with the teaching methods that the instructors actually use. The research study included 289 participants; 244 nursing student participants and 45 nursing faculty participants from four nursing departments in colleges in Pennsylvania. Overall, the results of the study found many statistically significant findings. The results of the ANOVA test revealed eight statistically significant findings among Generation Y, Generation X and Baby boomers. The preferred teaching methods included: lecture, self-directed learning, web-based course with no class meetings, important for faculty to know my name, classroom structure, know why I am learning what I am learning, learning for the sake of learning and grade is all that matters. Lecture was found to be the most frequently used teaching method by faculty as well as the most preferred teaching methods by students. Overall, the support for a variety of teaching methods was also found in the analysis of the data.

The Web as an educational tool for/in learning/teaching bioinformatics statistics.

PubMed

Oliver, J; Pisano, M E; Alonso, T; Roca, P

2005-12-01

Statistics provides essential tool in Bioinformatics to interpret the results of a database search or for the management of enormous amounts of information provided from genomics, proteomics and metabolomics. The goal of this project was the development of a software tool that would be as simple as possible to demonstrate the use of the Bioinformatics statistics. Computer Simulation Methods (CSMs) developed using Microsoft Excel were chosen for their broad range of applications, immediate and easy formula calculation, immediate testing and easy graphics representation, and of general use and acceptance by the scientific community. The result of these endeavours is a set of utilities which can be accessed from the following URL: http://gmein.uib.es/bioinformatica/statistics. When tested on students with previous coursework with traditional statistical teaching methods, the general opinion/overall consensus was that Web-based instruction had numerous advantages, but traditional methods with manual calculations were also needed for their theory and practice. Once having mastered the basic statistical formulas, Excel spreadsheets and graphics were shown to be very useful for trying many parameters in a rapid fashion without having to perform tedious calculations. CSMs will be of great importance for the formation of the students and professionals in the field of bioinformatics, and for upcoming applications of self-learning and continuous formation.

Neurophysiological Markers of Statistical Learning in Music and Language: Hierarchy, Entropy, and Uncertainty.

PubMed

Daikoku, Tatsuya

2018-06-19

Statistical learning (SL) is a method of learning based on the transitional probabilities embedded in sequential phenomena such as music and language. It has been considered an implicit and domain-general mechanism that is innate in the human brain and that functions independently of intention to learn and awareness of what has been learned. SL is an interdisciplinary notion that incorporates information technology, artificial intelligence, musicology, and linguistics, as well as psychology and neuroscience. A body of recent study has suggested that SL can be reflected in neurophysiological responses based on the framework of information theory. This paper reviews a range of work on SL in adults and children that suggests overlapping and independent neural correlations in music and language, and that indicates disability of SL. Furthermore, this article discusses the relationships between the order of transitional probabilities (TPs) (i.e., hierarchy of local statistics) and entropy (i.e., global statistics) regarding SL strategies in human's brains; claims importance of information-theoretical approaches to understand domain-general, higher-order, and global SL covering both real-world music and language; and proposes promising approaches for the application of therapy and pedagogy from various perspectives of psychology, neuroscience, computational studies, musicology, and linguistics.

A glossary for big data in population and public health: discussion and commentary on terminology and research methods.

PubMed

Fuller, Daniel; Buote, Richard; Stanley, Kevin

2017-11-01

The volume and velocity of data are growing rapidly and big data analytics are being applied to these data in many fields. Population and public health researchers may be unfamiliar with the terminology and statistical methods used in big data. This creates a barrier to the application of big data analytics. The purpose of this glossary is to define terms used in big data and big data analytics and to contextualise these terms. We define the five Vs of big data and provide definitions and distinctions for data mining, machine learning and deep learning, among other terms. We provide key distinctions between big data and statistical analysis methods applied to big data. We contextualise the glossary by providing examples where big data analysis methods have been applied to population and public health research problems and provide brief guidance on how to learn big data analysis methods. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Can blended learning and the flipped classroom improve student learning and satisfaction in Saudi Arabia?

PubMed

Sajid, Muhammad R; Laheji, Abrar F; Abothenain, Fayha; Salam, Yezan; AlJayar, Dina; Obeidat, Akef

2016-09-04

To evaluate student academic performance and perception towards blended learning and flipped classrooms in comparison to traditional teaching. This study was conducted during the hematology block on year three students. Five lectures were delivered online only. Asynchronous discussion boards were created where students could interact with colleagues and instructors. A flipped classroom was introduced with application exercises. Summative assessment results were compared with previous year results as a historical control for statistical significance. Student feedback regarding their blended learning experience was collected. A total of 127 responses were obtained. Approximately 22.8% students felt all lectures should be delivered through didactic lecturing, while almost 35% felt that 20% of total lectures should be given online. Students expressed satisfaction with blended learning as a new and effective learning approach. The majority of students reported blended learning was helpful for exam preparation and concept clarification. However, a comparison of grades did not show a statistically significant increase in the academic performance of students taught via the blended learning method. Learning experiences can be enriched by adopting a blended method of instruction at various stages of undergraduate and postgraduate education. Our results suggest that blended learning, a relatively new concept in Saudi Arabia, shows promising results with higher student satisfaction. Flipped classrooms replace passive lecturing with active student-centered learning that enhances critical thinking and application, including information retention.

Bridging the Gap: Cognitive and Social Approaches to Research in Second Language Learning and Teaching

ERIC Educational Resources Information Center

Hulstijn, Jan H.; Young, Richard F.; Ortega, Lourdes; Bigelow, Martha; DeKeyser, Robert; Ellis, Nick C.; Lantolf, James P.; Mackey, Alison; Talmy, Steven

2014-01-01

For some, research in learning and teaching of a second language (L2) runs the risk of disintegrating into irreconcilable approaches to L2 learning and use. On the one side, we find researchers investigating linguistic-cognitive issues, often using quantitative research methods including inferential statistics; on the other side, we find…

Learning coefficient of generalization error in Bayesian estimation and vandermonde matrix-type singularity.

PubMed

Aoyagi, Miki; Nagata, Kenji

2012-06-01

The term algebraic statistics arises from the study of probabilistic models and techniques for statistical inference using methods from algebra and geometry (Sturmfels, 2009 ). The purpose of our study is to consider the generalization error and stochastic complexity in learning theory by using the log-canonical threshold in algebraic geometry. Such thresholds correspond to the main term of the generalization error in Bayesian estimation, which is called a learning coefficient (Watanabe, 2001a , 2001b ). The learning coefficient serves to measure the learning efficiencies in hierarchical learning models. In this letter, we consider learning coefficients for Vandermonde matrix-type singularities, by using a new approach: focusing on the generators of the ideal, which defines singularities. We give tight new bound values of learning coefficients for the Vandermonde matrix-type singularities and the explicit values with certain conditions. By applying our results, we can show the learning coefficients of three-layered neural networks and normal mixture models.

Statistics in the Workplace: A Survey of Use by Recent Graduates with Higher Degrees

ERIC Educational Resources Information Center

Harraway, John A.; Barker, Richard J.

2005-01-01

A postal survey was conducted regarding statistical techniques, research methods and software used in the workplace by 913 graduates with PhD and Masters degrees in the biological sciences, psychology, business, economics, and statistics. The study identified gaps between topics and techniques learned at university and those used in the workplace,…

Improving Robot Locomotion Through Learning Methods for Expensive Black-Box Systems

DTIC Science & Technology

2013-11-01

development of a class of “gradient free” optimization techniques; these include local approaches, such as a Nelder- Mead simplex search (c.f. [73]), and global...1Note that this simple method differs from the Nelder Mead constrained nonlinear optimization method [73]. 39 the Non-dominated Sorting Genetic Algorithm...Kober, and Jan Peters. Model-free inverse reinforcement learning. In International Conference on Artificial Intelligence and Statistics, 2011. [12] George

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2004-10-01

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.

An analysis of K--5 teachers' beliefs regarding the uses of direct instruction, the discovery method, and the inquiry method in elementary science education

NASA Astrophysics Data System (ADS)

Kowalczyk, Donna Lee

The purpose of this study was to examine K--5 elementary teachers' reported beliefs about the use, function, and importance of Direct Instruction, the Discovery Method, and the Inquiry Method in the instruction of science in their classrooms. Eighty-two teachers completed questionnaires about their beliefs, opinions, uses, and ideas about each of the three instructional methods. Data were collected and analyzed using the Statistical Package of the Social Sciences (SPSS). Descriptive statistics and Chi-Square analyses indicated that the majority of teachers reported using all three methods to varying degrees in their classrooms. Guided Discovery was reported by the teachers as being the most frequently used method to teach science, while Pure Discovery was reportedly used the least frequently. The majority of teachers expressed the belief that a blend of all three instructional methods is the most effective strategy for teaching science at the elementary level. The teachers also reported a moderate level of confidence in teaching science. Students' ability levels, learning styles, and time/class schedule were identified as factors that most influence teachers' instructional choice. Student participation in hands-on activities, creative thinking ability, and developing an understanding of scientific concepts were reported as the learning behaviors most associated with student success in science. Data obtained from this study provide information about the nature and uses of Direct Instruction, the Discovery Method, and the Inquiry Method and teachers' perceptions and beliefs about each method's use in science education. Learning more about the science teaching and learning environment may help teachers, administrators, curriculum developers, and researchers gain greater insights about student learning, instructional effectiveness, and science curriculum development at the elementary level.

Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records

PubMed Central

Weiss, Jeremy C; Page, David; Peissig, Peggy L; Natarajan, Sriraam; McCarty, Catherine

2013-01-01

Electronic health records (EHRs) are an emerging relational domain with large potential to improve clinical outcomes. We apply two statistical relational learning (SRL) algorithms to the task of predicting primary myocardial infarction. We show that one SRL algorithm, relational functional gradient boosting, outperforms propositional learners particularly in the medically-relevant high recall region. We observe that both SRL algorithms predict outcomes better than their propositional analogs and suggest how our methods can augment current epidemiological practices. PMID:25360347

From inverse problems to learning: a Statistical Mechanics approach

NASA Astrophysics Data System (ADS)

Baldassi, Carlo; Gerace, Federica; Saglietti, Luca; Zecchina, Riccardo

2018-01-01

We present a brief introduction to the statistical mechanics approaches for the study of inverse problems in data science. We then provide concrete new results on inferring couplings from sampled configurations in systems characterized by an extensive number of stable attractors in the low temperature regime. We also show how these result are connected to the problem of learning with realistic weak signals in computational neuroscience. Our techniques and algorithms rely on advanced mean-field methods developed in the context of disordered systems.

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Haxby, James V.; Pollmann, Stefan

2009-01-01

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities. PMID:20582270

Learning planar Ising models

DOE PAGES

Johnson, Jason K.; Oyen, Diane Adele; Chertkov, Michael; ...

2016-12-01

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the bestmore » planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.« less

Learning planar Ising models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Jason K.; Oyen, Diane Adele; Chertkov, Michael

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the bestmore » planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.« less

Correlation Between University Students' Kinematic Achievement and Learning Styles

NASA Astrophysics Data System (ADS)

Çirkinoǧlu, A. G.; Dem&ircidot, N.

2007-04-01

In the literature, some researches on kinematics revealed that students have many difficulties in connecting graphs and physics. Also some researches showed that the method used in classroom affects students' further learning. In this study the correlation between university students' kinematics achieve and learning style are investigated. In this purpose Kinematics Achievement Test and Learning Style Inventory were applied to 573 students enrolled in general physics 1 courses at Balikesir University in the fall semester of 2005-2006. Kinematics Test, consists of 12 multiple choose and 6 open ended questions, was developed by researchers to assess students' understanding, interpreting, and drawing graphs. Learning Style Inventory, a 24 items test including visual, auditory, and kinesthetic learning styles, was developed and used by Barsch. The data obtained from in this study were analyzed necessary statistical calculations (T-test, correlation, ANOVA, etc.) by using SPSS statistical program. Based on the research findings, the tentative recommendations are made.

Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report.

PubMed

Kim, Dong Wook; Kim, Hwiyoung; Nam, Woong; Kim, Hyung Jun; Cha, In-Ho

2018-04-23

The aim of this study was to build and validate five types of machine learning models that can predict the occurrence of BRONJ associated with dental extraction in patients taking bisphosphonates for the management of osteoporosis. A retrospective review of the medical records was conducted to obtain cases and controls for the study. Total 125 patients consisting of 41 cases and 84 controls were selected for the study. Five machine learning prediction algorithms including multivariable logistic regression model, decision tree, support vector machine, artificial neural network, and random forest were implemented. The outputs of these models were compared with each other and also with conventional methods, such as serum CTX level. Area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the results. The performance of machine learning models was significantly superior to conventional statistical methods and single predictors. The random forest model yielded the best performance (AUC = 0.973), followed by artificial neural network (AUC = 0.915), support vector machine (AUC = 0.882), logistic regression (AUC = 0.844), decision tree (AUC = 0.821), drug holiday alone (AUC = 0.810), and CTX level alone (AUC = 0.630). Machine learning methods showed superior performance in predicting BRONJ associated with dental extraction compared to conventional statistical methods using drug holiday and serum CTX level. Machine learning can thus be applied in a wide range of clinical studies. Copyright © 2017. Published by Elsevier Inc.

Supervised Learning for Dynamical System Learning.

PubMed

Hefny, Ahmed; Downey, Carlton; Gordon, Geoffrey J

2015-01-01

Recently there has been substantial interest in spectral methods for learning dynamical systems. These methods are popular since they often offer a good tradeoff between computational and statistical efficiency. Unfortunately, they can be difficult to use and extend in practice: e.g., they can make it difficult to incorporate prior information such as sparsity or structure. To address this problem, we present a new view of dynamical system learning: we show how to learn dynamical systems by solving a sequence of ordinary supervised learning problems, thereby allowing users to incorporate prior knowledge via standard techniques such as L 1 regularization. Many existing spectral methods are special cases of this new framework, using linear regression as the supervised learner. We demonstrate the effectiveness of our framework by showing examples where nonlinear regression or lasso let us learn better state representations than plain linear regression does; the correctness of these instances follows directly from our general analysis.

Enrolment Statistics in Open Learning Systems. Coombe Lodge Working Paper. Information Bank Number 1582.

ERIC Educational Resources Information Center

Latcham, Jack

The development and extension of open learning systems in British institutions of further and higher education has given rise to a need for changes in the methods of recording student progress. Open learning systems' provision of a wide flexibility of pace so that students can extend or compress their periods of study as compared with traditional…

Use of personalized Dynamic Treatment Regimes (DTRs) and Sequential Multiple Assignment Randomized Trials (SMARTs) in mental health studies

PubMed Central

Liu, Ying; ZENG, Donglin; WANG, Yuanjia

2014-01-01

Summary Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each point where a clinical decision is made based on each patient’s time-varying characteristics and intermediate outcomes observed at earlier points in time. The complexity, patient heterogeneity, and chronicity of mental disorders call for learning optimal DTRs to dynamically adapt treatment to an individual’s response over time. The Sequential Multiple Assignment Randomized Trial (SMARTs) design allows for estimating causal effects of DTRs. Modern statistical tools have been developed to optimize DTRs based on personalized variables and intermediate outcomes using rich data collected from SMARTs; these statistical methods can also be used to recommend tailoring variables for designing future SMART studies. This paper introduces DTRs and SMARTs using two examples in mental health studies, discusses two machine learning methods for estimating optimal DTR from SMARTs data, and demonstrates the performance of the statistical methods using simulated data. PMID:25642116

Introduction to Statistics. Learning Packages in the Policy Sciences Series, PS-26. Revised Edition.

ERIC Educational Resources Information Center

Policy Studies Associates, Croton-on-Hudson, NY.

The primary objective of this booklet is to introduce students to basic statistical skills that are useful in the analysis of public policy data. A few, selected statistical methods are presented, and theory is not emphasized. Chapter 1 provides instruction for using tables, bar graphs, bar graphs with grouped data, trend lines, pie diagrams,…

Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications

PubMed Central

Qian, Guoqi; Wu, Yuehua; Ferrari, Davide; Qiao, Puxue; Hollande, Frédéric

2016-01-01

Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. PMID:27212939

Using statistical and machine learning to help institutions detect suspicious access to electronic health records.

PubMed

Boxwala, Aziz A; Kim, Jihoon; Grillo, Janice M; Ohno-Machado, Lucila

2011-01-01

To determine whether statistical and machine-learning methods, when applied to electronic health record (EHR) access data, could help identify suspicious (ie, potentially inappropriate) access to EHRs. From EHR access logs and other organizational data collected over a 2-month period, the authors extracted 26 features likely to be useful in detecting suspicious accesses. Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10-fold cross-validation sets of 1291 labeled events. The authors evaluated the sensitivity of final models on an external set of 58 events that were identified as truly inappropriate and investigated independently from this study using standard operating procedures. The area under the receiver operating characteristic curve of the models on the whole data set of 1291 events was 0.91 for LR, and 0.95 for SVM. The sensitivity of the baseline model on this set was 0.8. When the final models were evaluated on the set of 58 investigated events, all of which were determined as truly inappropriate, the sensitivity was 0 for the baseline method, 0.76 for LR, and 0.79 for SVM. The LR and SVM models may not generalize because of interinstitutional differences in organizational structures, applications, and workflows. Nevertheless, our approach for constructing the models using statistical and machine-learning techniques can be generalized. An important limitation is the relatively small sample used for the training set due to the effort required for its construction. The results suggest that statistical and machine-learning methods can play an important role in helping privacy officers detect suspicious accesses to EHRs.

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

PubMed

Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R

2016-12-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The Effect of Active Learning Methodologies on the Teaching of Pharmaceutical Care in a Brazilian Pharmacy Faculty

PubMed Central

2015-01-01

Background In recent years, pharmacists have been involved in expanded patient care responsibilities, for example patient counseling in self-medication, medication review and pharmaceutical care, which require graduates to develop the necessary competences. Consequently, reorientation of pharmacy education has become necessary. As such, active learning strategies have been introduced into classrooms to increase problem-solving and critical thinking skills of students. The objective of this study was to evaluate the performance and perceptions of competency of students in a new pharmaceutical care course that uses active learning methodologies. Methods This pharmaceutical care course was conducted in the first semester of 2014, in the Federal University of Sergipe. In the pharmaceutical care course, active learning methods were used, consisting of dialogic classroom expository, simulation and case studies. Student learning was evaluated using classroom tests and instruments that evaluated the perception of competency in pharmaceutical care practice. Furthermore, students' satisfaction with the course was evaluated. Results Thirty-three students completed the four evaluations used in the course (i.e., a discursive written exam, seminars, OSCE, and virtual patient); 25 were female (75.75%), and the median age was 23.43 (SD 2.82) years. The overall mean of student scores, in all evaluation methods was 7.97 (SD 0.59) on a scale of 0 to 10 points, and student performance on the virtual patient method was statistically superior to other methods. With respect to the perception of competency in pharmaceutical care practice, a comparison of pre- and post-test scores revealed statistically significant improvement for all evaluated competences. At the end of the semester, the students presented positive opinions of the pharmaceutical care course. Conclusions The results suggest that an active learning course can enhance the learning of pharmaceutical care competences. In future studies it will be necessary to compare active learning to traditional methods. PMID:25969991

Content-Based VLE Designs Improve Learning Efficiency in Constructivist Statistics Education

PubMed Central

Wessa, Patrick; De Rycker, Antoon; Holliday, Ian Edward

2011-01-01

Background We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific–purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. Objectives The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Methods Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. Results The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a content–based design outperforms the traditional VLE–based design. PMID:21998652

Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials.

PubMed

Potter, Christine E; Wang, Tianlin; Saffran, Jenny R

2017-04-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, 6 months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, whereas both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli. Copyright © 2016 Cognitive Science Society, Inc.

Second language experience facilitates statistical learning of novel linguistic materials

PubMed Central

Potter, Christine E.; Wang, Tianlin; Saffran, Jenny R.

2016-01-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In the present research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning a new language may also influence statistical learning by changing the regularities to which learners are sensitive. We tested two groups of participants, Mandarin Learners and Naïve Controls, at two time points, six months apart. At each time point, participants performed two different statistical learning tasks: an artificial tonal language statistical learning task and a visual statistical learning task. Only the Mandarin-learning group showed significant improvement on the linguistic task, while both groups improved equally on the visual task. These results support the view that there are multiple influences on statistical learning. Domain-relevant experiences may affect the regularities that learners can discover when presented with novel stimuli. PMID:27988939

Bayesian Statistics and Uncertainty Quantification for Safety Boundary Analysis in Complex Systems

NASA Technical Reports Server (NTRS)

He, Yuning; Davies, Misty Dawn

2014-01-01

The analysis of a safety-critical system often requires detailed knowledge of safe regions and their highdimensional non-linear boundaries. We present a statistical approach to iteratively detect and characterize the boundaries, which are provided as parameterized shape candidates. Using methods from uncertainty quantification and active learning, we incrementally construct a statistical model from only few simulation runs and obtain statistically sound estimates of the shape parameters for safety boundaries.

Beyond existence and aiming outside the laboratory: estimating frequency-dependent and pay-off-biased social learning strategies.

PubMed

McElreath, Richard; Bell, Adrian V; Efferson, Charles; Lubell, Mark; Richerson, Peter J; Waring, Timothy

2008-11-12

The existence of social learning has been confirmed in diverse taxa, from apes to guppies. In order to advance our understanding of the consequences of social transmission and evolution of behaviour, however, we require statistical tools that can distinguish among diverse social learning strategies. In this paper, we advance two main ideas. First, social learning is diverse, in the sense that individuals can take advantage of different kinds of information and combine them in different ways. Examining learning strategies for different information conditions illuminates the more detailed design of social learning. We construct and analyse an evolutionary model of diverse social learning heuristics, in order to generate predictions and illustrate the impact of design differences on an organism's fitness. Second, in order to eventually escape the laboratory and apply social learning models to natural behaviour, we require statistical methods that do not depend upon tight experimental control. Therefore, we examine strategic social learning in an experimental setting in which the social information itself is endogenous to the experimental group, as it is in natural settings. We develop statistical models for distinguishing among different strategic uses of social information. The experimental data strongly suggest that most participants employ a hierarchical strategy that uses both average observed pay-offs of options as well as frequency information, the same model predicted by our evolutionary analysis to dominate a wide range of conditions.

Learning and dynamics in social systems. Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

NASA Astrophysics Data System (ADS)

Dolfin, Marina

2016-03-01

The interesting novelty of the paper by Burini et al. [1] is that the authors present a survey and a new approach of collective learning based on suitable development of methods of the kinetic theory [2] and theoretical tools of evolutionary game theory [3]. Methods of statistical dynamics and kinetic theory lead naturally to stochastic and collective dynamics. Indeed, the authors propose the use of games where the state of the interacting entities is delivered by probability distributions.

3D Visualization of Machine Learning Algorithms with Astronomical Data

NASA Astrophysics Data System (ADS)

Kent, Brian R.

2016-01-01

We present innovative machine learning (ML) methods using unsupervised clustering with minimum spanning trees (MSTs) to study 3D astronomical catalogs. Utilizing Python code to build trees based on galaxy catalogs, we can render the results with the visualization suite Blender to produce interactive 360 degree panoramic videos. The catalogs and their ML results can be explored in a 3D space using mobile devices, tablets or desktop browsers. We compare the statistics of the MST results to a number of machine learning methods relating to optimization and efficiency.

What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?

PubMed

Binder, Harald

2014-07-01

This is a discussion of the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Epidermis area detection for immunofluorescence microscopy

NASA Astrophysics Data System (ADS)

Dovganich, Andrey; Krylov, Andrey; Nasonov, Andrey; Makhneva, Natalia

2018-04-01

We propose a novel image segmentation method for immunofluorescence microscopy images of skin tissue for the diagnosis of various skin diseases. The segmentation is based on machine learning algorithms. The feature vector is filled by three groups of features: statistical features, Laws' texture energy measures and local binary patterns. The images are preprocessed for better learning. Different machine learning algorithms have been used and the best results have been obtained with random forest algorithm. We use the proposed method to detect the epidermis region as a part of pemphigus diagnosis system.

Smoking Gun or Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants.

PubMed

Gagliano, Sarah A; Ravji, Reena; Barnes, Michael R; Weale, Michael E; Knight, Jo

2015-08-24

Although technology has triumphed in facilitating routine genome sequencing, new challenges have been created for the data-analyst. Genome-scale surveys of human variation generate volumes of data that far exceed capabilities for laboratory characterization. By incorporating functional annotations as predictors, statistical learning has been widely investigated for prioritizing genetic variants likely to be associated with complex disease. We compared three published prioritization procedures, which use different statistical learning algorithms and different predictors with regard to the quantity, type and coding. We also explored different combinations of algorithm and annotation set. As an application, we tested which methodology performed best for prioritizing variants using data from a large schizophrenia meta-analysis by the Psychiatric Genomics Consortium. Results suggest that all methods have considerable (and similar) predictive accuracies (AUCs 0.64-0.71) in test set data, but there is more variability in the application to the schizophrenia GWAS. In conclusion, a variety of algorithms and annotations seem to have a similar potential to effectively enrich true risk variants in genome-scale datasets, however none offer more than incremental improvement in prediction. We discuss how methods might be evolved for risk variant prediction to address the impending bottleneck of the new generation of genome re-sequencing studies.

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

PubMed

Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

2017-04-01

Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

Profiling Student Use of Calculators in the Learning of High School Mathematics

ERIC Educational Resources Information Center

Crowe, Cheryll E.; Ma, Xin

2010-01-01

Using data from the 2005 National Assessment of Educational Progress, students' use of calculators in the learning of high school mathematics was profiled based on their family background, curriculum background, and advanced mathematics coursework. A statistical method new to educational research--classification and regression trees--was applied…

The effectiveness of nurses' ability to interpret basic electrocardiogram strips accurately using different learning modalities.

PubMed

Spiva, LeeAnna; Johnson, Kimberly; Robertson, Bethany; Barrett, Darcy T; Jarrell, Nicole M; Hunter, Donna; Mendoza, Inocencia

2012-02-01

Historically, the instructional method of choice has been traditional lecture or face-to-face education; however, changes in the health care environment, including resource constraints, have necessitated examination of this practice. A descriptive pre-/posttest method was used to determine the effectiveness of alternative teaching modalities on nurses' knowledge and confidence in electrocardiogram (EKG) interpretation. A convenience sample of 135 nurses was recruited in an integrated health care system in the Southeastern United States. Nurses attended an instructor-led course, an online learning (e-learning) platform with no study time or 1 week of study time, or an e-learning platform coupled with a 2-hour post-course instructor-facilitated debriefing with no study time or 1 week of study time. Instruments included a confidence scale, an online EKG test, and a course evaluation. Statistically significant differences in knowledge and confidence were found for individual groups after nurses participated in the intervention. Statistically significant differences were found in pre-knowledge and post-confidence when groups were compared. Organizations that use various instructional methods to educate nurses in EKG interpretation can use different teaching modalities without negatively affecting nurses' knowledge or confidence in this skill. Copyright 2012, SLACK Incorporated.

A Comparison of Classroom and Online Asynchronous Problem-Based Learning for Students Undertaking Statistics Training as Part of a Public Health Masters Degree

ERIC Educational Resources Information Center

de Jong, N.; Verstegen, D. M. L.; Tan, F. E. S.; O'Connor, S. J.

2013-01-01

This case-study compared traditional, face-to-face classroom-based teaching with asynchronous online learning and teaching methods in two sets of students undertaking a problem-based learning module in the multilevel and exploratory factor analysis of longitudinal data as part of a Masters degree in Public Health at Maastricht University. Students…

Online incidental statistical learning of audiovisual word sequences in adults: a registered report.

PubMed

Kuppuraj, Sengottuvel; Duta, Mihaela; Thompson, Paul; Bishop, Dorothy

2018-02-01

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory-picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test-retest reliability ( r  = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process.

Online incidental statistical learning of audiovisual word sequences in adults: a registered report

PubMed Central

Duta, Mihaela; Thompson, Paul

2018-01-01

Statistical learning has been proposed as a key mechanism in language learning. Our main goal was to examine whether adults are capable of simultaneously extracting statistical dependencies in a task where stimuli include a range of structures amenable to statistical learning within a single paradigm. We devised an online statistical learning task using real word auditory–picture sequences that vary in two dimensions: (i) predictability and (ii) adjacency of dependent elements. This task was followed by an offline recall task to probe learning of each sequence type. We registered three hypotheses with specific predictions. First, adults would extract regular patterns from continuous stream (effect of grammaticality). Second, within grammatical conditions, they would show differential speeding up for each condition as a factor of statistical complexity of the condition and exposure. Third, our novel approach to measure online statistical learning would be reliable in showing individual differences in statistical learning ability. Further, we explored the relation between statistical learning and a measure of verbal short-term memory (STM). Forty-two participants were tested and retested after an interval of at least 3 days on our novel statistical learning task. We analysed the reaction time data using a novel regression discontinuity approach. Consistent with prediction, participants showed a grammaticality effect, agreeing with the predicted order of difficulty for learning different statistical structures. Furthermore, a learning index from the task showed acceptable test–retest reliability (r = 0.67). However, STM did not correlate with statistical learning. We discuss the findings noting the benefits of online measures in tracking the learning process. PMID:29515876

Improving Nursing Students' Learning Outcomes in Fundamentals of Nursing Course through Combination of Traditional and e-Learning Methods

PubMed Central

Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

2018-01-01

Background: Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. Materials and Methods: A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Results: Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Conclusions: Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills. PMID:29861761

Transforming Graph Data for Statistical Relational Learning

DTIC Science & Technology

2012-10-01

Jordan, 2003), PLSA (Hofmann, 1999), ? Classification via RMN (Taskar et al., 2003) or SVM (Hasan, Chaoji, Salem , & Zaki, 2006) ? Hierarchical...dimensionality reduction methods such as Principal 407 Rossi, McDowell, Aha, & Neville Component Analysis (PCA), Principal Factor Analysis ( PFA ), and...clustering algorithm. Journal of the Royal Statistical Society. Series C, Applied statistics, 28, 100–108. Hasan, M. A., Chaoji, V., Salem , S., & Zaki, M

Nowcasting Cloud Fields for U.S. Air Force Special Operations

DTIC Science & Technology

2017-03-01

application of Bayes’ Rule offers many advantages over Kernel Density Estimation (KDE) and other commonly used statistical post-processing methods...reflectance and probability of cloud. A statistical post-processing technique is applied using Bayesian estimation to train the system from a set of past...nowcasting, low cloud forecasting, cloud reflectance, ISR, Bayesian estimation, statistical post-processing, machine learning 15. NUMBER OF PAGES

Explorations in statistics: the log transformation.

PubMed

Curran-Everett, Douglas

2018-06-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This thirteenth installment of Explorations in Statistics explores the log transformation, an established technique that rescales the actual observations from an experiment so that the assumptions of some statistical analysis are better met. A general assumption in statistics is that the variability of some response Y is homogeneous across groups or across some predictor variable X. If the variability-the standard deviation-varies in rough proportion to the mean value of Y, a log transformation can equalize the standard deviations. Moreover, if the actual observations from an experiment conform to a skewed distribution, then a log transformation can make the theoretical distribution of the sample mean more consistent with a normal distribution. This is important: the results of a one-sample t test are meaningful only if the theoretical distribution of the sample mean is roughly normal. If we log-transform our observations, then we want to confirm the transformation was useful. We can do this if we use the Box-Cox method, if we bootstrap the sample mean and the statistic t itself, and if we assess the residual plots from the statistical model of the actual and transformed sample observations.

Statistical learning and selective inference.

PubMed

Taylor, Jonathan; Tibshirani, Robert J

2015-06-23

We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

Neural network approaches versus statistical methods in classification of multisource remote sensing data

NASA Technical Reports Server (NTRS)

Benediktsson, Jon A.; Swain, Philip H.; Ersoy, Okan K.

1990-01-01

Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.

Learning process mapping heuristics under stochastic sampling overheads

NASA Technical Reports Server (NTRS)

Ieumwananonthachai, Arthur; Wah, Benjamin W.

1991-01-01

A statistical method was developed previously for improving process mapping heuristics. The method systematically explores the space of possible heuristics under a specified time constraint. Its goal is to get the best possible heuristics while trading between the solution quality of the process mapping heuristics and their execution time. The statistical selection method is extended to take into consideration the variations in the amount of time used to evaluate heuristics on a problem instance. The improvement in performance is presented using the more realistic assumption along with some methods that alleviate the additional complexity.

Applications of "Integrated Data Viewer'' (IDV) in the classroom

NASA Astrophysics Data System (ADS)

Nogueira, R.; Cutrim, E. M.

2006-06-01

Conventionally, weather products utilized in synoptic meteorology reduce phenomena occurring in four dimensions to a 2-dimensional form. This constitutes a road-block for non-atmospheric-science majors who need to take meteorology as a non-mathematical and complementary course to their major programs. This research examines the use of Integrated Data Viewer-IDV as a teaching tool, as it allows a 4-dimensional representation of weather products. IDV was tested in the teaching of synoptic meteorology, weather analysis, and weather map interpretation to non-science students in the laboratory sessions of an introductory meteorology class at Western Michigan University. Comparison of student exam scores according to the laboratory teaching techniques, i.e., traditional lab manual and IDV was performed for short- and long-term learning. Results of the statistical analysis show that the Fall 2004 students in the IDV-based lab session retained learning. However, in the Spring 2005 the exam scores did not reflect retention in learning when compared with IDV-based and MANUAL-based lab scores (short term learning, i.e., exam taken one week after the lab exercise). Testing the long-term learning, seven weeks between the two exams in the Spring 2005, show no statistically significant difference between IDV-based group scores and MANUAL-based group scores. However, the IDV group obtained exam score average slightly higher than the MANUAL group. Statistical testing of the principal hypothesis in this study, leads to the conclusion that the IDV-based method did not prove to be a better teaching tool than the traditional paper-based method. Future studies could potentially find significant differences in the effectiveness of both manual and IDV methods if the conditions had been more controlled. That is, students in the control group should not be exposed to the weather analysis using IDV during lecture.

Attitudes toward statistics in medical postgraduates: measuring, evaluating and monitoring

PubMed Central

2012-01-01

Background In medical training, statistics is considered a very difficult course to learn and teach. Current studies have found that students’ attitudes toward statistics can influence their learning process. Measuring, evaluating and monitoring the changes of students’ attitudes toward statistics are important. Few studies have focused on the attitudes of postgraduates, especially medical postgraduates. Our purpose was to understand current attitudes regarding statistics held by medical postgraduates and explore their effects on students’ achievement. We also wanted to explore the influencing factors and the sources of these attitudes and monitor their changes after a systematic statistics course. Methods A total of 539 medical postgraduates enrolled in a systematic statistics course completed the pre-form of the Survey of Attitudes Toward Statistics −28 scale, and 83 postgraduates were selected randomly from among them to complete the post-form scale after the course. Results Most medical postgraduates held positive attitudes toward statistics, but they thought statistics was a very difficult subject. The attitudes mainly came from experiences in a former statistical or mathematical class. Age, level of statistical education, research experience, specialty and mathematics basis may influence postgraduate attitudes toward statistics. There were significant positive correlations between course achievement and attitudes toward statistics. In general, student attitudes showed negative changes after completing a statistics course. Conclusions The importance of student attitudes toward statistics must be recognized in medical postgraduate training. To make sure all students have a positive learning environment, statistics teachers should measure their students’ attitudes and monitor their change of status during a course. Some necessary assistance should be offered for those students who develop negative attitudes. PMID:23173770

A computational visual saliency model based on statistics and machine learning.

PubMed

Lin, Ru-Je; Lin, Wei-Song

2014-08-01

Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases. © 2014 ARVO.

Extracting laboratory test information from biomedical text

PubMed Central

Kang, Yanna Shen; Kayaalp, Mehmet

2013-01-01

Background: No previous study reported the efficacy of current natural language processing (NLP) methods for extracting laboratory test information from narrative documents. This study investigates the pathology informatics question of how accurately such information can be extracted from text with the current tools and techniques, especially machine learning and symbolic NLP methods. The study data came from a text corpus maintained by the U.S. Food and Drug Administration, containing a rich set of information on laboratory tests and test devices. Methods: The authors developed a symbolic information extraction (SIE) system to extract device and test specific information about four types of laboratory test entities: Specimens, analytes, units of measures and detection limits. They compared the performance of SIE and three prominent machine learning based NLP systems, LingPipe, GATE and BANNER, each implementing a distinct supervised machine learning method, hidden Markov models, support vector machines and conditional random fields, respectively. Results: Machine learning systems recognized laboratory test entities with moderately high recall, but low precision rates. Their recall rates were relatively higher when the number of distinct entity values (e.g., the spectrum of specimens) was very limited or when lexical morphology of the entity was distinctive (as in units of measures), yet SIE outperformed them with statistically significant margins on extracting specimen, analyte and detection limit information in both precision and F-measure. Its high recall performance was statistically significant on analyte information extraction. Conclusions: Despite its shortcomings against machine learning methods, a well-tailored symbolic system may better discern relevancy among a pile of information of the same type and may outperform a machine learning system by tapping into lexically non-local contextual information such as the document structure. PMID:24083058

The effect of active learning methodologies on the teaching of pharmaceutical care in a Brazilian pharmacy faculty.

PubMed

Mesquita, Alessandra R; Souza, Werlissandra M; Boaventura, Thays C; Barros, Izadora M C; Antoniolli, Angelo R; Silva, Wellington B; Lyra Júnior, Divaldo P

2015-01-01

In recent years, pharmacists have been involved in expanded patient care responsibilities, for example patient counseling in self-medication, medication review and pharmaceutical care, which require graduates to develop the necessary competences. Consequently, reorientation of pharmacy education has become necessary. As such, active learning strategies have been introduced into classrooms to increase problem-solving and critical thinking skills of students. The objective of this study was to evaluate the performance and perceptions of competency of students in a new pharmaceutical care course that uses active learning methodologies. This pharmaceutical care course was conducted in the first semester of 2014, in the Federal University of Sergipe. In the pharmaceutical care course, active learning methods were used, consisting of dialogic classroom expository, simulation and case studies. Student learning was evaluated using classroom tests and instruments that evaluated the perception of competency in pharmaceutical care practice. Furthermore, students' satisfaction with the course was evaluated. Thirty-three students completed the four evaluations used in the course (i.e., a discursive written exam, seminars, OSCE, and virtual patient); 25 were female (75.75%), and the median age was 23.43 (SD 2.82) years. The overall mean of student scores, in all evaluation methods was 7.97 (SD 0.59) on a scale of 0 to 10 points, and student performance on the virtual patient method was statistically superior to other methods. With respect to the perception of competency in pharmaceutical care practice, a comparison of pre- and post-test scores revealed statistically significant improvement for all evaluated competences. At the end of the semester, the students presented positive opinions of the pharmaceutical care course. The results suggest that an active learning course can enhance the learning of pharmaceutical care competences. In future studies it will be necessary to compare active learning to traditional methods.

