Stimulating Deep Learning Using Active Learning Techniques

ERIC Educational Resources Information Center

Yew, Tee Meng; Dawood, Fauziah K. P.; a/p S. Narayansany, Kannaki; a/p Palaniappa Manickam, M. Kamala; Jen, Leong Siok; Hoay, Kuan Chin

2016-01-01

When students and teachers behave in ways that reinforce learning as a spectator sport, the result can often be a classroom and overall learning environment that is mostly limited to transmission of information and rote learning rather than deep approaches towards meaningful construction and application of knowledge. A group of college instructors…

m-Learning and holography: Compatible techniques?

NASA Astrophysics Data System (ADS)

Calvo, Maria L.

2014-07-01

Since the last decades, cell phones have become increasingly popular and are nowadays ubiquitous. New generations of cell phones are now equipped with text messaging, internet, and camera features. They are now making their way into the classroom. This is creating a new teaching and learning technique, the so called m-Learning (or mobile-Learning). Because of the many benefits that cell phones offer, teachers could easily use them as a teaching and learning tool. However, an additional work from the teachers for introducing their students into the m-Learning in the classroom needs to be defined and developed. As an example, optical techniques, based upon interference and diffraction phenomena, such as holography, appear to be convenient topics for m-Learning. They can be approached with simple examples and experiments within the cell phones performances and classroom accessibility. We will present some results carried out at the Faculty of Physical Sciences in UCM to obtain very simple holographic recordings via cell phones. The activities were carried out inside the course on Optical Coherence and Laser, offered to students in the fourth course of the Grade in Physical Sciences. Some open conclusions and proposals will be presented.

Is Peer Interaction Necessary for Optimal Active Learning?

ERIC Educational Resources Information Center

Linton, Debra L.; Farmer, Jan Keith; Peterson, Ernie

2014-01-01

Meta-analyses of active-learning research consistently show that active-learning techniques result in greater student performance than traditional lecture-based courses. However, some individual studies show no effect of active-learning interventions. This may be due to inexperienced implementation of active learning. To minimize the effect of…

Incorporating active-learning techniques and competency assessment into a critical care elective course.

PubMed

Malcom, Daniel R; Hibbs, Jennifer L

2012-09-10

To design, implement, and measure the effectiveness of a critical care elective course for second-year students in a 3-year accelerated doctor of pharmacy (PharmD) program. A critical care elective course was developed that used active-learning techniques, including cooperative learning and group presentations, to deliver content on critical care topics. Group presentations had to include a disease state overview, practice guidelines, and clinical recommendations, and were evaluated by course faculty members and peers. Students' mean scores on a 20-question critical-care competency assessment administered before and after the course improved by 11% (p < 0.05). Course evaluations and comments were positive. A critical care elective course resulted in significantly improved competency in critical care and was well-received by students.

Active-learning processes used in US pharmacy education.

PubMed

Stewart, David W; Brown, Stacy D; Clavier, Cheri W; Wyatt, Jarrett

2011-05-10

To document the type and extent of active-learning techniques used in US colleges and schools of pharmacy as well as factors associated with use of these techniques. A survey instrument was developed to assess whether and to what extent active learning was used by faculty members of US colleges and schools of pharmacy. This survey instrument was distributed via the American Association of Colleges of Pharmacy (AACP) mailing list. Ninety-five percent (114) of all US colleges and schools of pharmacy were represented with at least 1 survey among the 1179 responses received. Eighty-seven percent of respondents used active-learning techniques in their classroom activities. The heavier the teaching workload the more active-learning strategies were used. Other factors correlated with higher use of active-learning strategies included younger faculty member age (inverse relationship), lower faculty member rank (inverse relationship), and departments that focused on practice, clinical and social, behavioral, and/or administrative sciences. Active learning has been embraced by pharmacy educators and is used to some extent by the majority of US colleges and schools of pharmacy. Future research should focus on how active-learning methods can be used most effectively within pharmacy education, how it can gain even broader acceptance throughout the academy, and how the effect of active learning on programmatic outcomes can be better documented.

Active-Learning Processes Used in US Pharmacy Education

PubMed Central

Brown, Stacy D.; Clavier, Cheri W.; Wyatt, Jarrett

2011-01-01

Objective To document the type and extent of active-learning techniques used in US colleges and schools of pharmacy as well as factors associated with use of these techniques. Methods A survey instrument was developed to assess whether and to what extent active learning was used by faculty members of US colleges and schools of pharmacy. This survey instrument was distributed via the American Association of Colleges of Pharmacy (AACP) mailing list. Results Ninety-five percent (114) of all US colleges and schools of pharmacy were represented with at least 1 survey among the 1179 responses received. Eighty-seven percent of respondents used active-learning techniques in their classroom activities. The heavier the teaching workload the more active-learning strategies were used. Other factors correlated with higher use of active-learning strategies included younger faculty member age (inverse relationship), lower faculty member rank (inverse relationship), and departments that focused on practice, clinical and social, behavioral, and/or administrative sciences. Conclusions Active learning has been embraced by pharmacy educators and is used to some extent by the majority of US colleges and schools of pharmacy. Future research should focus on how active-learning methods can be used most effectively within pharmacy education, how it can gain even broader acceptance throughout the academy, and how the effect of active learning on programmatic outcomes can be better documented. PMID:21769144

Status of the Usage of Active Learning and Teaching Method and Techniques by Social Studies Teachers

ERIC Educational Resources Information Center

Akman, Özkan

2016-01-01

The purpose of this study was to determine the active learning and teaching methods and techniques which are employed by the social studies teachers working in state schools of Turkey. This usage status was assessed using different variables. This was a case study, wherein the research was limited to 241 social studies teachers. These teachers…

eLearning techniques supporting problem based learning in clinical simulation.

PubMed

Docherty, Charles; Hoy, Derek; Topp, Helena; Trinder, Kathryn

2005-08-01

This paper details the results of the first phase of a project using eLearning to support students' learning within a simulated environment. The locus was a purpose built clinical simulation laboratory (CSL) where the School's philosophy of problem based learning (PBL) was challenged through lecturers using traditional teaching methods. a student-centred, problem based approach to the acquisition of clinical skills that used high quality learning objects embedded within web pages, substituting for lecturers providing instruction and demonstration. This encouraged student nurses to explore, analyse and make decisions within the safety of a clinical simulation. Learning was facilitated through network communications and reflection on video performances of self and others. Evaluations were positive, students demonstrating increased satisfaction with PBL, improved performance in exams, and increased self-efficacy in the performance of nursing activities. These results indicate that eLearning techniques can help students acquire clinical skills in the safety of a simulated environment within the context of a problem based learning curriculum.

Introduction of active learning method in learning physiology by MBBS students.

PubMed

Gilkar, Suhail Ahmad; Lone, Shabiruddin; Lone, Riyaz Ahmad

2016-01-01

Active learning has received considerable attention over the past several years, often presented or perceived as a radical change from traditional instruction methods. Current research on learning indicates that using a variety of teaching strategies in the classroom increases student participation and learning. To introduce active learning methodology, i.e., "jigsaw technique" in undergraduate medical education and assess the student and faculty response to it. This study was carried out in the Department of Physiology in a Medical College of North India. A topic was chosen and taught using one of the active learning methods (ALMs), i.e., jigsaw technique. An instrument (questionnaire) was developed in English through an extensive review of literature and was properly validated. The students were asked to give their response on a five-point Likert scale. The feedback was kept anonymous. Faculty also provided their feedback in a separately provided feedback proforma. The data were collected, compiled, and analyzed. Of 150 students of MBBS-first year batch 2014, 142 participated in this study along with 14 faculty members of the Physiology Department. The majority of the students (>90%) did welcome the introduction of ALM and strongly recommended the use of such methods in teaching many more topics in future. 100% faculty members were of the opinion that many more topics shall be taken up using ALMs. This study establishes the fact that both the medical students and faculty want a change from the traditional way of passive, teacher-centric learning, to the more active teaching-learning techniques.

The Effectiveness of Active and Traditional Teaching Techniques in the Orthopedic Assessment Laboratory

ERIC Educational Resources Information Center

Nottingham, Sara; Verscheure, Susan

2010-01-01

Active learning is a teaching methodology with a focus on student-centered learning that engages students in the educational process. This study implemented active learning techniques in an orthopedic assessment laboratory, and the effects of these teaching techniques. Mean scores from written exams, practical exams, and final course evaluations…

Experiential Learning and Learning Environments: The Case of Active Listening Skills

ERIC Educational Resources Information Center

Huerta-Wong, Juan Enrique; Schoech, Richard

2010-01-01

Social work education research frequently has suggested an interaction between teaching techniques and learning environments. However, this interaction has never been tested. This study compared virtual and face-to-face learning environments and included active listening concepts to test whether the effectiveness of learning environments depends…

Active Learning and Teaching: Improving Postsecondary Library Instruction.

ERIC Educational Resources Information Center

Allen, Eileen E.

1995-01-01

Discusses ways to improve postsecondary library instruction based on theories of active learning. Topics include a historical background of active learning; student achievement and attitudes; cognitive development; risks; active teaching; and instructional techniques, including modified lectures, brainstorming, small group work, cooperative…

Is There a Relationship between the Usage of Active and Collaborative Learning Techniques and International Students' Study Anxiety?

ERIC Educational Resources Information Center

Khoshlessan, Rezvan

2013-01-01

This study was designed to explore the relationships between the international students' perception of professors' instructional practices (the usage of active and collaborative learning techniques in class) and the international students' study anxiety. The dominant goal of this research was to investigate whether the professors' usage of active…

Adaptive Batch Mode Active Learning.

PubMed

Chakraborty, Shayok; Balasubramanian, Vineeth; Panchanathan, Sethuraman

2015-08-01

Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts toward a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set. Real-world applications require adaptive approaches for batch selection in active learning, depending on the complexity of the data stream in question. However, the existing work in this field has primarily focused on static or heuristic batch size selection. In this paper, we propose two novel optimization-based frameworks for adaptive batch mode active learning (BMAL), where the batch size as well as the selection criteria are combined in a single formulation. We exploit gradient-descent-based optimization strategies as well as properties of submodular functions to derive the adaptive BMAL algorithms. The solution procedures have the same computational complexity as existing state-of-the-art static BMAL techniques. Our empirical results on the widely used VidTIMIT and the mobile biometric (MOBIO) data sets portray the efficacy of the proposed frameworks and also certify the potential of these approaches in being used for real-world biometric recognition applications.

Variation in behavioral engagement during an active learning activity leads to differential knowledge gains in college students.

PubMed

LaDage, Lara D; Tornello, Samantha L; Vallejera, Jennilyn M; Baker, Emily E; Yan, Yue; Chowdhury, Anik

2018-03-01

There are many pedagogical techniques used by educators in higher education; however, some techniques and activities have been shown to be more beneficial to student learning than others. Research has demonstrated that active learning and learning in which students cognitively engage with the material in a multitude of ways result in better understanding and retention. The aim of the present study was to determine which of three pedagogical techniques led to improvement in learning and retention in undergraduate college students. Subjects partook in one of three different types of pedagogical engagement: hands-on learning with a model, observing someone else manipulate the model, and traditional lecture-based presentation. Students were then asked to take an online quiz that tested their knowledge of the new material, both immediately after learning the material and 2 wk later. Students who engaged in direct manipulation of the model scored higher on the assessment immediately after learning the material compared with the other two groups. However, there were no differences among the three groups when assessed after a 2-wk retention interval. Thus active learning techniques that involve direct interaction with the material can lead to learning benefits; however, how these techniques benefit long-term retention of the information is equivocal.

Classification of the Regional Ionospheric Disturbance Based on Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Terzi, Merve Begum; Arikan, Orhan; Karatay, Secil; Arikan, Feza; Gulyaeva, Tamara

2016-08-01

In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.

Feasibility of Active Machine Learning for Multiclass Compound Classification.

PubMed

Lang, Tobias; Flachsenberg, Florian; von Luxburg, Ulrike; Rarey, Matthias

2016-01-25

A common task in the hit-to-lead process is classifying sets of compounds into multiple, usually structural classes, which build the groundwork for subsequent SAR studies. Machine learning techniques can be used to automate this process by learning classification models from training compounds of each class. Gathering class information for compounds can be cost-intensive as the required data needs to be provided by human experts or experiments. This paper studies whether active machine learning can be used to reduce the required number of training compounds. Active learning is a machine learning method which processes class label data in an iterative fashion. It has gained much attention in a broad range of application areas. In this paper, an active learning method for multiclass compound classification is proposed. This method selects informative training compounds so as to optimally support the learning progress. The combination with human feedback leads to a semiautomated interactive multiclass classification procedure. This method was investigated empirically on 15 compound classification tasks containing 86-2870 compounds in 3-38 classes. The empirical results show that active learning can solve these classification tasks using 10-80% of the data which would be necessary for standard learning techniques.

Machine Learning Techniques in Clinical Vision Sciences.

PubMed

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration

Generalized query-based active learning to identify differentially methylated regions in DNA.

PubMed

Haque, Md Muksitul; Holder, Lawrence B; Skinner, Michael K; Cook, Diane J

2013-01-01

Active learning is a supervised learning technique that reduces the number of examples required for building a successful classifier, because it can choose the data it learns from. This technique holds promise for many biological domains in which classified examples are expensive and time-consuming to obtain. Most traditional active learning methods ask very specific queries to the Oracle (e.g., a human expert) to label an unlabeled example. The example may consist of numerous features, many of which are irrelevant. Removing such features will create a shorter query with only relevant features, and it will be easier for the Oracle to answer. We propose a generalized query-based active learning (GQAL) approach that constructs generalized queries based on multiple instances. By constructing appropriately generalized queries, we can achieve higher accuracy compared to traditional active learning methods. We apply our active learning method to find differentially DNA methylated regions (DMRs). DMRs are DNA locations in the genome that are known to be involved in tissue differentiation, epigenetic regulation, and disease. We also apply our method on 13 other data sets and show that our method is better than another popular active learning technique.

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.

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

ERIC Educational Resources Information Center

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

Focusing on Active, Meaningful Learning. IDEA Paper No. 34.

ERIC Educational Resources Information Center

Stalheim-Smith, Ann

This paper discusses active and meaningful learning and the application of this instructional approach to the college classroom, focusing on techniques used in the author's biology classes. Active and meaningful learning places emphasis on students actually doing things and thinking about what they are doing, relating new information to…

Active Learning and Cooperative Learning in the Organic Chemistry Lecture Class

NASA Astrophysics Data System (ADS)

Paulson, Donald R.

1999-08-01

Faculty in the physical sciences are one of the academic groups least receptive to the use of active learning strategies and cooperative learning in their classrooms. This is particularly so in traditional lecture classes. It is the objective of this paper to show how effective these techniques can be in improving student performance in classes. The use of active learning strategies and cooperative learning groups in my organic chemistry lecture classes has increased the overall pass rate in my classes by an astounding 20-30% over the traditional lecture mode. This has been accomplished without any reduction in "standards". The actual methods employed are presented as well as a discussion of how I came to radically change the way I teach my classes.

Animal-Centered Learning Activities in Pharmacy Education

PubMed Central

Lust, Elaine

2006-01-01

Objectives To assess the contribution of animal-centered activities to students achieving learning outcomes in a veterinary therapeutics course. Design Qualitative methods were used to assess the outcome of using “hands-on” animal interactions as tools of engagement in the course. Reflective commentary on animal-centered activities was collected and analyzed. Assessment Animal-centered learning activities are effective tools for engaging students and facilitating their understanding and application of veterinary therapeutic knowledge, skills, and attitudes. Analysis of qualitative data revealed themes of professional caring and caring behaviors as a direct result of animal-centered activities. Elements of empathy, caring, compassion, and self-awareness were strong undercurrents in student's comments. Conclusions Animal-centered learning activities provide an innovative learning environment for the application of veterinary pharmacy knowledge, skills, and attitudes directly to animal patients. The use of animals in the course is a successful active-learning technique to engage pharmacy students and assist them in developing caring attitudes and behaviors beneficial to future health care providers. PMID:17149415

Examining Online Learning Patterns with Data Mining Techniques in Peer-Moderated and Teacher-Moderated Courses

ERIC Educational Resources Information Center

Hung, Jui-Long; Crooks, Steven M.

2009-01-01

The student learning process is important in online learning environments. If instructors can "observe" online learning behaviors, they can provide adaptive feedback, adjust instructional strategies, and assist students in establishing patterns of successful learning activities. This study used data mining techniques to examine and…

Student knowledge and confidence in an elective clinical toxicology course using active-learning techniques.

PubMed

Thomas, Michael C; Macias-Moriarity, Liliairica Z

2014-06-17

To measure changes in students' knowledge and confidence scores after completing an elective clinical toxicology course in an accelerated doctor of pharmacy (PharmD) program. Various active-learning techniques were used to create a learner-centered environment. Approximately two-thirds of the course used student-led presentations. Some of those not presenting were assigned to be evaluators, responsible for asking the presenter a question or writing quiz questions based on the presented material. Other learner-centered activities included weekly quizzes and discussions at the conclusion of each presented topic. A test instrument designed to measure students' knowledge and associated level of confidence on each item was administered at the beginning and end of the course. Students' knowledge and confidence scores increased significantly from pretest to posttest. Students' increased confidence and knowledge scores were well correlated after course completion, indicating students were better able to self-assess these areas. These findings suggest that confidence could be an additional measure of students' metacognitive skill development.

Is Active Learning Like Broccoli? Student Perceptions of Active Learning in Large Lecture Classes

ERIC Educational Resources Information Center

Smith, C. Veronica; Cardaciotto, LeeAnn

2011-01-01

Although research suggests that active learning is associated with positive outcomes (e.g., memory, test performance), use of such techniques can be difficult to implement in large lecture-based classes. In the current study, 1,091 students completed out-of-class group exercises to complement course material in an Introductory Psychology class.…

Toward accelerating landslide mapping with interactive machine learning techniques

NASA Astrophysics Data System (ADS)

Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

2013-04-01

Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also

Active Learning in PhysicsTechnology and Research-based Techniques Emphasizing Interactive Lecture Demonstrations

NASA Astrophysics Data System (ADS)

Thornton, Ronald

2010-10-01

Physics education research has shown that learning environments that engage students and allow them to take an active part in their learning can lead to large conceptual gains compared to traditional instruction. Examples of successful curricula and methods include Peer Instruction, Just in Time Teaching, RealTime Physics, Workshop Physics, Scale-Up, and Interactive Lecture Demonstrations (ILDs). An active learning environment is often difficult to achieve in lecture sessions. This presentation will demonstrate the use of sequences of Interactive Lecture Demonstrations (ILDs) that use real experiments often involving real-time data collection and display combined with student interaction to create an active learning environment in large or small lecture classes. Interactive lecture demonstrations will be done in the area of mechanics using real-time motion probes and the Visualizer. A video tape of students involved in interactive lecture demonstrations will be shown. The results of a number of research studies at various institutions (including international) to measure the effectiveness of ILDs and guided inquiry conceptual laboratories will be presented.