Assessing Continuous Operator Workload With a Hybrid Scaffolded Neuroergonomic Modeling Approach.

PubMed

Borghetti, Brett J; Giametta, Joseph J; Rusnock, Christina F

2017-02-01

We aimed to predict operator workload from neurological data using statistical learning methods to fit neurological-to-state-assessment models. Adaptive systems require real-time mental workload assessment to perform dynamic task allocations or operator augmentation as workload issues arise. Neuroergonomic measures have great potential for informing adaptive systems, and we combine these measures with models of task demand as well as information about critical events and performance to clarify the inherent ambiguity of interpretation. We use machine learning algorithms on electroencephalogram (EEG) input to infer operator workload based upon Improved Performance Research Integration Tool workload model estimates. Cross-participant models predict workload of other participants, statistically distinguishing between 62% of the workload changes. Machine learning models trained from Monte Carlo resampled workload profiles can be used in place of deterministic workload profiles for cross-participant modeling without incurring a significant decrease in machine learning model performance, suggesting that stochastic models can be used when limited training data are available. We employed a novel temporary scaffold of simulation-generated workload profile truth data during the model-fitting process. A continuous workload profile serves as the target to train our statistical machine learning models. Once trained, the workload profile scaffolding is removed and the trained model is used directly on neurophysiological data in future operator state assessments. These modeling techniques demonstrate how to use neuroergonomic methods to develop operator state assessments, which can be employed in adaptive systems.

Improving Nursing Students' Learning Outcomes in Fundamentals of Nursing Course through Combination of Traditional and e-Learning Methods.

PubMed

Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

2018-01-01

Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills.

Evaluation of Team-Based Learning and Traditional Instruction in Teaching Removable Partial Denture Concepts.

PubMed

Echeto, Luisa F; Sposetti, Venita; Childs, Gail; Aguilar, Maria L; Behar-Horenstein, Linda S; Rueda, Luis; Nimmo, Arthur

2015-09-01

The aim of this study was to evaluate the effectiveness of team-based learning (TBL) methodology on dental students' retention of knowledge regarding removable partial denture (RPD) treatment. The process of learning RPD treatment requires that students first acquire foundational knowledge and then use critical thinking skills to apply that knowledge to a variety of clinical situations. The traditional approach to teaching, characterized by a reliance on lectures, is not the most effective method for learning clinical applications. To address the limitations of that approach, the teaching methodology of the RPD preclinical course at the University of Florida was changed to TBL, which has been shown to motivate student learning and improve clinical performance. A written examination was constructed to compare the impact of TBL with that of traditional teaching regarding students' retention of knowledge and their ability to evaluate, diagnose, and treatment plan a partially edentulous patient with an RPD prosthesis. Students taught using traditional and TBL methods took the same examination. The response rate (those who completed the examination) for the class of 2013 (traditional method) was 94% (79 students of 84); for the class of 2014 (TBL method), it was 95% (78 students of 82). The results showed that students who learned RPD with TBL scored higher on the examination than those who learned RPD with traditional methods. Compared to the students taught with the traditional method, the TBL students' proportion of passing grades was statistically significantly higher (p=0.002), and 23.7% more TBL students passed the examination. The mean score for the TBL class (0.758) compared to the conventional class (0.700) was statistically significant with a large effect size, also demonstrating the practical significance of the findings. The results of the study suggest that TBL methodology is a promising approach to teaching RPD with successful outcomes.

Dynamics of EEG functional connectivity during statistical learning.

PubMed

Tóth, Brigitta; Janacsek, Karolina; Takács, Ádám; Kóbor, Andrea; Zavecz, Zsófia; Nemeth, Dezso

2017-10-01

Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning. Copyright © 2017 Elsevier Inc. All rights reserved.

Incorporating conditional random fields and active learning to improve sentiment identification.

PubMed

Zhang, Kunpeng; Xie, Yusheng; Yang, Yi; Sun, Aaron; Liu, Hengchang; Choudhary, Alok

2014-10-01

Many machine learning, statistical, and computational linguistic methods have been developed to identify sentiment of sentences in documents, yielding promising results. However, most of state-of-the-art methods focus on individual sentences and ignore the impact of context on the meaning of a sentence. In this paper, we propose a method based on conditional random fields to incorporate sentence structure and context information in addition to syntactic information for improving sentiment identification. We also investigate how human interaction affects the accuracy of sentiment labeling using limited training data. We propose and evaluate two different active learning strategies for labeling sentiment data. Our experiments with the proposed approach demonstrate a 5%-15% improvement in accuracy on Amazon customer reviews compared to existing supervised learning and rule-based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Perceptual statistical learning over one week in child speech production.

PubMed

Richtsmeier, Peter T; Goffman, Lisa

2017-07-01

What cognitive mechanisms account for the trajectory of speech sound development, in particular, gradually increasing accuracy during childhood? An intriguing potential contributor is statistical learning, a type of learning that has been studied frequently in infant perception but less often in child speech production. To assess the relevance of statistical learning to developing speech accuracy, we carried out a statistical learning experiment with four- and five-year-olds in which statistical learning was examined over one week. Children were familiarized with and tested on word-medial consonant sequences in novel words. There was only modest evidence for statistical learning, primarily in the first few productions of the first session. This initial learning effect nevertheless aligns with previous statistical learning research. Furthermore, the overall learning effect was similar to an estimate of weekly accuracy growth based on normative studies. The results implicate other important factors in speech sound development, particularly learning via production. Copyright © 2017 Elsevier Inc. All rights reserved.

Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology

EPA Science Inventory

Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological datasets there is limited guidance on variable selection methods for RF modeling. Typically, e...

In silico prediction of post-translational modifications.

PubMed

Liu, Chunmei; Li, Hui

2011-01-01

Methods for predicting protein post-translational modifications have been developed extensively. In this chapter, we review major post-translational modification prediction strategies, with a particular focus on statistical and machine learning approaches. We present the workflow of the methods and summarize the advantages and disadvantages of the methods.

Quantitative Skills, Critical Thinking, and Writing Mechanics in Blended versus Face-to-Face Versions of a Research Methods and Statistics Course

ERIC Educational Resources Information Center

Goode, Christopher T.; Lamoreaux, Marika; Atchison, Kristin J.; Jeffress, Elizabeth C.; Lynch, Heather L.; Sheehan, Elizabeth

2018-01-01

Hybrid or blended learning (BL) has been shown to be equivalent to or better than face-to-face (FTF) instruction in a broad variety of contexts. We randomly assigned students to either 50/50 BL or 100% FTF versions of a research methods and statistics in psychology course. Students who took the BL version of the course scored significantly lower…

Application of scl - pbl method to increase quality learning of industrial statistics course in department of industrial engineering pancasila university

NASA Astrophysics Data System (ADS)

Darmawan, M.; Hidayah, N. Y.

2017-12-01

Currently, there has been a change of new paradigm in the learning model in college, ie from Teacher Centered Learning (TCL) model to Student Centered Learing (SCL). It is generally assumed that the SCL model is better than the TCL model. The Courses of 2nd Industrial Statistics in the Department Industrial Engineering Pancasila University is the subject that belongs to the Basic Engineering group. So far, the applied learning model refers more to the TCL model, and field facts show that the learning outcomes are less satisfactory. Of the three consecutive semesters, ie even semester 2013/2014, 2014/2015, and 2015/2016 obtained grade average is equal to 56.0; 61.1, and 60.5. In the even semester of 2016/2017, Classroom Action Research (CAR) is conducted for this course through the implementation of SCL model with Problem Based Learning (PBL) methods. The hypothesis proposed is that the SCL-PBL model will be able to improve the final grade of the course. The results shows that the average grade of the course can be increased to 73.27. This value was then tested using the ANOVA and the test results concluded that the average grade was significantly different from the average grade value in the previous three semesters.

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

PubMed

Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

2012-07-01

Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

Using Interactive "Shiny" Applications to Facilitate Research-Informed Learning and Teaching

ERIC Educational Resources Information Center

Fawcett, Lee

2018-01-01

In this article we discuss our attempt to incorporate research-informed learning and teaching activities into a final year undergraduate Statistics course. We make use of the Shiny web-based application framework for R to develop "Shiny apps" designed to help facilitate student interaction with methods from recently published papers in…

Impact of E-Learning and Digitalization in Primary and Secondary Schools

ERIC Educational Resources Information Center

Tunmibi, Sunday; Aregbesola, Ayooluwa; Adejobi, Pascal; Ibrahim, Olaniyi

2015-01-01

This study examines into the impact of e-learning and digitalization in primary and secondary schools, using Greensprings School in Lagos State, Nigeria as a case study. Questionnaire was used as a data collection instrument, and descriptive statistical method was adopted for analysis. Responses from students and teachers reveal that application…

Using Peer Feedback to Improve Learning via Online Peer Assessment

ERIC Educational Resources Information Center

Liu, Eric Zhi-Feng; Lee, Chun-Yi

2013-01-01

This study investigates the influence of various forms of peer observation and feedback on student learning. We recruited twelve graduate students enrolled in a course entitled, Statistics in Education and Psychology, at a university in northern Taiwan. Researchers adopted the case study method, and the course lasted for ten weeks. Students were…

How Social Network Position Relates to Knowledge Building in Online Learning Communities

ERIC Educational Resources Information Center

Wang, Lu

2010-01-01

Social Network Analysis, Statistical Analysis, Content Analysis and other research methods were used to research online learning communities at Capital Normal University, Beijing. Analysis of the two online courses resulted in the following conclusions: (1) Social networks of the two online courses form typical core-periphery structures; (2)…

Economics: A Discriminant Analysis of Students' Perceptions of Web-Based Learning.

ERIC Educational Resources Information Center

Usip, Ebenge E.; Bee, Richard H.

1998-01-01

Users and nonusers of Web-based instruction (WBI) in an undergraduate statistics classes at Youngstown State University were surveyed. Users concluded that distance learning via the Web was a good method of obtaining general information and useful tool in improving their academic performance. Nonusers thought the university should provide…

Embedding Evidence-based Practice Education into a Post-graduate Physiotherapy Program: Eight Years of pre-Post Course Evaluations.

PubMed

Perraton, L; Machotka, Z; Grimmer, K; Gibbs, C; Mahar, C; Kennedy, K

2017-04-01

Little has been published about the effectiveness of training postgraduate physiotherapy coursework students in research methods and evidence-based practice (EBP) theory. Graduate qualities in most universities include lifelong learning. Inclusion of EBP in post-graduate coursework students' training is one way for students to develop the knowledge and skills needed to implement current best evidence in their clinical practice after graduation, thereby facilitating lifelong learning. This paper reports on change in confidence and anxiety in knowledge of statistical terminology and concepts related to research design and EBP in eight consecutive years of post-graduate physiotherapy students at one Australian university. Pre-survey/post-survey instruments were administered to students in an intensive 3-week post-graduate course, which taught health research methods, biostatistics and EBP. This course was embedded into a post-graduate physiotherapy programme from 2007 to 2014. The organization and delivery of the course was based on best pedagogical evidence for effectively teaching adult physiotherapists. The course was first delivered each year in the programme, and no other course was delivered concurrently. There were significant improvements in confidence, significantly decreased anxiety and improvements in knowledge of statistical terminology and concepts related to research design and EBP, at course completion. Age, gender and country of origin were not confounders on learning outcomes, although there was a (non-significant) trend that years of practice negatively impacted on learning outcomes (p = 0.09). There was a greater improvement in confidence in statistical terminology than in concepts related to research design and EBP. An intensive teaching programme in health research methods and biostatistics and EBP, based on best practice adult physiotherapy learning principles, is effective immediately post-course, in decreasing anxiety and increasing confidence in the terminology used in research methods and EBP. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Does reviewing lead to better learning and decision making? Answers from a randomized stock market experiment.

PubMed

Wessa, Patrick; Holliday, Ian E

2012-01-01

The literature is not univocal about the effects of Peer Review (PR) within the context of constructivist learning. Due to the predominant focus on using PR as an assessment tool, rather than a constructivist learning activity, and because most studies implicitly assume that the benefits of PR are limited to the reviewee, little is known about the effects upon students who are required to review their peers. Much of the theoretical debate in the literature is focused on explaining how and why constructivist learning is beneficial. At the same time these discussions are marked by an underlying presupposition of a causal relationship between reviewing and deep learning. The purpose of the study is to investigate whether the writing of PR feedback causes students to benefit in terms of: perceived utility about statistics, actual use of statistics, better understanding of statistical concepts and associated methods, changed attitudes towards market risks, and outcomes of decisions that were made. We conducted a randomized experiment, assigning students randomly to receive PR or non-PR treatments and used two cohorts with a different time span. The paper discusses the experimental design and all the software components that we used to support the learning process: Reproducible Computing technology which allows students to reproduce or re-use statistical results from peers, Collaborative PR, and an AI-enhanced Stock Market Engine. The results establish that the writing of PR feedback messages causes students to experience benefits in terms of Behavior, Non-Rote Learning, and Attitudes, provided the sequence of PR activities are maintained for a period that is sufficiently long.

Students' perspectives of undergraduate research methods education at three public medical schools in Uganda.

PubMed

Munabi, Ian Guyton; Buwembo, William; Joseph, Ruberwa; Peter, Kawungezi; Bajunirwe, Francis; Mwaka, Erisa Sabakaki

2016-01-01

In this study we used a model of adult learning to explore undergraduate students' views on how to improve the teaching of research methods and biostatistics. This was a secondary analysis of survey data of 600 undergraduate students from three medical schools in Uganda. The analysis looked at student's responses to an open ended section of a questionnaire on their views on undergraduate teaching of research methods and biostatistics. Qualitative phenomenological data analysis was done with a bias towards principles of adult learning. Students appreciated the importance of learning research methods and biostatistics as a way of understanding research problems; appropriately interpreting statistical concepts during their training and post-qualification practice; and translating the knowledge acquired. Stressful teaching environment and inadequate educational resource materials were identified as impediments to effective learning. Suggestions for improved learning included: early and continuous exposure to the course; more active and practical approach to teaching; and a need for mentorship. The current methods of teaching research methods and biostatistics leave most of the students in the dissonance phase of learning resulting in none or poor student engagement that results in a failure to comprehend and/or appreciate the principles governing the use of different research methods.

Self-learning Monte Carlo method and cumulative update in fermion systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Junwei; Shen, Huitao; Qi, Yang

2017-06-07

In this study, we develop the self-learning Monte Carlo (SLMC) method, a general-purpose numerical method recently introduced to simulate many-body systems, for studying interacting fermion systems. Our method uses a highly efficient update algorithm, which we design and dub “cumulative update”, to generate new candidate configurations in the Markov chain based on a self-learned bosonic effective model. From a general analysis and a numerical study of the double exchange model as an example, we find that the SLMC with cumulative update drastically reduces the computational cost of the simulation, while remaining statistically exact. Remarkably, its computational complexity is far lessmore » than the conventional algorithm with local updates.« less

Comparison of Electronic Learning Versus Lecture-based Learning in Improving Emergency Medicine Residents' Knowledge About Mild Induced Hypothermia After Cardiac Arrest.

PubMed

Soleimanpour, Maryam; Rahmani, Farzad; Naghizadeh Golzari, Mehrad; Ala, Alireza; Morteza Bagi, Hamid Reza; Mehdizadeh Esfanjani, Robab; Soleimanpour, Hassan

2017-08-01

The process of medical education depends on several issues such as training materials, students, professors, educational fields, and the applied technologies. The current study aimed at comparing the impacts of e-learning and lecture-based learning of mild induced hypothermia (MIH) after cardiac arrest on the increase of knowledge among emergency medicine residents. In a pre- and post-intervention study, MIH after cardiac arrest was taught to 44 emergency medicine residents. Residents were randomly divided into 2 groups. The first group included 21 participants (lecture-based learning) and the second had 23 participants (e-learning). A 19-item questionnaire with approved validity and reliability was employed as the pretest and posttest. Then, data were analyzed with SPSS software version 17.0. There was no statistically significant difference in terms of the learning method between the test scores of the 2 groups (P = 0.977). E-learning and lecture-based learning methods was effective in augmentation of residents of emergency medicine knowledge about MIH after cardiac arrest; nevertheless, there was no significant difference between these mentioned methods.

Learning the Language of Statistics: Challenges and Teaching Approaches

ERIC Educational Resources Information Center

Dunn, Peter K.; Carey, Michael D.; Richardson, Alice M.; McDonald, Christine

2016-01-01

Learning statistics requires learning the language of statistics. Statistics draws upon words from general English, mathematical English, discipline-specific English and words used primarily in statistics. This leads to many linguistic challenges in teaching statistics and the way in which the language is used in statistics creates an extra layer…

Difficulties in learning and teaching statistics: teacher views

NASA Astrophysics Data System (ADS)

Koparan, Timur

2015-01-01

The purpose of this study is to define teacher views about the difficulties in learning and teaching middle school statistics subjects. To serve this aim, a number of interviews were conducted with 10 middle school maths teachers in 2011-2012 school year in the province of Trabzon. Of the qualitative descriptive research methods, the semi-structured interview technique was applied in the research. In accordance with the aim, teacher opinions about the statistics subjects were examined and analysed. Similar responses from the teachers were grouped and evaluated. The teachers stated that it was positive that middle school statistics subjects were taught gradually in every grade but some difficulties were experienced in the teaching of this subject. The findings are presented in eight themes which are context, sample, data representation, central tendency and dispersion measure, probability, variance, and other difficulties.

Teaching Biology through Statistics: Application of Statistical Methods in Genetics and Zoology Courses

PubMed Central

Colon-Berlingeri, Migdalisel; Burrowes, Patricia A.

2011-01-01

Incorporation of mathematics into biology curricula is critical to underscore for undergraduate students the relevance of mathematics to most fields of biology and the usefulness of developing quantitative process skills demanded in modern biology. At our institution, we have made significant changes to better integrate mathematics into the undergraduate biology curriculum. The curricular revision included changes in the suggested course sequence, addition of statistics and precalculus as prerequisites to core science courses, and incorporating interdisciplinary (math–biology) learning activities in genetics and zoology courses. In this article, we describe the activities developed for these two courses and the assessment tools used to measure the learning that took place with respect to biology and statistics. We distinguished the effectiveness of these learning opportunities in helping students improve their understanding of the math and statistical concepts addressed and, more importantly, their ability to apply them to solve a biological problem. We also identified areas that need emphasis in both biology and mathematics courses. In light of our observations, we recommend best practices that biology and mathematics academic departments can implement to train undergraduates for the demands of modern biology. PMID:21885822

Teaching biology through statistics: application of statistical methods in genetics and zoology courses.

PubMed

Colon-Berlingeri, Migdalisel; Burrowes, Patricia A

2011-01-01

Incorporation of mathematics into biology curricula is critical to underscore for undergraduate students the relevance of mathematics to most fields of biology and the usefulness of developing quantitative process skills demanded in modern biology. At our institution, we have made significant changes to better integrate mathematics into the undergraduate biology curriculum. The curricular revision included changes in the suggested course sequence, addition of statistics and precalculus as prerequisites to core science courses, and incorporating interdisciplinary (math-biology) learning activities in genetics and zoology courses. In this article, we describe the activities developed for these two courses and the assessment tools used to measure the learning that took place with respect to biology and statistics. We distinguished the effectiveness of these learning opportunities in helping students improve their understanding of the math and statistical concepts addressed and, more importantly, their ability to apply them to solve a biological problem. We also identified areas that need emphasis in both biology and mathematics courses. In light of our observations, we recommend best practices that biology and mathematics academic departments can implement to train undergraduates for the demands of modern biology.

Infusing Active Learning into the Research Methods Unit

ERIC Educational Resources Information Center

Bluestone, Cheryl

2007-01-01

The research methods unit of survey psychology classes introduces important concepts of scientific reasoning and fluency, making it an ideal course in which to deliver enhanced curricula. To increase interest and engagement, the author developed an expanded research methods and statistics module to give students the opportunity to explore…

A Selective Overview of Variable Selection in High Dimensional Feature Space

PubMed Central

Fan, Jianqing

2010-01-01

High dimensional statistical problems arise from diverse fields of scientific research and technological development. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional idea of best subset selection methods, which can be regarded as a specific form of penalized likelihood, is computationally too expensive for many modern statistical applications. Other forms of penalized likelihood methods have been successfully developed over the last decade to cope with high dimensionality. They have been widely applied for simultaneously selecting important variables and estimating their effects in high dimensional statistical inference. In this article, we present a brief account of the recent developments of theory, methods, and implementations for high dimensional variable selection. What limits of the dimensionality such methods can handle, what the role of penalty functions is, and what the statistical properties are rapidly drive the advances of the field. The properties of non-concave penalized likelihood and its roles in high dimensional statistical modeling are emphasized. We also review some recent advances in ultra-high dimensional variable selection, with emphasis on independence screening and two-scale methods. PMID:21572976

A Novel Weighted Kernel PCA-Based Method for Optimization and Uncertainty Quantification

NASA Astrophysics Data System (ADS)

Thimmisetty, C.; Talbot, C.; Chen, X.; Tong, C. H.

2016-12-01

It has been demonstrated that machine learning methods can be successfully applied to uncertainty quantification for geophysical systems through the use of the adjoint method coupled with kernel PCA-based optimization. In addition, it has been shown through weighted linear PCA how optimization with respect to both observation weights and feature space control variables can accelerate convergence of such methods. Linear machine learning methods, however, are inherently limited in their ability to represent features of non-Gaussian stochastic random fields, as they are based on only the first two statistical moments of the original data. Nonlinear spatial relationships and multipoint statistics leading to the tortuosity characteristic of channelized media, for example, are captured only to a limited extent by linear PCA. With the aim of coupling the kernel-based and weighted methods discussed, we present a novel mathematical formulation of kernel PCA, Weighted Kernel Principal Component Analysis (WKPCA), that both captures nonlinear relationships and incorporates the attribution of significance levels to different realizations of the stochastic random field of interest. We also demonstrate how new instantiations retaining defining characteristics of the random field can be generated using Bayesian methods. In particular, we present a novel WKPCA-based optimization method that minimizes a given objective function with respect to both feature space random variables and observation weights through which optimal snapshot significance levels and optimal features are learned. We showcase how WKPCA can be applied to nonlinear optimal control problems involving channelized media, and in particular demonstrate an application of the method to learning the spatial distribution of material parameter values in the context of linear elasticity, and discuss further extensions of the method to stochastic inversion.

Self-Regulated Learning Strategies in Relation with Statistics Anxiety

ERIC Educational Resources Information Center

Kesici, Sahin; Baloglu, Mustafa; Deniz, M. Engin

2011-01-01

Dealing with students' attitudinal problems related to statistics is an important aspect of statistics instruction. Employing the appropriate learning strategies may have a relationship with anxiety during the process of statistics learning. Thus, the present study investigated multivariate relationships between self-regulated learning strategies…

Minimizing Statistical Bias with Queries.

DTIC Science & Technology

1995-09-14

method for optimally selecting these points would o er enormous savings in time and money. An active learning system will typically attempt to select data...research in active learning assumes that the sec- ond term of Equation 2 is approximately zero, that is, that the learner is unbiased. If this is the case...outperforms the variance- minimizing algorithm and random exploration. and e ective strategy for active learning . I have given empirical evidence that, with

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

NASA Technical Reports Server (NTRS)

Sharma, Manali; Das, Kamalika; Bilgic, Mustafa; Matthews, Bryan; Nielsen, David Lynn; Oza, Nikunj C.

2016-01-01

A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

Problem Based Learning and the scientific process

NASA Astrophysics Data System (ADS)

Schuchardt, Daniel Shaner

This research project was developed to inspire students to constructively use problem based learning and the scientific process to learn middle school science content. The student population in this study consisted of male and female seventh grade students. Students were presented with authentic problems that are connected to physical and chemical properties of matter. The intent of the study was to have students use the scientific process of looking at existing knowledge, generating learning issues or questions about the problems, and then developing a course of action to research and design experiments to model resolutions to the authentic problems. It was expected that students would improve their ability to actively engage with others in a problem solving process to achieve a deeper understanding of Michigan's 7th Grade Level Content Expectations, the Next Generation Science Standards, and a scientific process. Problem based learning was statistically effective in students' learning of the scientific process. Students statistically showed improvement on pre to posttest scores. The teaching method of Problem Based Learning was effective for seventh grade science students at Dowagiac Middle School.

The Mediating Role of Organizational Learning in the Relationship of Organizational Intelligence and Organizational Agility.

PubMed

Bahrami, Mohammad Amin; Kiani, Mohammad Mehdi; Montazeralfaraj, Raziye; Zadeh, Hossein Fallah; Zadeh, Morteza Mohammad

2016-06-01

Organizational learning is defined as creating, absorbing, retaining, transferring, and application of knowledge within an organization. This article aims to examine the mediating role of organizational learning in the relationship of organizational intelligence and organizational agility. This analytical and cross-sectional study was conducted in 2015 at four teaching hospitals of Yazd city, Iran. A total of 370 administrative and medical staff contributed to the study. We used stratified-random method for sampling. Required data were gathered using three valid questionnaires including Alberkht (2003) organizational intelligence, Neefe (2001) organizational learning, and Sharifi and Zhang (1999) organizational agility questionnaires. Data analysis was done through R and SPSS 18 statistical software. The results showed that organizational learning acts as a mediator in the relationship of organizational intelligence and organizational agility (path coefficient = 0.943). Also, organizational learning has a statistical relationship with organizational agility (path coefficient = 0.382). Our findings suggest that the improvement of organizational learning abilities can affect an organization's agility which is crucial for its survival.

Statistical Mechanical Analysis of Online Learning with Weight Normalization in Single Layer Perceptron

NASA Astrophysics Data System (ADS)

Yoshida, Yuki; Karakida, Ryo; Okada, Masato; Amari, Shun-ichi

2017-04-01

Weight normalization, a newly proposed optimization method for neural networks by Salimans and Kingma (2016), decomposes the weight vector of a neural network into a radial length and a direction vector, and the decomposed parameters follow their steepest descent update. They reported that learning with the weight normalization achieves better converging speed in several tasks including image recognition and reinforcement learning than learning with the conventional parameterization. However, it remains theoretically uncovered how the weight normalization improves the converging speed. In this study, we applied a statistical mechanical technique to analyze on-line learning in single layer linear and nonlinear perceptrons with weight normalization. By deriving order parameters of the learning dynamics, we confirmed quantitatively that weight normalization realizes fast converging speed by automatically tuning the effective learning rate, regardless of the nonlinearity of the neural network. This property is realized when the initial value of the radial length is near the global minimum; therefore, our theory suggests that it is important to choose the initial value of the radial length appropriately when using weight normalization.

Identification of the Learning Styles and "On-the-Job" Learning Methods Implemented by Nurses for Promoting Their Professional Knowledge and Skills.

PubMed

Rassin, Michal; Kurzweil, Yaffa; Maoz, Yael

2015-05-09

The aim of this study was to identify the learning styles and methods used by nurses to promote their professional knowledge and skills. 928 nurses from 11 hospitals across Israel completed 2 questionnaires, (1) Kolb's Learning Style Inventory, Version 3.1. and (2) the On-The-Job Learning Styles Questionnaire for the Nursing Profession. The most common learning style was the convergent style. The other learning styles were rated in the following descending order: accommodation, assimilation, and divergence. The on-the-job learning style consistently ranked highest was experience of relevant situations. On the other hand, seeking knowledge from books, journals, television, or the Internet was ranked lowest on all the indicators examined. With respect to general and on-the-job learning styles, statistically significant differences were found between groups of nurses by: country of birth, gender, department, age, education, and role. Nurses required to take more personal responsibility for their own professional development by deepening their self-learning skills.

Relationship between perceptual learning in speech and statistical learning in younger and older adults

PubMed Central

Neger, Thordis M.; Rietveld, Toni; Janse, Esther

2014-01-01

Within a few sentences, listeners learn to understand severely degraded speech such as noise-vocoded speech. However, individuals vary in the amount of such perceptual learning and it is unclear what underlies these differences. The present study investigates whether perceptual learning in speech relates to statistical learning, as sensitivity to probabilistic information may aid identification of relevant cues in novel speech input. If statistical learning and perceptual learning (partly) draw on the same general mechanisms, then statistical learning in a non-auditory modality using non-linguistic sequences should predict adaptation to degraded speech. In the present study, 73 older adults (aged over 60 years) and 60 younger adults (aged between 18 and 30 years) performed a visual artificial grammar learning task and were presented with 60 meaningful noise-vocoded sentences in an auditory recall task. Within age groups, sentence recognition performance over exposure was analyzed as a function of statistical learning performance, and other variables that may predict learning (i.e., hearing, vocabulary, attention switching control, working memory, and processing speed). Younger and older adults showed similar amounts of perceptual learning, but only younger adults showed significant statistical learning. In older adults, improvement in understanding noise-vocoded speech was constrained by age. In younger adults, amount of adaptation was associated with lexical knowledge and with statistical learning ability. Thus, individual differences in general cognitive abilities explain listeners' variability in adapting to noise-vocoded speech. Results suggest that perceptual and statistical learning share mechanisms of implicit regularity detection, but that the ability to detect statistical regularities is impaired in older adults if visual sequences are presented quickly. PMID:25225475

Relationship between perceptual learning in speech and statistical learning in younger and older adults.

PubMed

Neger, Thordis M; Rietveld, Toni; Janse, Esther

2014-01-01

Within a few sentences, listeners learn to understand severely degraded speech such as noise-vocoded speech. However, individuals vary in the amount of such perceptual learning and it is unclear what underlies these differences. The present study investigates whether perceptual learning in speech relates to statistical learning, as sensitivity to probabilistic information may aid identification of relevant cues in novel speech input. If statistical learning and perceptual learning (partly) draw on the same general mechanisms, then statistical learning in a non-auditory modality using non-linguistic sequences should predict adaptation to degraded speech. In the present study, 73 older adults (aged over 60 years) and 60 younger adults (aged between 18 and 30 years) performed a visual artificial grammar learning task and were presented with 60 meaningful noise-vocoded sentences in an auditory recall task. Within age groups, sentence recognition performance over exposure was analyzed as a function of statistical learning performance, and other variables that may predict learning (i.e., hearing, vocabulary, attention switching control, working memory, and processing speed). Younger and older adults showed similar amounts of perceptual learning, but only younger adults showed significant statistical learning. In older adults, improvement in understanding noise-vocoded speech was constrained by age. In younger adults, amount of adaptation was associated with lexical knowledge and with statistical learning ability. Thus, individual differences in general cognitive abilities explain listeners' variability in adapting to noise-vocoded speech. Results suggest that perceptual and statistical learning share mechanisms of implicit regularity detection, but that the ability to detect statistical regularities is impaired in older adults if visual sequences are presented quickly.

Flipped Learning With Simulation in Undergraduate Nursing Education.

PubMed

Kim, HeaRan; Jang, YounKyoung

2017-06-01

Flipped learning has proliferated in various educational environments. This study aimed to verify the effects of flipped learning on the academic achievement, teamwork skills, and satisfaction levels of undergraduate nursing students. For the flipped learning group, simulation-based education via the flipped learning method was provided, whereas traditional, simulation-based education was provided for the control group. After completion of the program, academic achievement, teamwork skills, and satisfaction levels were assessed and analyzed. The flipped learning group received higher scores on academic achievement, teamwork skills, and satisfaction levels than the control group, including the areas of content knowledge and clinical nursing practice competency. In addition, this difference gradually increased between the two groups throughout the trial. The results of this study demonstrated the positive, statistically significant effects of the flipped learning method on simulation-based nursing education. [J Nurs Educ. 2017;56(6):329-336.]. Copyright 2017, SLACK Incorporated.

Learning style and teaching method preferences of Saudi students of physical therapy

PubMed Central

Al Maghraby, Mohamed A.; Alshami, Ali M.

2013-01-01

Context: To the researchers’ knowledge, there are no published studies that have investigated the learning styles and preferred teaching methods of physical therapy students in Saudi Arabia. Aim: The study was conducted to determine the learning styles and preferred teaching methods of Saudi physical therapy students. Settings and Design: A cross-sectional study design. Materials and Methods: Fifty-three Saudis studying physical therapy (21 males and 32 females) participated in the study. The principal researcher gave an introductory lecture to explain the different learning styles and common teaching methods. Upon completion of the lecture, questionnaires were distributed, and were collected on completion. Statistical Analysis Used: Percentages were calculated for the learning styles and teaching methods. Pearson’s correlations were performed to investigate the relationship between them. Results: More than 45 (85%) of the students rated hands-on training as the most preferred teaching method. Approximately 30 (57%) students rated the following teaching methods as the most preferred methods: “Advanced organizers,” “demonstrations,” and “multimedia activities.” Although 31 (59%) students rated the concrete-sequential learning style the most preferred, these students demonstrated mixed styles on the other style dimensions: Abstract-sequential, abstract-random, and concrete-random. Conclusions: The predominant concrete-sequential learning style is consistent with the most preferred teaching method (hands-on training). The high percentage of physical therapy students whose responses were indicative of mixed learning styles suggests that they can accommodate multiple teaching methods. It is recommended that educators consider the diverse learning styles of the students and utilize a variety of teaching methods in order to promote an optimal learning environment for the students. PMID:24672278

Meeting Contemporary Statistical Needs of Instructional Communication Research: Modeling Teaching and Learning as a Conditional Process. Forum: The Future of Instructional Communication

ERIC Educational Resources Information Center

Goodboy, Alan K.

2017-01-01

For decades, instructional communication scholars have relied predominantly on cross-sectional survey methods to generate empirical associations between effective teaching and student learning. These studies typically correlate students' perceptions of their instructor's teaching behaviors with subjective self-report assessments of their own…

Cooperative Learning Revisited: From an Instructional Method to a Way of Life

ERIC Educational Resources Information Center

Garfield, Joan

2013-01-01

Joan Garfield reflects on cooperative learning, 20 years after publishing an article on this topic in the inaugural issue of "Journal of Statistics Education" ("JSE") (Garfield 1993). Here, she writes that as she re-read the article, she realized that she still agrees with most of what she originally wrote regarding the use of…

Data-adaptive test statistics for microarray data.

PubMed

Mukherjee, Sach; Roberts, Stephen J; van der Laan, Mark J

2005-09-01

An important task in microarray data analysis is the selection of genes that are differentially expressed between different tissue samples, such as healthy and diseased. However, microarray data contain an enormous number of dimensions (genes) and very few samples (arrays), a mismatch which poses fundamental statistical problems for the selection process that have defied easy resolution. In this paper, we present a novel approach to the selection of differentially expressed genes in which test statistics are learned from data using a simple notion of reproducibility in selection results as the learning criterion. Reproducibility, as we define it, can be computed without any knowledge of the 'ground-truth', but takes advantage of certain properties of microarray data to provide an asymptotically valid guide to expected loss under the true data-generating distribution. We are therefore able to indirectly minimize expected loss, and obtain results substantially more robust than conventional methods. We apply our method to simulated and oligonucleotide array data. By request to the corresponding author.

Alternative and traditional assessments: Their comparative impact on students' attitudes and science learning outcomes. An exploratory study

NASA Astrophysics Data System (ADS)

Century, Daisy Nelson

This probing study focused on alternative and traditional assessments, their comparative impacts on students' attitudes and science learning outcomes. Four basic questions were asked: What type of science learning stemming from the instruction can best be assessed by the use of traditional paper-and pencil test? What type of science learning stemming from the instruction can best be assessed by the use of alternative assessment? What are the differences in the types of learning outcomes that can be assessed by the use of paper-pencil test and alternative assessment test? Is there a difference in students' attitude towards learning science when assessment of outcomes is by alternative assessment means compared to traditional means compared to traditional means? A mixed methodology involving quantitative and qualitative techniques was utilized. However, the study was essentially a case study. Quantitative data analysis included content achievement and attitude results, to which non-parametric statistics were applied. Analysis of qualitative data was done as a case study utilizing pre-set protocols resulting in a narrative summary style of report. These outcomes were combined in order to produce conclusions. This study revealed that the traditional method yielded more concrete cognitive content learning than did the alternative assessment. The alternative assessment yielded more psychomotor, cooperative learning and critical thinking skills. In both the alternative and the traditional methods the student's attitudes toward science were positive. There was no significant differences favoring either group. The quantitative findings of no statistically significant differences suggest that at a minimum there is no loss in the use of alternative assessment methods, in this instance, performance testing. Adding the results from the qualitative analysis to this suggests (1) that class groups were more satisfied when alternative methods were employed, and (2) that the two assessment methodologies are complementary to each other, and thus should probably be used together to produce maximum benefit.

Learning Might Not Equal Liking: Research Methods Course Changes Knowledge But Not Attitudes

ERIC Educational Resources Information Center

Sizemore, O. J.; Lewandowski, Gary W., Jr.

2009-01-01

Students completed surveys at the beginning and end of a sophomore-level course on research and statistics. We hypothesized that the course would produce advances in knowledge of research and statistics and that those changes would be accompanied by more favorable attitudes toward the subject matter. Results showed that knowledge did increase…

Infant Statistical-Learning Ability Is Related to Real-Time Language Processing

ERIC Educational Resources Information Center

Lany, Jill; Shoaib, Amber; Thompson, Abbie; Estes, Katharine Graf

2018-01-01

Infants are adept at learning statistical regularities in artificial language materials, suggesting that the ability to learn statistical structure may support language development. Indeed, infants who perform better on statistical learning tasks tend to be more advanced in parental reports of infants' language skills. Work with adults suggests…

Statistical Learning Is Related to Early Literacy-Related Skills

ERIC Educational Resources Information Center

Spencer, Mercedes; Kaschak, Michael P.; Jones, John L.; Lonigan, Christopher J.

2015-01-01

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one's environment, plays a role in young children's acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from…

Current and future health care professionals attitudes toward and knowledge of statistics: How confidence influences learning

PubMed Central

Baghi, Heibatollah; Kornides, Melanie L.

2014-01-01

Background Health care professionals require some understanding of statistics to successfully implement evidence based practice. Developing competency in statistical reasoning is necessary for students training in health care administration, research, and clinical care. Recently, the interest in healthcare professional's attitudes toward statistics has increased substantially due to evidence that these attitudes can hinder professionalism developing an understanding of statistical concepts. Methods In this study, we analyzed pre- and post-instruction attitudes towards and knowledge of statistics obtained from health science graduate students, including nurses and nurse practitioners, enrolled in an introductory graduate course in statistics (n = 165). Results and Conclusions Results show that the students already held generally positive attitudes toward statistics at the beginning of course. However, these attitudes—along with the students’ statistical proficiency—improved after 10 weeks of instruction. The results have implications for curriculum design and delivery methods as well as for health professionals’ effective use of statistics in critically evaluating and utilizing research in their practices. PMID:25419256

The Japanese and the American First-Line Supervisor.

ERIC Educational Resources Information Center

Bryan, Leslie A., Jr.

1982-01-01

Compares the American and Japanese first-line supervisor: production statistics, supervisory style, company loyalty, management style, and communication. Also suggests what Americans might learn from the Japanese methods. (CT)

A supervised learning approach for Crohn's disease detection using higher-order image statistics and a novel shape asymmetry measure.

PubMed

Mahapatra, Dwarikanath; Schueffler, Peter; Tielbeek, Jeroen A W; Buhmann, Joachim M; Vos, Franciscus M

2013-10-01

Increasing incidence of Crohn's disease (CD) in the Western world has made its accurate diagnosis an important medical challenge. The current reference standard for diagnosis, colonoscopy, is time-consuming and invasive while magnetic resonance imaging (MRI) has emerged as the preferred noninvasive procedure over colonoscopy. Current MRI approaches assess rate of contrast enhancement and bowel wall thickness, and rely on extensive manual segmentation for accurate analysis. We propose a supervised learning method for the identification and localization of regions in abdominal magnetic resonance images that have been affected by CD. Low-level features like intensity and texture are used with shape asymmetry information to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy-based shape asymmetry method and higher-order statistics like skewness and kurtosis. Multi-scale feature extraction renders the method robust. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods.

Research Methodologies Explored for a Paradigm Shift in University Teaching.

ERIC Educational Resources Information Center

Venter, I. M.; Blignaut, R. J.; Stoltz, D.

2001-01-01

Innovative teaching methods such as collaborative learning, teamwork, and mind maps were introduced to teach computer science and statistics courses at a South African university. Soft systems methodology was adapted and used to manage the research process of evaluating the effectiveness of the teaching methods. This research method provided proof…

The extraction and integration framework: a two-process account of statistical learning.

PubMed

Thiessen, Erik D; Kronstein, Alexandra T; Hufnagle, Daniel G

2013-07-01

The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other. 2013 APA, all rights reserved

Helping Students Develop Statistical Reasoning: Implementing a Statistical Reasoning Learning Environment

ERIC Educational Resources Information Center

Garfield, Joan; Ben-Zvi, Dani

2009-01-01

This article describes a model for an interactive, introductory secondary- or tertiary-level statistics course that is designed to develop students' statistical reasoning. This model is called a "Statistical Reasoning Learning Environment" and is built on the constructivist theory of learning.

Machine Learning Methods for Production Cases Analysis

NASA Astrophysics Data System (ADS)

Mokrova, Nataliya V.; Mokrov, Alexander M.; Safonova, Alexandra V.; Vishnyakov, Igor V.

2018-03-01

Approach to analysis of events occurring during the production process were proposed. Described machine learning system is able to solve classification tasks related to production control and hazard identification at an early stage. Descriptors of the internal production network data were used for training and testing of applied models. k-Nearest Neighbors and Random forest methods were used to illustrate and analyze proposed solution. The quality of the developed classifiers was estimated using standard statistical metrics, such as precision, recall and accuracy.

Evaluation of Deep Learning Representations of Spatial Storm Data

NASA Astrophysics Data System (ADS)

Gagne, D. J., II; Haupt, S. E.; Nychka, D. W.

2017-12-01

The spatial structure of a severe thunderstorm and its surrounding environment provide useful information about the potential for severe weather hazards, including tornadoes, hail, and high winds. Statistics computed over the area of a storm or from the pre-storm environment can provide descriptive information but fail to capture structural information. Because the storm environment is a complex, high-dimensional space, identifying methods to encode important spatial storm information in a low-dimensional form should aid analysis and prediction of storms by statistical and machine learning models. Principal component analysis (PCA), a more traditional approach, transforms high-dimensional data into a set of linearly uncorrelated, orthogonal components ordered by the amount of variance explained by each component. The burgeoning field of deep learning offers two potential approaches to this problem. Convolutional Neural Networks are a supervised learning method for transforming spatial data into a hierarchical set of feature maps that correspond with relevant combinations of spatial structures in the data. Generative Adversarial Networks (GANs) are an unsupervised deep learning model that uses two neural networks trained against each other to produce encoded representations of spatial data. These different spatial encoding methods were evaluated on the prediction of severe hail for a large set of storm patches extracted from the NCAR convection-allowing ensemble. Each storm patch contains information about storm structure and the near-storm environment. Logistic regression and random forest models were trained using the PCA and GAN encodings of the storm data and were compared against the predictions from a convolutional neural network. All methods showed skill over climatology at predicting the probability of severe hail. However, the verification scores among the methods were very similar and the predictions were highly correlated. Further evaluations are being performed to determine how the choice of input variables affects the results.

Using Microsoft Excel to teach statistics in a graduate advanced practice nursing program.

PubMed

DiMaria-Ghalili, Rose Ann; Ostrow, C Lynne

2009-02-01

This article describes the authors' experiences during 3 years of using Microsoft Excel to teach graduate-level statistics, as part of the research core required by the American Association of Colleges of Nursing for all professional graduate nursing programs. The advantages to using this program instead of specialized statistical programs are ease of accessibility, increased transferability of skills, and reduced cost for students. The authors share their insight about realistic goals for teaching statistics to master's-level students and the resources that are available to faculty to help them to learn and use Excel in their courses. Several online sites that are excellent resources for both faculty and students are discussed. Detailed attention is given to an online course (Carnegie-Mellon University Open Learning Initiative, n.d.), which the authors have incorporated into their graduate-level research methods course.

A Role for Chunk Formation in Statistical Learning of Second Language Syntax

ERIC Educational Resources Information Center

Hamrick, Phillip

2014-01-01

Humans are remarkably sensitive to the statistical structure of language. However, different mechanisms have been proposed to account for such statistical sensitivities. The present study compared adult learning of syntax and the ability of two models of statistical learning to simulate human performance: Simple Recurrent Networks, which learn by…

Statistical Learning is Related to Early Literacy-Related Skills

PubMed Central

Spencer, Mercedes; Kaschak, Michael P.; Jones, John L.; Lonigan, Christopher J.

2015-01-01

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one’s environment, plays a role in young children’s acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from fluent speech and the learning of syntactic structure, some recent studies have explored the extent to which individual differences in statistical learning are related to literacy-relevant knowledge and skills. The present study extends on this literature by investigating the relations between two measures of statistical learning and multiple measures of skills that are critical to the development of literacy—oral language, vocabulary knowledge, and phonological processing—within a single model. Our sample included a total of 553 typically developing children from prekindergarten through second grade. Structural equation modeling revealed that statistical learning accounted for a unique portion of the variance in these literacy-related skills. Practical implications for instruction and assessment are discussed. PMID:26478658

OBSERVATIONS ABOUT HOW WE LEARN ABOUT METHODOLOGY AND STATISTICS.

PubMed

Jose, Paul E

2017-06-01

The overarching theme of this monograph is to encourage developmental researchers to acquire cutting-edge and innovative design and statistical methods so that we can improve the studies that we execute on the topic of change. Card, the editor of the monograph, challenges the reader to think about works such as the present one as contributing to the new subdiscipline of developmental methodology within the broader field of developmental science. This thought-provoking stance served as the stimulus for the present commentary, which is a collection of observations on "how we learn about methodology and statistics." The point is made that we often learn critical new information from our colleagues, from seminal writings in the literature, and from conferences and workshop participation. It is encouraged that researchers pursue all three of these pathways as ways to acquire innovative knowledge and techniques. Finally, the role of developmental science societies in supporting the dissemination and uptake of this type of knowledge is discussed. © 2017 The Society for Research in Child Development, Inc.

Undergraduate medical student's perceptions on traditional and problem based curricula: pilot study.

PubMed

Meo, Sultan Ayoub

2014-07-01

To evaluate and compare students' perceptions about teaching and learning, knowledge and skills, outcomes of course materials and their satisfaction in traditional Lecture Based learning versus Problem-Based Learning curricula in two different medical schools. The comparative cross-sectional questionnaire-based study was conducted in the Department of Physiology, College of Medicine, King Saud University, Riyadh, Saudi Arabia, from July 2009 to January 2011. Two different undergraduate medical schools were selected; one followed the traditional curriculum, while the other followed the problem-based learning curriculum. Two equal groups of first year medical students were selected. They were taught in respiratory physiology and lung function lab according to their curriculum for a period of two weeks. At the completion of the study period, a five-point Likert scale was used to assess students' perceptions on satisfaction, academic environment, teaching and learning, knowledge and skills and outcomes of course materials about effectiveness of problem-based learning compared to traditional methods. SPSS 19 was used for statistical analysis. Students used to problem-based learning curriculum obtained marginally higher scores in their perceptions (24.10 +/- 3.63) compared to ones following the traditional curriculum (22.67 +/- 3.74). However, the difference in perceptions did not achieve a level of statistical significance. Students following problem-based learning curriculum have more positive perceptions on teaching and learning, knowledge and skills, outcomes of their course materials and satisfaction compared to the students belonging to the traditional style of medical school. However, the difference between the two groups was not statistically significant.

Use of iPhone technology in improving acetabular component position in total hip arthroplasty.

PubMed

Tay, Xiau Wei; Zhang, Benny Xu; Gayagay, George

2017-09-01

Improper acetabular cup positioning is associated with high risk of complications after total hip arthroplasty. The aim of our study is to objectively compare 3 methods, namely (1) free hand, (2) alignment jig (Sputnik), and (3) iPhone application to identify an easy, reproducible, and accurate method in improving acetabular cup placement. We designed a simple setup and carried out a simple experiment (see Method section). Using statistical analysis, the difference in inclination angles using iPhone application compared with the freehand method was found to be statistically significant ( F [2,51] = 4.17, P = .02) in the "untrained group". There is no statistical significance detected for the other groups. This suggests a potential role for iPhone applications in junior surgeons in overcoming the steep learning curve.

A Study of the Effectiveness of the Contextual Lab Activity in the Teaching and Learning Statistics at the UTHM (Universiti Tun Hussein Onn Malaysia)

ERIC Educational Resources Information Center

Kamaruddin, Nafisah Kamariah Md; Jaafar, Norzilaila bt; Amin, Zulkarnain Md

2012-01-01

Inaccurate concept in statistics contributes to the assumption by the students that statistics do not relate to the real world and are not relevant to the engineering field. There are universities which introduced learning statistics using statistics lab activities. However, the learning is more on the learning how to use software and not to…

Statistical Machine Learning for Structured and High Dimensional Data

DTIC Science & Technology

2014-09-17

AFRL-OSR-VA-TR-2014-0234 STATISTICAL MACHINE LEARNING FOR STRUCTURED AND HIGH DIMENSIONAL DATA Larry Wasserman CARNEGIE MELLON UNIVERSITY Final...Re . 8-98) v Prescribed by ANSI Std. Z39.18 14-06-2014 Final Dec 2009 - Aug 2014 Statistical Machine Learning for Structured and High Dimensional...area of resource-constrained statistical estimation. machine learning , high-dimensional statistics U U U UU John Lafferty 773-702-3813 > Research under

Dynamic Optimization

NASA Technical Reports Server (NTRS)

Laird, Philip

1992-01-01

We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.

Second Language Experience Facilitates Statistical Learning of Novel Linguistic Materials

ERIC Educational Resources Information Center

Potter, Christine E.; Wang, Tianlin; Saffran, Jenny R.

2017-01-01

Recent research has begun to explore individual differences in statistical learning, and how those differences may be related to other cognitive abilities, particularly their effects on language learning. In this research, we explored a different type of relationship between language learning and statistical learning: the possibility that learning…

Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial

PubMed Central

Worm, Bjarne Skjødt; Jensen, Kenneth

2013-01-01

Background and aims The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students’ learning ability. Methods One hundred and twenty Danish medical students were randomized to six groups all with 20 students (eCases level 1, eCases level 2, eCases level 2+, eTextbook level 1, eTextbook level 2, and eTextbook level 2+). All students participated in a pre-test, Group 1 participated in an interactive case-based e-learning program, while Group 2 was presented with textbook material electronically. The 2+ groups were able to discuss the material between themselves in a web forum. The subject was head injury and associated treatment and observation guidelines in the emergency room. Following the e-learning, all students completed a post-test. Pre- and post-tests both consisted of 25 questions randomly chosen from a pool of 50 different questions. Results All students concluded the study with comparable pre-test results. Students at Level 2 (in both groups) improved statistically significant compared to students at level 1 (p>0.05). There was no statistically significant difference between level 2 and level 2+. However, level 2+ was associated with statistically significant greater student's satisfaction than the rest of the students (p>0.05). Conclusions This study applies a new way of comparing different types of e-learning using a pre-defined level division and the possibility of peer learning. Our findings show that higher levels of e-learning does in fact provide better results when compared with the same type of e-learning at lower levels. While social interaction in web forums increase student satisfaction, learning ability does not seem to change. Both findings are relevant when designing new e-learning materials. PMID:24229729

Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial.

PubMed

Worm, Bjarne Skjødt; Jensen, Kenneth

2013-01-01

Background and aims The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students' learning ability. Methods One hundred and twenty Danish medical students were randomized to six groups all with 20 students (eCases level 1, eCases level 2, eCases level 2+, eTextbook level 1, eTextbook level 2, and eTextbook level 2+). All students participated in a pre-test, Group 1 participated in an interactive case-based e-learning program, while Group 2 was presented with textbook material electronically. The 2+ groups were able to discuss the material between themselves in a web forum. The subject was head injury and associated treatment and observation guidelines in the emergency room. Following the e-learning, all students completed a post-test. Pre- and post-tests both consisted of 25 questions randomly chosen from a pool of 50 different questions. Results All students concluded the study with comparable pre-test results. Students at Level 2 (in both groups) improved statistically significant compared to students at level 1 (p>0.05). There was no statistically significant difference between level 2 and level 2+. However, level 2+ was associated with statistically significant greater student's satisfaction than the rest of the students (p>0.05). Conclusions This study applies a new way of comparing different types of e-learning using a pre-defined level division and the possibility of peer learning. Our findings show that higher levels of e-learning does in fact provide better results when compared with the same type of e-learning at lower levels. While social interaction in web forums increase student satisfaction, learning ability does not seem to change. Both findings are relevant when designing new e-learning materials.

Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides.

PubMed

Stanislawski, Jerzy; Kotulska, Malgorzata; Unold, Olgierd

2013-01-17

Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning). We showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset proved representative enough to use simple statistical methods for testing the amylogenicity based only on six letter sequences. Statistical machine learning methods such as Alternating Decision Tree and Multilayer Perceptron can replace the energy based classifier, with advantage of very significantly reduced computational time and simplicity to perform the analysis. Additionally, a decision tree provides a set of very easily interpretable rules.

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bai, T; UT Southwestern Medical Center, Dallas, TX; Yan, H

2014-06-15

Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm inmore » a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application. A high zresolution is preferred to stabilize statistical iterative reconstruction. This work was supported in part by NIH(1R01CA154747-01), NSFC((No. 61172163), Research Fund for the Doctoral Program of Higher Education of China (No. 20110201110011), China Scholarship Council.« less

Computer Mediated Communication: Online Instruction and Interactivity.

ERIC Educational Resources Information Center

Lavooy, Maria J.; Newlin, Michael H.

2003-01-01

Explores the different forms and potential applications of computer mediated communication (CMC) for Web-based and Web-enhanced courses. Based on their experiences with three different Web courses (Research Methods in Psychology, Statistical Methods in Psychology, and Basic Learning Processes) taught repeatedly over the last five years, the…

Participant Interaction in Asynchronous Learning Environments: Evaluating Interaction Analysis Methods

ERIC Educational Resources Information Center

Blanchette, Judith

2012-01-01

The purpose of this empirical study was to determine the extent to which three different objective analytical methods--sequence analysis, surface cohesion analysis, and lexical cohesion analysis--can most accurately identify specific characteristics of online interaction. Statistically significant differences were found in all points of…

The unrealized promise of infant statistical word-referent learning

PubMed Central

Smith, Linda B.; Suanda, Sumarga H.; Yu, Chen

2014-01-01

Recent theory and experiments offer a new solution as to how infant learners may break into word learning, by using cross-situational statistics to find the underlying word-referent mappings. Computational models demonstrate the in-principle plausibility of this statistical learning solution and experimental evidence shows that infants can aggregate and make statistically appropriate decisions from word-referent co-occurrence data. We review these contributions and then identify the gaps in current knowledge that prevent a confident conclusion about whether cross-situational learning is the mechanism through which infants break into word learning. We propose an agenda to address that gap that focuses on detailing the statistics in the learning environment and the cognitive processes that make use of those statistics. PMID:24637154

Analysing the Opportunities and Challenges to Use of Information and Communication Technology Tools in Teaching-Learning Process

ERIC Educational Resources Information Center

Dastjerdi, Negin Barat

2016-01-01

The research aims at the evaluation of ICT use in teaching-learning process to the students of Isfahan elementary schools. The method of this research is descriptive-surveying. The statistical population of the study was all teachers of Isfahan elementary schools. The sample size was determined 350 persons that selected through cluster sampling…

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

PubMed

Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

2017-11-13

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 < 0.001). The AUCs were 0.84 (95% CI 0.78-0.89) for deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.

Calculation of precise firing statistics in a neural network model

NASA Astrophysics Data System (ADS)

Cho, Myoung Won

2017-08-01

A precise prediction of neural firing dynamics is requisite to understand the function of and the learning process in a biological neural network which works depending on exact spike timings. Basically, the prediction of firing statistics is a delicate manybody problem because the firing probability of a neuron at a time is determined by the summation over all effects from past firing states. A neural network model with the Feynman path integral formulation is recently introduced. In this paper, we present several methods to calculate firing statistics in the model. We apply the methods to some cases and compare the theoretical predictions with simulation results.

Potential Values of Incorporating a Multiple-Choice Question Construction in Physics Experimentation Instruction

NASA Astrophysics Data System (ADS)

Yu, Fu-Yun; Liu, Yu-Hsin

2005-09-01

The potential value of a multiple-choice question-construction instructional strategy for the support of students’ learning of physics experiments was examined in the study. Forty-two university freshmen participated in the study for a whole semester. A constant comparison method adopted to categorize students’ qualitative data indicated that the influences of multiple-choice question construction were evident in several significant ways (promoting constructive and productive studying habits; reflecting and previewing course-related materials; increasing in-group communication and interaction; breaking passive learning style and habits, etc.), which, worked together, not only enhanced students’ comprehension and retention of the obtained knowledge, but also helped distil a sense of empowerment and learning community within the participants. Analysis with one-group t-tests, using 3 as the expected mean, on quantitative data further found that students’ satisfaction toward past learning experience, and perceptions toward this strategy’s potentials for promoting learning were statistically significant at the 0.0005 level, while learning anxiety was not statistically significant. Suggestions for incorporating question-generation activities within classroom and topics for future studies were rendered.

An Alternative Approach to Analyze Ipsative Data. Revisiting Experiential Learning Theory.

PubMed

Batista-Foguet, Joan M; Ferrer-Rosell, Berta; Serlavós, Ricard; Coenders, Germà; Boyatzis, Richard E

2015-01-01

The ritualistic use of statistical models regardless of the type of data actually available is a common practice across disciplines which we dare to call type zero error. Statistical models involve a series of assumptions whose existence is often neglected altogether, this is specially the case with ipsative data. This paper illustrates the consequences of this ritualistic practice within Kolb's Experiential Learning Theory (ELT) operationalized through its Learning Style Inventory (KLSI). We show how using a well-known methodology in other disciplines-compositional data analysis (CODA) and log ratio transformations-KLSI data can be properly analyzed. In addition, the method has theoretical implications: a third dimension of the KLSI is unveiled providing room for future research. This third dimension describes an individual's relative preference for learning by prehension rather than by transformation. Using a sample of international MBA students, we relate this dimension with another self-assessment instrument, the Philosophical Orientation Questionnaire (POQ), and with an observer-assessed instrument, the Emotional and Social Competency Inventory (ESCI-U). Both show plausible statistical relationships. An intellectual operating philosophy (IOP) is linked to a preference for prehension, whereas a pragmatic operating philosophy (POP) is linked to transformation. Self-management and social awareness competencies are linked to a learning preference for transforming knowledge, whereas relationship management and cognitive competencies are more related to approaching learning by prehension.

An Alternative Approach to Analyze Ipsative Data. Revisiting Experiential Learning Theory

PubMed Central

Batista-Foguet, Joan M.; Ferrer-Rosell, Berta; Serlavós, Ricard; Coenders, Germà; Boyatzis, Richard E.

2015-01-01

The ritualistic use of statistical models regardless of the type of data actually available is a common practice across disciplines which we dare to call type zero error. Statistical models involve a series of assumptions whose existence is often neglected altogether, this is specially the case with ipsative data. This paper illustrates the consequences of this ritualistic practice within Kolb's Experiential Learning Theory (ELT) operationalized through its Learning Style Inventory (KLSI). We show how using a well-known methodology in other disciplines—compositional data analysis (CODA) and log ratio transformations—KLSI data can be properly analyzed. In addition, the method has theoretical implications: a third dimension of the KLSI is unveiled providing room for future research. This third dimension describes an individual's relative preference for learning by prehension rather than by transformation. Using a sample of international MBA students, we relate this dimension with another self-assessment instrument, the Philosophical Orientation Questionnaire (POQ), and with an observer-assessed instrument, the Emotional and Social Competency Inventory (ESCI-U). Both show plausible statistical relationships. An intellectual operating philosophy (IOP) is linked to a preference for prehension, whereas a pragmatic operating philosophy (POP) is linked to transformation. Self-management and social awareness competencies are linked to a learning preference for transforming knowledge, whereas relationship management and cognitive competencies are more related to approaching learning by prehension. PMID:26617561

A system for learning statistical motion patterns.

PubMed

Hu, Weiming; Xiao, Xuejuan; Fu, Zhouyu; Xie, Dan; Tan, Tieniu; Maybank, Steve

2006-09-01

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy K-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction.

Predominant learning styles among pharmacy students at the Federal University of Paraná, Brazil

PubMed Central

Czepula, Alexandra I.; Bottacin, Wallace E.; Hipólito, Edson; Baptista, Deise R.; Pontarolo, Roberto; Correr, Cassyano J.

2015-01-01

Background: Learning styles are cognitive, emotional, and physiological traits, as well as indicators of how learners perceive, interact, and respond to their learning environments. According to Honey-Mumford, learning styles are classified as active, reflexive, theoretical, and pragmatic. Objective: The purpose of this study was to identify the predominant learning styles among pharmacy students at the Federal University of Paraná, Brazil. Methods: An observational, cross-sectional, and descriptive study was conducted using the Honey-Alonso Learning Style Questionnaire. Students in the Bachelor of Pharmacy program were invited to participate in this study. The questionnaire comprised 80 randomized questions, 20 for each of the four learning styles. The maximum possible score was 20 points for each learning style, and cumulative scores indicated the predominant learning styles among the participants. Honey-Mumford (1986) proposed five preference levels for each style (very low, low, moderate, high, and very high), called a general interpretation scale, to avoid student identification with one learning style and ignoring the characteristics of the other styles. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 20.0. Results: This study included 297 students (70% of all pharmacy students at the time) with a median age of 21 years old. Women comprised 77.1% of participants. The predominant style among pharmacy students at the Federal University of Paraná was the pragmatist, with a median of 14 (high preference). The pragmatist style prevails in people who are able to discover techniques related to their daily learning because such people are curious to discover new strategies and attempt to verify whether the strategies are efficient and valid. Because these people are direct and objective in their actions, pragmatists prefer to focus on practical issues that are validated and on problem situations. There was no statistically significant difference between genders with regard to learning styles. Conclusion: The pragmatist style is the prevailing style among pharmacy students at the Federal University of Paraná. Although students may have a learning preference that preference is not the only manner in which students can learn, neither their preference is the only manner in which students can be taught. Awareness of students’ learning styles can be used to adapt the methodology used by teachers to render the teaching-learning process effective and long lasting. The content taught to students should be presented in different manners because varying teaching methods can develop learning skills in students. PMID:27011774

E-Learning in Croatian Higher Education: An Analysis of Students' Perceptions

NASA Astrophysics Data System (ADS)

Dukić, Darko; Andrijanić, Goran

2010-06-01

Over the last years, e-learning has taken an important role in Croatian higher education as a result of strategies defined and measures undertaken. Nonetheless, in comparison to the developed countries, the achievements in e-learning implementation are still unsatisfactory. Therefore, the efforts to advance e-learning within Croatian higher education need to be intensified. It is further necessary to undertake ongoing activities in order to solve possible problems in e-learning system functioning, which requires the development of adequate evaluation instruments and methods. One of the key steps in this process would be examining and analyzing users' attitudes. This paper presents a study of Croatian students' perceptions with regard to certain aspects of e-learning usage. Given the character of this research, adequate statistical methods were required for the data processing. The results of the analysis indicate that, for the most part, Croatian students have positive perceptions of e-learning, particularly as support to time-honored forms of teaching. However, they are not prepared to completely give up the traditional classroom. Using factor analysis, we identified four underlying factors of a collection of variables related to students' perceptions of e-learning. Furthermore, a certain number of statistically significant differences in student attitudes have been confirmed, in terms of gender and year of study. In our study we used discriminant analysis to determine discriminant functions that distinguished defined groups of students. With this research we managed to a certain degree to alleviate the current data insufficiency in the area of e-learning evaluation among Croatian students. Since this type of learning is gaining in importance within higher education, such analyses have to be conducted continuously.

Reward maximization justifies the transition from sensory selection at childhood to sensory integration at adulthood.

PubMed

Daee, Pedram; Mirian, Maryam S; Ahmadabadi, Majid Nili

2014-01-01

In a multisensory task, human adults integrate information from different sensory modalities--behaviorally in an optimal Bayesian fashion--while children mostly rely on a single sensor modality for decision making. The reason behind this change of behavior over age and the process behind learning the required statistics for optimal integration are still unclear and have not been justified by the conventional Bayesian modeling. We propose an interactive multisensory learning framework without making any prior assumptions about the sensory models. In this framework, learning in every modality and in their joint space is done in parallel using a single-step reinforcement learning method. A simple statistical test on confidence intervals on the mean of reward distributions is used to select the most informative source of information among the individual modalities and the joint space. Analyses of the method and the simulation results on a multimodal localization task show that the learning system autonomously starts with sensory selection and gradually switches to sensory integration. This is because, relying more on modalities--i.e. selection--at early learning steps (childhood) is more rewarding than favoring decisions learned in the joint space since, smaller state-space in modalities results in faster learning in every individual modality. In contrast, after gaining sufficient experiences (adulthood), the quality of learning in the joint space matures while learning in modalities suffers from insufficient accuracy due to perceptual aliasing. It results in tighter confidence interval for the joint space and consequently causes a smooth shift from selection to integration. It suggests that sensory selection and integration are emergent behavior and both are outputs of a single reward maximization process; i.e. the transition is not a preprogrammed phenomenon.

The effectiveness of concept mapping and retrieval practice as learning strategies in an undergraduate physiology course.

PubMed

Burdo, Joseph; O'Dwyer, Laura

2015-12-01

Concept mapping and retrieval practice are both educational methods that have separately been reported to provide significant benefits for learning in diverse settings. Concept mapping involves diagramming a hierarchical representation of relationships between distinct pieces of information, whereas retrieval practice involves retrieving information that was previously coded into memory. The relative benefits of these two methods have never been tested against each other in a classroom setting. Our study was designed to investigate whether or not concept mapping or retrieval practice produced a significant learning benefit in an undergraduate physiology course as measured by exam performance and, if so, was the benefit of one method significantly greater than the other. We found that there was a trend toward increased exam scores for the retrieval practice group compared with both the control group and concept mapping group, and that trend achieved statistical significance for one of the four module exams in the course. We also found that women performed statistically better than men on the module exam that contained a substantial amount of material relating to female reproductive physiology. Copyright © 2015 The American Physiological Society.

Is Statistical Learning Constrained by Lower Level Perceptual Organization?

PubMed Central

Emberson, Lauren L.; Liu, Ran; Zevin, Jason D.

2013-01-01

In order for statistical information to aid in complex developmental processes such as language acquisition, learning from higher-order statistics (e.g. across successive syllables in a speech stream to support segmentation) must be possible while perceptual abilities (e.g. speech categorization) are still developing. The current study examines how perceptual organization interacts with statistical learning. Adult participants were presented with multiple exemplars from novel, complex sound categories designed to reflect some of the spectral complexity and variability of speech. These categories were organized into sequential pairs and presented such that higher-order statistics, defined based on sound categories, could support stream segmentation. Perceptual similarity judgments and multi-dimensional scaling revealed that participants only perceived three perceptual clusters of sounds and thus did not distinguish the four experimenter-defined categories, creating a tension between lower level perceptual organization and higher-order statistical information. We examined whether the resulting pattern of learning is more consistent with statistical learning being “bottom-up,” constrained by the lower levels of organization, or “top-down,” such that higher-order statistical information of the stimulus stream takes priority over the perceptual organization, and perhaps influences perceptual organization. We consistently find evidence that learning is constrained by perceptual organization. Moreover, participants generalize their learning to novel sounds that occupy a similar perceptual space, suggesting that statistical learning occurs based on regions of or clusters in perceptual space. Overall, these results reveal a constraint on learning of sound sequences, such that statistical information is determined based on lower level organization. These findings have important implications for the role of statistical learning in language acquisition. PMID:23618755

Fuzzy self-learning control for magnetic servo system

NASA Technical Reports Server (NTRS)

Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

1994-01-01

It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

Effects of lattice parameters on piezoelectric constants in wurtzite materials: A theoretical study using first-principles and statistical-learning methods

NASA Astrophysics Data System (ADS)

Momida, Hiroyoshi; Oguchi, Tamio

2018-04-01

Longitudinal piezoelectric constant (e 33) values of wurtzite materials, which are listed in a structure database, are calculated and analyzed by using first-principles and statistical learning methods. It is theoretically shown that wurtzite materials with high e 33 generally have small lattice constant ratios (c/a) almost independent of constituent elements, and approximately expressed as e 33 ∝ c/a - (c/a)0 with ideal lattice constant ratio (c/a)0. This relation also holds for highly-piezoelectric ternary materials such as Sc x Al1- x N. We conducted a search for high-piezoelectric wurtzite materials by identifying materials with smaller c/a values. It is proposed that the piezoelectricity of ZnO can be significantly enhanced by substitutions of Zn with Ca.

Distinct contributions of attention and working memory to visual statistical learning and ensemble processing.

PubMed

Hall, Michelle G; Mattingley, Jason B; Dux, Paul E

2015-08-01

The brain exploits redundancies in the environment to efficiently represent the complexity of the visual world. One example of this is ensemble processing, which provides a statistical summary of elements within a set (e.g., mean size). Another is statistical learning, which involves the encoding of stable spatial or temporal relationships between objects. It has been suggested that ensemble processing over arrays of oriented lines disrupts statistical learning of structure within the arrays (Zhao, Ngo, McKendrick, & Turk-Browne, 2011). Here we asked whether ensemble processing and statistical learning are mutually incompatible, or whether this disruption might occur because ensemble processing encourages participants to process the stimulus arrays in a way that impedes statistical learning. In Experiment 1, we replicated Zhao and colleagues' finding that ensemble processing disrupts statistical learning. In Experiments 2 and 3, we found that statistical learning was unimpaired by ensemble processing when task demands necessitated (a) focal attention to individual items within the stimulus arrays and (b) the retention of individual items in working memory. Together, these results are consistent with an account suggesting that ensemble processing and statistical learning can operate over the same stimuli given appropriate stimulus processing demands during exposure to regularities. (c) 2015 APA, all rights reserved).

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes

PubMed Central

2017-01-01

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274, 1926–1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105, 2745–2750; Thiessen & Yee 2010 Child Development 81, 1287–1303; Saffran 2002 Journal of Memory and Language 47, 172–196; Misyak & Christiansen 2012 Language Learning 62, 302–331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39, 246–263; Thiessen et al. 2013 Psychological Bulletin 139, 792–814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37, 310–343). This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences'. PMID:27872374

What's statistical about learning? Insights from modelling statistical learning as a set of memory processes.

PubMed

Thiessen, Erik D

2017-01-05

Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274: , 1926-1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105: , 2745-2750; Thiessen & Yee 2010 Child Development 81: , 1287-1303; Saffran 2002 Journal of Memory and Language 47: , 172-196; Misyak & Christiansen 2012 Language Learning 62: , 302-331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39: , 246-263; Thiessen et al. 2013 Psychological Bulletin 139: , 792-814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37: , 310-343).This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

ADDIE Model Application Promoting Interactive Multimedia

NASA Astrophysics Data System (ADS)

Baharuddin, B.

2018-02-01

This paper presents the benefits of interactive learning in a vocational high school, which is developed by Research and Developmet (R&D) method. The questionnaires, documentations, and instrument tests are used to obtain data and it is analyzed by descriptive statistic. The results show the students’ competence is generated up to 80.00 %, and the subject matter aspects of the content is up to 90.00 %. The learning outcomes average is 85. This type media fulfils the proposed objective which can enhance the learning outcome.

Data Mining in Health and Medical Information.

ERIC Educational Resources Information Center

Bath, Peter A.

2004-01-01

Presents a literature review that covers the following topics related to data mining (DM) in health and medical information: the potential of DM in health and medicine; statistical methods; evaluation of methods; DM tools for health and medicine; inductive learning of symbolic rules; application of DM tools in diagnosis and prognosis; and…

Musicians' edge: A comparison of auditory processing, cognitive abilities and statistical learning.

PubMed

Mandikal Vasuki, Pragati Rao; Sharma, Mridula; Demuth, Katherine; Arciuli, Joanne

2016-12-01

It has been hypothesized that musical expertise is associated with enhanced auditory processing and cognitive abilities. Recent research has examined the relationship between musicians' advantage and implicit statistical learning skills. In the present study, we assessed a variety of auditory processing skills, cognitive processing skills, and statistical learning (auditory and visual forms) in age-matched musicians (N = 17) and non-musicians (N = 18). Musicians had significantly better performance than non-musicians on frequency discrimination, and backward digit span. A key finding was that musicians had better auditory, but not visual, statistical learning than non-musicians. Performance on the statistical learning tasks was not correlated with performance on auditory and cognitive measures. Musicians' superior performance on auditory (but not visual) statistical learning suggests that musical expertise is associated with an enhanced ability to detect statistical regularities in auditory stimuli. Copyright © 2016 Elsevier B.V. All rights reserved.

Learning style preferences of dental students at a single institution in Riyadh, Saudi Arabia, evaluated using the VARK questionnaire

PubMed Central

Aldosari, Mohammad A; Aljabaa, Aljazi H; Al-Sehaibany, Fares S; Albarakati, Sahar F

2018-01-01

Background Students differ in their preferred methods of acquiring, processing, and recalling new information. The aim of this study was to investigate the learning style preferences of undergraduate dental students and examine the influence of gender, Grade Point Average (GPA), and academic year levels on these preferences. Methods The Arabic version of the visual, aural, read/write, and kinesthetic (VARK) questionnaire was administered to 491 students from the first- to the fifth-year academic classes at the College of Dentistry, King Saud University. Descriptive statistics were used to characterize the learning styles of the students, and Chi-square test and Fisher’s test were used to compare the learning preferences between genders and among academic years. Significance was set at a p-value of <0.05. Results A total of 368 dental students completed the questionnaire. The multimodal learning style was preferred by 63.04% of the respondents, with the remaining 36% having a unimodal style preference. The aural (A) and the kinesthetic (K) styles were the most preferred unimodal styles. The most common style overall was the quadmodal (VARK) style with 23.64% having this preference. These differences did not reach statistical significance (p>0.05). Females were more likely to prefer a bimodal learning style over a unimodal style (relative risk =2.37). Students with a GPA of “C” were less likely to have a bimodal or a quadmodal style preference compared to students with a GPA of “A” (relative risk =0.34 and 0.36, respectively). Second-year students were less likely to prefer a bimodal over a unimodal style compared to first-year students (relative risk =0.34). Conclusion The quadmodal VARK style is the preferred learning method chosen by dental students, followed by unimodal aural and kinesthetic styles. Gender was found to influence learning style preferences. Students with a “C” GPA tend to prefer unimodal learning style preferences. The VARK questionnaire is a relatively quick and simple tool to reveal the learning style preferences on an individual or a group level. Dental educators should adjust their delivery methods to approximate the learning preferences of their students. Dental students are encouraged to adapt a multimodal style of learning to improve their academic results. PMID:29593441

The impact of blended teaching on knowledge, satisfaction, and self-directed learning in nursing undergraduates: a randomized, controlled trial.