The use of an active learning approach in a SCALE-UP learning space improves academic performance in undergraduate General Biology.

PubMed

Hacisalihoglu, Gokhan; Stephens, Desmond; Johnson, Lewis; Edington, Maurice

2018-01-01

Active learning is a pedagogical approach that involves students engaging in collaborative learning, which enables them to take more responsibility for their learning and improve their critical thinking skills. While prior research examined student performance at majority universities, this study focuses on specifically Historically Black Colleges and Universities (HBCUs) for the first time. Here we present work that focuses on the impact of active learning interventions at Florida A&M University, where we measured the impact of active learning strategies coupled with a SCALE-UP (Student Centered Active Learning Environment with Upside-down Pedagogies) learning environment on student success in General Biology. In biology sections where active learning techniques were employed, students watched online videos and completed specific activities before class covering information previously presented in a traditional lecture format. In-class activities were then carefully planned to reinforce critical concepts and enhance critical thinking skills through active learning techniques such as the one-minute paper, think-pair-share, and the utilization of clickers. Students in the active learning and control groups covered the same topics, took the same summative examinations and completed identical homework sets. In addition, the same instructor taught all of the sections included in this study. Testing demonstrated that these interventions increased learning gains by as much as 16%, and students reported an increase in their positive perceptions of active learning and biology. Overall, our results suggest that active learning approaches coupled with the SCALE-UP environment may provide an added opportunity for student success when compared with the standard modes of instruction in General Biology.

Prostate Cancer Probability Prediction By Machine Learning Technique.

PubMed

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

Automatic Earthquake Detection by Active Learning

NASA Astrophysics Data System (ADS)

Bergen, K.; Beroza, G. C.

2017-12-01

In recent years, advances in machine learning have transformed fields such as image recognition, natural language processing and recommender systems. Many of these performance gains have relied on the availability of large, labeled data sets to train high-accuracy models; labeled data sets are those for which each sample includes a target class label, such as waveforms tagged as either earthquakes or noise. Earthquake seismologists are increasingly leveraging machine learning and data mining techniques to detect and analyze weak earthquake signals in large seismic data sets. One of the challenges in applying machine learning to seismic data sets is the limited labeled data problem; learning algorithms need to be given examples of earthquake waveforms, but the number of known events, taken from earthquake catalogs, may be insufficient to build an accurate detector. Furthermore, earthquake catalogs are known to be incomplete, resulting in training data that may be biased towards larger events and contain inaccurate labels. This challenge is compounded by the class imbalance problem; the events of interest, earthquakes, are infrequent relative to noise in continuous data sets, and many learning algorithms perform poorly on rare classes. In this work, we investigate the use of active learning for automatic earthquake detection. Active learning is a type of semi-supervised machine learning that uses a human-in-the-loop approach to strategically supplement a small initial training set. The learning algorithm incorporates domain expertise through interaction between a human expert and the algorithm, with the algorithm actively posing queries to the user to improve detection performance. We demonstrate the potential of active machine learning to improve earthquake detection performance with limited available training data.

Is Peer Interaction Necessary for Optimal Active Learning?

PubMed

Linton, Debra L; Farmer, Jan Keith; Peterson, Ernie

2014-01-01

Meta-analyses of active-learning research consistently show that active-learning techniques result in greater student performance than traditional lecture-based courses. However, some individual studies show no effect of active-learning interventions. This may be due to inexperienced implementation of active learning. To minimize the effect of inexperience, we should try to provide more explicit implementation recommendations based on research into the key components of effective active learning. We investigated the optimal implementation of active-learning exercises within a "lecture" course. Two sections of nonmajors biology were taught by the same instructor, in the same semester, using the same instructional materials and assessments. Students in one section completed in-class active-learning exercises in cooperative groups, while students in the other section completed the same activities individually. Performance on low-level, multiple-choice assessments was not significantly different between sections. However, students who worked in cooperative groups on the in-class activities significantly outperformed students who completed the activities individually on the higher-level, extended-response questions. Our results provide additional evidence that group processing of activities should be the recommended mode of implementation for in-class active-learning exercises. © 2014 D. L. Linton et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Study of CT image texture using deep learning techniques

NASA Astrophysics Data System (ADS)

Dutta, Sandeep; Fan, Jiahua; Chevalier, David

2018-03-01

For CT imaging, reduction of radiation dose while improving or maintaining image quality (IQ) is currently a very active research and development topic. Iterative Reconstruction (IR) approaches have been suggested to be able to offer better IQ to dose ratio compared to the conventional Filtered Back Projection (FBP) reconstruction. However, it has been widely reported that often CT image texture from IR is different compared to that from FBP. Researchers have proposed different figure of metrics to quantitate the texture from different reconstruction methods. But there is still a lack of practical and robust method in the field for texture description. This work applied deep learning method for CT image texture study. Multiple dose scans of a 20cm diameter cylindrical water phantom was performed on Revolution CT scanner (GE Healthcare, Waukesha) and the images were reconstructed with FBP and four different IR reconstruction settings. The training images generated were randomly allotted (80:20) to a training and validation set. An independent test set of 256-512 images/class were collected with the same scan and reconstruction settings. Multiple deep learning (DL) networks with Convolution, RELU activation, max-pooling, fully-connected, global average pooling and softmax activation layers were investigated. Impact of different image patch size for training was investigated. Original pixel data as well as normalized image data were evaluated. DL models were reliably able to classify CT image texture with accuracy up to 99%. Results show that the deep learning techniques suggest that CT IR techniques may help lower the radiation dose compared to FBP.

The Ticket to Retention: A Classroom Assessment Technique Designed to Improve Student Learning

ERIC Educational Resources Information Center

Divoll, Kent A.; Browning, Sandra T.; Vesey, Winona M.

2012-01-01

Classroom assessment techniques (CATs) or other closure activities are widely promoted for use in college classrooms. However, research on whether CATs improve student learning are mixed. The authors posit that the results are mixed because CATs were designed to "help teachers find out what students are learning in the classroom and how well…

Collaborative and Cooperative Learning Techniques. Learning Package No. 6.

ERIC Educational Resources Information Center

Compton, Joe; Smith, Carl, Comp.

Originally developed for the Department of Defense Schools (DoDDS) system, this learning package on collaborative and cooperative learning techniques is designed for teachers who wish to upgrade or expand their teaching skills on their own. The package includes a comprehensive search of the ERIC database; a lecture giving an overview on the topic;…

Improving Students' Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology.

PubMed

Dunlosky, John; Rawson, Katherine A; Marsh, Elizabeth J; Nathan, Mitchell J; Willingham, Daniel T

2013-01-01

Many students are being left behind by an educational system that some people believe is in crisis. Improving educational outcomes will require efforts on many fronts, but a central premise of this monograph is that one part of a solution involves helping students to better regulate their learning through the use of effective learning techniques. Fortunately, cognitive and educational psychologists have been developing and evaluating easy-to-use learning techniques that could help students achieve their learning goals. In this monograph, we discuss 10 learning techniques in detail and offer recommendations about their relative utility. We selected techniques that were expected to be relatively easy to use and hence could be adopted by many students. Also, some techniques (e.g., highlighting and rereading) were selected because students report relying heavily on them, which makes it especially important to examine how well they work. The techniques include elaborative interrogation, self-explanation, summarization, highlighting (or underlining), the keyword mnemonic, imagery use for text learning, rereading, practice testing, distributed practice, and interleaved practice. To offer recommendations about the relative utility of these techniques, we evaluated whether their benefits generalize across four categories of variables: learning conditions, student characteristics, materials, and criterion tasks. Learning conditions include aspects of the learning environment in which the technique is implemented, such as whether a student studies alone or with a group. Student characteristics include variables such as age, ability, and level of prior knowledge. Materials vary from simple concepts to mathematical problems to complicated science texts. Criterion tasks include different outcome measures that are relevant to student achievement, such as those tapping memory, problem solving, and comprehension. We attempted to provide thorough reviews for each technique, so this

Incorporation of Socio-scientific Content into Active Learning Activities

NASA Astrophysics Data System (ADS)

King, D. B.; Lewis, J. E.; Anderson, K.; Latch, D.; Sutheimer, S.; Webster, G.; Moog, R.