PubMed

Gagnon, Marie-Pierre; Gagnon, Johanne; Desmartis, Marie; Njoya, Merlin

2013-01-01

This study aimed to assess the effectiveness of a blended-teaching intervention using Internet-based tutorials coupled with traditional lectures in an introduction to research undergraduate nursing course. Effects of the intervention were compared with conventional, face-to-face classroom teaching on three outcomes: knowledge, satisfaction, and self-learning readiness. A two-group, randomized, controlled design was used, involving 112 participants. Descriptive statistics and analysis of covariance (ANCOVA) were performed. The teaching method was found to have no direct impact on knowledge acquisition, satisfaction, and self-learning readiness. However, motivation and teaching method had an interaction effect on knowledge acquisition by students. Among less motivated students, those in the intervention group performed better than those who received traditional training. These findings suggest that this blended-teaching method could better suit some students, depending on their degree of motivation and level of self-directed learning readiness.

Teaching beyond the walls: A mixed method study of prospective elementary teacher's belief systems about science instruction

NASA Astrophysics Data System (ADS)

Asim, Sumreen

This mixed method study investigated K-6 teacher candidates' beliefs about informal science instruction prior to and after their experiences in a 15-week science methods course and in comparison to a non-intervention group. The study is predicated by the literature that supports the extent to which teachers' beliefs influence their instructional practices. The intervention integrated the six strands of learning science in informal science education (NRC, 2009) and exposed candidates to out-of-school-time environments (NRC, 2010). Participants included 17 candidates in the intervention and 75 in the comparison group. All were undergraduate K-6 teacher candidates at one university enrolled in different sections of a required science methods course. All the participants completed the Beliefs about Science Teaching (BAT) survey. Reflective journals, drawings, interviews, and microteaching protocols were collected from participants in the intervention. There was no statistically significant difference in pre or post BAT scores of the two groups; However, there was a statistically significant interaction effect for the intervention group over time. Analysis of the qualitative data revealed that the intervention candidates displayed awareness of each of the six strands of learning science in informal environments and commitment to out-of-school-time learning of science. This study supports current reform efforts favoring integration of informal science instructional strategies in science methods courses of elementary teacher education programs.

Explorations in Statistics: Hypothesis Tests and P Values

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This second installment of "Explorations in Statistics" delves into test statistics and P values, two concepts fundamental to the test of a scientific null hypothesis. The essence of a test statistic is that it compares what…

Statistical Learning Is Constrained to Less Abstract Patterns in Complex Sensory Input (but not the Least)

PubMed Central

Emberson, Lauren L.; Rubinstein, Dani

2016-01-01

The influence of statistical information on behavior (either through learning or adaptation) is quickly becoming foundational to many domains of cognitive psychology and cognitive neuroscience, from language comprehension to visual development. We investigate a central problem impacting these diverse fields: when encountering input with rich statistical information, are there any constraints on learning? This paper examines learning outcomes when adult learners are given statistical information across multiple levels of abstraction simultaneously: from abstract, semantic categories of everyday objects to individual viewpoints on these objects. After revealing statistical learning of abstract, semantic categories with scrambled individual exemplars (Exp. 1), participants viewed pictures where the categories as well as the individual objects predicted picture order (e.g., bird1—dog1, bird2—dog2). Our findings suggest that participants preferentially encode the relationships between the individual objects, even in the presence of statistical regularities linking semantic categories (Exps. 2 and 3). In a final experiment we investigate whether learners are biased towards learning object-level regularities or simply construct the most detailed model given the data (and therefore best able to predict the specifics of the upcoming stimulus) by investigating whether participants preferentially learn from the statistical regularities linking individual snapshots of objects or the relationship between the objects themselves (e.g., bird_picture1— dog_picture1, bird_picture2—dog_picture2). We find that participants fail to learn the relationships between individual snapshots, suggesting a bias towards object-level statistical regularities as opposed to merely constructing the most complete model of the input. This work moves beyond the previous existence proofs that statistical learning is possible at both very high and very low levels of abstraction (categories vs. individual objects) and suggests that, at least with the current categories and type of learner, there are biases to pick up on statistical regularities between individual objects even when robust statistical information is present at other levels of abstraction. These findings speak directly to emerging theories about how systems supporting statistical learning and prediction operate in our structure-rich environments. Moreover, the theoretical implications of the current work across multiple domains of study is already clear: statistical learning cannot be assumed to be unconstrained even if statistical learning has previously been established at a given level of abstraction when that information is presented in isolation. PMID:27139779

Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm.

PubMed

Sandoval, Michelle; Patterson, Dianne; Dai, Huanping; Vance, Christopher J; Plante, Elena

2017-01-01

The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the "rules" for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system.

Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm

PubMed Central

Sandoval, Michelle; Patterson, Dianne; Dai, Huanping; Vance, Christopher J.; Plante, Elena

2017-01-01

The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the “rules” for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system. PMID:28798703

Non-Gaussian Methods for Causal Structure Learning.

PubMed

Shimizu, Shohei

2018-05-22

Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

Learn the game but don't play it: nurses' perspectives on learning and applying statistics in practice.

PubMed

Gaudet, Julie; Singh, Mina D; Epstein, Iris; Santa Mina, Elaine; Gula, Taras

2014-07-01

An integrative review regarding undergraduate level statistics pedagogy for nurses revealed a paucity of research to inform curricula development and delivery. The aim of the study was to explore alumni nurses' perspectives about statistics education and its application to practice. A mixed-method approach was used whereby a quantitative approach was used to complement and develop the qualitative aspect. This study was conducted in Toronto, Ontario, Canada. Participants were nursing alumni who graduated from four types of nursing degree programs (BScN) in two Ontario universities between the years 2005-2009. Data were collected via surveys (n=232) followed by interviews (n=36). Participants reported that they did not fear statistics and that they thought their math skills were very good or excellent. They felt that statistics courses were important to their nursing practice but they were not required to use statistics. Qualitative findings emerged in the two major themes: 1) nurses value statistics and 2) nurses do not feel comfortable using statistics. Nurses recognize the inherent value of statistics to improve their professional image and interprofessional communication; yet they feel denied of full participation in application to their practice. Our findings have major implications for changes in pedagogy and practice. Copyright © 2013 Elsevier Ltd. All rights reserved.

[Statistics for statistics?--Thoughts about psychological tools].

PubMed

Berger, Uwe; Stöbel-Richter, Yve

2007-12-01

Statistical methods take a prominent place among psychologists' educational programs. Being known as difficult to understand and heavy to learn, students fear of these contents. Those, who do not aspire after a research carrier at the university, will forget the drilled contents fast. Furthermore, because it does not apply for the work with patients and other target groups at a first glance, the methodological education as a whole was often questioned. For many psychological practitioners the statistical education makes only sense by enforcing respect against other professions, namely physicians. For the own business, statistics is rarely taken seriously as a professional tool. The reason seems to be clear: Statistics treats numbers, while psychotherapy treats subjects. So, does statistics ends in itself? With this article, we try to answer the question, if and how statistical methods were represented within the psychotherapeutical and psychological research. Therefore, we analyzed 46 Originals of a complete volume of the journal Psychotherapy, Psychosomatics, Psychological Medicine (PPmP). Within the volume, 28 different analyse methods were applied, from which 89 per cent were directly based upon statistics. To be able to write and critically read Originals as a backbone of research, presumes a high degree of statistical education. To ignore statistics means to ignore research and at least to reveal the own professional work to arbitrariness.

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

Online neural monitoring of statistical learning

PubMed Central

Batterink, Laura J.; Paller, Ken A.

2017-01-01

The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the RT task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning. PMID:28324696

You Can Lead Students to Water, but You Can't Make Them Think: An Assessment of Student Engagement and Learning through Student-Centered Teaching

ERIC Educational Resources Information Center

Bradford, Jennifer; Mowder, Denise; Bohte, Joy

2016-01-01

The current project conducted an assessment of specific, directed use of student-centered teaching techniques in a criminal justice and criminology research methods and statistics class. The project sought to ascertain to what extent these techniques improved or impacted student learning and engagement in this traditionally difficult course.…

Feelings and Performance in the First Year at University: Learning-Related Emotions as Predictors of Achievement Outcomes in Mathematics and Statistics

ERIC Educational Resources Information Center

Niculescu, Alexandra C.; Templelaar, Dirk; Leppink, Jimmie; Dailey-Hebert, Amber; Segers, Mien; Gijselaers, Wim

2015-01-01

Introduction: This study examined the predictive value of four learning-related emotions--Enjoyment, Anxiety, Boredom and Hopelessness for achievement outcomes in the first year of study at university. Method: We used a large sample (N = 2337) of first year university students enrolled over three consecutive academic years in a mathematics and…

Learning styles of medical students, general surgery residents, and general surgeons: implications for surgical education

PubMed Central

2010-01-01

Background Surgical education is evolving under the dual pressures of an enlarging body of knowledge required during residency and mounting work-hour restrictions. Changes in surgical residency training need to be based on available educational models and research to ensure successful training of surgeons. Experiential learning theory, developed by David Kolb, demonstrates the importance of individual learning styles in improving learning. This study helps elucidate the way in which medical students, surgical residents, and surgical faculty learn. Methods The Kolb Learning Style Inventory, which divides individual learning styles into Accommodating, Diverging, Converging, and Assimilating categories, was administered to the second year undergraduate medical students, general surgery resident body, and general surgery faculty at the University of Alberta. Results A total of 241 faculty, residents, and students were surveyed with an overall response rate of 73%. The predominant learning style of the medical students was assimilating and this was statistically significant (p < 0.03) from the converging learning style found in the residents and faculty. The predominant learning styles of the residents and faculty were convergent and accommodative, with no statistically significant differences between the residents and the faculty. Conclusions We conclude that medical students have a significantly different learning style from general surgical trainees and general surgeons. This has important implications in the education of general surgery residents. PMID:20591159

A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA

USGS Publications Warehouse

Nolan, Bernard T.; Fienen, Michael N.; Lorenz, David L.

2015-01-01

We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well to new data. The statistical learning framework involves cross-validation (CV) training and testing data and a separate hold-out data set for model evaluation, with the goal of optimizing predictive performance by controlling for model overfit. The order of prediction performance according to both CV testing R2 and that for the hold-out data set was BRT > BN > ANN. For each method we identified two models based on CV testing results: that with maximum testing R2 and a version with R2 within one standard error of the maximum (the 1SE model). The former yielded CV training R2 values of 0.94–1.0. Cross-validation testing R2 values indicate predictive performance, and these were 0.22–0.39 for the maximum R2 models and 0.19–0.36 for the 1SE models. Evaluation with hold-out data suggested that the 1SE BRT and ANN models predicted better for an independent data set compared with the maximum R2 versions, which is relevant to extrapolation by mapping. Scatterplots of predicted vs. observed hold-out data obtained for final models helped identify prediction bias, which was fairly pronounced for ANN and BN. Lastly, the models were compared with multiple linear regression (MLR) and a previous random forest regression (RFR) model. Whereas BRT results were comparable to RFR, MLR had low hold-out R2 (0.07) and explained less than half the variation in the training data. Spatial patterns of predictions by the final, 1SE BRT model agreed reasonably well with previously observed patterns of nitrate occurrence in groundwater of the Central Valley.

A delivery mode study: The effect of self-paced video learning on first-year college students' achievement in calculus

NASA Astrophysics Data System (ADS)

Oktaviyanthi, Rina; Herman, Tatang

2016-10-01

In this paper, the effect of two different modes of deliver are proposed. The use of self-paced video learning and conventional learning methods in mathematics are compared. The research design classified as a quasi-experiment. The participants were 80 students in the first-year college and divided into two groups. One group as an experiment class received self-paced video learning method and the other group as a control group taught by conventional learning method. Pre and posttest were employed to measure the students' achievement, while questionnaire and interviews were applied to support the pre and posttest data. Statistical analysis included the independent samples t-test showed differences (p < 0.05) in posttest between the experimental and control groups, it means that the use of self-paced video contributed on students' achievement and students' attitudes. In addition, related to corresponding to the students' answer, there are five positive gains in using self-paced video in learning Calculus, such as appropriate learning for both audio and visual of students' characteristics, useful to learn Calculus, assisting students to be more engaging and paying attention in learning, helping students in making the concepts of Calculus are visible, interesting media and motivating students to learn independently.

Statistical learning and language acquisition

PubMed Central

Romberg, Alexa R.; Saffran, Jenny R.

2011-01-01

Human learners, including infants, are highly sensitive to structure in their environment. Statistical learning refers to the process of extracting this structure. A major question in language acquisition in the past few decades has been the extent to which infants use statistical learning mechanisms to acquire their native language. There have been many demonstrations showing infants’ ability to extract structures in linguistic input, such as the transitional probability between adjacent elements. This paper reviews current research on how statistical learning contributes to language acquisition. Current research is extending the initial findings of infants’ sensitivity to basic statistical information in many different directions, including investigating how infants represent regularities, learn about different levels of language, and integrate information across situations. These current directions emphasize studying statistical language learning in context: within language, within the infant learner, and within the environment as a whole. PMID:21666883

Learning Across Senses: Cross-Modal Effects in Multisensory Statistical Learning

PubMed Central

Mitchel, Aaron D.; Weiss, Daniel J.

2014-01-01

It is currently unknown whether statistical learning is supported by modality-general or modality-specific mechanisms. One issue within this debate concerns the independence of learning in one modality from learning in other modalities. In the present study, the authors examined the extent to which statistical learning across modalities is independent by simultaneously presenting learners with auditory and visual streams. After establishing baseline rates of learning for each stream independently, they systematically varied the amount of audiovisual correspondence across 3 experiments. They found that learners were able to segment both streams successfully only when the boundaries of the audio and visual triplets were in alignment. This pattern of results suggests that learners are able to extract multiple statistical regularities across modalities provided that there is some degree of cross-modal coherence. They discuss the implications of their results in light of recent claims that multisensory statistical learning is guided by modality-independent mechanisms. PMID:21574745

Infant Statistical Learning

PubMed Central

Saffran, Jenny R.; Kirkham, Natasha Z.

2017-01-01

Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories. PMID:28793812

Communicating Patient Status: Comparison of Teaching Strategies in Prelicensure Nursing Education.

PubMed

Lanz, Amelia S; Wood, Felecia G

Research indicates that nurses lack adequate preparation for reporting patient status. This study compared 2 instructional methods focused on patient status reporting in the clinical setting using a randomized posttest-only comparison group design. Reporting performance using a standardized communication framework and student perceptions of satisfaction and confidence with learning were measured in a simulated event that followed the instruction. Between the instructional methods, there was no statistical difference in student reporting performance or perceptions of learning. Performance evaluations provided helpful insights for the nurse educator.

Learning Statistics at the Farmers Market? A Comparison of Academic Service Learning and Case Studies in an Introductory Statistics Course

ERIC Educational Resources Information Center

Hiedemann, Bridget; Jones, Stacey M.

2010-01-01

We compare the effectiveness of academic service learning to that of case studies in an undergraduate introductory business statistics course. Students in six sections of the course were assigned either an academic service learning project (ASL) or business case studies (CS). We examine two learning outcomes: students' performance on the final…

Electrophysiological evidence of heterogeneity in visual statistical learning in young children with ASD.

PubMed

Jeste, Shafali S; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F N; Johnson, Scott P

2015-01-01

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism spectrum disorder (ASD) using an event-related potential shape learning paradigm, and we examined the relation between visual statistical learning and cognitive function. Compared to typically developing (TD) controls, the ASD group as a whole showed reduced evidence of learning as defined by N1 (early visual discrimination) and P300 (attention to novelty) components. Upon further analysis, in the ASD group there was a positive correlation between N1 amplitude difference and non-verbal IQ, and a positive correlation between P300 amplitude difference and adaptive social function. Children with ASD and a high non-verbal IQ and high adaptive social function demonstrated a distinctive pattern of learning. This is the first study to identify electrophysiological markers of visual statistical learning in children with ASD. Through this work we have demonstrated heterogeneity in statistical learning in ASD that maps onto non-verbal cognition and adaptive social function. © 2014 John Wiley & Sons Ltd.

Changing viewer perspectives reveals constraints to implicit visual statistical learning.

PubMed

Jiang, Yuhong V; Swallow, Khena M

2014-10-07

Statistical learning-learning environmental regularities to guide behavior-likely plays an important role in natural human behavior. One potential use is in search for valuable items. Because visual statistical learning can be acquired quickly and without intention or awareness, it could optimize search and thereby conserve energy. For this to be true, however, visual statistical learning needs to be viewpoint invariant, facilitating search even when people walk around. To test whether implicit visual statistical learning of spatial information is viewpoint independent, we asked participants to perform a visual search task from variable locations around a monitor placed flat on a stand. Unbeknownst to participants, the target was more often in some locations than others. In contrast to previous research on stationary observers, visual statistical learning failed to produce a search advantage for targets in high-probable regions that were stable within the environment but variable relative to the viewer. This failure was observed even when conditions for spatial updating were optimized. However, learning was successful when the rich locations were referenced relative to the viewer. We conclude that changing viewer perspective disrupts implicit learning of the target's location probability. This form of learning shows limited integration with spatial updating or spatiotopic representations. © 2014 ARVO.

Using the Flipped Classroom to Bridge the Gap to Generation Y

PubMed Central

Gillispie, Veronica

2016-01-01

Background: The flipped classroom is a student-centered approach to learning that increases active learning for the student compared to traditional classroom-based instruction. In the flipped classroom model, students are first exposed to the learning material through didactics outside of the classroom, usually in the form of written material, voice-over lectures, or videos. During the formal teaching time, an instructor facilitates student-driven discussion of the material via case scenarios, allowing for complex problem solving, peer interaction, and a deep understanding of the concepts. A successful flipped classroom should have three goals: (1) allow the students to become critical thinkers, (2) fully engage students and instructors, and (3) stimulate the development of a deep understanding of the material. The flipped classroom model includes teaching and learning methods that can appeal to all four generations in the academic environment. Methods: During the 2015 academic year, we implemented the flipped classroom in the obstetrics and gynecology clerkship for the Ochsner Clinical School in New Orleans, LA. Voice-over presentations of the lectures that had been given to students in prior years were recorded and made available to the students through an online classroom. Weekly problem-based learning sessions matched to the subjects of the traditional lectures were held, and the faculty who had previously presented the information in the traditional lecture format facilitated the problem-based learning sessions. The knowledge base of students was evaluated at the end of the rotation via a multiple-choice question examination and the Objective Structured Clinical Examination (OSCE) as had been done in previous years. We compared demographic information and examination scores for traditional teaching and flipped classroom groups of students. The traditional teaching group consisted of students from Rotation 2 and Rotation 3 of the 2014 academic year who received traditional classroom-based instruction. The flipped classroom group consisted of students from Rotation 2 and Rotation 3 of the 2015 academic year who received formal didactics via voice-over presentation and had the weekly problem-based learning sessions. Results: When comparing the students taught by traditional methods to those taught in the flipped classroom model, we saw a statistically significant increase in test scores on the multiple-choice question examination in both the obstetrics and gynecology sections in Rotation 2. While the average score for the flipped classroom group increased in Rotation 3 on the obstetrics section of the multiple-choice question examination, the difference was not statistically significant. Unexpectedly, the average score on the gynecology portion of the multiple-choice question examination decreased among the flipped classroom group compared to the traditional teaching group, and this decrease was statistically significant. For both the obstetrics and the gynecology portions of the OSCE, we saw statistically significant increases in the scores for the flipped classroom group in both Rotation 2 and Rotation 3 compared to the traditional teaching group. With the exception of the gynecology portion of the multiple-choice question examination in Rotation 3, we saw improvement in scores after the implementation of the flipped classroom. Conclusion: The flipped classroom is a feasible and useful alternative to the traditional classroom. It is a method that embraces Generation Y's need for active learning in a group setting while maintaining a traditional classroom method for introducing the information. Active learning increases student engagement and can lead to improved retention of material as demonstrated on standard examinations. PMID:27046401

Functional Differences between Statistical Learning with and without Explicit Training

ERIC Educational Resources Information Center

Batterink, Laura J.; Reber, Paul J.; Paller, Ken A.

2015-01-01

Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and…

Statistically Modeling Individual Students' Learning over Successive Collaborative Practice Opportunities

ERIC Educational Resources Information Center

Olsen, Jennifer; Aleven, Vincent; Rummel, Nikol

2017-01-01

Within educational data mining, many statistical models capture the learning of students working individually. However, not much work has been done to extend these statistical models of individual learning to a collaborative setting, despite the effectiveness of collaborative learning activities. We extend a widely used model (the additive factors…

Statistical Learning as a Key to Cracking Chinese Orthographic Codes

ERIC Educational Resources Information Center

He, Xinjie; Tong, Xiuli

2017-01-01

This study examines statistical learning as a mechanism for Chinese orthographic learning among children in Grades 3-5. Using an artificial orthography, children were repeatedly exposed to positional, phonetic, and semantic regularities of radicals. Children showed statistical learning of all three regularities. Regularities' levels of consistency…

Integrated approach to e-learning enhanced both subjective and objective knowledge of aEEG in a neonatal intensive care unit

PubMed Central

Poon, Woei Bing; Tagamolila, Vina; Toh, Ying Pin Anne; Cheng, Zai Ru

2015-01-01

INTRODUCTION Various meta-analyses have shown that e-learning is as effective as traditional methods of continuing professional education. However, there are some disadvantages to e-learning, such as possible technical problems, the need for greater self-discipline, cost involved in developing programmes and limited direct interaction. Currently, most strategies for teaching amplitude-integrated electroencephalography (aEEG) in neonatal intensive care units (NICUs) worldwide depend on traditional teaching methods. METHODS We implemented a programme that utilised an integrated approach to e-learning. The programme consisted of three sessions of supervised protected time e-learning in an NICU. The objective and subjective effectiveness of the approach was assessed through surveys administered to participants before and after the programme. RESULTS A total of 37 NICU staff (32 nurses and 5 doctors) participated in the study. 93.1% of the participants appreciated the need to acquire knowledge of aEEG. We also saw a statistically significant improvement in the subjective knowledge score (p = 0.041) of the participants. The passing rates for identifying abnormal aEEG tracings (defined as ≥ 3 correct answers out of 5) also showed a statistically significant improvement (from 13.6% to 81.8%, p < 0.001). Among the participants who completed the survey, 96.0% felt the teaching was well structured, 77.8% felt the duration was optimal, 80.0% felt that they had learnt how to systematically interpret aEEGs, and 70.4% felt that they could interpret normal aEEG with confidence. CONCLUSION An integrated approach to e-learning can help improve subjective and objective knowledge of aEEG. PMID:25820847

Explorations in Statistics: the Bootstrap

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fourth installment of Explorations in Statistics explores the bootstrap. The bootstrap gives us an empirical approach to estimate the theoretical variability among possible values of a sample statistic such as the…

Statistical Optimality in Multipartite Ranking and Ordinal Regression.

PubMed

Uematsu, Kazuki; Lee, Yoonkyung

2015-05-01

Statistical optimality in multipartite ranking is investigated as an extension of bipartite ranking. We consider the optimality of ranking algorithms through minimization of the theoretical risk which combines pairwise ranking errors of ordinal categories with differential ranking costs. The extension shows that for a certain class of convex loss functions including exponential loss, the optimal ranking function can be represented as a ratio of weighted conditional probability of upper categories to lower categories, where the weights are given by the misranking costs. This result also bridges traditional ranking methods such as proportional odds model in statistics with various ranking algorithms in machine learning. Further, the analysis of multipartite ranking with different costs provides a new perspective on non-smooth list-wise ranking measures such as the discounted cumulative gain and preference learning. We illustrate our findings with simulation study and real data analysis.

Learning Rotation-Invariant Local Binary Descriptor.

PubMed

Duan, Yueqi; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie

2017-08-01

In this paper, we propose a rotation-invariant local binary descriptor (RI-LBD) learning method for visual recognition. Compared with hand-crafted local binary descriptors, such as local binary pattern and its variants, which require strong prior knowledge, local binary feature learning methods are more efficient and data-adaptive. Unlike existing learning-based local binary descriptors, such as compact binary face descriptor and simultaneous local binary feature learning and encoding, which are susceptible to rotations, our RI-LBD first categorizes each local patch into a rotational binary pattern (RBP), and then jointly learns the orientation for each pattern and the projection matrix to obtain RI-LBDs. As all the rotation variants of a patch belong to the same RBP, they are rotated into the same orientation and projected into the same binary descriptor. Then, we construct a codebook by a clustering method on the learned binary codes, and obtain a histogram feature for each image as the final representation. In order to exploit higher order statistical information, we extend our RI-LBD to the triple rotation-invariant co-occurrence local binary descriptor (TRICo-LBD) learning method, which learns a triple co-occurrence binary code for each local patch. Extensive experimental results on four different visual recognition tasks, including image patch matching, texture classification, face recognition, and scene classification, show that our RI-LBD and TRICo-LBD outperform most existing local descriptors.

A Low-Cost Method for Multiple Disease Prediction.

PubMed

Bayati, Mohsen; Bhaskar, Sonia; Montanari, Andrea

Recently, in response to the rising costs of healthcare services, employers that are financially responsible for the healthcare costs of their workforce have been investing in health improvement programs for their employees. A main objective of these so called "wellness programs" is to reduce the incidence of chronic illnesses such as cardiovascular disease, cancer, diabetes, and obesity, with the goal of reducing future medical costs. The majority of these wellness programs include an annual screening to detect individuals with the highest risk of developing chronic disease. Once these individuals are identified, the company can invest in interventions to reduce the risk of those individuals. However, capturing many biomarkers per employee creates a costly screening procedure. We propose a statistical data-driven method to address this challenge by minimizing the number of biomarkers in the screening procedure while maximizing the predictive power over a broad spectrum of diseases. Our solution uses multi-task learning and group dimensionality reduction from machine learning and statistics. We provide empirical validation of the proposed solution using data from two different electronic medical records systems, with comparisons to a statistical benchmark.

Peer Teaching to Foster Learning in Physiology

PubMed Central

Srivastava, Tripti K; Waghmare, Lalitbhushan S.; Mishra, Ved Prakash; Rawekar, Alka T; Quazi, Nazli; Jagzape, Arunita T

2015-01-01

Introduction Peer teaching is an effective tool to promote learning and retention of knowledge. By preparing to teach, students are encouraged to construct their own learning program, so that they can explain effectively to fellow learners. Peer teaching is introduced in present study to foster learning and pedagogical skills amongst first year medical under-graduates in physiology with a Hypothesis that teaching is linked to learning on part of the teacher. Materials and Methods Non-randomized, Interventional study, with mixed methods design. Cases experienced peer teaching whereas controls underwent tutorials for four consecutive classes. Quantitative Evaluation was done through pre/post test score analysis for Class average normalized gain and tests of significance, difference in average score in surprise class test after one month and percentage of responses in closed ended items of feedback questionnaire. Qualitative Evaluation was done through categorization of open ended items and coding of reflective statements. Results The average pre and post test score was statistically significant within cases (p = 0.01) and controls (p = 0.023). The average post test scores was more for cases though not statistically significant. The class average normalized gain (g) for Tutorials was 49% and for peer teaching 53%. Surprise test had average scoring of 36 marks (out of 50) for controls and 41 marks for cases. Analysed section wise, the average score was better for Long answer question (LAQ) in cases. Section wise analysis suggested that through peer teaching, retention was better for descriptive answers as LAQ has better average score in cases. Feedback responses were predominantly positive for efficacy of peer teaching as a learning method. The reflective statements were sorted into reflection in action, reflection on action, claiming evidence, describing experience, and recognizing discrepancies. Conclusion Teaching can stimulate further learning as it involves interplay of three processes: metacognitive awareness; deliberate practice, and self-explanation. Coupled with immediate feedback and reflective exercises, learning can be measurably enhanced along with improved teaching skills. PMID:26435969

Methods for Assessment of Memory Reactivation.

PubMed

Liu, Shizhao; Grosmark, Andres D; Chen, Zhe

2018-04-13

It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.

Dialogue as a tool for meaning making

NASA Astrophysics Data System (ADS)

Bruni, Angela Suzanne Dudley

In order to empower citizens to analyze the effects, risk, and value of science, a knowledge of scientific concepts is necessary (Mejlgaard, 2009). The formal educational system plays a role in this endeavor (Gil-Perez & Vilches, 2005). One proposed constructivist practice is the use of social learning activities using verbalized, shared cognition among learners. In an effort to investigate the effects of verbally shared cognition, this project sought to determine if social learning opportunities affect the mastery of content in gateway biology courses and to identify the types of dialogue students engage in during cognitive collaboration. Fifty-seven students enrolled in a small southern community college were randomly assigned into treatment groups for each of nine units of instruction. The treatment variable was participation in verbalized social learning activities. Treatment differences based on a pretest/posttest design were analyzed using various statistical methods and recorded student discussions were analyzed for characteristics of talk based on a model developed by Mercer. Findings support the use of social learning activities as a way to improve content knowledge. Students in the treatment group had higher posttest and gain scores than those in the control group, with statistical significance reached in some cases. Types of talk were examined to support the constructivist method of learning. Findings support the use of non-confrontational talk as the vector of meaning making within the classroom.

Juvenile zebra finches learn the underlying structural regularities of their fathers’ song

PubMed Central

Menyhart, Otília; Kolodny, Oren; Goldstein, Michael H.; DeVoogd, Timothy J.; Edelman, Shimon

2015-01-01

Natural behaviors, such as foraging, tool use, social interaction, birdsong, and language, exhibit branching sequential structure. Such structure should be learnable if it can be inferred from the statistics of early experience. We report that juvenile zebra finches learn such sequential structure in song. Song learning in finches has been extensively studied, and it is generally believed that young males acquire song by imitating tutors (Zann, 1996). Variability in the order of elements in an individual’s mature song occurs, but the degree to which variation in a zebra finch’s song follows statistical regularities has not been quantified, as it has typically been dismissed as production error (Sturdy et al., 1999). Allowing for the possibility that such variation in song is non-random and learnable, we applied a novel analytical approach, based on graph-structured finite-state grammars, to each individual’s full corpus of renditions of songs. This method does not assume syllable-level correspondence between individuals. We find that song variation can be described by probabilistic finite-state graph grammars that are individually distinct, and that the graphs of juveniles are more similar to those of their fathers than to those of other adult males. This grammatical learning is a new parallel between birdsong and language. Our method can be applied across species and contexts to analyze complex variable learned behaviors, as distinct as foraging, tool use, and language. PMID:26005428

CORSSA: The Community Online Resource for Statistical Seismicity Analysis

USGS Publications Warehouse

Michael, Andrew J.; Wiemer, Stefan

2010-01-01

Statistical seismology is the application of rigorous statistical methods to earthquake science with the goal of improving our knowledge of how the earth works. Within statistical seismology there is a strong emphasis on the analysis of seismicity data in order to improve our scientific understanding of earthquakes and to improve the evaluation and testing of earthquake forecasts, earthquake early warning, and seismic hazards assessments. Given the societal importance of these applications, statistical seismology must be done well. Unfortunately, a lack of educational resources and available software tools make it difficult for students and new practitioners to learn about this discipline. The goal of the Community Online Resource for Statistical Seismicity Analysis (CORSSA) is to promote excellence in statistical seismology by providing the knowledge and resources necessary to understand and implement the best practices, so that the reader can apply these methods to their own research. This introduction describes the motivation for and vision of CORRSA. It also describes its structure and contents.

Analysis of Machine Learning Techniques for Heart Failure Readmissions.

PubMed

Mortazavi, Bobak J; Downing, Nicholas S; Bucholz, Emily M; Dharmarajan, Kumar; Manhapra, Ajay; Li, Shu-Xia; Negahban, Sahand N; Krumholz, Harlan M

2016-11-01

The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates. © 2016 American Heart Association, Inc.

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges

PubMed Central

Goldstein, Benjamin A.; Navar, Ann Marie; Carter, Rickey E.

2017-01-01

Abstract Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. PMID:27436868

What You Learn is What You See: Using Eye Movements to Study Infant Cross-Situational Word Learning

PubMed Central

Smith, Linda

2016-01-01

Recent studies show that both adults and young children possess powerful statistical learning capabilities to solve the word-to-world mapping problem. However, the underlying mechanisms that make statistical learning possible and powerful are not yet known. With the goal of providing new insights into this issue, the research reported in this paper used an eye tracker to record the moment-by-moment eye movement data of 14-month-old babies in statistical learning tasks. Various measures are applied to such fine-grained temporal data, such as looking duration and shift rate (the number of shifts in gaze from one visual object to the other) trial by trial, showing different eye movement patterns between strong and weak statistical learners. Moreover, an information-theoretic measure is developed and applied to gaze data to quantify the degree of learning uncertainty trial by trial. Next, a simple associative statistical learning model is applied to eye movement data and these simulation results are compared with empirical results from young children, showing strong correlations between these two. This suggests that an associative learning mechanism with selective attention can provide a cognitively plausible model of cross-situational statistical learning. The work represents the first steps to use eye movement data to infer underlying real-time processes in statistical word learning. PMID:22213894

Relationship between the learning style preferences of medical students and academic achievement

PubMed Central

Almigbal, Turky H.

2015-01-01

Objectives: To investigate the relationship between the learning style preferences of Saudi medical students and their academic achievements. Methods: A cross-sectional study was conducted among 600 medical students at King Saud University in Riyadh, Kingdom of Saudi Arabia from October 2012 to July 2013. The Visual, Aural, Read/Write, and Kinesthetic questionnaire (VARK) questionnaire was used to categorize learning style preferences. Descriptive and analytical statistics were used to identify the learning style preferences of medical students and their relationship to academic achievement, gender, marital status, residency, different teaching curricula, and study resources (for example, teachers’ PowerPoint slides, textbooks, and journals). Results: The results indicated that 261 students (43%) preferred to learn using all VARK modalities. There was a significant difference in learning style preferences between genders (p=0.028). The relationship between learning style preferences and students in different teaching curricula was also statistically significant (p=0.047). However, learning style preferences are not related to a student’s academic achievements, marital status, residency, or study resources (for example, teachers’ PowerPoint slides, textbooks, and journals). Also, after being adjusted to other studies’ variables, the learning style preferences were not related to GPA. Conclusion: Our findings can be used to improve the quality of teaching in Saudi Arabia; students would be advantaged if teachers understood the factors that can be related to students’ learning styles. PMID:25737179

Statistical Learning Is Not Affected by a Prior Bout of Physical Exercise.

PubMed

Stevens, David J; Arciuli, Joanne; Anderson, David I

2016-05-01

This study examined the effect of a prior bout of exercise on implicit cognition. Specifically, we examined whether a prior bout of moderate intensity exercise affected performance on a statistical learning task in healthy adults. A total of 42 participants were allocated to one of three conditions-a control group, a group that exercised for 15 min prior to the statistical learning task, and a group that exercised for 30 min prior to the statistical learning task. The participants in the exercise groups cycled at 60% of their respective V˙O2 max. Each group demonstrated significant statistical learning, with similar levels of learning among the three groups. Contrary to previous research that has shown that a prior bout of exercise can affect performance on explicit cognitive tasks, the results of the current study suggest that the physiological stress induced by moderate-intensity exercise does not affect implicit cognition as measured by statistical learning. Copyright © 2015 Cognitive Science Society, Inc.

Bias-Free Chemically Diverse Test Sets from Machine Learning.

PubMed

Swann, Ellen T; Fernandez, Michael; Coote, Michelle L; Barnard, Amanda S

2017-08-14

Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal analysis and K-means clustering have previously been used to summarize large sets of nanoparticles however molecules are more diverse and not as easily characterized by descriptors. In this work, we compare three sets of descriptors based on the one-, two-, and three-dimensional structure of a molecule. Using data from the NIST Computational Chemistry Comparison and Benchmark Database and machine learning techniques, we demonstrate the functional relationship between these structural descriptors and the electronic energy of molecules. Archetypes and prototypes found with topological or Coulomb matrix descriptors can be used to identify smaller, statistically significant test sets that better capture the diversity of chemical space. We apply this same method to find a diverse subset of organic molecules to demonstrate how the methods can easily be reapplied to individual research projects. Finally, we use our bias-free test sets to assess the performance of density functional theory and quantum Monte Carlo methods.

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

Electrophysiological Evidence of Heterogeneity in Visual Statistical Learning in Young Children with ASD

ERIC Educational Resources Information Center

Jeste, Shafali S.; Kirkham, Natasha; Senturk, Damla; Hasenstab, Kyle; Sugar, Catherine; Kupelian, Chloe; Baker, Elizabeth; Sanders, Andrew J.; Shimizu, Christina; Norona, Amanda; Paparella, Tanya; Freeman, Stephanny F. N.; Johnson, Scott P.

2015-01-01

Statistical learning is characterized by detection of regularities in one's environment without an awareness or intention to learn, and it may play a critical role in language and social behavior. Accordingly, in this study we investigated the electrophysiological correlates of visual statistical learning in young children with autism…

Effects of Mobile Learning in Medical Education: A Counterfactual Evaluation.

PubMed

Briz-Ponce, Laura; Juanes-Méndez, Juan Antonio; García-Peñalvo, Francisco José; Pereira, Anabela

2016-06-01

The aim of this research is to contribute to the general system education providing new insights and resources. This study performs a quasi-experimental study at University of Salamanca with 30 students to compare results between using an anatomic app for learning and the formal traditional method conducted by a teacher. The findings of the investigation suggest that the performance of learners using mobile apps is statistical better than the students using the traditional method. However, mobile devices should be considered as an additional tool to complement the teachers' explanation and it is necessary to overcome different barriers and challenges to adopt these pedagogical methods at University.

Statistical mechanics of complex neural systems and high dimensional data

NASA Astrophysics Data System (ADS)

Advani, Madhu; Lahiri, Subhaneil; Ganguli, Surya

2013-03-01

Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks for understanding how dynamical network processes cooperate across widely disparate spatiotemporal scales to solve important computational problems? Second, how can we extract meaningful models of neuronal systems from high dimensional datasets? To aid in these challenges, we give a pedagogical review of a collection of ideas and theoretical methods arising at the intersection of statistical physics, computer science and neurobiology. We introduce the interrelated replica and cavity methods, which originated in statistical physics as powerful ways to quantitatively analyze large highly heterogeneous systems of many interacting degrees of freedom. We also introduce the closely related notion of message passing in graphical models, which originated in computer science as a distributed algorithm capable of solving large inference and optimization problems involving many coupled variables. We then show how both the statistical physics and computer science perspectives can be applied in a wide diversity of contexts to problems arising in theoretical neuroscience and data analysis. Along the way we discuss spin glasses, learning theory, illusions of structure in noise, random matrices, dimensionality reduction and compressed sensing, all within the unified formalism of the replica method. Moreover, we review recent conceptual connections between message passing in graphical models, and neural computation and learning. Overall, these ideas illustrate how statistical physics and computer science might provide a lens through which we can uncover emergent computational functions buried deep within the dynamical complexities of neuronal networks.

The results of STEM education methods for enhancing critical thinking and problem solving skill in physics the 10th grade level

NASA Astrophysics Data System (ADS)

Soros, P.; Ponkham, K.; Ekkapim, S.

2018-01-01

This research aimed to: 1) compare the critical think and problem solving skills before and after learning using STEM Education plan, 2) compare student achievement before and after learning about force and laws of motion using STEM Education plan, and 3) the satisfaction of learning by using STEM Education. The sample used were 37 students from grade 10 at Borabu School, Borabu District, Mahasarakham Province, semester 2, Academic year 2016. Tools used in this study consist of: 1) STEM Education plan about the force and laws of motion for grade 10 students of 1 schemes with total of 14 hours, 2) The test of critical think and problem solving skills with multiple-choice type of 5 options and 2 option of 30 items, 3) achievement test on force and laws of motion with multiple-choice of 4 options of 30 items, 4) satisfaction learning with 5 Rating Scale of 20 items. The statistics used in data analysis were percentage, mean, standard deviation, and t-test (Dependent). The results showed that 1) The student with learning using STEM Education plan have score of critical think and problem solving skills on post-test higher than pre-test with statistically significant level .01. 2) The student with learning using STEM Education plan have achievement score on post-test higher than pre-test with statistically significant level of .01. 3) The student'level of satisfaction toward the learning by using STEM Education plan was at a high level (X ¯ = 4.51, S.D=0.56).

Online neural monitoring of statistical learning.

PubMed

Batterink, Laura J; Paller, Ken A

2017-05-01

The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the reaction time (RT) task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparative study on the measurement of learning outcomes after powerpoint presentation and problem based learning with discussion in family medicine amongst fifth year medical students.

PubMed

Khobragade, Sujata; Abas, Adinegara Lutfi; Khobragade, Yadneshwar Sudam

2016-01-01

Learning outcomes after traditional teaching methods were compared with problem-based learning (PBL) among fifth year medical students. Six students participated each in traditional teaching and PBL methods, respectively. Traditional teaching method involved PowerPoint (PPT) presentation and PBL included study on case scenario and discussion. Both methods were effective in improving performance of students. Postteaching, we did not find significant differences in learning outcomes between these two teaching methods. (1) Study was conducted with an intention to find out which method of learning is more effective; traditional or PBL. (2) To assess the level of knowledge and understanding in anemia/zoonotic diseases as against diabetes/hypertension. All the students posted from February 3, 2014, to March 14, 2014, participated in this study. Six students were asked to prepare and present a lecture (PPT) and subsequent week other six students were asked to present PBL. Both groups presented different topics. Since it was a pre- and post-test, same students were taken as control. To maintain uniformity and to avoid bias due cultural diversity, language etc., same questions were administered. After taking verbal consent, all 34 students were given pretest on anemia and zoonotic diseases. Then lecture (PPT) by six students on the same topic was given it followed by posttest questionnaire. Subsequent week pretest was conducted on hypertension and diabetes. Then case scenario presentation and discussion (PBL) was done by different six students followed by posttest. Both the methods were compared. Analysis was done manually and standard error of means and students t -test was used to find out statistical significance. We found statistically significant improvement in performance of students after PPT presentation as well as PBL. Both methods are equally effective. However, Pretest results of students in anemia and zoonotic diseases (Group A) were poor compared to pretest results of students in hypertension and diabetes (Group B). The students who participated in presentation did not influence their performance as they were covering a small part of the topic and there were no differences in their marks compared to other students. We did not find significant differences in outcome after teaching between PBL and traditional methods. Performances of students were poor in anemia and zoonotic diseases which need remedial teaching. Assessment may influence retention ability and performance.

Prediction of Enzyme Mutant Activity Using Computational Mutagenesis and Incremental Transduction

PubMed Central

Basit, Nada; Wechsler, Harry

2011-01-01

Wet laboratory mutagenesis to determine enzyme activity changes is expensive and time consuming. This paper expands on standard one-shot learning by proposing an incremental transductive method (T2bRF) for the prediction of enzyme mutant activity during mutagenesis using Delaunay tessellation and 4-body statistical potentials for representation. Incremental learning is in tune with both eScience and actual experimentation, as it accounts for cumulative annotation effects of enzyme mutant activity over time. The experimental results reported, using cross-validation, show that overall the incremental transductive method proposed, using random forest as base classifier, yields better results compared to one-shot learning methods. T2bRF is shown to yield 90% on T4 and LAC (and 86% on HIV-1). This is significantly better than state-of-the-art competing methods, whose performance yield is at 80% or less using the same datasets. PMID:22007208

Potential application of machine learning in health outcomes research and some statistical cautions.

PubMed

Crown, William H

2015-03-01

Traditional analytic methods are often ill-suited to the evolving world of health care big data characterized by massive volume, complexity, and velocity. In particular, methods are needed that can estimate models efficiently using very large datasets containing healthcare utilization data, clinical data, data from personal devices, and many other sources. Although very large, such datasets can also be quite sparse (e.g., device data may only be available for a small subset of individuals), which creates problems for traditional regression models. Many machine learning methods address such limitations effectively but are still subject to the usual sources of bias that commonly arise in observational studies. Researchers using machine learning methods such as lasso or ridge regression should assess these models using conventional specification tests. Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models

DTIC Science & Technology

2015-09-12

AFRL-AFOSR-VA-TR-2015-0278 DERIVATIVE FREE OPTIMIZATION OF COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS Katya Scheinberg...COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-11-1-0239 5c.  PROGRAM ELEMENT...developed, which has been the focus of our research. 15. SUBJECT TERMS optimization, Derivative-Free Optimization, Statistical Machine Learning 16. SECURITY

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

PubMed

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

Introducing StatHand: A Cross-Platform Mobile Application to Support Students' Statistical Decision Making.

PubMed

Allen, Peter J; Roberts, Lynne D; Baughman, Frank D; Loxton, Natalie J; Van Rooy, Dirk; Rock, Adam J; Finlay, James

2016-01-01

Although essential to professional competence in psychology, quantitative research methods are a known area of weakness for many undergraduate psychology students. Students find selecting appropriate statistical tests and procedures for different types of research questions, hypotheses and data types particularly challenging, and these skills are not often practiced in class. Decision trees (a type of graphic organizer) are known to facilitate this decision making process, but extant trees have a number of limitations. Furthermore, emerging research suggests that mobile technologies offer many possibilities for facilitating learning. It is within this context that we have developed StatHand, a free cross-platform application designed to support students' statistical decision making. Developed with the support of the Australian Government Office for Learning and Teaching, StatHand guides users through a series of simple, annotated questions to help them identify a statistical test or procedure appropriate to their circumstances. It further offers the guidance necessary to run these tests and procedures, then interpret and report their results. In this Technology Report we will overview the rationale behind StatHand, before describing the feature set of the application. We will then provide guidelines for integrating StatHand into the research methods curriculum, before concluding by outlining our road map for the ongoing development and evaluation of StatHand.

A comparison of distance education instructional methods in occupational therapy.

PubMed

Jedlicka, Janet S; Brown, Sarah W; Bunch, Ashley E; Jaffe, Lynn E

2002-01-01

The progression of technology is rapidly bringing new opportunities to students and academic institutions, resulting in a need for additional information to determine the most effective strategies for teaching distance learners. The purpose of this study was to compare the effectiveness of three instructional strategies (two-way interactive video and audio, chat rooms, and independent learning) and student preferences regarding instructional methods in a mental health programming distance learning course. Precourse and postcourse surveys were completed by 22 occupational therapy students enrolled in the course. Effectiveness of the teaching methods was determined based on the results of students' examinations. The findings indicated that there were no statistically significant differences in student performance on multiple-choice examinations using the three instructional methods. Of students, 77% indicated a preference for two-way interactive video and audio instruction. To provide effective education via distance learning methods, faculty members need to structure assignments that facilitate interaction and communication among learners. As distance education becomes more commonplace, it is important to identify the methods of instruction that are the most effective in delivering essential course content and the methods that provide the atmosphere most conducive to learning.

Investigating Students' Acceptance of a Statistics Learning Platform Using Technology Acceptance Model

ERIC Educational Resources Information Center

Song, Yanjie; Kong, Siu-Cheung

2017-01-01

The study aims at investigating university students' acceptance of a statistics learning platform to support the learning of statistics in a blended learning context. Three kinds of digital resources, which are simulations, online videos, and online quizzes, were provided on the platform. Premised on the technology acceptance model, we adopted a…

Computational Modeling of Statistical Learning: Effects of Transitional Probability versus Frequency and Links to Word Learning

ERIC Educational Resources Information Center

Mirman, Daniel; Estes, Katharine Graf; Magnuson, James S.

2010-01-01

Statistical learning mechanisms play an important role in theories of language acquisition and processing. Recurrent neural network models have provided important insights into how these mechanisms might operate. We examined whether such networks capture two key findings in human statistical learning. In Simulation 1, a simple recurrent network…

The Impact of Language Experience on Language and Reading: A Statistical Learning Approach

ERIC Educational Resources Information Center

Seidenberg, Mark S.; MacDonald, Maryellen C.

2018-01-01

This article reviews the important role of statistical learning for language and reading development. Although statistical learning--the unconscious encoding of patterns in language input--has become widely known as a force in infants' early interpretation of speech, the role of this kind of learning for language and reading comprehension in…

Reducing statistics anxiety and enhancing statistics learning achievement: effectiveness of a one-minute strategy.

PubMed

Chiou, Chei-Chang; Wang, Yu-Min; Lee, Li-Tze

2014-08-01

Statistical knowledge is widely used in academia; however, statistics teachers struggle with the issue of how to reduce students' statistics anxiety and enhance students' statistics learning. This study assesses the effectiveness of a "one-minute paper strategy" in reducing students' statistics-related anxiety and in improving students' statistics-related achievement. Participants were 77 undergraduates from two classes enrolled in applied statistics courses. An experiment was implemented according to a pretest/posttest comparison group design. The quasi-experimental design showed that the one-minute paper strategy significantly reduced students' statistics anxiety and improved students' statistics learning achievement. The strategy was a better instructional tool than the textbook exercise for reducing students' statistics anxiety and improving students' statistics achievement.

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2017-01-01

This study proposes an adaptive-learning-based method for machine faulty detection and health degradation monitoring. The kernel of the proposed method is an "evolving" model that uses an unsupervised online learning scheme, in which an adaptive hidden Markov model (AHMM) is used for online learning the dynamic health changes of machines in their full life. A statistical index is developed for recognizing the new health states in the machines. Those new health states are then described online by adding of new hidden states in AHMM. Furthermore, the health degradations in machines are quantified online by an AHMM-based health index (HI) that measures the similarity between two density distributions that describe the historic and current health states, respectively. When necessary, the proposed method characterizes the distinct operating modes of the machine and can learn online both abrupt as well as gradual health changes. Our method overcomes some drawbacks of the HIs (e.g., relatively low comprehensibility and applicability) based on fixed monitoring models constructed in the offline phase. Results from its application in a bearing life test reveal that the proposed method is effective in online detection and adaptive assessment of machine health degradation. This study provides a useful guide for developing a condition-based maintenance (CBM) system that uses an online learning method without considerable human intervention.

Predicting recreational water quality advisories: A comparison of statistical methods

USGS Publications Warehouse

Brooks, Wesley R.; Corsi, Steven R.; Fienen, Michael N.; Carvin, Rebecca B.

2016-01-01

Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a result. In order to avoid the 24 h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.

Learning to improve iterative repair scheduling

NASA Technical Reports Server (NTRS)

Zweben, Monte; Davis, Eugene

1992-01-01

This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone.

Assessing segmentation processes by click detection: online measure of statistical learning, or simple interference?

PubMed

Franco, Ana; Gaillard, Vinciane; Cleeremans, Axel; Destrebecqz, Arnaud

2015-12-01

Statistical learning can be used to extract the words from continuous speech. Gómez, Bion, and Mehler (Language and Cognitive Processes, 26, 212-223, 2011) proposed an online measure of statistical learning: They superimposed auditory clicks on a continuous artificial speech stream made up of a random succession of trisyllabic nonwords. Participants were instructed to detect these clicks, which could be located either within or between words. The results showed that, over the length of exposure, reaction times (RTs) increased more for within-word than for between-word clicks. This result has been accounted for by means of statistical learning of the between-word boundaries. However, even though statistical learning occurs without an intention to learn, it nevertheless requires attentional resources. Therefore, this process could be affected by a concurrent task such as click detection. In the present study, we evaluated the extent to which the click detection task indeed reflects successful statistical learning. Our results suggest that the emergence of RT differences between within- and between-word click detection is neither systematic nor related to the successful segmentation of the artificial language. Therefore, instead of being an online measure of learning, the click detection task seems to interfere with the extraction of statistical regularities.

37: COMPARISON OF TWO METHODS: TBL-BASED AND LECTURE-BASED LEARNING IN NURSING CARE OF PATIENTS WITH DIABETES IN NURSING STUDENTS

PubMed Central

Khodaveisi, Masoud; Qaderian, Khosro; Oshvandi, Khodayar; Soltanian, Ali Reza; Vardanjani, Mehdi molavi

2017-01-01

Background and aims learning plays an important role in developing nursing skills and right care-taking. The Present study aims to evaluate two learning methods based on team –based learning and lecture-based learning in learning care-taking of patients with diabetes in nursing students. Method In this quasi-experimental study, 64 students in term 4 in nursing college of Bukan and Miandoab were included in the study based on knowledge and performance questionnaire including 15 questions based on knowledge and 5 questions based on performance on care-taking in patients with diabetes were used as data collection tool whose reliability was confirmed by cronbach alpha (r=0.83) by the researcher. To compare the mean score of knowledge and performance in each group in pre-test step and post-test step, pair –t test and to compare mean of scores in two groups of control and intervention, the independent t- test was used. Results There was not significant statistical difference between two groups in pre terms of knowledge and performance score (p=0.784). There was significant difference between the mean of knowledge scores and diabetes performance in the post-test in the team-based learning group and lecture-based learning group (p=0.001). There was significant difference between the mean score of knowledge of diabetes care in pre-test and post-test in base learning groups (p=0.001). Conclusion In both methods team-based and lecture-based learning approaches resulted in improvement in learning in students, but the rate of learning in the team-based learning approach is greater compared to that of lecture-based learning and it is recommended that this method be used as a higher education method in the education of students.

A comparative study of traditional lecture methods and interactive lecture methods in introductory geology courses for non-science majors at the college level

NASA Astrophysics Data System (ADS)

Hundley, Stacey A.

In recent years there has been a national call for reform in undergraduate science education. The goal of this reform movement in science education is to develop ways to improve undergraduate student learning with an emphasis on developing more effective teaching practices. Introductory science courses at the college level are generally taught using a traditional lecture format. Recent studies have shown incorporating active learning strategies within the traditional lecture classroom has positive effects on student outcomes. This study focuses on incorporating interactive teaching methods into the traditional lecture classroom to enhance student learning for non-science majors enrolled in introductory geology courses at a private university. Students' experience and instructional preferences regarding introductory geology courses were identified from survey data analysis. The information gained from responses to the questionnaire was utilized to develop an interactive lecture introductory geology course for non-science majors. Student outcomes were examined in introductory geology courses based on two teaching methods: interactive lecture and traditional lecture. There were no significant statistical differences between the groups based on the student outcomes and teaching methods. Incorporating interactive lecture methods did not statistically improve student outcomes when compared to traditional lecture teaching methods. However, the responses to the survey revealed students have a preference for introductory geology courses taught with lecture and instructor-led discussions and students prefer to work independently or in small groups. The results of this study are useful to individuals who teach introductory geology courses and individuals who teach introductory science courses for non-science majors at the college level.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

PubMed Central

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-01-01

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0. PMID:27892471

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.

PubMed

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-11-28

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

NASA Astrophysics Data System (ADS)

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-11-01

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Statistical learning using real-world scenes: extracting categorical regularities without conscious intent.

PubMed

Brady, Timothy F; Oliva, Aude

2008-07-01

Recent work has shown that observers can parse streams of syllables, tones, or visual shapes and learn statistical regularities in them without conscious intent (e.g., learn that A is always followed by B). Here, we demonstrate that these statistical-learning mechanisms can operate at an abstract, conceptual level. In Experiments 1 and 2, observers incidentally learned which semantic categories of natural scenes covaried (e.g., kitchen scenes were always followed by forest scenes). In Experiments 3 and 4, category learning with images of scenes transferred to words that represented the categories. In each experiment, the category of the scenes was irrelevant to the task. Together, these results suggest that statistical-learning mechanisms can operate at a categorical level, enabling generalization of learned regularities using existing conceptual knowledge. Such mechanisms may guide learning in domains as disparate as the acquisition of causal knowledge and the development of cognitive maps from environmental exploration.

Computer and laboratory simulation in the teaching of neonatal nursing: innovation and impact on learning 1

PubMed Central

Fonseca, Luciana Mara Monti; Aredes, Natália Del' Angelo; Fernandes, Ananda Maria; Batalha, Luís Manuel da Cunha; Apóstolo, Jorge Manuel Amado; Martins, José Carlos Amado; Rodrigues, Manuel Alves

2016-01-01

ABSTRACT Objectives: to evaluate the cognitive learning of nursing students in neonatal clinical evaluation from a blended course with the use of computer and laboratory simulation; to compare the cognitive learning of students in a control and experimental group testing the laboratory simulation; and to assess the extracurricular blended course offered on the clinical assessment of preterm infants, according to the students. Method: a quasi-experimental study with 14 Portuguese students, containing pretest, midterm test and post-test. The technologies offered in the course were serious game e-Baby, instructional software of semiology and semiotechnique, and laboratory simulation. Data collection tools developed for this study were used for the course evaluation and characterization of the students. Nonparametric statistics were used: Mann-Whitney and Wilcoxon. Results: the use of validated digital technologies and laboratory simulation demonstrated a statistically significant difference (p = 0.001) in the learning of the participants. The course was evaluated as very satisfactory for them. The laboratory simulation alone did not represent a significant difference in the learning. Conclusions: the cognitive learning of participants increased significantly. The use of technology can be partly responsible for the course success, showing it to be an important teaching tool for innovation and motivation of learning in healthcare. PMID:27737376

An Assessment of Teaching and Learning Practices: A Questionnaire Study for Dental Educators of Karnataka

PubMed Central

Meenakshi, S.; Raghunath, N.; Shreeshyla, H. S.

2017-01-01

Aims and Objectives: Faculty members of dental institutions are being asked to assume new academic duties for which they have received no formal training. To succeed in new teaching tasks, faculty development through assessment of teaching skills is essential. Materials and Methods: A Self-Assessment Questionnaire consisting 18 closed-ended questions was sent to various faculty members of dental colleges of Karnataka. A total of 210 faculty members volunteered to participate in the study. The response rate was 69.8%. Data gathered were statistically analyzed using SPSS software version 16, Chi-square test, and descriptive statistics. Results: In the present study, 27.3% of participants were unaware of andragogy, 33.3% were unaware of teachers development programs, 44.6% do not obtain student feedback after teaching, 52.6% were unaware of peer review of teaching skills, and 50% were unaware of interprofessional education initiatives. Conclusion: By incorporating teaching and learning skills, dental faculty could acquire competencies and academic credentials to become valuable contributors to the institution. This study emphasizes the areas of improvement in dental school learning environment, based on activation of prior knowledge, elaboration of new learning, learning in context, transfer of learning, and organization of knowledge toward learning. PMID:29285474

Statistics at a glance.

PubMed

Ector, Hugo

2010-12-01

I still remember my first book on statistics: "Elementary statistics with applications in medicine and the biological sciences" by Frederick E. Croxton. For me, it has been the start of pursuing understanding statistics in daily life and in medical practice. It was the first volume in a long row of books. In his introduction, Croxton pretends that"nearly everyone involved in any aspect of medicine needs to have some knowledge of statistics". The reality is that for many clinicians, statistics are limited to a "P < 0.05 = ok". I do not blame my colleagues who omit the paragraph on statistical methods. They have never had the opportunity to learn concise and clear descriptions of the key features. I have experienced how some authors can describe difficult methods in a well understandable language. Others fail completely. As a teacher, I tell my students that life is impossible without a basic knowledge of statistics. This feeling has resulted in an annual seminar of 90 minutes. This tutorial is the summary of this seminar. It is a summary and a transcription of the best pages I have detected.

Inferring Demographic History Using Two-Locus Statistics.

PubMed

Ragsdale, Aaron P; Gutenkunst, Ryan N

2017-06-01

Population demographic history may be learned from contemporary genetic variation data. Methods based on aggregating the statistics of many single loci into an allele frequency spectrum (AFS) have proven powerful, but such methods ignore potentially informative patterns of linkage disequilibrium (LD) between neighboring loci. To leverage such patterns, we developed a composite-likelihood framework for inferring demographic history from aggregated statistics of pairs of loci. Using this framework, we show that two-locus statistics are more sensitive to demographic history than single-locus statistics such as the AFS. In particular, two-locus statistics escape the notorious confounding of depth and duration of a bottleneck, and they provide a means to estimate effective population size based on the recombination rather than mutation rate. We applied our approach to a Zambian population of Drosophila melanogaster Notably, using both single- and two-locus statistics, we inferred a substantially lower ancestral effective population size than previous works and did not infer a bottleneck history. Together, our results demonstrate the broad potential for two-locus statistics to enable powerful population genetic inference. Copyright © 2017 by the Genetics Society of America.

Students' attitudes towards learning statistics

NASA Astrophysics Data System (ADS)

Ghulami, Hassan Rahnaward; Hamid, Mohd Rashid Ab; Zakaria, Roslinazairimah

2015-05-01

Positive attitude towards learning is vital in order to master the core content of the subject matters under study. This is unexceptional in learning statistics course especially at the university level. Therefore, this study investigates the students' attitude towards learning statistics. Six variables or constructs have been identified such as affect, cognitive competence, value, difficulty, interest, and effort. The instrument used for the study is questionnaire that was adopted and adapted from the reliable instrument of Survey of Attitudes towards Statistics(SATS©). This study is conducted to engineering undergraduate students in one of the university in the East Coast of Malaysia. The respondents consist of students who were taking the applied statistics course from different faculties. The results are analysed in terms of descriptive analysis and it contributes to the descriptive understanding of students' attitude towards the teaching and learning process of statistics.

Jointly learning word embeddings using a corpus and a knowledge base

PubMed Central

Bollegala, Danushka; Maehara, Takanori; Kawarabayashi, Ken-ichi

2018-01-01

Methods for representing the meaning of words in vector spaces purely using the information distributed in text corpora have proved to be very valuable in various text mining and natural language processing (NLP) tasks. However, these methods still disregard the valuable semantic relational structure between words in co-occurring contexts. These beneficial semantic relational structures are contained in manually-created knowledge bases (KBs) such as ontologies and semantic lexicons, where the meanings of words are represented by defining the various relationships that exist among those words. We combine the knowledge in both a corpus and a KB to learn better word embeddings. Specifically, we propose a joint word representation learning method that uses the knowledge in the KBs, and simultaneously predicts the co-occurrences of two words in a corpus context. In particular, we use the corpus to define our objective function subject to the relational constrains derived from the KB. We further utilise the corpus co-occurrence statistics to propose two novel approaches, Nearest Neighbour Expansion (NNE) and Hedged Nearest Neighbour Expansion (HNE), that dynamically expand the KB and therefore derive more constraints that guide the optimisation process. Our experimental results over a wide-range of benchmark tasks demonstrate that the proposed method statistically significantly improves the accuracy of the word embeddings learnt. It outperforms a corpus-only baseline and reports an improvement of a number of previously proposed methods that incorporate corpora and KBs in both semantic similarity prediction and word analogy detection tasks. PMID:29529052

Maximum entropy methods for extracting the learned features of deep neural networks.

PubMed

Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

A statistical learning strategy for closed-loop control of fluid flows

NASA Astrophysics Data System (ADS)

Guéniat, Florimond; Mathelin, Lionel; Hussaini, M. Yousuff

2016-12-01

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system's dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz'63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well.

Thinking Style Diversity and Collaborative Design Learning

NASA Astrophysics Data System (ADS)

Volpentesta, Antonio P.; Ammirato, Salvatore; Sofo, Francesco

The paper explores the impact of structured learning experiences that were designed to challenge students’ ways of thinking and promote creativity. The aim was to develop the ability of students, coming from different engineering disciplines and characterized by particular thinking style profiles, to collaboratively work on a project-based learning experience in an educational environment. Three project-based learning experiences were structured using critical thinking methods to stimulate creativity. Pre and post-survey data using a specially modified thinking style inventory for 202 design students indicated a thinking style profile of preferences with a focus on exploring and questioning. Statistically significant results showed students successfully developed empathy and openness to multiple perspectives.

Statistically optimal perception and learning: from behavior to neural representations

PubMed Central

Fiser, József; Berkes, Pietro; Orbán, Gergő; Lengyel, Máté

2010-01-01

Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and reevaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty. PMID:20153683

Evaluating Computer-Based Simulations, Multimedia and Animations that Help Integrate Blended Learning with Lectures in First Year Statistics

ERIC Educational Resources Information Center

Neumann, David L.; Neumann, Michelle M.; Hood, Michelle

2011-01-01

The discipline of statistics seems well suited to the integration of technology in a lecture as a means to enhance student learning and engagement. Technology can be used to simulate statistical concepts, create interactive learning exercises, and illustrate real world applications of statistics. The present study aimed to better understand the…

Attitudes of Medical Graduate and Undergraduate Students toward the Learning and Application of Medical Statistics

ERIC Educational Resources Information Center

Wu, Yazhou; Zhang, Ling; Liu, Ling; Zhang, Yanqi; Liu, Xiaoyu; Yi, Dong

2015-01-01

It is clear that the teaching of medical statistics needs to be improved, yet areas for priority are unclear as medical students' learning and application of statistics at different levels is not well known. Our goal is to assess the attitudes of medical students toward the learning and application of medical statistics, and discover their…

Developing Conceptual Understanding in a Statistics Course: Merrill's First Principles and Real Data at Work

ERIC Educational Resources Information Center

Tu, Wendy; Snyder, Martha M.

2017-01-01

Difficulties in learning statistics primarily at the college-level led to a reform movement in statistics education in the early 1990s. Although much work has been done, effective learning designs that facilitate active learning, conceptual understanding of statistics, and the use of real-data in the classroom are needed. Guided by Merrill's First…

Effects of an Interteaching Probe on Learning and Generalization of American Psychological Association (APA) Style

ERIC Educational Resources Information Center

Slezak, Jonathan M.; Faas, Caitlin

2017-01-01

This study implemented the components of interteaching as a probe to teach American Psychological Association (APA) Style to undergraduate university students in a psychology research methods and statistics course. The interteaching method was compared to the traditional lecture-based approach between two sections of the course with the same…

Improving Student Learning: Action Principles for Families, Classrooms, Schools, Districts, and States

ERIC Educational Resources Information Center

Walberg, Herbert J.

2010-01-01

This book summarizes the major research findings that show how to substantially increase student achievement. This book draws on a number of investigators who have statistically synthesized many studies. A new education method showing superior results in 90% of the studies concerning it has more credibility than a method that shows results in only…

Statistical Learning and Language: An Individual Differences Study

ERIC Educational Resources Information Center

Misyak, Jennifer B.; Christiansen, Morten H.

2012-01-01

Although statistical learning and language have been assumed to be intertwined, this theoretical presupposition has rarely been tested empirically. The present study investigates the relationship between statistical learning and language using a within-subject design embedded in an individual-differences framework. Participants were administered…

Statistical Learning of Probabilistic Nonadjacent Dependencies by Multiple-Cue Integration

ERIC Educational Resources Information Center

van den Bos, Esther; Christiansen, Morten H.; Misyak, Jennifer B.

2012-01-01

Previous studies have indicated that dependencies between nonadjacent elements can be acquired by statistical learning when each element predicts only one other element (deterministic dependencies). The present study investigates statistical learning of probabilistic nonadjacent dependencies, in which each element predicts several other elements…

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

Do statistical segmentation abilities predict lexical-phonological and lexical-semantic abilities in children with and without SLI?

PubMed Central

Mainela-Arnold, Elina; Evans, Julia L.

2014-01-01

This study tested the predictions of the procedural deficit hypothesis by investigating the relationship between sequential statistical learning and two aspects of lexical ability, lexical-phonological and lexical-semantic, in children with and without specific language impairment (SLI). Participants included 40 children (ages 8;5–12;3), 20 children with SLI and 20 with typical development. Children completed Saffran’s statistical word segmentation task, a lexical-phonological access task (gating task), and a word definition task. Poor statistical learners were also poor at managing lexical-phonological competition during the gating task. However, statistical learning was not a significant predictor of semantic richness in word definitions. The ability to track statistical sequential regularities may be important for learning the inherently sequential structure of lexical-phonology, but not as important for learning lexical-semantic knowledge. Consistent with the procedural/declarative memory distinction, the brain networks associated with the two types of lexical learning are likely to have different learning properties. PMID:23425593

Personalized summarization using user preference for m-learning

NASA Astrophysics Data System (ADS)

Lee, Sihyoung; Yang, Seungji; Ro, Yong Man; Kim, Hyoung Joong

2008-02-01

As the Internet and multimedia technology is becoming advanced, the number of digital multimedia contents is also becoming abundant in learning area. In order to facilitate the access of digital knowledge and to meet the need of a lifelong learning, e-learning could be the helpful alternative way to the conventional learning paradigms. E-learning is known as a unifying term to express online, web-based and technology-delivered learning. Mobile-learning (m-learning) is defined as e-learning through mobile devices using wireless transmission. In a survey, more than half of the people remarked that the re-consumption was one of the convenient features in e-learning. However, it is not easy to find user's preferred segmentation from a full version of lengthy e-learning content. Especially in m-learning, a content-summarization method is strongly required because mobile devices are limited to low processing power and battery capacity. In this paper, we propose a new user preference model for re-consumption to construct personalized summarization for re-consumption. The user preference for re-consumption is modeled based on user actions with statistical model. Based on the user preference model for re-consumption with personalized user actions, our method discriminates preferred parts over the entire content. Experimental results demonstrated successful personalized summarization.

Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification.

PubMed

Zhang, Xiang; Guan, Naiyang; Jia, Zhilong; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application because labels are quite expensive in the clinical cancer research community. This paper proposes a semi-supervised projective non-negative matrix factorization method (Semi-PNMF) to learn an effective classifier from both labeled and unlabeled samples, thus boosting subsequent cancer classification performance. In particular, Semi-PNMF jointly learns a non-negative subspace from concatenated labeled and unlabeled samples and indicates classes by the positions of the maximum entries of their coefficients. Because Semi-PNMF incorporates statistical information from the large volume of unlabeled samples in the learned subspace, it can learn more representative subspaces and boost classification performance. We developed a multiplicative update rule (MUR) to optimize Semi-PNMF and proved its convergence. The experimental results of cancer classification for two multiclass cancer gene expression profile datasets show that Semi-PNMF outperforms the representative methods.

Target attribute-based false alarm rejection in small infrared target detection

NASA Astrophysics Data System (ADS)

Kim, Sungho

2012-11-01

Infrared search and track is an important research area in military applications. Although there are a lot of works on small infrared target detection methods, we cannot apply them in real field due to high false alarm rate caused by clutters. This paper presents a novel target attribute extraction and machine learning-based target discrimination method. Eight kinds of target features are extracted and analyzed statistically. Learning-based classifiers such as SVM and Adaboost are developed and compared with conventional classifiers for real infrared images. In addition, the generalization capability is also inspected for various infrared clutters.

An Evaluation of Feature Learning Methods for High Resolution Image Classification

NASA Astrophysics Data System (ADS)

Tokarczyk, P.; Montoya, J.; Schindler, K.

2012-07-01

Automatic image classification is one of the fundamental problems of remote sensing research. The classification problem is even more challenging in high-resolution images of urban areas, where the objects are small and heterogeneous. Two questions arise, namely which features to extract from the raw sensor data to capture the local radiometry and image structure at each pixel or segment, and which classification method to apply to the feature vectors. While classifiers are nowadays well understood, selecting the right features remains a largely empirical process. Here we concentrate on the features. Several methods are evaluated which allow one to learn suitable features from unlabelled image data by analysing the image statistics. In a comparative study, we evaluate unsupervised feature learning with different linear and non-linear learning methods, including principal component analysis (PCA) and deep belief networks (DBN). We also compare these automatically learned features with popular choices of ad-hoc features including raw intensity values, standard combinations like the NDVI, a few PCA channels, and texture filters. The comparison is done in a unified framework using the same images, the target classes, reference data and a Random Forest classifier.

Grounding statistical learning in context: The effects of learning and retrieval contexts on cross-situational word learning.

PubMed

Chen, Chi-Hsin; Yu, Chen

2017-06-01

Natural language environments usually provide structured contexts for learning. This study examined the effects of semantically themed contexts-in both learning and retrieval phases-on statistical word learning. Results from 2 experiments consistently showed that participants had higher performance in semantically themed learning contexts. In contrast, themed retrieval contexts did not affect performance. Our work suggests that word learners are sensitive to statistical regularities not just at the level of individual word-object co-occurrences but also at another level containing a whole network of associations among objects and their properties.

Statistical learning and auditory processing in children with music training: An ERP study.

PubMed

Mandikal Vasuki, Pragati Rao; Sharma, Mridula; Ibrahim, Ronny; Arciuli, Joanne

2017-07-01

The question whether musical training is associated with enhanced auditory and cognitive abilities in children is of considerable interest. In the present study, we compared children with music training versus those without music training across a range of auditory and cognitive measures, including the ability to detect implicitly statistical regularities in input (statistical learning). Statistical learning of regularities embedded in auditory and visual stimuli was measured in musically trained and age-matched untrained children between the ages of 9-11years. In addition to collecting behavioural measures, we recorded electrophysiological measures to obtain an online measure of segmentation during the statistical learning tasks. Musically trained children showed better performance on melody discrimination, rhythm discrimination, frequency discrimination, and auditory statistical learning. Furthermore, grand-averaged ERPs showed that triplet onset (initial stimulus) elicited larger responses in the musically trained children during both auditory and visual statistical learning tasks. In addition, children's music skills were associated with performance on auditory and visual behavioural statistical learning tasks. Our data suggests that individual differences in musical skills are associated with children's ability to detect regularities. The ERP data suggest that musical training is associated with better encoding of both auditory and visual stimuli. Although causality must be explored in further research, these results may have implications for developing music-based remediation strategies for children with learning impairments. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Learning curve evaluation using cumulative summation analysis-a clinical example of pediatric robot-assisted laparoscopic pyeloplasty.

PubMed

Cundy, Thomas P; Gattas, Nicholas E; White, Alan D; Najmaldin, Azad S

2015-08-01

The cumulative summation (CUSUM) method for learning curve analysis remains under-utilized in the surgical literature in general, and is described in only a small number of publications within the field of pediatric surgery. This study introduces the CUSUM analysis technique and applies it to evaluate the learning curve for pediatric robot-assisted laparoscopic pyeloplasty (RP). Clinical data were prospectively recorded for consecutive pediatric RP cases performed by a single-surgeon. CUSUM charts and tests were generated for set-up time, docking time, console time, operating time, total operating room time, and postoperative complications. Conversions and avoidable operating room delay were separately evaluated with respect to case experience. Comparisons between case experience and time-based outcomes were assessed using the Student's t-test and ANOVA for bi-phasic and multi-phasic learning curves respectively. Comparison between case experience and complication frequency was assessed using the Kruskal-Wallis test. A total of 90 RP cases were evaluated. The learning curve transitioned beyond the learning phase at cases 10, 15, 42, 57, and 58 for set-up time, docking time, console time, operating time, and total operating room time respectively. All comparisons of mean operating times between the learning phase and subsequent phases were statistically significant (P=<0.001-0.01). No significant difference was observed between case experience and frequency of post-operative complications (P=0.125), although the CUSUM chart demonstrated a directional change in slope for the last 12 cases in which there were high proportions of re-do cases and patients <6 months of age. The CUSUM method has a valuable role for learning curve evaluation and outcome quality monitoring. In applying this statistical technique to the largest reported single surgeon series of pediatric RP, we demonstrate numerous distinctly shaped learning curves and well-defined learning phase transition points. Copyright © 2015 Elsevier Inc. All rights reserved.

Metamodels for Computer-Based Engineering Design: Survey and Recommendations

NASA Technical Reports Server (NTRS)

Simpson, Timothy W.; Peplinski, Jesse; Koch, Patrick N.; Allen, Janet K.

1997-01-01

The use of statistical techniques to build approximations of expensive computer analysis codes pervades much of todays engineering design. These statistical approximations, or metamodels, are used to replace the actual expensive computer analyses, facilitating multidisciplinary, multiobjective optimization and concept exploration. In this paper we review several of these techniques including design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We survey their existing application in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of statistical approximation techniques in given situations and how common pitfalls can be avoided.

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Integrated approach to e-learning enhanced both subjective and objective knowledge of aEEG in a neonatal intensive care unit.

PubMed

Poon, W B; Tagamolila, V; Toh, Y P; Cheng, Z R

2015-03-01

Various meta-analyses have shown that e-learning is as effective as traditional methods of continuing professional education. However, there are some disadvantages to e-learning, such as possible technical problems, the need for greater self-discipline, cost involved in developing programmes and limited direct interaction. Currently, most strategies for teaching amplitude-integrated electroencephalography (aEEG) in neonatal intensive care units (NICUs) worldwide depend on traditional teaching methods. We implemented a programme that utilised an integrated approach to e-learning. The programme consisted of three sessions of supervised protected time e-learning in an NICU. The objective and subjective effectiveness of the approach was assessed through surveys administered to participants before and after the programme. A total of 37 NICU staff (32 nurses and 5 doctors) participated in the study. 93.1% of the participants appreciated the need to acquire knowledge of aEEG. We also saw a statistically significant improvement in the subjective knowledge score (p = 0.041) of the participants. The passing rates for identifying abnormal aEEG tracings (defined as ≥ 3 correct answers out of 5) also showed a statistically significant improvement (from 13.6% to 81.8%, p < 0.001). Among the participants who completed the survey, 96.0% felt the teaching was well structured, 77.8% felt the duration was optimal, 80.0% felt that they had learnt how to systematically interpret aEEGs, and 70.4% felt that they could interpret normal aEEG with confidence. An integrated approach to e-learning can help improve subjective and objective knowledge of aEEG.