2014-12-01

Active learning has gained increasing support as an effective pedagogical technique to improve student learning. One way to promote active learning in the classroom is the use of in-class activities in place of lecturing. As part of an NSF-funded project, a set of in-class activities have been created that use climate change topics to teach chemistry content. These activities use the Process Oriented Guided Inquiry Learning (POGIL) methodology. In this pedagogical approach a set of models and a series of critical thinking questions are used to guide students through the introduction to or application of course content. Students complete the activities in their groups, with the faculty member as a facilitator of learning. Through assigned group roles and intentionally designed activity structure, process skills, such as teamwork, communication, and information processing, are developed during completion of the activity. Each of these climate change activities contains a socio-scientific component, e.g., social, ethical and economic data. In one activity, greenhouse gases are used to explain the concept of dipole moment. Data about natural and anthropogenic production rates, global warming potential and atmospheric lifetimes for a list of greenhouse gases are presented. The students are asked to identify which greenhouse gas they would regulate, with a corresponding explanation for their choice. They are also asked to identify the disadvantages of regulating the gas they chose in the previous question. In another activity, where carbon sequestration is used to demonstrate the utility of a phase diagram, students use economic and environmental data to choose the best location for sequestration. Too often discussions about climate change (both in and outside the classroom) consist of purely emotional responses. These activities force students to use data to support their arguments and hypothesize about what other data could be used in the corresponding discussion to

Learning a Taxonomy of Predefined and Discovered Activity Patterns

PubMed Central

Krishnan, Narayanan; Cook, Diane J.; Wemlinger, Zachary

2013-01-01

Many intelligent systems that focus on the needs of a human require information about the activities that are being performed by the human. At the core of this capability is activity recognition. Activity recognition techniques have become robust but rarely scale to handle more than a few activities. They also rarely learn from more than one smart home data set because of inherent differences between labeling techniques. In this paper we investigate a data-driven approach to creating an activity taxonomy from sensor data found in disparate smart home datasets. We investigate how the resulting taxonomy can help analyze the relationship between classes of activities. We also analyze how the taxonomy can be used to scale activity recognition to a large number of activity classes and training datasets. We describe our approach and evaluate it on 34 smart home datasets. The results of the evaluation indicate that the hierarchical modeling can reduce training time while maintaining accuracy of the learned model. PMID:25302084

The Effect of Higher Education Faculty Training in Improvisational Theatre Techniques on Student Learning and Perceptions of Engagement and Faculty Perceptions of Teaching and Learning

ERIC Educational Resources Information Center

Massie, DeAnna

2017-01-01

College instructors are content experts but ineffective at creating engaging and productive learning environments. This mixed methods study explored how improvisational theatre techniques affect college instructors' ability to increase student engagement and learning. Theoretical foundations included engagement, active learning, collaboration and…

Active Learning in a Finnish Engineering University Course

ERIC Educational Resources Information Center

Larson, Debra; Ahonen, Anna-Maija

2004-01-01

This paper is a case study on the use of active learning techniques in an upper-level engineering course at the Helsinki University of Technology. The paper describes how these techniques were introduced and successfully used within the Finnish university classroom. The cultural subtext is explored and attention is given to teaching techniques…

The Effect of Listening Education Practices Organized by Active Learning Techniques on the Attitudes of 6th Grade Students towards Turkish Course

ERIC Educational Resources Information Center

Aytan, Talat

2017-01-01

In this study, it was aimed to determine the effect of listening education practices that organized by active learning techniques on the attitudes of 6th grade students towards Turkish course. The sample of the study conducted at a secondary school in the Black Sea region of Turkey consisted of twenty students--ten girls and ten boys. During…

Technique Feature Analysis or Involvement Load Hypothesis: Estimating Their Predictive Power in Vocabulary Learning.

PubMed

Gohar, Manoochehr Jafari; Rahmanian, Mahboubeh; Soleimani, Hassan

2018-02-05

Vocabulary learning has always been a great concern and has attracted the attention of many researchers. Among the vocabulary learning hypotheses, involvement load hypothesis and technique feature analysis have been proposed which attempt to bring some concepts like noticing, motivation, and generation into focus. In the current study, 90 high proficiency EFL students were assigned into three vocabulary tasks of sentence making, composition, and reading comprehension in order to examine the power of involvement load hypothesis and technique feature analysis frameworks in predicting vocabulary learning. It was unraveled that involvement load hypothesis cannot be a good predictor, and technique feature analysis was a good predictor in pretest to posttest score change and not in during-task activity. The implications of the results will be discussed in the light of preparing vocabulary tasks.

Active Learning of Biochemistry Made Easy (for the Teacher)

ERIC Educational Resources Information Center

Bobich, Joseph A.

2008-01-01

This active learning pedagogical technique aims to improve students' learning in a two-semester, upper-division biochemistry course sequence in which the vast majority of students enrolled will continue on to medical or graduate schools. Instead of lecturing, the Instructor moves to the side of the room, thereby becoming "the guide on the side".…

Machine learning of molecular properties: Locality and active learning

NASA Astrophysics Data System (ADS)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

The Effect of Learning Based on Technology Model and Assessment Technique toward Thermodynamic Learning Achievement

NASA Astrophysics Data System (ADS)

Makahinda, T.

2018-02-01

The purpose of this research is to find out the effect of learning model based on technology and assessment technique toward thermodynamic achievement by controlling students intelligence. This research is an experimental research. The sample is taken through cluster random sampling with the total respondent of 80 students. The result of the research shows that the result of learning of thermodynamics of students who taught the learning model of environmental utilization is higher than the learning result of student thermodynamics taught by simulation animation, after controlling student intelligence. There is influence of student interaction, and the subject between models of technology-based learning with assessment technique to student learning result of Thermodynamics, after controlling student intelligence. Based on the finding in the lecture then should be used a thermodynamic model of the learning environment with the use of project assessment technique.

Analyzing the Effects of Various Concept Mapping Techniques on Learning Achievement under Different Learning Styles

ERIC Educational Resources Information Center

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

2017-01-01

This study explored the effectiveness of different concept mapping techniques on the learning achievement of senior accounting students and whether achievements attained using various techniques are affected by different learning styles. The techniques are computer-assisted construct-by-self-concept mapping (CACSB), computer-assisted…

Active learning of cortical connectivity from two-photon imaging data.

PubMed

Bertrán, Martín A; Martínez, Natalia L; Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this "active learning" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

The Development of Teaching and Learning in Bright-Field Microscopy Technique

ERIC Educational Resources Information Center

Iskandar, Yulita Hanum P.; Mahmud, Nurul Ethika; Wahab, Wan Nor Amilah Wan Abdul; Jamil, Noor Izani Noor; Basir, Nurlida

2013-01-01

E-learning should be pedagogically-driven rather than technologically-driven. The objectives of this study are to develop an interactive learning system in bright-field microscopy technique in order to support students' achievement of their intended learning outcomes. An interactive learning system on bright-field microscopy technique was…

Active learning for semi-supervised clustering based on locally linear propagation reconstruction.

PubMed

Chang, Chin-Chun; Lin, Po-Yi

2015-03-01

The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

Investigating the Relationship among Extracurricular Activities, Learning Approach and Academic Outcomes: A Case Study

ERIC Educational Resources Information Center

Chan, Yiu-Kong

2016-01-01

Learning effectiveness requires an understanding of the relationship among extracurricular activities, learning approach and academic performance and, it is argued, this helps educators develop techniques designed to enrich learning effectiveness. Biggs' Presage-Process-Product model on student learning has identified the relationship among…

Challenges of Using Learning Analytics Techniques to Support Mobile Learning

ERIC Educational Resources Information Center

Arrigo, Marco; Fulantelli, Giovanni; Taibi, Davide

2015-01-01

Evaluation of Mobile Learning remains an open research issue, especially as regards the activities that take place outside the classroom. In this context, Learning Analytics can provide answers, and offer the appropriate tools to enhance Mobile Learning experiences. In this poster we introduce a task-interaction framework, using learning analytics…

Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

ERIC Educational Resources Information Center

Firdausiah Mansur, Andi Besse; Yusof, Norazah

2013-01-01

Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

Comparative evaluation of features and techniques for identifying activity type and estimating energy cost from accelerometer data

PubMed Central

Kate, Rohit J.; Swartz, Ann M.; Welch, Whitney A.; Strath, Scott J.

2016-01-01

Wearable accelerometers can be used to objectively assess physical activity. However, the accuracy of this assessment depends on the underlying method used to process the time series data obtained from accelerometers. Several methods have been proposed that use this data to identify the type of physical activity and estimate its energy cost. Most of the newer methods employ some machine learning technique along with suitable features to represent the time series data. This paper experimentally compares several of these techniques and features on a large dataset of 146 subjects doing eight different physical activities wearing an accelerometer on the hip. Besides features based on statistics, distance based features and simple discrete features straight from the time series were also evaluated. On the physical activity type identification task, the results show that using more features significantly improve results. Choice of machine learning technique was also found to be important. However, on the energy cost estimation task, choice of features and machine learning technique were found to be less influential. On that task, separate energy cost estimation models trained specifically for each type of physical activity were found to be more accurate than a single model trained for all types of physical activities. PMID:26862679

Learning Programming Technique through Visual Programming Application as Learning Media with Fuzzy Rating

ERIC Educational Resources Information Center

Buditjahjanto, I. G. P. Asto; Nurlaela, Luthfiyah; Ekohariadi; Riduwan, Mochamad

2017-01-01

Programming technique is one of the subjects at Vocational High School in Indonesia. This subject contains theory and application of programming utilizing Visual Programming. Students experience some difficulties to learn textual learning. Therefore, it is necessary to develop media as a tool to transfer learning materials. The objectives of this…

Improving Word Learning in Children Using an Errorless Technique

ERIC Educational Resources Information Center

Warmington, Meesha; Hitch, Graham J.; Gathercole, Susan E.

2013-01-01

The current experiment examined the relative advantage of an errorless learning technique over an errorful one in the acquisition of novel names for unfamiliar objects in typically developing children aged between 7 and 9 years. Errorless learning led to significantly better learning than did errorful learning. Processing speed and vocabulary…

Incorporating active-learning techniques into the photonics-related teaching in the Erasmus Mundus Master in "Color in Informatics and Media Technology"

NASA Astrophysics Data System (ADS)

Pozo, Antonio M.; Rubiño, Manuel; Hernández-Andrés, Javier; Nieves, Juan L.