Gr-GDHP: A New Architecture for Globalized Dual Heuristic Dynamic Programming.

PubMed

Zhong, Xiangnan; Ni, Zhen; He, Haibo

2017-10-01

Goal representation globalized dual heuristic dynamic programming (Gr-GDHP) method is proposed in this paper. A goal neural network is integrated into the traditional GDHP method providing an internal reinforcement signal and its derivatives to help the control and learning process. From the proposed architecture, it is shown that the obtained internal reinforcement signal and its derivatives can be able to adjust themselves online over time rather than a fixed or predefined function in literature. Furthermore, the obtained derivatives can directly contribute to the objective function of the critic network, whose learning process is thus simplified. Numerical simulation studies are applied to show the performance of the proposed Gr-GDHP method and compare the results with other existing adaptive dynamic programming designs. We also investigate this method on a ball-and-beam balancing system. The statistical simulation results are presented for both the Gr-GDHP and the GDHP methods to demonstrate the improved learning and controlling performance.

A 3D model retrieval approach based on Bayesian networks lightfield descriptor

NASA Astrophysics Data System (ADS)

Xiao, Qinhan; Li, Yanjun

2009-12-01

A new 3D model retrieval methodology is proposed by exploiting a novel Bayesian networks lightfield descriptor (BNLD). There are two key novelties in our approach: (1) a BN-based method for building lightfield descriptor; and (2) a 3D model retrieval scheme based on the proposed BNLD. To overcome the disadvantages of the existing 3D model retrieval methods, we explore BN for building a new lightfield descriptor. Firstly, 3D model is put into lightfield, about 300 binary-views can be obtained along a sphere, then Fourier descriptors and Zernike moments descriptors can be calculated out from binaryviews. Then shape feature sequence would be learned into a BN model based on BN learning algorithm; Secondly, we propose a new 3D model retrieval method by calculating Kullback-Leibler Divergence (KLD) between BNLDs. Beneficial from the statistical learning, our BNLD is noise robustness as compared to the existing methods. The comparison between our method and the lightfield descriptor-based approach is conducted to demonstrate the effectiveness of our proposed methodology.

Using the Flipped Classroom to Bridge the Gap to Generation Y.

PubMed

Gillispie, Veronica

2016-01-01

The flipped classroom is a student-centered approach to learning that increases active learning for the student compared to traditional classroom-based instruction. In the flipped classroom model, students are first exposed to the learning material through didactics outside of the classroom, usually in the form of written material, voice-over lectures, or videos. During the formal teaching time, an instructor facilitates student-driven discussion of the material via case scenarios, allowing for complex problem solving, peer interaction, and a deep understanding of the concepts. A successful flipped classroom should have three goals: (1) allow the students to become critical thinkers, (2) fully engage students and instructors, and (3) stimulate the development of a deep understanding of the material. The flipped classroom model includes teaching and learning methods that can appeal to all four generations in the academic environment. During the 2015 academic year, we implemented the flipped classroom in the obstetrics and gynecology clerkship for the Ochsner Clinical School in New Orleans, LA. Voice-over presentations of the lectures that had been given to students in prior years were recorded and made available to the students through an online classroom. Weekly problem-based learning sessions matched to the subjects of the traditional lectures were held, and the faculty who had previously presented the information in the traditional lecture format facilitated the problem-based learning sessions. The knowledge base of students was evaluated at the end of the rotation via a multiple-choice question examination and the Objective Structured Clinical Examination (OSCE) as had been done in previous years. We compared demographic information and examination scores for traditional teaching and flipped classroom groups of students. The traditional teaching group consisted of students from Rotation 2 and Rotation 3 of the 2014 academic year who received traditional classroom-based instruction. The flipped classroom group consisted of students from Rotation 2 and Rotation 3 of the 2015 academic year who received formal didactics via voice-over presentation and had the weekly problem-based learning sessions. When comparing the students taught by traditional methods to those taught in the flipped classroom model, we saw a statistically significant increase in test scores on the multiple-choice question examination in both the obstetrics and gynecology sections in Rotation 2. While the average score for the flipped classroom group increased in Rotation 3 on the obstetrics section of the multiple-choice question examination, the difference was not statistically significant. Unexpectedly, the average score on the gynecology portion of the multiple-choice question examination decreased among the flipped classroom group compared to the traditional teaching group, and this decrease was statistically significant. For both the obstetrics and the gynecology portions of the OSCE, we saw statistically significant increases in the scores for the flipped classroom group in both Rotation 2 and Rotation 3 compared to the traditional teaching group. With the exception of the gynecology portion of the multiple-choice question examination in Rotation 3, we saw improvement in scores after the implementation of the flipped classroom. The flipped classroom is a feasible and useful alternative to the traditional classroom. It is a method that embraces Generation Y's need for active learning in a group setting while maintaining a traditional classroom method for introducing the information. Active learning increases student engagement and can lead to improved retention of material as demonstrated on standard examinations.

Concurrent Movement Impairs Incidental but Not Intentional Statistical Learning

ERIC Educational Resources Information Center

Stevens, David J.; Arciuli, Joanne; Anderson, David I.

2015-01-01

The effect of concurrent movement on incidental versus intentional statistical learning was examined in two experiments. In Experiment 1, participants learned the statistical regularities embedded within familiarization stimuli implicitly, whereas in Experiment 2 they were made aware of the embedded regularities and were instructed explicitly to…

Explorations in Statistics: Correlation

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2010-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This sixth installment of "Explorations in Statistics" explores correlation, a familiar technique that estimates the magnitude of a straight-line relationship between two variables. Correlation is meaningful only when the…

Learning Styles Preferences of Statistics Students: A Study in the Faculty of Business and Economics at the UAE University

ERIC Educational Resources Information Center

Yousef, Darwish Abdulrahman

2016-01-01

Purpose: Although there are many studies addressing the learning styles of business students as well as students of other disciplines, there are few studies which address the learning style preferences of statistics students. The purpose of this study is to explore the learning style preferences of statistics students at a United Arab Emirates…

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Statistics Anxiety, Trait Anxiety, Learning Behavior, and Academic Performance

ERIC Educational Resources Information Center

Macher, Daniel; Paechter, Manuela; Papousek, Ilona; Ruggeri, Kai

2012-01-01

The present study investigated the relationship between statistics anxiety, individual characteristics (e.g., trait anxiety and learning strategies), and academic performance. Students enrolled in a statistics course in psychology (N = 147) filled in a questionnaire on statistics anxiety, trait anxiety, interest in statistics, mathematical…

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods

PubMed Central

Hancock, Matthew C.; Magnan, Jerry F.

2016-01-01

Abstract. In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists’ annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 (±1.14)%, which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 (±0.012), which increases to 0.949 (±0.007) when diameter and volume features are included and has an accuracy of 88.08 (±1.11)%. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification. PMID:27990453

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods.

PubMed

Hancock, Matthew C; Magnan, Jerry F

2016-10-01

In the assessment of nodules in CT scans of the lungs, a number of image-derived features are diagnostically relevant. Currently, many of these features are defined only qualitatively, so they are difficult to quantify from first principles. Nevertheless, these features (through their qualitative definitions and interpretations thereof) are often quantified via a variety of mathematical methods for the purpose of computer-aided diagnosis (CAD). To determine the potential usefulness of quantified diagnostic image features as inputs to a CAD system, we investigate the predictive capability of statistical learning methods for classifying nodule malignancy. We utilize the Lung Image Database Consortium dataset and only employ the radiologist-assigned diagnostic feature values for the lung nodules therein, as well as our derived estimates of the diameter and volume of the nodules from the radiologists' annotations. We calculate theoretical upper bounds on the classification accuracy that are achievable by an ideal classifier that only uses the radiologist-assigned feature values, and we obtain an accuracy of 85.74 [Formula: see text], which is, on average, 4.43% below the theoretical maximum of 90.17%. The corresponding area-under-the-curve (AUC) score is 0.932 ([Formula: see text]), which increases to 0.949 ([Formula: see text]) when diameter and volume features are included and has an accuracy of 88.08 [Formula: see text]. Our results are comparable to those in the literature that use algorithmically derived image-based features, which supports our hypothesis that lung nodules can be classified as malignant or benign using only quantified, diagnostic image features, and indicates the competitiveness of this approach. We also analyze how the classification accuracy depends on specific features and feature subsets, and we rank the features according to their predictive power, statistically demonstrating the top four to be spiculation, lobulation, subtlety, and calcification.

Blended learning with Moodle in medical statistics: an assessment of knowledge, attitudes and practices relating to e-learning.

PubMed

Luo, Li; Cheng, Xiaohua; Wang, Shiyuan; Zhang, Junxue; Zhu, Wenbo; Yang, Jiaying; Liu, Pei

2017-09-19

Blended learning that combines a modular object-oriented dynamic learning environment (Moodle) with face-to-face teaching was applied to a medical statistics course to improve learning outcomes and evaluate the impact factors of students' knowledge, attitudes and practices (KAP) relating to e-learning. The same real-name questionnaire was administered before and after the intervention. The summed scores of every part (knowledge, attitude and practice) were calculated using the entropy method. A mixed linear model was fitted using the SAS PROC MIXED procedure to analyse the impact factors of KAP. Educational reform, self-perceived character, registered permanent residence and hours spent online per day were significant impact factors of e-learning knowledge. Introversion and middle type respondents' average scores were higher than those of extroversion type respondents. Regarding e-learning attitudes, educational reform, community number, Internet age and hours spent online per day had a significant impact. Specifically, participants whose Internet age was no greater than 6 years scored 7.00 points lower than those whose Internet age was greater than 10 years. Regarding e-learning behaviour, educational reform and parents' literacy had a significant impact, as the average score increased 10.05 points (P < 0.0001). This educational reform that combined Moodle with a traditional class achieved good results in terms of students' e-learning KAP. Additionally, this type of blended course can be implemented in many other curriculums.

Study of the Impact of Educational Behavioral Interventions on Fatigue in Mothers in the Postpartum Period in the Groups of Face-to-Face and Electronic Training

PubMed Central

Gholami, Zahra; Mohammadirizi, Soheila; Bahadoran, Parvin

2017-01-01

Background: Maternal fatigue in the postpartum period include factors that affect the quality of life and health of both the mother and newborn. This study aimed to investigate two educational approaches regarding mother's fatigue in the postpartum period. Materials and Methods: This experimental study was performed among 110 pregnant mothers during their postpartum care using random sampling. The participants were divided in three groups, namely, face-to-face, e-learning, and control groups. Interventions included individual meetings between the researcher and mothers in the face-to-face group and giving educational compact disc to the e-learning department to improve maternal fatigue. Personal information and fertility data was obtained (before training); the maternal fatigue questionnaire Fatigue Severity Scale (FSS) was completed before and after any type of (face-to-face, e-learning, and control) education. Obtained data were analyzed using one-way analysis of variance (ANOVA) and repeated-measures ANOVA. Results: Results showed that both face-to-face and e-learning methods had similar maternal fatigue scores. The average change on the maternal fatigue score in the second treatment was (p = 0.02) and the third treatment was (p < 0.001)among three groups that was indicative of significant statistical differences. Similarly, there was no statistically significant difference in the average maternal fatigue score between the two groups before the intervention and in the second and third groups after the intervention. Therefore, over time, the training was unaffected. Conclusions: The findings of this study indicate that both face-to-face and e-learning methods are effective to reduce maternal postpartum fatigue. PMID:29184586

Statistical Learning in a Natural Language by 8-Month-Old Infants

PubMed Central

Pelucchi, Bruna; Hay, Jessica F.; Saffran, Jenny R.

2013-01-01

Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants’ ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition. PMID:19489896

Statistical learning in a natural language by 8-month-old infants.

PubMed

Pelucchi, Bruna; Hay, Jessica F; Saffran, Jenny R

2009-01-01

Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants' ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition.

Musical Experience Influences Statistical Learning of a Novel Language

PubMed Central

Shook, Anthony; Marian, Viorica; Bartolotti, James; Schroeder, Scott R.

2014-01-01

Musical experience may benefit learning a new language by enhancing the fidelity with which the auditory system encodes sound. In the current study, participants with varying degrees of musical experience were exposed to two statistically-defined languages consisting of auditory Morse-code sequences which varied in difficulty. We found an advantage for highly-skilled musicians, relative to less-skilled musicians, in learning novel Morse-code based words. Furthermore, in the more difficult learning condition, performance of lower-skilled musicians was mediated by their general cognitive abilities. We suggest that musical experience may lead to enhanced processing of statistical information and that musicians’ enhanced ability to learn statistical probabilities in a novel Morse-code language may extend to natural language learning. PMID:23505962

Teaching statistics in biology: using inquiry-based learning to strengthen understanding of statistical analysis in biology laboratory courses.

PubMed

Metz, Anneke M

2008-01-01

There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly at the lower-division level. Elements of statistics were incorporated into an introductory biology course, including a review of statistics concepts and opportunity for students to perform statistical analysis in a biological context. Learning gains were measured with an 11-item statistics learning survey instrument developed for the course. Students showed a statistically significant 25% (p < 0.005) increase in statistics knowledge after completing introductory biology. Students improved their scores on the survey after completing introductory biology, even if they had previously completed an introductory statistics course (9%, improvement p < 0.005). Students retested 1 yr after completing introductory biology showed no loss of their statistics knowledge as measured by this instrument, suggesting that the use of statistics in biology course work may aid long-term retention of statistics knowledge. No statistically significant differences in learning were detected between male and female students in the study.

Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

PubMed

Goldstein, Benjamin A; Navar, Ann Marie; Carter, Rickey E

2017-06-14

Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of acute myocardial infarction. We use data derived from our institution's electronic health record and abstract data on 13 regularly measured laboratory markers. We walk through different challenges that arise in modelling these data and then introduce different machine-learning approaches. Finally, we discuss general issues in the application of machine-learning methods including tuning parameters, loss functions, variable importance, and missing data. Overall, this review serves as an introduction for those working on risk modelling to approach the diffuse field of machine learning. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Cardiology.

Learning styles: The learning methods of air traffic control students

NASA Astrophysics Data System (ADS)

Jackson, Dontae L.

In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

Explorations in Statistics: Power

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2010-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fifth installment of "Explorations in Statistics" revisits power, a concept fundamental to the test of a null hypothesis. Power is the probability that we reject the null hypothesis when it is false. Four…

Explorations in Statistics: Confidence Intervals

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This third installment of "Explorations in Statistics" investigates confidence intervals. A confidence interval is a range that we expect, with some level of confidence, to include the true value of a population parameter…

Explorations in Statistics: The Analysis of Change

ERIC Educational Resources Information Center

Curran-Everett, Douglas; Williams, Calvin L.

2015-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This tenth installment of "Explorations in Statistics" explores the analysis of a potential change in some physiological response. As researchers, we often express absolute change as percent change so we can…

Statistical Learning of Phonetic Categories: Insights from a Computational Approach

ERIC Educational Resources Information Center

McMurray, Bob; Aslin, Richard N.; Toscano, Joseph C.

2009-01-01

Recent evidence (Maye, Werker & Gerken, 2002) suggests that statistical learning may be an important mechanism for the acquisition of phonetic categories in the infant's native language. We examined the sufficiency of this hypothesis and its implications for development by implementing a statistical learning mechanism in a computational model…

Infant Directed Speech Enhances Statistical Learning in Newborn Infants: An ERP Study

PubMed Central

Teinonen, Tuomas; Tervaniemi, Mari; Huotilainen, Minna

2016-01-01

Statistical learning and the social contexts of language addressed to infants are hypothesized to play important roles in early language development. Previous behavioral work has found that the exaggerated prosodic contours of infant-directed speech (IDS) facilitate statistical learning in 8-month-old infants. Here we examined the neural processes involved in on-line statistical learning and investigated whether the use of IDS facilitates statistical learning in sleeping newborns. Event-related potentials (ERPs) were recorded while newborns were exposed to12 pseudo-words, six spoken with exaggerated pitch contours of IDS and six spoken without exaggerated pitch contours (ADS) in ten alternating blocks. We examined whether ERP amplitudes for syllable position within a pseudo-word (word-initial vs. word-medial vs. word-final, indicating statistical word learning) and speech register (ADS vs. IDS) would interact. The ADS and IDS registers elicited similar ERP patterns for syllable position in an early 0–100 ms component but elicited different ERP effects in both the polarity and topographical distribution at 200–400 ms and 450–650 ms. These results provide the first evidence that the exaggerated pitch contours of IDS result in differences in brain activity linked to on-line statistical learning in sleeping newborns. PMID:27617967

Region growing using superpixels with learned shape prior

NASA Astrophysics Data System (ADS)

Borovec, Jiří; Kybic, Jan; Sugimoto, Akihiro

2017-11-01

Region growing is a classical image segmentation method based on hierarchical region aggregation using local similarity rules. Our proposed method differs from classical region growing in three important aspects. First, it works on the level of superpixels instead of pixels, which leads to a substantial speed-up. Second, our method uses learned statistical shape properties that encourage plausible shapes. In particular, we use ray features to describe the object boundary. Third, our method can segment multiple objects and ensure that the segmentations do not overlap. The problem is represented as an energy minimization and is solved either greedily or iteratively using graph cuts. We demonstrate the performance of the proposed method and compare it with alternative approaches on the task of segmenting individual eggs in microscopy images of Drosophila ovaries.

Statistical Methods in Ai: Rare Event Learning Using Associative Rules and Higher-Order Statistics

NASA Astrophysics Data System (ADS)

Iyer, V.; Shetty, S.; Iyengar, S. S.

2015-07-01

Rare event learning has not been actively researched since lately due to the unavailability of algorithms which deal with big samples. The research addresses spatio-temporal streams from multi-resolution sensors to find actionable items from a perspective of real-time algorithms. This computing framework is independent of the number of input samples, application domain, labelled or label-less streams. A sampling overlap algorithm such as Brooks-Iyengar is used for dealing with noisy sensor streams. We extend the existing noise pre-processing algorithms using Data-Cleaning trees. Pre-processing using ensemble of trees using bagging and multi-target regression showed robustness to random noise and missing data. As spatio-temporal streams are highly statistically correlated, we prove that a temporal window based sampling from sensor data streams converges after n samples using Hoeffding bounds. Which can be used for fast prediction of new samples in real-time. The Data-cleaning tree model uses a nonparametric node splitting technique, which can be learned in an iterative way which scales linearly in memory consumption for any size input stream. The improved task based ensemble extraction is compared with non-linear computation models using various SVM kernels for speed and accuracy. We show using empirical datasets the explicit rule learning computation is linear in time and is only dependent on the number of leafs present in the tree ensemble. The use of unpruned trees (t) in our proposed ensemble always yields minimum number (m) of leafs keeping pre-processing computation to n × t log m compared to N2 for Gram Matrix. We also show that the task based feature induction yields higher Qualify of Data (QoD) in the feature space compared to kernel methods using Gram Matrix.

Exploring Meteorology Education in Community College: Lecture-based Instruction and Dialogue-based Group Learning

NASA Astrophysics Data System (ADS)

Finley, Jason Paul

This study examined the impact of dialogue-based group instruction on student learning and engagement in community college meteorology education. A quasi-experimental design was used to compare lecture-based instruction with dialogue-based group instruction during two class sessions at one community college in southern California. Pre- and post-tests were used to measure learning and interest, while surveys were conducted two days after the learning events to assess engagement, perceived learning, and application of content. The results indicated that the dialogue-based group instruction was more successful in helping students learn than the lecture-based instruction. Each question that assessed learning had a higher score for the dialogue group that was statistically significant (alpha < 0.05) compared to the lecture group. The survey questions about perceived learning and application of content also exhibited higher scores that were statistically significant for the dialogue group. The qualitative portion of these survey questions supported the quantitative results and showed that the dialogue students were able to remember more concepts and apply these concepts to their lives. Dialogue students were also more engaged, as three out of the five engagement-related survey questions revealed statistically significantly higher scores for them. The qualitative data also supported increased engagement for the dialogue students. Interest in specific meteorological topics did not change significantly for either group of students; however, interest in learning about severe weather was higher for the dialogue group. Neither group found the learning events markedly meaningful, although more students from the dialogue group found pronounced meaning centered on applying severe weather knowledge to their lives. Active engagement in the dialogue approach kept these students from becoming distracted and allowed them to become absorbed in the learning event. This higher engagement most likely contributed to the resulting higher learning. Together, these results indicate that dialogue education, especially compared to lecture methods, has a great potential for helping students learn meteorology. Dialogue education can also help students engage in weather-related concepts and potentially develop better-informed citizens in a world with a changing climate.

External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches.

PubMed

Morales, Daniel R; Flynn, Rob; Zhang, Jianguo; Trucco, Emmanuel; Quint, Jennifer K; Zutis, Kris

2018-05-01

Several models for predicting the risk of death in people with chronic obstructive pulmonary disease (COPD) exist but have not undergone large scale validation in primary care. The objective of this study was to externally validate these models using statistical and machine learning approaches. We used a primary care COPD cohort identified using data from the UK Clinical Practice Research Datalink. Age-standardised mortality rates were calculated for the population by gender and discrimination of ADO (age, dyspnoea, airflow obstruction), COTE (COPD-specific comorbidity test), DOSE (dyspnoea, airflow obstruction, smoking, exacerbations) and CODEX (comorbidity, dyspnoea, airflow obstruction, exacerbations) at predicting death over 1-3 years measured using logistic regression and a support vector machine learning (SVM) method of analysis. The age-standardised mortality rate was 32.8 (95%CI 32.5-33.1) and 25.2 (95%CI 25.4-25.7) per 1000 person years for men and women respectively. Complete data were available for 54879 patients to predict 1-year mortality. ADO performed the best (c-statistic of 0.730) compared with DOSE (c-statistic 0.645), COTE (c-statistic 0.655) and CODEX (c-statistic 0.649) at predicting 1-year mortality. Discrimination of ADO and DOSE improved at predicting 1-year mortality when combined with COTE comorbidities (c-statistic 0.780 ADO + COTE; c-statistic 0.727 DOSE + COTE). Discrimination did not change significantly over 1-3 years. Comparable results were observed using SVM. In primary care, ADO appears superior at predicting death in COPD. Performance of ADO and DOSE improved when combined with COTE comorbidities suggesting better models may be generated with additional data facilitated using novel approaches. Copyright © 2018. Published by Elsevier Ltd.

Applying Bayesian statistics to the study of psychological trauma: A suggestion for future research.

PubMed

Yalch, Matthew M

2016-03-01

Several contemporary researchers have noted the virtues of Bayesian methods of data analysis. Although debates continue about whether conventional or Bayesian statistics is the "better" approach for researchers in general, there are reasons why Bayesian methods may be well suited to the study of psychological trauma in particular. This article describes how Bayesian statistics offers practical solutions to the problems of data non-normality, small sample size, and missing data common in research on psychological trauma. After a discussion of these problems and the effects they have on trauma research, this article explains the basic philosophical and statistical foundations of Bayesian statistics and how it provides solutions to these problems using an applied example. Results of the literature review and the accompanying example indicates the utility of Bayesian statistics in addressing problems common in trauma research. Bayesian statistics provides a set of methodological tools and a broader philosophical framework that is useful for trauma researchers. Methodological resources are also provided so that interested readers can learn more. (c) 2016 APA, all rights reserved).

TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning.

PubMed

Mareschal, Denis; French, Robert M

2017-01-05

Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning

PubMed Central

French, Robert M.

2017-01-01

Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872375

An enriched multimedia eBook application to facilitate learning of anatomy.

PubMed

Stirling, Allan; Birt, James

2014-01-01

This pilot study compared the use of an enriched multimedia eBook with traditional methods for teaching the gross anatomy of the heart and great vessels. Seventy-one first-year students from an Australian medical school participated in the study. Students' abilities were examined by pretest, intervention, and post-test measurements. Perceptions and attitudes toward eBook technology were examined by survey questions. Results indicated a strongly positive user experience coupled with increased marks; however, there were no statistically significant results for the eBook method of delivery alone outperforming the traditional anatomy practical session. Results did show a statistically significant difference in the final marks achieved based on the sequencing of the learning modalities. With initial interaction with the multimedia content followed by active experimentation in the anatomy lab, students' performance was improved in the final test. Obtained data support the role of eBook technology in modern anatomy curriculum being a useful adjunct to traditional methods. Further study is needed to investigate the importance of sequencing of teaching interventions. © 2013 American Association of Anatomists.

Aggregative Learning Method and Its Application for Communication Quality Evaluation

NASA Astrophysics Data System (ADS)

Akhmetov, Dauren F.; Kotaki, Minoru

2007-12-01

In this paper, so-called Aggregative Learning Method (ALM) is proposed to improve and simplify the learning and classification abilities of different data processing systems. It provides a universal basis for design and analysis of mathematical models of wide class. A procedure was elaborated for time series model reconstruction and analysis for linear and nonlinear cases. Data approximation accuracy (during learning phase) and data classification quality (during recall phase) are estimated from introduced statistic parameters. The validity and efficiency of the proposed approach have been demonstrated through its application for monitoring of wireless communication quality, namely, for Fixed Wireless Access (FWA) system. Low memory and computation resources were shown to be needed for the procedure realization, especially for data classification (recall) stage. Characterized with high computational efficiency and simple decision making procedure, the derived approaches can be useful for simple and reliable real-time surveillance and control system design.

Evaluating the use of simulation with beginning nursing students.

PubMed

Alfes, Celeste M

2011-02-01

The purpose of this quasi-experimental study was to evaluate and compare the effectiveness of simulation versus a traditional skills laboratory method in promoting self-confidence and satisfaction with learning among beginning nursing students. A single convenience sample of 63 first-semester baccalaureate nursing students learning effective comfort care measures were recruited to compare the two teaching methods. Students participating in the simulation experience were statistically more confident than students participating in the traditional group. There was a slight, nonsignificant difference in satisfaction with learning between the two groups. Bivariate analysis revealed a significant positive relationship between self-confidence and satisfaction. Students in both groups reported higher levels of self-confidence following the learning experiences. Findings may influence the development of simulation experiences for beginning nursing students and encourage the implementation of simulation as a strand from beginning to end in nursing curricula. Copyright 2011, SLACK Incorporated.

Adapt-Mix: learning local genetic correlation structure improves summary statistics-based analyses

PubMed Central

Park, Danny S.; Brown, Brielin; Eng, Celeste; Huntsman, Scott; Hu, Donglei; Torgerson, Dara G.; Burchard, Esteban G.; Zaitlen, Noah

2015-01-01

Motivation: Approaches to identifying new risk loci, training risk prediction models, imputing untyped variants and fine-mapping causal variants from summary statistics of genome-wide association studies are playing an increasingly important role in the human genetics community. Current summary statistics-based methods rely on global ‘best guess’ reference panels to model the genetic correlation structure of the dataset being studied. This approach, especially in admixed populations, has the potential to produce misleading results, ignores variation in local structure and is not feasible when appropriate reference panels are missing or small. Here, we develop a method, Adapt-Mix, that combines information across all available reference panels to produce estimates of local genetic correlation structure for summary statistics-based methods in arbitrary populations. Results: We applied Adapt-Mix to estimate the genetic correlation structure of both admixed and non-admixed individuals using simulated and real data. We evaluated our method by measuring the performance of two summary statistics-based methods: imputation and joint-testing. When using our method as opposed to the current standard of ‘best guess’ reference panels, we observed a 28% decrease in mean-squared error for imputation and a 73.7% decrease in mean-squared error for joint-testing. Availability and implementation: Our method is publicly available in a software package called ADAPT-Mix available at https://github.com/dpark27/adapt_mix. Contact: noah.zaitlen@ucsf.edu PMID:26072481

Machine learning: Trends, perspectives, and prospects.

PubMed

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Can machine learning complement traditional medical device surveillance? A case study of dual-chamber implantable cardioverter-defibrillators.

PubMed

Ross, Joseph S; Bates, Jonathan; Parzynski, Craig S; Akar, Joseph G; Curtis, Jeptha P; Desai, Nihar R; Freeman, James V; Gamble, Ginger M; Kuntz, Richard; Li, Shu-Xia; Marinac-Dabic, Danica; Masoudi, Frederick A; Normand, Sharon-Lise T; Ranasinghe, Isuru; Shaw, Richard E; Krumholz, Harlan M

2017-01-01

Machine learning methods may complement traditional analytic methods for medical device surveillance. Using data from the National Cardiovascular Data Registry for implantable cardioverter-defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). The first approach used PS-SME and cumulative incidence (time-to-event), the second approach used PS-SME and cumulative risk (Data Extraction and Longitudinal Trend Analysis [DELTA]), and the third approach used PS-ML and cumulative risk (embedded feature selection). Safety-signal surveillance was conducted for eleven dual-chamber ICD models implanted at least 2,000 times over 3 years. Between 2006 and 2010, there were 71,948 Medicare fee-for-service beneficiaries who received dual-chamber ICDs. Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%-20.9%; nonfatal ICD-related adverse events, 19.3%-26.3%; and death from any cause or nonfatal ICD-related adverse event, 27.1%-37.6%. Agreement among safety signals detected/not detected between the time-to-event and DELTA approaches was 90.9% (360 of 396, k =0.068), between the time-to-event and embedded feature-selection approaches was 91.7% (363 of 396, k =-0.028), and between the DELTA and embedded feature selection approaches was 88.1% (349 of 396, k =-0.042). Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. Ensemble methods may be needed to detect all safety signals for further evaluation during medical device surveillance.

Predicting radiotherapy outcomes using statistical learning techniques

NASA Astrophysics Data System (ADS)

El Naqa, Issam; Bradley, Jeffrey D.; Lindsay, Patricia E.; Hope, Andrew J.; Deasy, Joseph O.

2009-09-01

Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model variables. These models have the capacity to predict on unseen data. Part of this work was first presented at the Seventh International Conference on Machine Learning and Applications, San Diego, CA, USA, 11-13 December 2008.

Measuring medical students' motivation to learning anatomy by cadaveric dissection.

PubMed

Abdel Meguid, Eiman M; Khalil, Mohammed K

2017-07-01

Motivation and learning are inter-related. It is well known that motivating learners is clearly a complex endeavor, which can be influenced by the educational program and the learning environment. Limited research has been conducted to examine students' motivation as a method to assess the effectiveness of dissection in medical education. This study aimed to assess and analyze students' motivation following their dissection experience. A 29-item survey was developed based on the Attention, Relevance, Confidence, and Satisfaction model of motivation. Descriptive statistics were undertaken to describe students' motivation to the dissection experience. T-test and ANOVA were used to compare differences in motivational scores between gender and educational characteristics of students. Dissection activities appear to promote students' motivation. Gender difference was statistically significant as males were more motivated by the dissection experience than females. Comparison between students with different knowledge of anatomy was also significantly different. The study is an important step in the motivational design to improve students' motivation to learn. The outcome of this study provides guidance to the selection of specific strategies to increase motivation by generating motivational strategies/tactics to facilitate learning. Anat Sci Educ 10: 363-371. © 2016 American Association of Anatomists. © 2016 American Association of Anatomists.

A Comparison of Didactic and Inquiry Teaching Methods in a Rural Community College Earth Science Course

NASA Astrophysics Data System (ADS)

Beam, Margery Elizabeth

The combination of increasing enrollment and the importance of providing transfer students a solid foundation in science calls for science faculty to evaluate teaching methods in rural community colleges. The purpose of this study was to examine and compare the effectiveness of two teaching methods, inquiry teaching methods and didactic teaching methods, applied in a rural community college earth science course. Two groups of students were taught the same content via inquiry and didactic teaching methods. Analysis of quantitative data included a non-parametric ranking statistical testing method in which the difference between the rankings and the median of the post-test scores was analyzed for significance. Results indicated there was not a significant statistical difference between the teaching methods for the group of students participating in the research. The practical and educational significance of this study provides valuable perspectives on teaching methods and student learning styles in rural community colleges.

Evolution of social versus individual learning in a subdivided population revisited: comparative analysis of three coexistence mechanisms using the inclusive-fitness method.

PubMed

Kobayashi, Yutaka; Ohtsuki, Hisashi

2014-03-01

Learning abilities are categorized into social (learning from others) and individual learning (learning on one's own). Despite the typically higher cost of individual learning, there are mechanisms that allow stable coexistence of both learning modes in a single population. In this paper, we investigate by means of mathematical modeling how the effect of spatial structure on evolutionary outcomes of pure social and individual learning strategies depends on the mechanisms for coexistence. We model a spatially structured population based on the infinite-island framework and consider three scenarios that differ in coexistence mechanisms. Using the inclusive-fitness method, we derive the equilibrium frequency of social learners and the genetic load of social learning (defined as average fecundity reduction caused by the presence of social learning) in terms of some summary statistics, such as relatedness, for each of the three scenarios and compare the results. This comparative analysis not only reconciles previous models that made contradictory predictions as to the effect of spatial structure on the equilibrium frequency of social learners but also derives a simple mathematical rule that determines the sign of the genetic load (i.e. whether or not social learning contributes to the mean fecundity of the population). Copyright © 2013 Elsevier Inc. All rights reserved.

[Problem-based learning, a comparison in the acquisition of transversal competencies].

PubMed

González Pascual, Juan Luis; López Martin, Inmaculada; Toledo Gómez, David

2009-01-01

In the European Higher Education Area (EEES in Spanish reference), a change in the pedagogical model has occurred: from teaching centered on the figure of the professor to learning centered on students, from an integral perspective. This learning must bring together the full set of competencies included in the program requirements necessary to obtain a degree. The specific competencies characterize a profession and distinguish one from others. The transversal competencies surpass the limits of one particular discipline to be potentially developed in all; these are subdivided in three types: instrumental, interpersonal and systemic. The authors describe and compare the acquisition of transversal competencies connected to students' portfolios and Problem-based Learning as pedagogical methods from the perspective of second year nursing students at the European University in Madrid during the 2007-8 academic year To do so, the authors carried out a transversal descriptive study; data was collected by a purpose-made questionnaire the authors developed which they based on the transversal competencies of the Tuning Nursing Project. Variables included age, sex, pedagogical method, perception on acquisition of those 24 competencies by means of a Likert Scale. U de Mann-Whitney descriptive and analytical statistics. The authors conclude that the portfolio and Problem-based Learning are useful pedagogical methods for acquiring transversal competencies; these results coincide with those of other studies. Comparing both methods, the authors share the opinion that the Problem-based Learning method could stimulate the search for information better than the portfolio method.

The effect of distance learning via SMS on academic achievement and satisfaction of medical students.

PubMed

Sichani, Mehrdad Mohammadi; Mobarakeh, Shadi Reissizadeh; Omid, Athar

2018-01-01

Recently, medical education has made significant progress, and medical teachers are trying to find methods that have most impressive effects on learning. One of the useful learning methods is student active participation. One of the helpful teaching aids in this method is mobile technology. The present study aimed to determine the effect of sending educational questions through short message service (SMS) on academic achievement and satisfaction of medical students and compare that with lecture teaching. In an semi-experimental, two chapters of urology reference book, Smiths General Urology 17 th edition, were taught to 47 medical students of Isfahan University of Medical Sciences in urology course in 2013 academic year. Kidney tumors chapter was educated by sending questions through SMS, and bladder tumors part was taught in a lecture session. For each method, pretest and posttest were held, each consisting of thirty multiple choice questions. To examine the knowledge retention, a test session was held on the same terms for each chapter, 1 month later. At the end, survey forms were distributed to assess student's satisfaction with SMS learning method. Data were analyzed through using SPSS 20. The findings demonstrated a statistically significant difference between the two learning methods in the medication test scores. Evaluation of the satisfaction showed 78.72% of participants were not satisfied. The results of the study showed that distance learning through SMS in medical students could lead to increase knowledge, however, it was not effective on their satisfaction.

AstroML: Python-powered Machine Learning for Astronomy

NASA Astrophysics Data System (ADS)

Vander Plas, Jake; Connolly, A. J.; Ivezic, Z.

2014-01-01

As astronomical data sets grow in size and complexity, automated machine learning and data mining methods are becoming an increasingly fundamental component of research in the field. The astroML project (http://astroML.org) provides a common repository for practical examples of the data mining and machine learning tools used and developed by astronomical researchers, written in Python. The astroML module contains a host of general-purpose data analysis and machine learning routines, loaders for openly-available astronomical datasets, and fast implementations of specific computational methods often used in astronomy and astrophysics. The associated website features hundreds of examples of these routines being used for analysis of real astronomical datasets, while the associated textbook provides a curriculum resource for graduate-level courses focusing on practical statistics, machine learning, and data mining approaches within Astronomical research. This poster will highlight several of the more powerful and unique examples of analysis performed with astroML, all of which can be reproduced in their entirety on any computer with the proper packages installed.

Deriving Signatures of In Vivo Toxicity Using Both Efficacy and Potency Information from In Vitro Assays: Evaluating Model Performance as a Function of Increasing Variability in Experimental Data

EPA Science Inventory

The US EPA ToxCast program aims to develop methods for mechanistically-based chemical prioritization using a suite of high throughput, in vitro assays that probe relevant biological pathways, and coupling them with statistical and machine learning methods that produce predictive ...

Explorations in Statistics: The Analysis of Ratios and Normalized Data

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2013-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This ninth installment of "Explorations in Statistics" explores the analysis of ratios and normalized--or standardized--data. As researchers, we compute a ratio--a numerator divided by a denominator--to compute a…

A Constructivist Approach in a Blended E-Learning Environment for Statistics

ERIC Educational Resources Information Center

Poelmans, Stephan; Wessa, Patrick

2015-01-01

In this study, we report on the students' evaluation of a self-constructed constructivist e-learning environment for statistics, the compendium platform (CP). The system was built to endorse deeper learning with the incorporation of statistical reproducibility and peer review practices. The deployment of the CP, with interactive workshops and…

Statistical Learning Effects in Musicians and Non-Musicians: An MEG Study

ERIC Educational Resources Information Center

Paraskevopoulos, Evangelos; Kuchenbuch, Anja; Herholz, Sibylle C.; Pantev, Christo

2012-01-01

This study aimed to assess the effect of musical training in statistical learning of tone sequences using Magnetoencephalography (MEG). Specifically, MEG recordings were used to investigate the neural and functional correlates of the pre-attentive ability for detection of deviance, from a statistically learned tone sequence. The effect of…

Enhancing predictive accuracy and reproducibility in clinical evaluation research: Commentary on the special section of the Journal of Evaluation in Clinical Practice.

PubMed

Bryant, Fred B

2016-12-01

This paper introduces a special section of the current issue of the Journal of Evaluation in Clinical Practice that includes a set of 6 empirical articles showcasing a versatile, new machine-learning statistical method, known as optimal data (or discriminant) analysis (ODA), specifically designed to produce statistical models that maximize predictive accuracy. As this set of papers clearly illustrates, ODA offers numerous important advantages over traditional statistical methods-advantages that enhance the validity and reproducibility of statistical conclusions in empirical research. This issue of the journal also includes a review of a recently published book that provides a comprehensive introduction to the logic, theory, and application of ODA in empirical research. It is argued that researchers have much to gain by using ODA to analyze their data. © 2016 John Wiley & Sons, Ltd.

Anomaly detection for machine learning redshifts applied to SDSS galaxies

NASA Astrophysics Data System (ADS)

Hoyle, Ben; Rau, Markus Michael; Paech, Kerstin; Bonnett, Christopher; Seitz, Stella; Weller, Jochen

2015-10-01

We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million `clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 `anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed `anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80 per cent when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.

Physical fitness modulates incidental but not intentional statistical learning of simultaneous auditory sequences during concurrent physical exercise.

PubMed

Daikoku, Tatsuya; Takahashi, Yuji; Futagami, Hiroko; Tarumoto, Nagayoshi; Yasuda, Hideki

2017-02-01

In real-world auditory environments, humans are exposed to overlapping auditory information such as those made by human voices and musical instruments even during routine physical activities such as walking and cycling. The present study investigated how concurrent physical exercise affects performance of incidental and intentional learning of overlapping auditory streams, and whether physical fitness modulates the performances of learning. Participants were grouped with 11 participants with lower and higher fitness each, based on their Vo 2 max value. They were presented simultaneous auditory sequences with a distinct statistical regularity each other (i.e. statistical learning), while they were pedaling on the bike and seating on a bike at rest. In experiment 1, they were instructed to attend to one of the two sequences and ignore to the other sequence. In experiment 2, they were instructed to attend to both of the two sequences. After exposure to the sequences, learning effects were evaluated by familiarity test. In the experiment 1, performance of statistical learning of ignored sequences during concurrent pedaling could be higher in the participants with high than low physical fitness, whereas in attended sequence, there was no significant difference in performance of statistical learning between high than low physical fitness. Furthermore, there was no significant effect of physical fitness on learning while resting. In the experiment 2, the both participants with high and low physical fitness could perform intentional statistical learning of two simultaneous sequences in the both exercise and rest sessions. The improvement in physical fitness might facilitate incidental but not intentional statistical learning of simultaneous auditory sequences during concurrent physical exercise.

Ensemble Methods

NASA Astrophysics Data System (ADS)

Re, Matteo; Valentini, Giorgio

2012-03-01

Ensemble methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. Ensembles are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote ensemble, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall ensemble [158]. In the literature, a plethora of terms other than ensembles has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term ensemble in its widest meaning, in order to include the whole range of combination methods. Nowadays, ensemble methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on ensemble methods is witnessed by conferences and workshops specifically devoted to ensembles, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been proposed to explain the characteristics and the successful application of ensembles to different application domains. For instance, Allwein, Schapire, and Singer interpreted the improved generalization capabilities of ensembles of learning machines in the framework of large margin classifiers [4,177], Kleinberg in the context of stochastic discrimination theory [112], and Breiman and Friedman in the light of the bias-variance analysis borrowed from classical statistics [21,70]. Empirical studies showed that both in classification and regression problems, ensembles improve on single learning machines, and moreover large experimental studies compared the effectiveness of different ensemble methods on benchmark data sets [10,11,49,188]. The interest in this research area is motivated also by the availability of very fast computers and networks of workstations at a relatively low cost that allow the implementation and the experimentation of complex ensemble methods using off-the-shelf computer platforms. However, as explained in Section 26.2 there are deeper reasons to use ensembles of learning machines, motivated by the intrinsic characteristics of the ensemble methods. The main aim of this chapter is to introduce ensemble methods and to provide an overview and a bibliography of the main areas of research, without pretending to be exhaustive or to explain the detailed characteristics of each ensemble method. The paper is organized as follows. In the next section, the main theoretical and practical reasons for combining multiple learners are introduced. Section 26.3 depicts the main taxonomies on ensemble methods proposed in the literature. In Section 26.4 and 26.5, we present an overview of the main supervised ensemble methods reported in the literature, adopting a simple taxonomy, originally proposed in Ref. [201]. Applications of ensemble methods are only marginally considered, but a specific section on some relevant applications of ensemble methods in astronomy and astrophysics has been added (Section 26.6). The conclusion (Section 26.7) ends this paper and lists some issues not covered in this work.

Probability, statistics, and computational science.

PubMed

Beerenwinkel, Niko; Siebourg, Juliane

2012-01-01

In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.

Smart-system of distance learning of visually impaired people based on approaches of artificial intelligence

NASA Astrophysics Data System (ADS)

Samigulina, Galina A.; Shayakhmetova, Assem S.

2016-11-01

Research objective is the creation of intellectual innovative technology and information Smart-system of distance learning for visually impaired people. The organization of the available environment for receiving quality education for visually impaired people, their social adaptation in society are important and topical issues of modern education.The proposed Smart-system of distance learning for visually impaired people can significantly improve the efficiency and quality of education of this category of people. The scientific novelty of proposed Smart-system is using intelligent and statistical methods of processing multi-dimensional data, and taking into account psycho-physiological characteristics of perception and awareness learning information by visually impaired people.

Teaching Statistics in Biology: Using Inquiry-based Learning to Strengthen Understanding of Statistical Analysis in Biology Laboratory Courses

PubMed Central

2008-01-01

There is an increasing need for students in the biological sciences to build a strong foundation in quantitative approaches to data analyses. Although most science, engineering, and math field majors are required to take at least one statistics course, statistical analysis is poorly integrated into undergraduate biology course work, particularly at the lower-division level. Elements of statistics were incorporated into an introductory biology course, including a review of statistics concepts and opportunity for students to perform statistical analysis in a biological context. Learning gains were measured with an 11-item statistics learning survey instrument developed for the course. Students showed a statistically significant 25% (p < 0.005) increase in statistics knowledge after completing introductory biology. Students improved their scores on the survey after completing introductory biology, even if they had previously completed an introductory statistics course (9%, improvement p < 0.005). Students retested 1 yr after completing introductory biology showed no loss of their statistics knowledge as measured by this instrument, suggesting that the use of statistics in biology course work may aid long-term retention of statistics knowledge. No statistically significant differences in learning were detected between male and female students in the study. PMID:18765754

Quantification of histone modification ChIP-seq enrichment for data mining and machine learning applications

PubMed Central

2011-01-01

Background The advent of ChIP-seq technology has made the investigation of epigenetic regulatory networks a computationally tractable problem. Several groups have applied statistical computing methods to ChIP-seq datasets to gain insight into the epigenetic regulation of transcription. However, methods for estimating enrichment levels in ChIP-seq data for these computational studies are understudied and variable. Since the conclusions drawn from these data mining and machine learning applications strongly depend on the enrichment level inputs, a comparison of estimation methods with respect to the performance of statistical models should be made. Results Various methods were used to estimate the gene-wise ChIP-seq enrichment levels for 20 histone methylations and the histone variant H2A.Z. The Multivariate Adaptive Regression Splines (MARS) algorithm was applied for each estimation method using the estimation of enrichment levels as predictors and gene expression levels as responses. The methods used to estimate enrichment levels included tag counting and model-based methods that were applied to whole genes and specific gene regions. These methods were also applied to various sizes of estimation windows. The MARS model performance was assessed with the Generalized Cross-Validation Score (GCV). We determined that model-based methods of enrichment estimation that spatially weight enrichment based on average patterns provided an improvement over tag counting methods. Also, methods that included information across the entire gene body provided improvement over methods that focus on a specific sub-region of the gene (e.g., the 5' or 3' region). Conclusion The performance of data mining and machine learning methods when applied to histone modification ChIP-seq data can be improved by using data across the entire gene body, and incorporating the spatial distribution of enrichment. Refinement of enrichment estimation ultimately improved accuracy of model predictions. PMID:21834981

Pitfalls in statistical landslide susceptibility modelling

NASA Astrophysics Data System (ADS)

Schröder, Boris; Vorpahl, Peter; Märker, Michael; Elsenbeer, Helmut

2010-05-01

The use of statistical methods is a well-established approach to predict landslide occurrence probabilities and to assess landslide susceptibility. This is achieved by applying statistical methods relating historical landslide inventories to topographic indices as predictor variables. In our contribution, we compare several new and powerful methods developed in machine learning and well-established in landscape ecology and macroecology for predicting the distribution of shallow landslides in tropical mountain rainforests in southern Ecuador (among others: boosted regression trees, multivariate adaptive regression splines, maximum entropy). Although these methods are powerful, we think it is necessary to follow a basic set of guidelines to avoid some pitfalls regarding data sampling, predictor selection, and model quality assessment, especially if a comparison of different models is contemplated. We therefore suggest to apply a novel toolbox to evaluate approaches to the statistical modelling of landslide susceptibility. Additionally, we propose some methods to open the "black box" as an inherent part of machine learning methods in order to achieve further explanatory insights into preparatory factors that control landslides. Sampling of training data should be guided by hypotheses regarding processes that lead to slope failure taking into account their respective spatial scales. This approach leads to the selection of a set of candidate predictor variables considered on adequate spatial scales. This set should be checked for multicollinearity in order to facilitate model response curve interpretation. Model quality assesses how well a model is able to reproduce independent observations of its response variable. This includes criteria to evaluate different aspects of model performance, i.e. model discrimination, model calibration, and model refinement. In order to assess a possible violation of the assumption of independency in the training samples or a possible lack of explanatory information in the chosen set of predictor variables, the model residuals need to be checked for spatial auto¬correlation. Therefore, we calculate spline correlograms. In addition to this, we investigate partial dependency plots and bivariate interactions plots considering possible interactions between predictors to improve model interpretation. Aiming at presenting this toolbox for model quality assessment, we investigate the influence of strategies in the construction of training datasets for statistical models on model quality.

Online Learning and the Development of Counseling Self-Efficacy Beliefs

ERIC Educational Resources Information Center

Watson, Joshua C.

2012-01-01

This study examined the relationship between enrollment in online counseling courses and students' counseling selfefficacy beliefs. Results indicate that students enrolled in online courses report statistically significant higher selfefficacy beliefs than students in traditional FTF courses. Online instructional method may increase counselor…

Assessing Creative Problem-Solving with Automated Text Grading

ERIC Educational Resources Information Center

Wang, Hao-Chuan; Chang, Chun-Yen; Li, Tsai-Yen

2008-01-01

The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational-statistical machine learning methods to grade students' natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit…

Effect of an EBM course in combination with case method learning sessions: an RCT on professional performance, job satisfaction, and self-efficacy of occupational physicians.

PubMed

Hugenholtz, Nathalie I R; Schaafsma, Frederieke G; Nieuwenhuijsen, Karen; van Dijk, Frank J H

2008-10-01

An intervention existing of an evidence-based medicine (EBM) course in combination with case method learning sessions (CMLSs) was designed to enhance the professional performance, self-efficacy and job satisfaction of occupational physicians. A cluster randomized controlled trial was set up and data were collected through questionnaires at baseline (T0), directly after the intervention (T1) and 7 months after baseline (T2). The data of the intervention group [T0 (n = 49), T1 (n = 31), T2 (n = 29)] and control group [T0 (n = 49), T1 (n = 28), T2 (n = 28)] were analysed in mixed model analyses. Mean scores of the perceived value of the CMLS were calculated in the intervention group. The overall effect of the intervention over time comparing the intervention with the control group was statistically significant for professional performance (p < 0.001). Job satisfaction and self-efficacy changes were small and not statistically significant between the groups. The perceived value of the CMLS to gain new insights and to improve the quality of their performance increased with the number of sessions followed. An EBM course in combination with case method learning sessions is perceived as valuable and offers evidence to enhance the professional performance of occupational physicians. However, it does not seem to influence their self-efficacy and job satisfaction.

Advanced building energy management system demonstration for Department of Defense buildings.

PubMed

O'Neill, Zheng; Bailey, Trevor; Dong, Bing; Shashanka, Madhusudana; Luo, Dong

2013-08-01

This paper presents an advanced building energy management system (aBEMS) that employs advanced methods of whole-building performance monitoring combined with statistical methods of learning and data analysis to enable identification of both gradual and discrete performance erosion and faults. This system assimilated data collected from multiple sources, including blueprints, reduced-order models (ROM) and measurements, and employed advanced statistical learning algorithms to identify patterns of anomalies. The results were presented graphically in a manner understandable to facilities managers. A demonstration of aBEMS was conducted in buildings at Naval Station Great Lakes. The facility building management systems were extended to incorporate the energy diagnostics and analysis algorithms, producing systematic identification of more efficient operation strategies. At Naval Station Great Lakes, greater than 20% savings were demonstrated for building energy consumption by improving facility manager decision support to diagnose energy faults and prioritize alternative, energy-efficient operation strategies. The paper concludes with recommendations for widespread aBEMS success. © 2013 New York Academy of Sciences.

Data Science in the Research Domain Criteria Era: Relevance of Machine Learning to the Study of Stress Pathology, Recovery, and Resilience

PubMed Central

Galatzer-Levy, Isaac R.; Ruggles, Kelly; Chen, Zhe

2017-01-01

Diverse environmental and biological systems interact to influence individual differences in response to environmental stress. Understanding the nature of these complex relationships can enhance the development of methods to: (1) identify risk, (2) classify individuals as healthy or ill, (3) understand mechanisms of change, and (4) develop effective treatments. The Research Domain Criteria (RDoC) initiative provides a theoretical framework to understand health and illness as the product of multiple inter-related systems but does not provide a framework to characterize or statistically evaluate such complex relationships. Characterizing and statistically evaluating models that integrate multiple levels (e.g. synapses, genes, environmental factors) as they relate to outcomes that a free from prior diagnostic benchmarks represents a challenge requiring new computational tools that are capable to capture complex relationships and identify clinically relevant populations. In the current review, we will summarize machine learning methods that can achieve these goals. PMID:29527592

Applications of machine learning in cancer prediction and prognosis.

PubMed

Cruz, Joseph A; Wishart, David S

2007-02-11

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on "older" technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Co-occurrence statistics as a language-dependent cue for speech segmentation.

PubMed

Saksida, Amanda; Langus, Alan; Nespor, Marina

2017-05-01

To what extent can language acquisition be explained in terms of different associative learning mechanisms? It has been hypothesized that distributional regularities in spoken languages are strong enough to elicit statistical learning about dependencies among speech units. Distributional regularities could be a useful cue for word learning even without rich language-specific knowledge. However, it is not clear how strong and reliable the distributional cues are that humans might use to segment speech. We investigate cross-linguistic viability of different statistical learning strategies by analyzing child-directed speech corpora from nine languages and by modeling possible statistics-based speech segmentations. We show that languages vary as to which statistical segmentation strategies are most successful. The variability of the results can be partially explained by systematic differences between languages, such as rhythmical differences. The results confirm previous findings that different statistical learning strategies are successful in different languages and suggest that infants may have to primarily rely on non-statistical cues when they begin their process of speech segmentation. © 2016 John Wiley & Sons Ltd.

Unsupervised learning toward brain imaging data analysis: cigarette craving and resistance related neuronal activations from functional magnetic resonance imaging data analysis

NASA Astrophysics Data System (ADS)

Kim, Dong-Youl; Lee, Jong-Hwan

2014-05-01

A data-driven unsupervised learning such as an independent component analysis was gainfully applied to bloodoxygenation- level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data compared to a model-based general linear model (GLM). This is due to an ability of this unsupervised learning method to extract a meaningful neuronal activity from BOLD signal that is a mixture of confounding non-neuronal artifacts such as head motions and physiological artifacts as well as neuronal signals. In this study, we support this claim by identifying neuronal underpinnings of cigarette craving and cigarette resistance. The fMRI data were acquired from heavy cigarette smokers (n = 14) while they alternatively watched images with and without cigarette smoking. During acquisition of two fMRI runs, they were asked to crave when they watched cigarette smoking images or to resist the urge to smoke. Data driven approaches of group independent component analysis (GICA) method based on temporal concatenation (TC) and TCGICA with an extension of iterative dual-regression (TC-GICA-iDR) were applied to the data. From the results, cigarette craving and cigarette resistance related neuronal activations were identified in the visual area and superior frontal areas, respectively with a greater statistical significance from the TC-GICA-iDR method than the TC-GICA method. On the other hand, the neuronal activity levels in many of these regions were not statistically different from the GLM method between the cigarette craving and cigarette resistance due to potentially aberrant BOLD signals.

Redefining "Learning" in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities?

PubMed

Siegelman, Noam; Bogaerts, Louisa; Kronenfeld, Ofer; Frost, Ram

2017-10-07

From a theoretical perspective, most discussions of statistical learning (SL) have focused on the possible "statistical" properties that are the object of learning. Much less attention has been given to defining what "learning" is in the context of "statistical learning." One major difficulty is that SL research has been monitoring participants' performance in laboratory settings with a strikingly narrow set of tasks, where learning is typically assessed offline, through a set of two-alternative-forced-choice questions, which follow a brief visual or auditory familiarization stream. Is that all there is to characterizing SL abilities? Here we adopt a novel perspective for investigating the processing of regularities in the visual modality. By tracking online performance in a self-paced SL paradigm, we focus on the trajectory of learning. In a set of three experiments we show that this paradigm provides a reliable and valid signature of SL performance, and it offers important insights for understanding how statistical regularities are perceived and assimilated in the visual modality. This demonstrates the promise of integrating different operational measures to our theory of SL. © 2017 Cognitive Science Society, Inc.

Dataset on Investigating the role of onsite learning in the optimisation of craft gang's productivity in the construction industry.

PubMed

Ugulu, Rex Asibuodu; Allen, Stephen

2017-12-01

The data presented in this article is an original data on "Investigating the role of onsite learning in the optimisation of craft gang's productivity in the construction industry". This article describes the constraints influencing craft gang's productivity and the influence of onsite learning on the blockwork craft gang's productivity. It also presented the method of data collection, using a semi-structured interview and an observation method to collect data from construction organisations. We provided statistics on the top most important constraints affecting the craft gang's productivity using 3-D Bar charts. In addition, we computed the correlation coefficients and the regression model on the influence of onsite learning on craft gang's productivity using the man-hour as the dependent variable. The relationship between blockwork inputs and cycle numbers was determined at 5% significance level. Finally, we presented data information on the application of the learning curve theory using the unit straight-line model equations and computed the learning rate of the observed craft gang's blockwork repetitive work.

Explorations in Statistics: Standard Deviations and Standard Errors

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2008-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This series in "Advances in Physiology Education" provides an opportunity to do just that: we will investigate basic concepts in statistics using the free software package R. Because this series uses R solely as a vehicle…

Educational Statistics Authentic Learning CAPSULES: Community Action Projects for Students Utilizing Leadership and E-Based Statistics

ERIC Educational Resources Information Center

Thompson, Carla J.

2009-01-01

Since educational statistics is a core or general requirement of all students enrolled in graduate education programs, the need for high quality student engagement and appropriate authentic learning experiences is critical for promoting student interest and student success in the course. Based in authentic learning theory and engagement theory…

The effect of distance learning via SMS on academic achievement and satisfaction of medical students

PubMed Central

Sichani, Mehrdad Mohammadi; Mobarakeh, Shadi Reissizadeh; Omid, Athar

2018-01-01

INTRODUCTION: Recently, medical education has made significant progress, and medical teachers are trying to find methods that have most impressive effects on learning. One of the useful learning methods is student active participation. One of the helpful teaching aids in this method is mobile technology. The present study aimed to determine the effect of sending educational questions through short message service (SMS) on academic achievement and satisfaction of medical students and compare that with lecture teaching. SUBJECTS AND METHODS: In an semi-experimental, two chapters of urology reference book, Smiths General Urology 17th edition, were taught to 47 medical students of Isfahan University of Medical Sciences in urology course in 2013 academic year. Kidney tumors chapter was educated by sending questions through SMS, and bladder tumors part was taught in a lecture session. For each method, pretest and posttest were held, each consisting of thirty multiple choice questions. To examine the knowledge retention, a test session was held on the same terms for each chapter, 1 month later. At the end, survey forms were distributed to assess student's satisfaction with SMS learning method. Data were analyzed through using SPSS 20. RESULTS: The findings demonstrated a statistically significant difference between the two learning methods in the medication test scores. Evaluation of the satisfaction showed 78.72% of participants were not satisfied. CONCLUSIONS: The results of the study showed that distance learning through SMS in medical students could lead to increase knowledge, however, it was not effective on their satisfaction. PMID:29629390

Natural-Annotation-based Unsupervised Construction of Korean-Chinese Domain Dictionary

NASA Astrophysics Data System (ADS)

Liu, Wuying; Wang, Lin

2018-03-01

The large-scale bilingual parallel resource is significant to statistical learning and deep learning in natural language processing. This paper addresses the automatic construction issue of the Korean-Chinese domain dictionary, and presents a novel unsupervised construction method based on the natural annotation in the raw corpus. We firstly extract all Korean-Chinese word pairs from Korean texts according to natural annotations, secondly transform the traditional Chinese characters into the simplified ones, and finally distill out a bilingual domain dictionary after retrieving the simplified Chinese words in an extra Chinese domain dictionary. The experimental results show that our method can automatically build multiple Korean-Chinese domain dictionaries efficiently.

Automated Analysis of Renewable Energy Datasets ('EE/RE Data Mining')

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bush, Brian; Elmore, Ryan; Getman, Dan

This poster illustrates methods to substantially improve the understanding of renewable energy data sets and the depth and efficiency of their analysis through the application of statistical learning methods ('data mining') in the intelligent processing of these often large and messy information sources. The six examples apply methods for anomaly detection, data cleansing, and pattern mining to time-series data (measurements from metering points in buildings) and spatiotemporal data (renewable energy resource datasets).

Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data.

PubMed

Koutsoukas, Alexios; Monaghan, Keith J; Li, Xiaoli; Huan, Jun

2017-06-28

In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful tools in drug-discovery and toxicology research. The aim of this work was dual, first large number of hyper-parameter configurations were explored to investigate how they affect the performance of DNNs and could act as starting points when tuning DNNs and second their performance was compared to popular methods widely employed in the field of cheminformatics namely Naïve Bayes, k-nearest neighbor, random forest and support vector machines. Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed. The open-source Caffe deep-learning framework and modern NVidia GPU units were utilized to carry out this study, allowing large number of DNN configurations to be explored. We show that feed-forward deep neural networks are capable of achieving strong classification performance and outperform shallow methods across diverse activity classes when optimized. Hyper-parameters that were found to play critical role are the activation function, dropout regularization, number hidden layers and number of neurons. When compared to the rest methods, tuned DNNs were found to statistically outperform, with p value <0.01 based on Wilcoxon statistical test. DNN achieved on average MCC units of 0.149 higher than NB, 0.092 than kNN, 0.052 than SVM with linear kernel, 0.021 than RF and finally 0.009 higher than SVM with radial basis function kernel. When exploring robustness to noise, non-linear methods were found to perform well when dealing with low levels of noise, lower than or equal to 20%, however when dealing with higher levels of noise, higher than 30%, the Naïve Bayes method was found to perform well and even outperform at the highest level of noise 50% more sophisticated methods across several datasets.

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

PubMed Central

Shi, Tiezhu; Liu, Huizeng; Chen, Yiyun; Fei, Teng; Wang, Junjie; Wu, Guofeng

2017-01-01

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar’s test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies. PMID:28471412

Under the hood of statistical learning: A statistical MMN reflects the magnitude of transitional probabilities in auditory sequences.

PubMed

Koelsch, Stefan; Busch, Tobias; Jentschke, Sebastian; Rohrmeier, Martin

2016-02-02

Within the framework of statistical learning, many behavioural studies investigated the processing of unpredicted events. However, surprisingly few neurophysiological studies are available on this topic, and no statistical learning experiment has investigated electroencephalographic (EEG) correlates of processing events with different transition probabilities. We carried out an EEG study with a novel variant of the established statistical learning paradigm. Timbres were presented in isochronous sequences of triplets. The first two sounds of all triplets were equiprobable, while the third sound occurred with either low (10%), intermediate (30%), or high (60%) probability. Thus, the occurrence probability of the third item of each triplet (given the first two items) was varied. Compared to high-probability triplet endings, endings with low and intermediate probability elicited an early anterior negativity that had an onset around 100 ms and was maximal at around 180 ms. This effect was larger for events with low than for events with intermediate probability. Our results reveal that, when predictions are based on statistical learning, events that do not match a prediction evoke an early anterior negativity, with the amplitude of this mismatch response being inversely related to the probability of such events. Thus, we report a statistical mismatch negativity (sMMN) that reflects statistical learning of transitional probability distributions that go beyond auditory sensory memory capabilities.

11.2 YIP Human In the Loop Statistical RelationalLearners

DTIC Science & Technology

2017-10-23

learning formalisms including inverse reinforcement learning [4] and statistical relational learning [7, 5, 8]. We have also applied our algorithms in...one introduced for label preferences. 4 Figure 2: Active Advice Seeking for Inverse Reinforcement Learning. active advice seeking is in selecting the...learning tasks. 1.2.1 Sequential Decision-Making Our previous work on advice for inverse reinforcement learning (IRL) defined advice as action

Effectiveness of teaching International Caries Detection and Assessment System II and its e-learning program to freshman dental students on occlusal caries detection

PubMed Central

El-Damanhoury, Hatem M.; Fakhruddin, Kausar Sadia; Awad, Manal A.

2014-01-01

Objective: To assess the feasibility of teaching International Caries Detection and Assessment System (ICDAS) II and its e-learning program as tools for occlusal caries detection to freshmen dental students in comparison to dental graduates with 2 years of experience. Materials and Methods: Eighty-four freshmen and 32 dental graduates examined occlusal surfaces of molars/premolars (n = 72) after a lecture and a hands-on workshop. The same procedure was repeated after 1 month following the training with ICDAS II e-learning program. Validation of ICDAS II codes was done histologically. Intra- and inter-examiner reproducibility of ICDAS II severity scores were assessed before and after e-learning using (Fleiss's kappa). Results: The kappa values showed inter-examiner reproducibility ranged from 0.53 (ICDAS II code cut off ≥ 1) to 0.70 (ICDAS II code cut off ≥ 3) by undergraduates and 0.69 (ICDAS II code cut off ≥ 1) to 0.95 (ICDAS II code cut off ≥ 3) by graduates. The inter-examiner reproducibility ranged from 0.64 (ICDAS II code cut off ≥ 1) to 0.89 (ICDAS II code cut off ≥ 3). No statistically significant difference was found between both groups in intra-examiner agreements for assessing ICDAS II codes. A high statistically significant difference (P ≤ 0.01) in correct identification of codes 1, 2, and 4 from before to after e-learning were observed in both groups. The bias indices for the undergraduate group were higher than those of the graduate group. Conclusions: Early exposure of students to ICDAS II is a valuable method of teaching caries detection and its e-learning program significantly improves their caries diagnostic skills. PMID:25512730

The Effects of Cooperative Learning and Feedback on E-Learning in Statistics

ERIC Educational Resources Information Center

Krause, Ulrike-Marie; Stark, Robin; Mandl, Heinz

2009-01-01

This study examined whether cooperative learning and feedback facilitate situated, example-based e-learning in the field of statistics. The factors "social context" (individual vs. cooperative) and "feedback intervention" (available vs. not available) were varied; participants were 137 university students. Results showed that…

Automatic vetting of planet candidates from ground based surveys: Machine learning with NGTS

NASA Astrophysics Data System (ADS)

Armstrong, David J.; Günther, Maximilian N.; McCormac, James; Smith, Alexis M. S.; Bayliss, Daniel; Bouchy, François; Burleigh, Matthew R.; Casewell, Sarah; Eigmüller, Philipp; Gillen, Edward; Goad, Michael R.; Hodgkin, Simon T.; Jenkins, James S.; Louden, Tom; Metrailler, Lionel; Pollacco, Don; Poppenhaeger, Katja; Queloz, Didier; Raynard, Liam; Rauer, Heike; Udry, Stéphane; Walker, Simon R.; Watson, Christopher A.; West, Richard G.; Wheatley, Peter J.

2018-05-01

State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the autovet code used to implement the algorithm publicly accessible. autovet is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context.

Walking through the statistical black boxes of plant breeding.

PubMed

Xavier, Alencar; Muir, William M; Craig, Bruce; Rainey, Katy Martin

2016-10-01

The main statistical procedures in plant breeding are based on Gaussian process and can be computed through mixed linear models. Intelligent decision making relies on our ability to extract useful information from data to help us achieve our goals more efficiently. Many plant breeders and geneticists perform statistical analyses without understanding the underlying assumptions of the methods or their strengths and pitfalls. In other words, they treat these statistical methods (software and programs) like black boxes. Black boxes represent complex pieces of machinery with contents that are not fully understood by the user. The user sees the inputs and outputs without knowing how the outputs are generated. By providing a general background on statistical methodologies, this review aims (1) to introduce basic concepts of machine learning and its applications to plant breeding; (2) to link classical selection theory to current statistical approaches; (3) to show how to solve mixed models and extend their application to pedigree-based and genomic-based prediction; and (4) to clarify how the algorithms of genome-wide association studies work, including their assumptions and limitations.

Active learning for noisy oracle via density power divergence.

PubMed

Sogawa, Yasuhiro; Ueno, Tsuyoshi; Kawahara, Yoshinobu; Washio, Takashi

2013-10-01

The accuracy of active learning is critically influenced by the existence of noisy labels given by a noisy oracle. In this paper, we propose a novel pool-based active learning framework through robust measures based on density power divergence. By minimizing density power divergence, such as β-divergence and γ-divergence, one can estimate the model accurately even under the existence of noisy labels within data. Accordingly, we develop query selecting measures for pool-based active learning using these divergences. In addition, we propose an evaluation scheme for these measures based on asymptotic statistical analyses, which enables us to perform active learning by evaluating an estimation error directly. Experiments with benchmark datasets and real-world image datasets show that our active learning scheme performs better than several baseline methods. Copyright © 2013 Elsevier Ltd. All rights reserved.

Statistical learning of novel graphotactic constraints in children and adults.

PubMed

Samara, Anna; Caravolas, Markéta

2014-05-01

The current study explored statistical learning processes in the acquisition of orthographic knowledge in school-aged children and skilled adults. Learning of novel graphotactic constraints on the position and context of letter distributions was induced by means of a two-phase learning task adapted from Onishi, Chambers, and Fisher (Cognition, 83 (2002) B13-B23). Following incidental exposure to pattern-embedding stimuli in Phase 1, participants' learning generalization was tested in Phase 2 with legality judgments about novel conforming/nonconforming word-like strings. Test phase performance was above chance, suggesting that both types of constraints were reliably learned even after relatively brief exposure. As hypothesized, signal detection theory d' analyses confirmed that learning permissible letter positions (d'=0.97) was easier than permissible neighboring letter contexts (d'=0.19). Adults were more accurate than children in all but a strict analysis of the contextual constraints condition. Consistent with the statistical learning perspective in literacy, our results suggest that statistical learning mechanisms contribute to children's and adults' acquisition of knowledge about graphotactic constraints similar to those existing in their orthography. Copyright © 2013 Elsevier Inc. All rights reserved.

Using Guided Reinvention to Develop Teachers' Understanding of Hypothesis Testing Concepts

ERIC Educational Resources Information Center

Dolor, Jason; Noll, Jennifer

2015-01-01

Statistics education reform efforts emphasize the importance of informal inference in the learning of statistics. Research suggests statistics teachers experience similar difficulties understanding statistical inference concepts as students and how teacher knowledge can impact student learning. This study investigates how teachers reinvented an…

Statistical learning in social action contexts.

PubMed

Monroy, Claire; Meyer, Marlene; Gerson, Sarah; Hunnius, Sabine

2017-01-01

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and-if so-whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together ('Joint' condition) or stated the intention to act alone ('Parallel' condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor's action reliably predicted the second actor's action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects.

Statistical learning in social action contexts

PubMed Central

Meyer, Marlene; Gerson, Sarah; Hunnius, Sabine

2017-01-01

Sensitivity to the regularities and structure contained within sequential, goal-directed actions is an important building block for generating expectations about the actions we observe. Until now, research on statistical learning for actions has solely focused on individual action sequences, but many actions in daily life involve multiple actors in various interaction contexts. The current study is the first to investigate the role of statistical learning in tracking regularities between actions performed by different actors, and whether the social context characterizing their interaction influences learning. That is, are observers more likely to track regularities across actors if they are perceived as acting jointly as opposed to in parallel? We tested adults and toddlers to explore whether social context guides statistical learning and—if so—whether it does so from early in development. In a between-subjects eye-tracking experiment, participants were primed with a social context cue between two actors who either shared a goal of playing together (‘Joint’ condition) or stated the intention to act alone (‘Parallel’ condition). In subsequent videos, the actors performed sequential actions in which, for certain action pairs, the first actor’s action reliably predicted the second actor’s action. We analyzed predictive eye movements to upcoming actions as a measure of learning, and found that both adults and toddlers learned the statistical regularities across actors when their actions caused an effect. Further, adults with high statistical learning performance were sensitive to social context: those who observed actors with a shared goal were more likely to correctly predict upcoming actions. In contrast, there was no effect of social context in the toddler group, regardless of learning performance. These findings shed light on how adults and toddlers perceive statistical regularities across actors depending on the nature of the observed social situation and the resulting effects. PMID:28475619

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

Best practices for measuring students' attitudes toward learning science.

PubMed

Lovelace, Matthew; Brickman, Peggy

2013-01-01

Science educators often characterize the degree to which tests measure different facets of college students' learning, such as knowing, applying, and problem solving. A casual survey of scholarship of teaching and learning research studies reveals that many educators also measure how students' attitudes influence their learning. Students' science attitudes refer to their positive or negative feelings and predispositions to learn science. Science educators use attitude measures, in conjunction with learning measures, to inform the conclusions they draw about the efficacy of their instructional interventions. The measurement of students' attitudes poses similar but distinct challenges as compared with measurement of learning, such as determining validity and reliability of instruments and selecting appropriate methods for conducting statistical analyses. In this review, we will describe techniques commonly used to quantify students' attitudes toward science. We will also discuss best practices for the analysis and interpretation of attitude data.

Best Practices for Measuring Students’ Attitudes toward Learning Science

PubMed Central

Lovelace, Matthew; Brickman, Peggy

2013-01-01

Science educators often characterize the degree to which tests measure different facets of college students’ learning, such as knowing, applying, and problem solving. A casual survey of scholarship of teaching and learning research studies reveals that many educators also measure how students’ attitudes influence their learning. Students’ science attitudes refer to their positive or negative feelings and predispositions to learn science. Science educators use attitude measures, in conjunction with learning measures, to inform the conclusions they draw about the efficacy of their instructional interventions. The measurement of students’ attitudes poses similar but distinct challenges as compared with measurement of learning, such as determining validity and reliability of instruments and selecting appropriate methods for conducting statistical analyses. In this review, we will describe techniques commonly used to quantify students’ attitudes toward science. We will also discuss best practices for the analysis and interpretation of attitude data. PMID:24297288

Infants' statistical learning: 2- and 5-month-olds' segmentation of continuous visual sequences.

PubMed

Slone, Lauren Krogh; Johnson, Scott P

2015-05-01

Past research suggests that infants have powerful statistical learning abilities; however, studies of infants' visual statistical learning offer differing accounts of the developmental trajectory of and constraints on this learning. To elucidate this issue, the current study tested the hypothesis that young infants' segmentation of visual sequences depends on redundant statistical cues to segmentation. A sample of 20 2-month-olds and 20 5-month-olds observed a continuous sequence of looming shapes in which unit boundaries were defined by both transitional probability and co-occurrence frequency. Following habituation, only 5-month-olds showed evidence of statistically segmenting the sequence, looking longer to a statistically improbable shape pair than to a probable pair. These results reaffirm the power of statistical learning in infants as young as 5 months but also suggest considerable development of statistical segmentation ability between 2 and 5 months of age. Moreover, the results do not support the idea that infants' ability to segment visual sequences based on transitional probabilities and/or co-occurrence frequencies is functional at the onset of visual experience, as has been suggested previously. Rather, this type of statistical segmentation appears to be constrained by the developmental state of the learner. Factors contributing to the development of statistical segmentation ability during early infancy, including memory and attention, are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Dental students' evaluations of an interactive histology software.

PubMed

Rosas, Cristian; Rubí, Rafael; Donoso, Manuel; Uribe, Sergio

2012-11-01

This study assessed dental students' evaluations of a new Interactive Histology Software (IHS) developed by the authors and compared students' assessment of the extent to which this new software, as well as other histology teaching methods, supported their learning. The IHS is a computer-based tool for histology learning that presents high-resolution images of histology basics as well as specific oral histologies at different magnifications and with text labels. Survey data were collected from 204 first-year dental students at the Universidad Austral de Chile. The survey consisted of questions for the respondents to evaluate the characteristics of the IHS and the contribution of various teaching methods to their histology learning. The response rate was 85 percent. Student evaluations were positive for the design, usability, and theoretical-practical integration of the IHS, and the students reported they would recommend the method to future students. The students continued to value traditional teaching methods for histological lab work and did not think this new technology would replace traditional methods. With respect to the contribution of each teaching method to students' learning, no statistically significant differences (p>0.05) were found for an evaluation of IHS, light microscopy, and slide presentations. However, these student assessments were significantly more positive than the evaluations of other digital or printed materials. Overall, the students evaluated the IHS very positively in terms of method quality and contribution to their learning; they also evaluated use of light microscopy and teacher slide presentations positively.