2014-07-01

In this work, we present a teaching methodology using active-learning techniques in the course "Devices and Instrumentation" of the Erasmus Mundus Master's Degree in "Color in Informatics and Media Technology" (CIMET). A part of the course "Devices and Instrumentation" of this Master's is dedicated to the study of image sensors and methods to evaluate their image quality. The teaching methodology that we present consists of incorporating practical activities during the traditional lectures. One of the innovative aspects of this teaching methodology is that students apply the concepts and methods studied in class to real devices. For this, students use their own digital cameras, webcams, or cellphone cameras in class. These activities provide students a better understanding of the theoretical subject given in class and encourage the active participation of students.

Learning Physics through Project-Based Learning Game Techniques

ERIC Educational Resources Information Center

Baran, Medine; Maskan, Abdulkadir; Yasar, Seyma

2018-01-01

The aim of the present study, in which Project and game techniques are used together, is to examine the impact of project-based learning games on students' physics achievement. Participants of the study consist of 34 9th grade students (N = 34). The data were collected using achievement tests and a questionnaire. Throughout the applications, the…

Finite Element Learning Modules as Active Learning Tools

ERIC Educational Resources Information Center

Brown, Ashland O.; Jensen, Daniel; Rencis, Joseph; Wood, Kristin; Wood, John; White, Christina; Raaberg, Kristen Kaufman; Coffman, Josh

2012-01-01

The purpose of active learning is to solicit participation by students beyond the passive mode of traditional classroom lectures. Reading, writing, participating in discussions, hands-on activities, engaging in active problem solving, and collaborative learning can all be involved. The skills acquired during active learning tend to go above and…

Collaborative Learning in the Dance Technique Class

ERIC Educational Resources Information Center

Raman, Tanja

2009-01-01

This research was designed to enhance dance technique learning by promoting critical thinking amongst students studying on a degree programme at the University of Wales Institute, Cardiff. Students were taught Cunningham-based dance technique using pair work together with the traditional demonstration/copying method. To evaluate the study,…

Enhancing learning in geosciences and water engineering via lab activities

NASA Astrophysics Data System (ADS)

Valyrakis, Manousos; Cheng, Ming

2016-04-01

This study focuses on the utilisation of lab based activities to enhance the learning experience of engineering students studying Water Engineering and Geosciences. In particular, the use of modern highly visual and tangible presentation techniques within an appropriate laboratory based space are used to introduce undergraduate students to advanced engineering concepts. A specific lab activity, namely "Flood-City", is presented as a case study to enhance the active engagement rate, improve the learning experience of the students and better achieve the intended learning objectives of the course within a broad context of the engineering and geosciences curriculum. Such activities, have been used over the last few years from the Water Engineering group @ Glasgow, with success for outreach purposes (e.g. Glasgow Science Festival and demos at the Glasgow Science Centre and Kelvingrove museum). The activity involves a specific setup of the demonstration flume in a sand-box configuration, with elements and activities designed so as to gamely the overall learning activity. Social media platforms can also be used effectively to the same goals, particularly in cases were the students already engage in these online media. To assess the effectiveness of this activity a purpose designed questionnaire is offered to the students. Specifically, the questionnaire covers several aspects that may affect student learning, performance and satisfaction, such as students' motivation, factors to effective learning (also assessed by follow-up quizzes), and methods of communication and assessment. The results, analysed to assess the effectiveness of the learning activity as the students perceive it, offer a promising potential for the use of such activities in outreach and learning.

The influence of curricular and extracurricular learning activities on students' choice of chiropractic technique

PubMed Central

Sikorski, David M.; KizhakkeVeettil, Anupama; Tobias, Gene S.

2016-01-01

Objective: Surveys for the National Board of Chiropractic Examiners indicate that diversified chiropractic technique is the most commonly used chiropractic manipulation method. The study objective was to investigate the influences of our diversified core technique curriculum, a technique survey course, and extracurricular technique activities on students' future practice technique preferences. Methods: We conducted an anonymous, voluntary survey of 1st, 2nd, and 3rd year chiropractic students at our institution. Surveys were pretested for face validity, and data were analyzed using descriptive and inferential statistics. Results: We had 164 students (78% response rate) participate in the survey. Diversified was the most preferred technique for future practice by students, and more than half who completed the chiropractic technique survey course reported changing their future practice technique choice as a result. The students surveyed agreed that the chiropractic technique curriculum and their experiences with chiropractic practitioners were the two greatest bases for their current practice technique preference, and that their participation in extracurricular technique clubs and seminars was less influential. Conclusions: Students appear to have the same practice technique preferences as practicing chiropractors. The chiropractic technique curriculum and the students' experience with chiropractic practitioners seem to have the greatest influence on their choice of chiropractic technique for future practice. Extracurricular activities, including technique clubs and seminars, although well attended, showed a lesser influence on students' practice technique preferences. PMID:26655282

The influence of curricular and extracurricular learning activities on students' choice of chiropractic technique.

PubMed

Sikorski, David M; KizhakkeVeettil, Anupama; Tobias, Gene S

2016-03-01

Surveys for the National Board of Chiropractic Examiners indicate that diversified chiropractic technique is the most commonly used chiropractic manipulation method. The study objective was to investigate the influences of our diversified core technique curriculum, a technique survey course, and extracurricular technique activities on students' future practice technique preferences. We conducted an anonymous, voluntary survey of 1st, 2nd, and 3rd year chiropractic students at our institution. Surveys were pretested for face validity, and data were analyzed using descriptive and inferential statistics. We had 164 students (78% response rate) participate in the survey. Diversified was the most preferred technique for future practice by students, and more than half who completed the chiropractic technique survey course reported changing their future practice technique choice as a result. The students surveyed agreed that the chiropractic technique curriculum and their experiences with chiropractic practitioners were the two greatest bases for their current practice technique preference, and that their participation in extracurricular technique clubs and seminars was less influential. Students appear to have the same practice technique preferences as practicing chiropractors. The chiropractic technique curriculum and the students' experience with chiropractic practitioners seem to have the greatest influence on their choice of chiropractic technique for future practice. Extracurricular activities, including technique clubs and seminars, although well attended, showed a lesser influence on students' practice technique preferences.

Improvements in Students' Understanding from Increased Implementation of Active Learning Strategies

NASA Astrophysics Data System (ADS)

Hayes-Gehrke, Melissa N.; Prather, E. E.; Rudolph, A. L.; Collaboration of Astronomy Teaching Scholars CATS

2011-01-01

Many instructors are hesitant to implement active learning strategies in their introductory astronomy classrooms because they are not sure which techniques they should use, how to implement those techniques, and question whether the investment in changing their course will really bring the advertised learning gains. We present an example illustrating how thoughtful and systematic implementation of active learning strategies into a traditionally taught Astro 101 class can translate into significant increases in students' understanding. We detail the journey of one instructor, over several years, as she changes the instruction and design of her course from one that focuses almost exclusively on lecture to a course that provides an integrated use of several active learning techniques such as Lecture-Tutorials and Think-Pair-Share questions. The students in the initial lecture-only course achieved a low normalized gain score of only 0.2 on the Light and Spectroscopy Concept Inventory (LSCI), while the students in the re-designed learner-centered course achieved a significantly better normalized gain of 0.43. This material is based upon work supported by the National Science Foundation under Grant No. 0715517, a CCLI Phase III Grant for the Collaboration of Astronomy Teaching Scholars (CATS), and Grant No. 0847170, a PAARE Grant for the Calfornia-Arizona Minority Partnership for Astronomy Research and Education (CAMPARE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Less is more: Sampling chemical space with active learning

NASA Astrophysics Data System (ADS)

Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas; Isayev, Olexandr; Roitberg, Adrian E.

2018-06-01

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials. It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble's prediction. QBC allows the presented AL algorithm to automatically sample regions of chemical space where the ML potential fails to accurately predict the potential energy. AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques. To provide validation of our AL approach, we develop the COmprehensive Machine-learning Potential (COMP6) benchmark (publicly available on GitHub) which contains a diverse set of organic molecules. Active learning-based ANI potentials outperform the original random sampled ANI-1 potential with only 10% of the data, while the final active learning-based model vastly outperforms ANI-1 on the COMP6 benchmark after training to only 25% of the data. Finally, we show that our proposed AL technique develops a universal ANI potential (ANI-1x) that provides accurate energy and force predictions on the entire COMP6 benchmark. This universal ML potential achieves a level of accuracy on par with the best ML potentials for single molecules or materials, while remaining applicable to the general class of organic molecules composed of the elements CHNO.

Active Learning Using Hint Information.

PubMed

Li, Chun-Liang; Ferng, Chun-Sung; Lin, Hsuan-Tien

2015-08-01

The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.

A Bridge to Active Learning: A Summer Bridge Program Helps Students Maximize Their Active-Learning Experiences and the Active-Learning Experiences of Others

PubMed Central

Cooper, Katelyn M.; Ashley, Michael; Brownell, Sara E.