Peer Teaching to Foster Learning in Physiology.

PubMed

Srivastava, Tripti K; Waghmare, Lalitbhushan S; Mishra, Ved Prakash; Rawekar, Alka T; Quazi, Nazli; Jagzape, Arunita T

2015-08-01

Peer teaching is an effective tool to promote learning and retention of knowledge. By preparing to teach, students are encouraged to construct their own learning program, so that they can explain effectively to fellow learners. Peer teaching is introduced in present study to foster learning and pedagogical skills amongst first year medical under-graduates in physiology with a Hypothesis that teaching is linked to learning on part of the teacher. Non-randomized, Interventional study, with mixed methods design. Cases experienced peer teaching whereas controls underwent tutorials for four consecutive classes. Quantitative Evaluation was done through pre/post test score analysis for Class average normalized gain and tests of significance, difference in average score in surprise class test after one month and percentage of responses in closed ended items of feedback questionnaire. Qualitative Evaluation was done through categorization of open ended items and coding of reflective statements. The average pre and post test score was statistically significant within cases (p = 0.01) and controls (p = 0.023). The average post test scores was more for cases though not statistically significant. The class average normalized gain (g) for Tutorials was 49% and for peer teaching 53%. Surprise test had average scoring of 36 marks (out of 50) for controls and 41 marks for cases. Analysed section wise, the average score was better for Long answer question (LAQ) in cases. Section wise analysis suggested that through peer teaching, retention was better for descriptive answers as LAQ has better average score in cases. Feedback responses were predominantly positive for efficacy of peer teaching as a learning method. The reflective statements were sorted into reflection in action, reflection on action, claiming evidence, describing experience, and recognizing discrepancies. Teaching can stimulate further learning as it involves interplay of three processes: metacognitive awareness; deliberate practice, and self-explanation. Coupled with immediate feedback and reflective exercises, learning can be measurably enhanced along with improved teaching skills.

Applications of Stochastic Analyses for Collaborative Learning and Cognitive Assessment

DTIC Science & Technology

2007-04-01

models (Visser, Maartje, Raijmakers, & Molenaar , 2002). The second part of this paper illustrates two applications of the methods described in the...clustering three-way data sets. Computational Statistics and Data Analysis, 51 (11), 5368–5376. Visser, I., Maartje, E., Raijmakers, E. J., & Molenaar

A Psychometric Evaluation of the Digital Logic Concept Inventory

ERIC Educational Resources Information Center

Herman, Geoffrey L.; Zilles, Craig; Loui, Michael C.

2014-01-01

Concept inventories hold tremendous promise for promoting the rigorous evaluation of teaching methods that might remedy common student misconceptions and promote deep learning. The measurements from concept inventories can be trusted only if the concept inventories are evaluated both by expert feedback and statistical scrutiny (psychometric…

Machine learning in cardiovascular medicine: are we there yet?

PubMed

Shameer, Khader; Johnson, Kipp W; Glicksberg, Benjamin S; Dudley, Joel T; Sengupta, Partho P

2018-01-19

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Accurate landmarking of three-dimensional facial data in the presence of facial expressions and occlusions using a three-dimensional statistical facial feature model.

PubMed

Zhao, Xi; Dellandréa, Emmanuel; Chen, Liming; Kakadiaris, Ioannis A

2011-10-01

Three-dimensional face landmarking aims at automatically localizing facial landmarks and has a wide range of applications (e.g., face recognition, face tracking, and facial expression analysis). Existing methods assume neutral facial expressions and unoccluded faces. In this paper, we propose a general learning-based framework for reliable landmark localization on 3-D facial data under challenging conditions (i.e., facial expressions and occlusions). Our approach relies on a statistical model, called 3-D statistical facial feature model, which learns both the global variations in configurational relationships between landmarks and the local variations of texture and geometry around each landmark. Based on this model, we further propose an occlusion classifier and a fitting algorithm. Results from experiments on three publicly available 3-D face databases (FRGC, BU-3-DFE, and Bosphorus) demonstrate the effectiveness of our approach, in terms of landmarking accuracy and robustness, in the presence of expressions and occlusions.

Competitive Processes in Cross-Situational Word Learning

PubMed Central

Yurovsky, Daniel; Yu, Chen; Smith, Linda B.

2013-01-01

Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. But the information that learners pick up from these regularities is dependent on their learning mechanism. This paper investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input, and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple scales – both within and across trials/situations –learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is then considered from the perspective of a process-level understanding of cross-situational learning. PMID:23607610

Competitive processes in cross-situational word learning.

PubMed

Yurovsky, Daniel; Yu, Chen; Smith, Linda B

2013-07-01

Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple scales-both within and across trials/situations-learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is considered from the perspective of a process-level understanding of cross-situational learning. Copyright © 2013 Cognitive Science Society, Inc.

A comparison of problem-based learning and conventional teaching in nursing ethics education.

PubMed

Lin, Chiou-Fen; Lu, Meei-Shiow; Chung, Chun-Chih; Yang, Che-Ming

2010-05-01

The aim of this study was to compare the learning effectiveness of peer tutored problem-based learning and conventional teaching of nursing ethics in Taiwan. The study adopted an experimental design. The peer tutored problem-based learning method was applied to an experimental group and the conventional teaching method to a control group. The study sample consisted of 142 senior nursing students who were randomly assigned to the two groups. All the students were tested for their nursing ethical discrimination ability both before and after the educational intervention. A learning satisfaction survey was also administered to both groups at the end of each course. After the intervention, both groups showed a significant increase in ethical discrimination ability. There was a statistically significant difference between the ethical discrimination scores of the two groups (P < 0.05), with the experimental group on average scoring higher than the control group. There were significant differences in satisfaction with self-motivated learning and critical thinking between the groups. Peer tutored problem-based learning and lecture-type conventional teaching were both effective for nursing ethics education, but problem-based learning was shown to be more effective. Peer tutored problem-based learning has the potential to enhance the efficacy of teaching nursing ethics in situations in which there are personnel and resource constraints.

Optical diagnosis of cervical cancer by higher order spectra and boosting

NASA Astrophysics Data System (ADS)

Pratiher, Sawon; Mukhopadhyay, Sabyasachi; Barman, Ritwik; Pratiher, Souvik; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2017-03-01

In this contribution, we report the application of higher order statistical moments using decision tree and ensemble based learning methodology for the development of diagnostic algorithms for optical diagnosis of cancer. The classification results were compared to those obtained with an independent feature extractors like linear discriminant analysis (LDA). The performance and efficacy of these methodology using higher order statistics as a classifier using boosting has higher specificity and sensitivity while being much faster as compared to other time-frequency domain based methods.

Some Variables in Relation to Students' Anxiety in Learning Statistics.

ERIC Educational Resources Information Center

Sutarso, Toto

The purpose of this study was to investigate some variables that relate to students' anxiety in learning statistics. The variables included sex, class level, students' achievement, school, mathematical background, previous statistics courses, and race. The instrument used was the 24-item Students' Attitudes Toward Statistics (STATS), which was…

Distance learning in discriminative vector quantization.

PubMed

Schneider, Petra; Biehl, Michael; Hammer, Barbara

2009-10-01

Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions thereof offer efficient and intuitive classifiers based on the representation of classes by prototypes. The original methods, however, rely on the Euclidean distance corresponding to the assumption that the data can be represented by isotropic clusters. For this reason, extensions of the methods to more general metric structures have been proposed, such as relevance adaptation in generalized LVQ (GLVQ) and matrix learning in GLVQ. In these approaches, metric parameters are learned based on the given classification task such that a data-driven distance measure is found. In this letter, we consider full matrix adaptation in advanced LVQ schemes. In particular, we introduce matrix learning to a recent statistical formalization of LVQ, robust soft LVQ, and we compare the results on several artificial and real-life data sets to matrix learning in GLVQ, a derivation of LVQ-like learning based on a (heuristic) cost function. In all cases, matrix adaptation allows a significant improvement of the classification accuracy. Interestingly, however, the principled behavior of the models with respect to prototype locations and extracted matrix dimensions shows several characteristic differences depending on the data sets.

Pattern Activity Clustering and Evaluation (PACE)

NASA Astrophysics Data System (ADS)

Blasch, Erik; Banas, Christopher; Paul, Michael; Bussjager, Becky; Seetharaman, Guna

2012-06-01

With the vast amount of network information available on activities of people (i.e. motions, transportation routes, and site visits) there is a need to explore the salient properties of data that detect and discriminate the behavior of individuals. Recent machine learning approaches include methods of data mining, statistical analysis, clustering, and estimation that support activity-based intelligence. We seek to explore contemporary methods in activity analysis using machine learning techniques that discover and characterize behaviors that enable grouping, anomaly detection, and adversarial intent prediction. To evaluate these methods, we describe the mathematics and potential information theory metrics to characterize behavior. A scenario is presented to demonstrate the concept and metrics that could be useful for layered sensing behavior pattern learning and analysis. We leverage work on group tracking, learning and clustering approaches; as well as utilize information theoretical metrics for classification, behavioral and event pattern recognition, and activity and entity analysis. The performance evaluation of activity analysis supports high-level information fusion of user alerts, data queries and sensor management for data extraction, relations discovery, and situation analysis of existing data.

PredPsych: A toolbox for predictive machine learning-based approach in experimental psychology research.

PubMed

Koul, Atesh; Becchio, Cristina; Cavallo, Andrea

2017-12-12

Recent years have seen an increased interest in machine learning-based predictive methods for analyzing quantitative behavioral data in experimental psychology. While these methods can achieve relatively greater sensitivity compared to conventional univariate techniques, they still lack an established and accessible implementation. The aim of current work was to build an open-source R toolbox - "PredPsych" - that could make these methods readily available to all psychologists. PredPsych is a user-friendly, R toolbox based on machine-learning predictive algorithms. In this paper, we present the framework of PredPsych via the analysis of a recently published multiple-subject motion capture dataset. In addition, we discuss examples of possible research questions that can be addressed with the machine-learning algorithms implemented in PredPsych and cannot be easily addressed with univariate statistical analysis. We anticipate that PredPsych will be of use to researchers with limited programming experience not only in the field of psychology, but also in that of clinical neuroscience, enabling computational assessment of putative bio-behavioral markers for both prognosis and diagnosis.

Functional differences between statistical learning with and without explicit training

PubMed Central

Reber, Paul J.; Paller, Ken A.

2015-01-01

Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and prepare for incoming input. In this study, we ask whether the function of statistical learning may be enhanced through supplementary explicit training, in which underlying regularities are explicitly taught rather than simply abstracted through exposure. Learners were randomly assigned either to an explicit group or an implicit group. All learners were exposed to a continuous stream of repeating nonsense words. Prior to this implicit training, learners in the explicit group received supplementary explicit training on the nonsense words. Statistical learning was assessed through a speeded reaction-time (RT) task, which measured the extent to which learners used acquired statistical knowledge to optimize online processing. Both RTs and brain potentials revealed significant differences in online processing as a function of training condition. RTs showed a crossover interaction; responses in the explicit group were faster to predictable targets and marginally slower to less predictable targets relative to responses in the implicit group. P300 potentials to predictable targets were larger in the explicit group than in the implicit group, suggesting greater recruitment of controlled, effortful processes. Taken together, these results suggest that information abstracted through passive exposure during statistical learning may be processed more automatically and with less effort than information that is acquired explicitly. PMID:26472644

Random forests for classification in ecology

USGS Publications Warehouse

Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J.

2007-01-01

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

Current and future health care professionals attitudes toward and knowledge of statistics: How confidence influences learning.

PubMed

Baghi, Heibatollah; Kornides, Melanie L

2013-01-01

Health care professionals require some understanding of statistics to successfully implement evidence based practice. Developing competency in statistical reasoning is necessary for students training in health care administration, research, and clinical care. Recently, the interest in healthcare professional's attitudes toward statistics has increased substantially due to evidence that these attitudes can hinder professionalism developing an understanding of statistical concepts. In this study, we analyzed pre- and post-instruction attitudes towards and knowledge of statistics obtained from health science graduate students, including nurses and nurse practitioners, enrolled in an introductory graduate course in statistics (n = 165). Results show that the students already held generally positive attitudes toward statistics at the beginning of course. However, these attitudes-along with the students' statistical proficiency-improved after 10 weeks of instruction. The results have implications for curriculum design and delivery methods as well as for health professionals' effective use of statistics in critically evaluating and utilizing research in their practices.

Can machine learning complement traditional medical device surveillance? A case study of dual-chamber implantable cardioverter–defibrillators

PubMed Central

Ross, Joseph S; Bates, Jonathan; Parzynski, Craig S; Akar, Joseph G; Curtis, Jeptha P; Desai, Nihar R; Freeman, James V; Gamble, Ginger M; Kuntz, Richard; Li, Shu-Xia; Marinac-Dabic, Danica; Masoudi, Frederick A; Normand, Sharon-Lise T; Ranasinghe, Isuru; Shaw, Richard E; Krumholz, Harlan M

2017-01-01

Background Machine learning methods may complement traditional analytic methods for medical device surveillance. Methods and results Using data from the National Cardiovascular Data Registry for implantable cardioverter–defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). The first approach used PS-SME and cumulative incidence (time-to-event), the second approach used PS-SME and cumulative risk (Data Extraction and Longitudinal Trend Analysis [DELTA]), and the third approach used PS-ML and cumulative risk (embedded feature selection). Safety-signal surveillance was conducted for eleven dual-chamber ICD models implanted at least 2,000 times over 3 years. Between 2006 and 2010, there were 71,948 Medicare fee-for-service beneficiaries who received dual-chamber ICDs. Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%–20.9%; nonfatal ICD-related adverse events, 19.3%–26.3%; and death from any cause or nonfatal ICD-related adverse event, 27.1%–37.6%. Agreement among safety signals detected/not detected between the time-to-event and DELTA approaches was 90.9% (360 of 396, k=0.068), between the time-to-event and embedded feature-selection approaches was 91.7% (363 of 396, k=−0.028), and between the DELTA and embedded feature selection approaches was 88.1% (349 of 396, k=−0.042). Conclusion Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. Ensemble methods may be needed to detect all safety signals for further evaluation during medical device surveillance. PMID:28860874

Learning semantic and visual similarity for endomicroscopy video retrieval.

PubMed

Andre, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2012-06-01

Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis support. However, traditional CBIR systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called "Dense-Sift," that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much shorter than them. In our resulting retrieval system, we decide to use visual signatures for perceived similarity learning and retrieval, and semantic signatures for the output of an additional information, expressed in the endoscopist own language, which provides a relevant semantic translation of the visual retrieval outputs.

Applications of Machine Learning in Cancer Prediction and Prognosis

PubMed Central

Cruz, Joseph A.; Wishart, David S.

2006-01-01

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression. PMID:19458758

"If You're Doubting Yourself Then, What's the Fun in That?" An Exploration of Why Prospective Secondary Mathematics Teachers Perceive Statistics as Difficult

ERIC Educational Resources Information Center

Leavy, Aisling M.; Hannigan, Ailish; Fitzmaurice, Olivia

2013-01-01

Most research into prospective secondary mathematics teachers' attitudes towards statistics indicates generally positive attitudes but a perception that statistics is difficult to learn. These perceptions of statistics as a difficult subject to learn may impact the approaches of prospective teachers to teaching statistics and in turn their…

Statistical learning in reading: variability in irrelevant letters helps children learn phonics skills.

PubMed

Apfelbaum, Keith S; Hazeltine, Eliot; McMurray, Bob

2013-07-01

Early reading abilities are widely considered to derive in part from statistical learning of regularities between letters and sounds. Although there is substantial evidence from laboratory work to support this, how it occurs in the classroom setting has not been extensively explored; there are few investigations of how statistics among letters and sounds influence how children actually learn to read or what principles of statistical learning may improve learning. We examined 2 conflicting principles that may apply to learning grapheme-phoneme-correspondence (GPC) regularities for vowels: (a) variability in irrelevant units may help children derive invariant relationships and (b) similarity between words may force children to use a deeper analysis of lexical structure. We trained 224 first-grade students on a small set of GPC regularities for vowels, embedded in words with either high or low consonant similarity, and tested their generalization to novel tasks and words. Variability offered a consistent benefit over similarity for trained and new words in both trained and new tasks.

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

PubMed Central

Belo, David; Gamboa, Hugo

2017-01-01

The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components. PMID:28831239

Critical Analysis of an e-Learning and Interactive Teaching Module with Respect to the Interpretation of Emergency Computed Tomography of the Brain.

PubMed

Groth, Michael; Barthe, Käthe Greta; Riemer, Martin; Ernst, Marielle; Herrmann, Jochen; Fiehler, Jens; Buhk, Jan-Hendrik

2018-04-01

 To compare the learning benefit of three different teaching strategies on the interpretation of emergency cerebral computed tomography (CT) pathologies by medical students.  Three groups of students with different types of teaching (e-learning, interactive teaching, and standard curricular education in neuroradiology) were tested with respect to the detection of seven CT pathologies. The test results of each group were compared for each CT pathology using the chi-square test. A p-value ≤ 0.05 was considered to be significant.  Opposed to the results of the comparison group (curricular education), the e-learning group and interactive teaching tutorial group both showed a significantly better performance in detecting hyperdense middle cerebral artery sign (p = 0.001 and p < 0.0001) as well as subarachnoid hemorrhage (p = 0.03 and p = 0.001) on CT. Moreover, an increase in performance for the detection of subdural hematoma and skull fracture could be observed for both the interactive teaching group and the e-learning group, with statistical significance in the latter (p = 0.03 and p < 0.0001, respectively). No statistically significant differences were found for the detection of intracranial and epidural hemorrhage, as well as midline shift, among the groups studied.  Our study demonstrates potential learning benefits for both the interactive teaching tutorial and e-learning module group with respect to reading CT scans with slightly different advantages. Thus, the introduction of new learning methods in radiological education might be reasonable at an undergraduate stage but requires learning content-based considerations.   · E-learning can offer benefits regarding the reading of cerebral CT scans by students. · Interactive tutorial can offer benefits regarding the reading of cerebral CT scans by students. · E-learning and interactive tutorial feature different strengths for student learning in radiology. · Application of interactive teaching methods in radiology requires learning content-based considerations. · Groth M, Barthe KG, Riemer M et al. Critical Analysis of an e-Learning and Interactive Teaching Module with Respect to the Interpretation of Emergency Computed Tomography of the Brain. Fortschr Röntgenstr 2017; 190: 334 - 340. © Georg Thieme Verlag KG Stuttgart · New York.

Innovative intelligent technology of distance learning for visually impaired people

NASA Astrophysics Data System (ADS)

Samigulina, Galina; Shayakhmetova, Assem; Nuysuppov, Adlet

2017-12-01

The aim of the study is to develop innovative intelligent technology and information systems of distance education for people with impaired vision (PIV). To solve this problem a comprehensive approach has been proposed, which consists in the aggregate of the application of artificial intelligence methods and statistical analysis. Creating an accessible learning environment, identifying the intellectual, physiological, psychophysiological characteristics of perception and information awareness by this category of people is based on cognitive approach. On the basis of fuzzy logic the individually-oriented learning path of PIV is con- structed with the aim of obtaining high-quality engineering education with modern equipment in the joint use laboratories.

Intelligence and Creativity Are Pretty Similar After All

ERIC Educational Resources Information Center

Silvia, Paul J.

2015-01-01

This article reviews the history of thought on how intelligence and creativity, two individual differences important to teaching and learning, are connected. For decades, intelligence and creativity have been seen as essentially unrelated abilities. Recently, however, new theories, assessment methods, and statistical tools have caused a shift in…

Transportability of Equivalence-Based Programmed Instruction: Efficacy and Efficiency in a College Classroom

ERIC Educational Resources Information Center

Fienup, Daniel M.; Critchfield, Thomas S.

2011-01-01

College students in a psychology research-methods course learned concepts related to inferential statistics and hypothesis decision making. One group received equivalence-based instruction on conditional discriminations that were expected to promote the emergence of many untaught, academically useful abilities (i.e., stimulus equivalence group). A…

How to Engage Medical Students in Chronobiology: An Example on Autorhythmometry

ERIC Educational Resources Information Center

Rol de Lama, M. A.; Lozano, J. P.; Ortiz, V.; Sanchez-Vazquez, F. J.; Madrid, J. A.

2005-01-01

This contribution describes a new laboratory experience that improves medical students' learning of chronobiology by introducing them to basic chronobiology concepts as well as to methods and statistical analysis tools specific for circadian rhythms. We designed an autorhythmometry laboratory session where students simultaneously played the role…

Aiding Participation and Engagement in a Blended Learning Environment

ERIC Educational Resources Information Center

Alrushiedat, Nimer; Olfman, Lorne

2013-01-01

This research was conducted as a field experiment that explored the potential benefits of anchoring in asynchronous online discussions for business statistics classes required for information systems majors. These classes are usually taught using traditional methods with emphasis on lecturing, knowledge reproduction, and treatment of students as…

Effect of moderate learning style–teaching mode mismatch on academic performance among 2nd year medical students in Pakistan

PubMed Central

Hamza, Muhammad; Inam-Ul-Haq; Hamid, Sidra; Nadir, Maha; Mehmood, Nadir

2018-01-01

Introduction: The vagueness surrounding “learning style–teaching mode mismatch” makes its effects uncertain. This study tried to tackle that controversy by comparing and assessing the effect of different learning styles on performance in physiology examination when teaching mode was somewhat different than learning preferences of the 2nd year medical students. Methods: A total of 102 2nd year medical students participated in this study. Honey and Mumford learning style questionnaire was used to categorize the participants into one of the four learning styles (activist, reflector, theorist, and pragmatist). Many teaching modes were used in the medical college. The first professional theory and practical physiology scores of these 102 students of University of Health Sciences were obtained online. Learning styles were compared with physiology scores and age using one-way analysis of variance and post hoc statistical analysis and between males and females by using Chi-square test. Results: Pragmatists had the lowest total physiology score (P < 0.001), while theorists had the highest total physiology scores (P < 0.001). Activists and reflectors had scores in between pragmatists and theorists, and there was no statistical difference between these two styles of learning (P = 0.9). No student scored below 60%. Conclusion: This study demonstrated that the effect of moderate teaching–learning mismatch is different for different learners. Theorists excelled as they had the highest physiology score, while pragmatists lagged in comparison. Reflectors and activists performed better than pragmatists but were worse than theorists. Despite this, none of the students scored below 60%. This shows that a moderate learning style–teaching mode mismatch is not harmful for learning. PMID:29736072

The Role of Statistical Learning and Working Memory in L2 Speakers' Pattern Learning

ERIC Educational Resources Information Center

McDonough, Kim; Trofimovich, Pavel

2016-01-01

This study investigated whether second language (L2) speakers' morphosyntactic pattern learning was predicted by their statistical learning and working memory abilities. Across three experiments, Thai English as a Foreign Language (EFL) university students (N = 140) were exposed to either the transitive construction in Esperanto (e.g., "tauro…

Enhancing interest in statistics among computer science students using computer tool entrepreneur role play

NASA Astrophysics Data System (ADS)

Judi, Hairulliza Mohamad; Sahari @ Ashari, Noraidah; Eksan, Zanaton Hj

2017-04-01

Previous research in Malaysia indicates that there is a problem regarding attitude towards statistics among students. They didn't show positive attitude in affective, cognitive, capability, value, interest and effort aspects although did well in difficulty. This issue should be given substantial attention because students' attitude towards statistics may give impacts on the teaching and learning process of the subject. Teaching statistics using role play is an appropriate attempt to improve attitudes to statistics, to enhance the learning of statistical techniques and statistical thinking, and to increase generic skills. The objectives of the paper are to give an overview on role play in statistics learning and to access the effect of these activities on students' attitude and learning in action research framework. The computer tool entrepreneur role play is conducted in a two-hour tutorial class session of first year students in Faculty of Information Sciences and Technology (FTSM), Universiti Kebangsaan Malaysia, enrolled in Probability and Statistics course. The results show that most students feel that they have enjoyable and great time in the role play. Furthermore, benefits and disadvantages from role play activities were highlighted to complete the review. Role play is expected to serve as an important activities that take into account students' experience, emotions and responses to provide useful information on how to modify student's thinking or behavior to improve learning.

Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

NASA Astrophysics Data System (ADS)

Veronesi, F.; Grassi, S.

2016-09-01

Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.

Estimation of Alpine Skier Posture Using Machine Learning Techniques

PubMed Central

Nemec, Bojan; Petrič, Tadej; Babič, Jan; Supej, Matej

2014-01-01

High precision Global Navigation Satellite System (GNSS) measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier's neck. A key issue is how to estimate other more relevant parameters of the skier's body, like the center of mass (COM) and ski trajectories. Previously, these parameters were estimated by modeling the skier's body with an inverted-pendulum model that oversimplified the skier's body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier's body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing. PMID:25313492

Measuring Student Learning in Social Statistics: A Pretest-Posttest Study of Knowledge Gain

ERIC Educational Resources Information Center

Delucchi, Michael

2014-01-01

This study used a pretest-posttest design to measure student learning in undergraduate statistics. Data were derived from 185 students enrolled in six different sections of a social statistics course taught over a seven-year period by the same sociology instructor. The pretest-posttest instrument reveals statistically significant gains in…

Survey of Native English Speakers and Spanish-Speaking English Language Learners in Tertiary Introductory Statistics

ERIC Educational Resources Information Center

Lesser, Lawrence M.; Wagler, Amy E.; Esquinca, Alberto; Valenzuela, M. Guadalupe

2013-01-01

The framework of linguistic register and case study research on Spanish-speaking English language learners (ELLs) learning statistics informed the construction of a quantitative instrument, the Communication, Language, And Statistics Survey (CLASS). CLASS aims to assess whether ELLs and non-ELLs approach the learning of statistics differently with…

Statistical learning of multisensory regularities is enhanced in musicians: An MEG study.

PubMed

Paraskevopoulos, Evangelos; Chalas, Nikolas; Kartsidis, Panagiotis; Wollbrink, Andreas; Bamidis, Panagiotis

2018-07-15

The present study used magnetoencephalography (MEG) to identify the neural correlates of audiovisual statistical learning, while disentangling the differential contributions of uni- and multi-modal statistical mismatch responses in humans. The applied paradigm was based on a combination of a statistical learning paradigm and a multisensory oddball one, combining an audiovisual, an auditory and a visual stimulation stream, along with the corresponding deviances. Plasticity effects due to musical expertise were investigated by comparing the behavioral and MEG responses of musicians to non-musicians. The behavioral results indicated that the learning was successful for both musicians and non-musicians. The unimodal MEG responses are consistent with previous studies, revealing the contribution of Heschl's gyrus for the identification of auditory statistical mismatches and the contribution of medial temporal and visual association areas for the visual modality. The cortical network underlying audiovisual statistical learning was found to be partly common and partly distinct from the corresponding unimodal networks, comprising right temporal and left inferior frontal sources. Musicians showed enhanced activation in superior temporal and superior frontal gyrus. Connectivity and information processing flow amongst the sources comprising the cortical network of audiovisual statistical learning, as estimated by transfer entropy, was reorganized in musicians, indicating enhanced top-down processing. This neuroplastic effect showed a cross-modal stability between the auditory and audiovisual modalities. Copyright © 2018 Elsevier Inc. All rights reserved.

Active Learning with Statistical Models.

DTIC Science & Technology

1995-01-01

Active Learning with Statistical Models ASC-9217041, NSF CDA-9309300 6. AUTHOR(S) David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan 7. PERFORMING...TERMS 15. NUMBER OF PAGES Al, MIT, Artificial Intelligence, active learning , queries, locally weighted 6 regression, LOESS, mixtures of gaussians...COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES A.I. Memo No. 1522 January 9. 1995 C.B.C.L. Paper No. 110 Active Learning with

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wurtz, R.; Kaplan, A.

Pulse shape discrimination (PSD) is a variety of statistical classifier. Fully-­realized statistical classifiers rely on a comprehensive set of tools for designing, building, and implementing. PSD advances rely on improvements to the implemented algorithm. PSD advances can be improved by using conventional statistical classifier or machine learning methods. This paper provides the reader with a glossary of classifier-­building elements and their functions in a fully-­designed and operational classifier framework that can be used to discover opportunities for improving PSD classifier projects. This paper recommends reporting the PSD classifier’s receiver operating characteristic (ROC) curve and its behavior at a gamma rejectionmore » rate (GRR) relevant for realistic applications.« less

Scaling up to address data science challenges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wendelberger, Joanne R.

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

Estimating procedure times for surgeries by determining location parameters for the lognormal model.

PubMed

Spangler, William E; Strum, David P; Vargas, Luis G; May, Jerrold H

2004-05-01

We present an empirical study of methods for estimating the location parameter of the lognormal distribution. Our results identify the best order statistic to use, and indicate that using the best order statistic instead of the median may lead to less frequent incorrect rejection of the lognormal model, more accurate critical value estimates, and higher goodness-of-fit. Using simulation data, we constructed and compared two models for identifying the best order statistic, one based on conventional nonlinear regression and the other using a data mining/machine learning technique. Better surgical procedure time estimates may lead to improved surgical operations.

Scaling up to address data science challenges

DOE PAGES

Wendelberger, Joanne R.

2017-04-27

Statistics and Data Science provide a variety of perspectives and technical approaches for exploring and understanding Big Data. Partnerships between scientists from different fields such as statistics, machine learning, computer science, and applied mathematics can lead to innovative approaches for addressing problems involving increasingly large amounts of data in a rigorous and effective manner that takes advantage of advances in computing. Here, this article will explore various challenges in Data Science and will highlight statistical approaches that can facilitate analysis of large-scale data including sampling and data reduction methods, techniques for effective analysis and visualization of large-scale simulations, and algorithmsmore » and procedures for efficient processing.« less

Statistics and Machine Learning based Outlier Detection Techniques for Exoplanets

NASA Astrophysics Data System (ADS)

Goel, Amit; Montgomery, Michele

2015-08-01

Architectures of planetary systems are observable snapshots in time that can indicate formation and dynamic evolution of planets. The observable key parameters that we consider are planetary mass and orbital period. If planet masses are significantly less than their host star masses, then Keplerian Motion is defined as P^2 = a^3 where P is the orbital period in units of years and a is the orbital period in units of Astronomical Units (AU). Keplerian motion works on small scales such as the size of the Solar System but not on large scales such as the size of the Milky Way Galaxy. In this work, for confirmed exoplanets of known stellar mass, planetary mass, orbital period, and stellar age, we analyze Keplerian motion of systems based on stellar age to seek if Keplerian motion has an age dependency and to identify outliers. For detecting outliers, we apply several techniques based on statistical and machine learning methods such as probabilistic, linear, and proximity based models. In probabilistic and statistical models of outliers, the parameters of a closed form probability distributions are learned in order to detect the outliers. Linear models use regression analysis based techniques for detecting outliers. Proximity based models use distance based algorithms such as k-nearest neighbour, clustering algorithms such as k-means, or density based algorithms such as kernel density estimation. In this work, we will use unsupervised learning algorithms with only the proximity based models. In addition, we explore the relative strengths and weaknesses of the various techniques by validating the outliers. The validation criteria for the outliers is if the ratio of planetary mass to stellar mass is less than 0.001. In this work, we present our statistical analysis of the outliers thus detected.

Impact of network aided platforms as educational tools on academic performance and attitude of pharmacology students

PubMed Central

Khan, Aftab Ahmed; Siddiqui, Adel Zia; Mohsin, Syed Fareed; Momani, Mohammed Mahmoud Al; Mirza, Eraj Humayun

2017-01-01

Objective: This cross-sectional study aimed to examine the impact of learning management system and WhatsApp application as educational tools on students’ academic achievement and attitude. Methods: The sample population was the students of six medical colleges of Riyadh, Saudi Arabia attending Medical Pharmacology’s semester course in Bachelor of Medicine, Bachelor of Surgery (MBBS) program from September 2016 to January 2017. An exploratory approach was adopted based on a comparison between students exposed to only in-class lectures (Group-N), in-class lectures together with WhatsApp platform to disseminate the lecture slides (Group-W) and students group with in-class lectures facility blended with Learning Management System (LMS) and WhatsApp platform (Group-WL). The students’ grades were assessed using unified multiple choice questions at the end of the semester. Data were analyzed using descriptive statistics and Pearson correlation (p<0.01). Results: Using learning management system (LMS) and/or WhatsApp messenger tool showed a significant positive correlation in improving students’ grades. Additionally, use of WhatsApp enhances students’ in-class attendance though statistically insignificant. Conclusion: The results are pivotal for a paradigm shift of in-class lectures and discussion to mobile learning (M-learning). M-learning through WhatsApp may be as an alternative, innovative, and collaborative tool in achieving the required goals in medical education. PMID:29492081

Statistical word learning in children with autism spectrum disorder and specific language impairment.

PubMed

Haebig, Eileen; Saffran, Jenny R; Ellis Weismer, Susan

2017-11-01

Word learning is an important component of language development that influences child outcomes across multiple domains. Despite the importance of word knowledge, word-learning mechanisms are poorly understood in children with specific language impairment (SLI) and children with autism spectrum disorder (ASD). This study examined underlying mechanisms of word learning, specifically, statistical learning and fast-mapping, in school-aged children with typical and atypical development. Statistical learning was assessed through a word segmentation task and fast-mapping was examined in an object-label association task. We also examined children's ability to map meaning onto newly segmented words in a third task that combined exposure to an artificial language and a fast-mapping task. Children with SLI had poorer performance on the word segmentation and fast-mapping tasks relative to the typically developing and ASD groups, who did not differ from one another. However, when children with SLI were exposed to an artificial language with phonemes used in the subsequent fast-mapping task, they successfully learned more words than in the isolated fast-mapping task. There was some evidence that word segmentation abilities are associated with word learning in school-aged children with typical development and ASD, but not SLI. Follow-up analyses also examined performance in children with ASD who did and did not have a language impairment. Children with ASD with language impairment evidenced intact statistical learning abilities, but subtle weaknesses in fast-mapping abilities. As the Procedural Deficit Hypothesis (PDH) predicts, children with SLI have impairments in statistical learning. However, children with SLI also have impairments in fast-mapping. Nonetheless, they are able to take advantage of additional phonological exposure to boost subsequent word-learning performance. In contrast to the PDH, children with ASD appear to have intact statistical learning, regardless of language status; however, fast-mapping abilities differ according to broader language skills. © 2017 Association for Child and Adolescent Mental Health.

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

PubMed Central

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present. PMID:27213008

Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography.

PubMed

Umut, İlhan; Çentik, Güven

2016-01-01

The number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.

Effect of an EBM course in combination with case method learning sessions: an RCT on professional performance, job satisfaction, and self-efficacy of occupational physicians

PubMed Central

Schaafsma, Frederieke G.; Nieuwenhuijsen, Karen; van Dijk, Frank J. H.

2008-01-01

Objective An intervention existing of an evidence-based medicine (EBM) course in combination with case method learning sessions (CMLSs) was designed to enhance the professional performance, self-efficacy and job satisfaction of occupational physicians. Methods A cluster randomized controlled trial was set up and data were collected through questionnaires at baseline (T0), directly after the intervention (T1) and 7 months after baseline (T2). The data of the intervention group [T0 (n = 49), T1 (n = 31), T2 (n = 29)] and control group [T0 (n = 49), T1 (n = 28), T2 (n = 28)] were analysed in mixed model analyses. Mean scores of the perceived value of the CMLS were calculated in the intervention group. Results The overall effect of the intervention over time comparing the intervention with the control group was statistically significant for professional performance (p < 0.001). Job satisfaction and self-efficacy changes were small and not statistically significant between the groups. The perceived value of the CMLS to gain new insights and to improve the quality of their performance increased with the number of sessions followed. Conclusion An EBM course in combination with case method learning sessions is perceived as valuable and offers evidence to enhance the professional performance of occupational physicians. However, it does not seem to influence their self-efficacy and job satisfaction. PMID:18386046

Blended learning is an effective strategy for acquiring competence in public health biostatistics.

PubMed

Milic, Natasa; Masic, Srdjan; Bjegovic-Mikanovic, Vesna; Trajkovic, Goran; Marinkovic, Jelena; Milin-Lazovic, Jelena; Bukumiric, Zoran; Savic, Marko; Cirkovic, Andja; Gajic, Milan; Stanisavljevic, Dejana

2018-04-01

We sought to determine whether blended learning is an effective strategy for acquiring competence in public health biostatistics. The trial was conducted with 69 Masters' students of public health attending the School of Public Health at University of Belgrade. Students were exposed to the traditional and blended learning styles. Blended learning included a combination of face-to-face and distance learning methodologies integrated into a single course. Curriculum development was guided by competencies as suggested by the Association of Schools of Public Health in the European Region (ASPHER). Teaching methods were compared according to the final competence score. Forty-four students were enrolled in the traditional method of education delivery, and 25 to the blended learning format. Mean exam scores for the blended learning group were higher than for the on-site group for both the final statistics score (89.65 ± 6.93 vs. 78.21 ± 13.26; p < 0.001) and knowledge test score (35.89 ± 3.66 vs. 22.56 ± 7.12; p < 0.001), with estimated large effect size (d > 0.8). A blended learning approach is an attractive and effective way of acquiring biostatistics competence for Masters of Public Health (MPH) graduate students.

Mere exposure alters category learning of novel objects.

PubMed

Folstein, Jonathan R; Gauthier, Isabel; Palmeri, Thomas J

2010-01-01

We investigated how mere exposure to complex objects with correlated or uncorrelated object features affects later category learning of new objects not seen during exposure. Correlations among pre-exposed object dimensions influenced later category learning. Unlike other published studies, the collection of pre-exposed objects provided no information regarding the categories to be learned, ruling out unsupervised or incidental category learning during pre-exposure. Instead, results are interpreted with respect to statistical learning mechanisms, providing one of the first demonstrations of how statistical learning can influence visual object learning.