2017-01-01

National calls to improve student academic success in college have sparked the development of bridge programs designed to help students transition from high school to college. We designed a 2-week Summer Bridge program that taught introductory biology content in an active-learning way. Through a set of exploratory interviews, we unexpectedly identified that Bridge students had developed sophisticated views of active learning, even though this was not an explicit goal of the program. We conducted an additional set of semistructured interviews that focused on active learning and compared the interviews of Bridge students with those from non-Bridge students who had been eligible for but did not participate in the program. We used the constant comparative method to identify themes from the interviews. We found that Bridge students perceived that, because they knew how to approach active learning and viewed it as important, they benefited more from active learning in introductory biology than non-Bridge students. Specifically, Bridge students seemed to be more aware of their own learning gains from participating in active learning. Compared with the majority of non-Bridge students, the majority of Bridge students described using a greater variety of strategies to maximize their experiences in active learning. Finally, in contrast to non-Bridge students, Bridge students indicated that they take an equitable approach to group work. These findings suggest that we may be able to prime students to maximize their own and other’s experiences in active learning. PMID:28232588

Development of active learning modules in pharmacology for small group teaching.

PubMed

Tripathi, Raakhi K; Sarkate, Pankaj V; Jalgaonkar, Sharmila V; Rege, Nirmala N

2015-01-01

Current teaching in pharmacology in undergraduate medical curriculum in India is primarily drug centered and stresses imparting factual knowledge rather than on pharmacotherapeutic skills. These skills would be better developed through active learning by the students. Hence modules that will encourage active learning were developed and compared with traditional methods within the Seth GS Medical College, Mumbai. After Institutional Review Board approval, 90 second year undergraduate medical students who consented were randomized into six sub-groups, each with 15 students. Pre-test was administered. The three sub-groups were taught a topic using active learning modules (active learning groups), which included problems on case scenarios, critical appraisal of prescriptions and drug identification. The remaining three sub-groups were taught the same topic in a conventional tutorial mode (tutorial learning groups). There was crossover for the second topic. Performance was assessed using post-test. Questionnaires with Likert-scaled items were used to assess feedback on teaching technique, student interaction and group dynamics. The active and tutorial learning groups differed significantly in their post-test scores (11.3 ± 1.9 and 15.9 ± 2.7, respectively, P < 0.05). In students' feedback, 69/90 students had perceived the active learning session as interactive (vs. 37/90 students in tutorial group) and enhanced their understanding vs. 56/90 in tutorial group), aroused intellectual curiosity (47/90 students of active learning group vs. 30/90 in tutorial group) and provoked self-learning (41/90 active learning group vs. 14/90 in tutorial group). Sixty-four students in the active learning group felt that questioning each other helped in understanding the topic, which was the experience of 25/90 students in tutorial group. Nevertheless, students (55/90) preferred tutorial mode of learning to help them score better in their examinations. In this study, students preferred

Active Learning Framework for Non-Intrusive Load Monitoring: Preprint

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

Jin, Xin

2016-05-16

Non-Intrusive Load Monitoring (NILM) is a set of techniques that estimate the electricity usage of individual appliances from power measurements taken at a limited number of locations in a building. One of the key challenges in NILM is having too much data without class labels yet being unable to label the data manually for cost or time constraints. This paper presents an active learning framework that helps existing NILM techniques to overcome this challenge. Active learning is an advanced machine learning method that interactively queries a user for the class label information. Unlike most existing NILM systems that heuristically requestmore » user inputs, the proposed method only needs minimally sufficient information from a user to build a compact and yet highly representative load signature library. Initial results indicate the proposed method can reduce the user inputs by up to 90% while still achieving similar disaggregation performance compared to a heuristic method. Thus, the proposed method can substantially reduce the burden on the user, improve the performance of a NILM system with limited user inputs, and overcome the key market barriers to the wide adoption of NILM technologies.« less

The application of machine learning techniques in the clinical drug therapy.

PubMed

Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

2018-05-25

The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Bidirectional Active Learning: A Two-Way Exploration Into Unlabeled and Labeled Data Set.

PubMed

Zhang, Xiao-Yu; Wang, Shupeng; Yun, Xiaochun

2015-12-01

In practical machine learning applications, human instruction is indispensable for model construction. To utilize the precious labeling effort effectively, active learning queries the user with selective sampling in an interactive way. Traditional active learning techniques merely focus on the unlabeled data set under a unidirectional exploration framework and suffer from model deterioration in the presence of noise. To address this problem, this paper proposes a novel bidirectional active learning algorithm that explores into both unlabeled and labeled data sets simultaneously in a two-way process. For the acquisition of new knowledge, forward learning queries the most informative instances from unlabeled data set. For the introspection of learned knowledge, backward learning detects the most suspiciously unreliable instances within the labeled data set. Under the two-way exploration framework, the generalization ability of the learning model can be greatly improved, which is demonstrated by the encouraging experimental results.

Predicting Solar Activity Using Machine-Learning Methods

NASA Astrophysics Data System (ADS)

Bobra, M.

2017-12-01

Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

Three visual techniques to enhance interprofessional learning.

PubMed

Parsell, G; Gibbs, T; Bligh, J

1998-07-01

Many changes in the delivery of healthcare in the UK have highlighted the need for healthcare professionals to learn to work together as teams for the benefit of patients. Whatever the profession or level, whether for postgraduate education and training, continuing professional development, or for undergraduates, learners should have an opportunity to learn about and with, other healthcare practitioners in a stimulating and exciting way. Learning to understand how people think, feel, and react, and the parts they play at work, both as professionals and individuals, can only be achieved through sensitive discussion and exchange of views. Teaching and learning methods must provide opportunities for this to happen. This paper describes three small-group teaching techniques which encourage a high level of learner collaboration and team-working. Learning content is focused on real-life health-care issues and strong visual images are used to stimulate lively discussion and debate. Each description includes the learning objectives of each exercise, basic equipment and resources, and learning outcomes.

A Severe Weather Laboratory Exercise for an Introductory Weather and Climate Class Using Active Learning Techniques

ERIC Educational Resources Information Center

Grundstein, Andrew; Durkee, Joshua; Frye, John; Andersen, Theresa; Lieberman, Jordan

2011-01-01

This paper describes a new severe weather laboratory exercise for an Introductory Weather and Climate class, appropriate for first and second year college students (including nonscience majors), that incorporates inquiry-based learning techniques. In the lab, students play the role of meteorologists making forecasts for severe weather. The…

The Effect of Active Learning Techniques on Class Teacher Candidates' Success Rates and Attitudes toward Their Museum Theory and Application Unit in Their Visual Arts Course

ERIC Educational Resources Information Center

Dilmac, Oguz

2016-01-01

The purpose of this study is to examine the effect that using active learning techniques during museum and gallery visits has on teacher candidates' academic success rates in and attitudes toward their Visual Arts Course. In this study, the importance and requirement of education to take place in museums and art galleries is emphasized. The…

Active Learning Methods

ERIC Educational Resources Information Center

Zayapragassarazan, Z.; Kumar, Santosh

2012-01-01

Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…

Imaging and machine learning techniques for diagnosis of Alzheimer's disease.

PubMed

Mirzaei, Golrokh; Adeli, Anahita; Adeli, Hojjat

2016-12-01

Alzheimer's disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.

Active-learning strategies in computer-assisted drug discovery.

PubMed

Reker, Daniel; Schneider, Gisbert

2015-04-01

High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the selection process by focusing on areas of chemical space that have the greatest chance of success while considering structural novelty. The core feature of these algorithms is their ability to adapt the structure-activity landscapes through feedback. Instead of full-deck screening, only focused subsets of compounds are tested, and the experimental readout is used to refine molecule selection for subsequent screening cycles. Once implemented, these techniques have the potential to reduce costs and save precious materials. Here, we provide a comprehensive overview of the various computational active-learning approaches and outline their potential for drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effects of Enhancement Techniques on L2 Incidental Vocabulary Learning

ERIC Educational Resources Information Center

Duan, Shiping

2018-01-01

Enhancement Techniques are conducive to incidental vocabulary learning. This study investigated the effects of two types of enhancement techniques-multiple-choice glosses (MC) and L1 single-gloss (SG) on L2 incidental learning of new words and retention of them. A total of 89 university learners of English as a Foreign Language (EFL) were asked to…

How Students Learn: Improving Teaching Techniques for Business Discipline Courses

ERIC Educational Resources Information Center

Cluskey, Bob; Elbeck, Matt; Hill, Kathy L.; Strupeck, Dave

2011-01-01

The focus of this paper is to familiarize business discipline faculty with cognitive psychology theories of how students learn together with teaching techniques to assist and improve student learning. Student learning can be defined as the outcome from the retrieval (free recall) of desired information. Student learning occurs in two processes.…

A Bridge to Active Learning: A Summer Bridge Program Helps Students Maximize Their Active-Learning Experiences and the Active-Learning Experiences of Others.

PubMed

Cooper, Katelyn M; Ashley, Michael; Brownell, Sara E

2017-01-01

National calls to improve student academic success in college have sparked the development of bridge programs designed to help students transition from high school to college. We designed a 2-week Summer Bridge program that taught introductory biology content in an active-learning way. Through a set of exploratory interviews, we unexpectedly identified that Bridge students had developed sophisticated views of active learning, even though this was not an explicit goal of the program. We conducted an additional set of semistructured interviews that focused on active learning and compared the interviews of Bridge students with those from non-Bridge students who had been eligible for but did not participate in the program. We used the constant comparative method to identify themes from the interviews. We found that Bridge students perceived that, because they knew how to approach active learning and viewed it as important, they benefited more from active learning in introductory biology than non-Bridge students. Specifically, Bridge students seemed to be more aware of their own learning gains from participating in active learning. Compared with the majority of non-Bridge students, the majority of Bridge students described using a greater variety of strategies to maximize their experiences in active learning. Finally, in contrast to non-Bridge students, Bridge students indicated that they take an equitable approach to group work. These findings suggest that we may be able to prime students to maximize their own and other's experiences in active learning. © 2017 K. M. Cooper et al. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Contemporary machine learning: techniques for practitioners in the physical sciences

NASA Astrophysics Data System (ADS)

Spears, Brian

2017-10-01

Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Exploring a Monetary Union among Nations through Active Learning

ERIC Educational Resources Information Center

Goma, Ophelia D.

2002-01-01

This article presents a classroom project that employs various techniques of active learning including role-playing, collaborative group work and writing. The project explores the recent creation of the European Monetary Union (EMU) with special emphasis on the introduction of the euro. The project assumes that the Americas have begun preliminary…

Machine Learning Techniques for Stellar Light Curve Classification

NASA Astrophysics Data System (ADS)

Hinners, Trisha A.; Tat, Kevin; Thorp, Rachel

2018-07-01

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time-series data. We preprocessed over 94 GB of Kepler light curves from the Mikulski Archive for Space Telescopes (MAST) to classify according to 10 distinct physical properties using both representation learning and feature engineering approaches. Studies using machine learning in the field have been primarily done on simulated data, making our study one of the first to use real light-curve data for machine learning approaches. We tuned our data using previous work with simulated data as a template and achieved mixed results between the two approaches. Representation learning using a long short-term memory recurrent neural network produced no successful predictions, but our work with feature engineering was successful for both classification and regression. In particular, we were able to achieve values for stellar density, stellar radius, and effective temperature with low error (∼2%–4%) and good accuracy (∼75%) for classifying the number of transits for a given star. The results show promise for improvement for both approaches upon using larger data sets with a larger minority class. This work has the potential to provide a foundation for future tools and techniques to aid in the analysis of astrophysical data.

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

Precision Learning Assessment: An Alternative to Traditional Assessment Techniques.

ERIC Educational Resources Information Center

Caltagirone, Paul J.; Glover, Christopher E.

1985-01-01

A continuous and curriculum-based assessment method, Precision Learning Assessment (PLA), which integrates precision teaching and norm-referenced techniques, was applied to a math computation curriculum for 214 third graders. The resulting districtwide learning curves defining average annual progress through the computation curriculum provided…

Practising What We Teach: Vocational Teachers Learn to Research through Applying Action Learning Techniques

ERIC Educational Resources Information Center

Lasky, Barbara; Tempone, Irene

2004-01-01

Action learning techniques are well suited to the teaching of organisation behaviour students because of their flexibility, inclusiveness, openness, and respect for individuals. They are no less useful as a tool for change for vocational teachers, learning, of necessity, to become researchers. Whereas traditional universities have always had a…

Epileptic seizure detection in EEG signal using machine learning techniques.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2018-03-01

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.

Active learning of cortical connectivity from two-photon imaging data

PubMed Central

Wang, Ye; Dunson, David; Sapiro, Guillermo; Ringach, Dario

2018-01-01

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this “active learning” method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model. PMID:29718955

Active Learning Using Arbitrary Binary Valued Queries

DTIC Science & Technology

1990-10-01

active learning in the sense that the learner has complete choice in the information received. Specifically, we allow the learner to ask arbitrary yes...no questions. We consider both active learning under a fixed distribution and distribution-free active learning . In the case of active learning , the...a concept class is actively learnable iff it is finite, so that active learning is in fact less powerful than the usual passive learning model. We

Active Learning with Irrelevant Examples

NASA Technical Reports Server (NTRS)

Mazzoni, Dominic; Wagstaff, Kiri L.; Burl, Michael

2006-01-01

Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there may exist unlabeled items that are irrelevant to the user's classification goals. Queries about these points slow down learning because they provide no information about the problem of interest. We have observed that when irrelevant items are present, active learning can perform worse than random selection, requiring more time (queries) to achieve the same level of accuracy. Therefore, we propose a novel approach, Relevance Bias, in which the active learner combines its default selection heuristic with the output of a simultaneously trained relevance classifier to favor items that are likely to be both informative and relevant. In our experiments on a real-world problem and two benchmark datasets, the Relevance Bias approach significantly improved the learning rate of three different active learning approaches.

Applying machine learning classification techniques to automate sky object cataloguing

NASA Astrophysics Data System (ADS)

Fayyad, Usama M.; Doyle, Richard J.; Weir, W. Nick; Djorgovski, Stanislav

1993-08-01

We describe the application of an Artificial Intelligence machine learning techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Mt. Palomar Northern Sky Survey is nearly completed. This survey provides comprehensive coverage of the northern celestial hemisphere in the form of photographic plates. The plates are being transformed into digitized images whose quality will probably not be surpassed in the next ten to twenty years. The images are expected to contain on the order of 107 galaxies and 108 stars. Astronomers wish to determine which of these sky objects belong to various classes of galaxies and stars. Unfortunately, the size of this data set precludes analysis in an exclusively manual fashion. Our approach is to develop a software system which integrates the functions of independently developed techniques for image processing and data classification. Digitized sky images are passed through image processing routines to identify sky objects and to extract a set of features for each object. These routines are used to help select a useful set of attributes for classifying sky objects. Then GID3 (Generalized ID3) and O-B Tree, two inductive learning techniques, learns classification decision trees from examples. These classifiers will then be applied to new data. These developmnent process is highly interactive, with astronomer input playing a vital role. Astronomers refine the feature set used to construct sky object descriptions, and evaluate the performance of the automated classification technique on new data. This paper gives an overview of the machine learning techniques with an emphasis on their general applicability, describes the details of our specific application, and reports the initial encouraging results. The results indicate that our machine learning approach is well-suited to the problem. The primary benefit of the approach is increased data reduction throughput. Another benefit is

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

NASA Astrophysics Data System (ADS)

Singh, Harpreet; Rana, Prashant Singh; Singh, Urvinder

2018-04-01

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug-drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

A preclustering-based ensemble learning technique for acute appendicitis diagnoses.

PubMed

Lee, Yen-Hsien; Hu, Paul Jen-Hwa; Cheng, Tsang-Hsiang; Huang, Te-Chia; Chuang, Wei-Yao

2013-06-01

Acute appendicitis is a common medical condition, whose effective, timely diagnosis can be difficult. A missed diagnosis not only puts the patient in danger but also requires additional resources for corrective treatments. An acute appendicitis diagnosis constitutes a classification problem, for which a further fundamental challenge pertains to the skewed outcome class distribution of instances in the training sample. A preclustering-based ensemble learning (PEL) technique aims to address the associated imbalanced sample learning problems and thereby support the timely, accurate diagnosis of acute appendicitis. The proposed PEL technique employs undersampling to reduce the number of majority-class instances in a training sample, uses preclustering to group similar majority-class instances into multiple groups, and selects from each group representative instances to create more balanced samples. The PEL technique thereby reduces potential information loss from random undersampling. It also takes advantage of ensemble learning to improve performance. We empirically evaluate this proposed technique with 574 clinical cases obtained from a comprehensive tertiary hospital in southern Taiwan, using several prevalent techniques and a salient scoring system as benchmarks. The comparative results show that PEL is more effective and less biased than any benchmarks. The proposed PEL technique seems more sensitive to identifying positive acute appendicitis than the commonly used Alvarado scoring system and exhibits higher specificity in identifying negative acute appendicitis. In addition, the sensitivity and specificity values of PEL appear higher than those of the investigated benchmarks that follow the resampling approach. Our analysis suggests PEL benefits from the more representative majority-class instances in the training sample. According to our overall evaluation results, PEL records the best overall performance, and its area under the curve measure reaches 0.619. The

Current Developments in Machine Learning Techniques in Biological Data Mining.

PubMed

Dumancas, Gerard G; Adrianto, Indra; Bello, Ghalib; Dozmorov, Mikhail

2017-01-01

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

Active Learning through Online Instruction

ERIC Educational Resources Information Center

Gulbahar, Yasemin; Kalelioglu, Filiz

2010-01-01

This article explores the use of proper instructional techniques in online discussions that lead to meaningful learning. The research study looks at the effective use of two instructional techniques within online environments, based on qualitative measures. "Brainstorming" and "Six Thinking Hats" were selected and implemented…

STEM learning activity among home-educating families

NASA Astrophysics Data System (ADS)

Bachman, Jennifer

2011-12-01

Science, technology, engineering, and mathematics (STEM) learning was studied among families in a group of home-educators in the Pacific Northwest. Ethnographic methods recorded learning activity (video, audio, fieldnotes, and artifacts) which was analyzed using a unique combination of Cultural-Historical Activity Theory (CHAT) and Mediated Action (MA), enabling analysis of activity at multiple levels. Findings indicate that STEM learning activity is family-led, guided by parents' values and goals for learning, and negotiated with children to account for learner interests and differences, and available resources. Families' STEM education practice is dynamic, evolves, and influenced by larger societal STEM learning activity. Parents actively seek support and resources for STEM learning within their home-school community, working individually and collectively to share their funds of knowledge. Home-schoolers also access a wide variety of free-choice learning resources: web-based materials, museums, libraries, and community education opportunities (e.g. afterschool, weekend and summer programs, science clubs and classes, etc.). A lesson-heuristic, grounded in Mediated Action, represents and analyzes home STEM learning activity in terms of tensions between parental goals, roles, and lesson structure. One tension observed was between 'academic' goals or school-like activity and 'lifelong' goals or everyday learning activity. Theoretical and experiential learning was found in both activity, though parents with academic goals tended to focus more on theoretical learning and those with lifelong learning goals tended to be more experiential. Examples of the National Research Council's science learning strands (NRC, 2009) were observed in the STEM practices of all these families. Findings contribute to the small but growing body of empirical CHAT research in science education, specifically to the empirical base of family STEM learning practices at home. It also fills a

Exploring Machine Learning Techniques Using Patient Interactions in Online Health Forums to Classify Drug Safety

ERIC Educational Resources Information Center

Chee, Brant Wah Kwong

2011-01-01

This dissertation explores the use of personal health messages collected from online message forums to predict drug safety using natural language processing and machine learning techniques. Drug safety is defined as any drug with an active safety alert from the US Food and Drug Administration (FDA). It is believed that this is the first…

Incorporating active learning in psychiatry education.

PubMed

Kumar, Sonia; McLean, Loyola; Nash, Louise; Trigwell, Keith

2017-06-01

We aim to summarise the active learning literature in higher education and consider its relevance for postgraduate psychiatry trainees, to inform the development of a new Formal Education Course (FEC): the Master of Medicine (Psychiatry) at the University of Sydney. We undertook a literature search on 'active learning', 'flipped classroom', 'problem-based learning' and 'psychiatry education'. The effectiveness of active learning pedagogy in higher education is well supported by evidence; however, there have been few psychiatry-specific studies. A new 'flipped classroom' format was developed for the Master of Medicine (Psychiatry). Postgraduate psychiatry training is an active learning environment; the pedagogical approach to FECs requires further evaluation.

A Comparison of Professional-Level Faculty and Student Perceptions of Active Learning: Its Current Use, Effectiveness, and Barriers

ERIC Educational Resources Information Center

Miller, Cynthia J.; Metz, Michael J.

2014-01-01

Active learning is an instructional method in which students become engaged participants in the classroom through the use of in-class written exercises, games, problem sets, audience-response systems, debates, class discussions, etc. Despite evidence supporting the effectiveness of active learning strategies, minimal adoption of the technique has…

Instructional Utility and Learning Efficacy of Common Active Learning Strategies

ERIC Educational Resources Information Center

McConell, David A.; Chapman, LeeAnna; Czaijka, C. Douglas; Jones, Jason P.; Ryker, Katherine D.; Wiggen, Jennifer

2017-01-01

The adoption of active learning instructional practices in college science, technology, engineering, and mathematics (STEM) courses has been shown to result in improvements in student learning, contribute to increased retention rates, and reduce the achievement gap among different student populations. Descriptions of active learning strategies…

The Effect of Student Learning Styles, Race and Gender on Learning Outcomes: The Case of Public Goods

ERIC Educational Resources Information Center

Devaraj, Nirupama; Raman, Jaishankar

2014-01-01

We investigate the impact of active learning techniques, specifically experiment based learning, in a Principles of Economics class. Our case study demonstrates that when using pedagogical techniques intended to facilitate active learning, teachers should be intentional about incorporating components of learning that appeal to students with…

Effective, Active Learning Strategies for the Oceanography Classroom

NASA Astrophysics Data System (ADS)

Dmochowski, J. E.; Marinov, I.

2014-12-01

A decline in enrollment in STEM fields at the university level has prompted extensive research on alternative ways of teaching and learning science. Inquiry-based learning as well as the related "flipped" or "active" lectures, and similar teaching methods and philosophies have been proposed as more effective ways to disseminate knowledge in science classes than the traditional lecture. We will provide a synopsis of our experiences in implementing some of these practices into our Introductory Oceanography, Global Climate Change, and Ocean Atmosphere Dynamics undergraduate courses at the University of Pennsylvania, with both smaller and larger enrollments. By implementing tools such as at-home modules; computer labs; incorporation of current research; pre- and post-lecture quizzes; reflective, qualitative writing assignments; peer review; and a variety of in-class learning strategies, we aim to increase the science literacy of the student population and help students gain a more comprehensive knowledge of the topic, enhance their critical thinking skills, and correct misconceptions. While implementing these teaching techniques with college students is not without complications, we argue that a blended class that flexibly and creatively accounts for class size and science level improves the learning experience and the acquired knowledge. We will present examples of student assignments and activities as well as describe the lessons we have learned, and propose ideas for moving forward to best utilize innovative teaching tools in order to increase science literacy in oceanography and other climate-related courses.

Acquisition of an instrumental activity of daily living in patients with Korsakoff's syndrome: a comparison of trial and error and errorless learning.

PubMed

Oudman, Erik; Nijboer, Tanja C W; Postma, Albert; Wijnia, Jan W; Kerklaan, Sandra; Lindsen, Karen; Van der Stigchel, Stefan

2013-01-01

Patients with Korsakoff's syndrome show devastating amnesia and executive deficits. Consequently, the ability to perform instrumental activities such as making coffee is frequently diminished. It is currently unknown whether patients with Korsakoff's syndrome are able to (re)learn instrumental activities. A good candidate for an effective teaching technique in Korsakoff's syndrome is errorless learning as it is based on intact implicit memory functioning. Therefore, the aim of the current study was two-fold: to investigate whether patients with Korsakoff's syndrome are able to (re)learn instrumental activities, and to compare the effectiveness of errorless learning with trial and error learning in the acquisition and maintenance of an instrumental activity, namely using a washing machine to do the laundry. Whereas initial learning performance in the errorless learning condition was superior, both intervention techniques resulted in similar improvement over eight learning sessions. Moreover, performance in a different spatial layout showed a comparable improvement. Notably, in follow-up sessions starting after four weeks without practice, performance was still elevated in the errorless learning condition, but not in the trial and error condition. The current study demonstrates that (re)learning and maintenance of an instrumental activity is possible in patients with Korsakoff's syndrome.

Encouraging junior community netball players to learn correct safe landing technique.

PubMed

White, Peta E; Ullah, Shahid; Donaldson, Alex; Otago, Leonie; Saunders, Natalie; Romiti, Maria; Finch, Caroline F

2012-01-01

Behavioural factors and beliefs are important determinants of the adoption of sports injury interventions. This study aimed to understand behavioural factors associated with junior community netball players' intentions to learn correct landing technique during coach-led training sessions, proposed as a means of reducing their risk of lower limb injury. Cross-sectional survey. 287 female players from 58 junior netball teams in the 2007/2008-summer competition completed a 13-item questionnaire developed from the Theory of Planned Behaviour (TPB). This assessed players' attitudes (four items), subjective norms (four), perceived behavioural control (four) and intentions (one) around the safety behaviour of learning correct landing technique at netball training. All items were rated on a seven-point bipolar scale. Cluster-adjusted logistic regression was used to assess which TPB constructs were most associated with strong intentions. Players had positive intentions and attitudes towards learning safe landing technique and perceived positive social pressure from significant others. They also perceived themselves to have considerable control over engaging (or not) in this behaviour. Players' attitudes (p<0.001) and subjective norms (p<0.001), but not perceived behavioural control (p=0.49), were associated with strong intentions to learn correct landing technique at training. Injury prevention implementation strategies aimed at maximising junior players' participation in correct landing training programs should emphasise the benefits of learning correct landing technique (i.e. change attitudes) and involve significant others and role models whom junior players admire (i.e. capitalise on social norms) in the promotion of such programs. Copyright © 2011 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Active Learning for Directed Exploration of Complex Systems

NASA Technical Reports Server (NTRS)

Burl, Michael C.; Wang, Esther

2009-01-01

Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.

The training and learning process of transseptal puncture using a modified technique.

PubMed

Yao, Yan; Ding, Ligang; Chen, Wensheng; Guo, Jun; Bao, Jingru; Shi, Rui; Huang, Wen; Zhang, Shu; Wong, Tom

2013-12-01

As the transseptal (TS) puncture has become an integral part of many types of cardiac interventional procedures, its technique that was initial reported for measurement of left atrial pressure in 1950s, continue to evolve. Our laboratory adopted a modified technique which uses only coronary sinus catheter as the landmark to accomplishing TS punctures under fluoroscopy. The aim of this study is prospectively to evaluate the training and learning process for TS puncture guided by this modified technique. Guided by the training protocol, TS puncture was performed in 120 consecutive patients by three trainees without previous personal experience in TS catheterization and one experienced trainer as a controller. We analysed the following parameters: one puncture success rate, total procedure time, fluoroscopic time, and radiation dose. The learning curve was analysed using curve-fitting methodology. The first attempt at TS crossing was successful in 74 (82%), a second attempt was successful in 11 (12%), and 5 patients failed to puncture the interatrial septal finally. The average starting process time was 4.1 ± 0.8 min, and the estimated mean learning plateau was 1.2 ± 0.2 min. The estimated mean learning rate for process time was 25 ± 3 cases. Important aspects of learning curve can be estimated by fitting inverse curves for TS puncture. The study demonstrated that this technique was a simple, safe, economic, and effective approach for learning of TS puncture. Base on the statistical analysis, approximately 29 TS punctures will be needed for trainee to pass the steepest area of learning curve.

Training hydrologists to be ecohydrologists: A ';how-you-can-do-it' example leveraging an active learning environment

NASA Astrophysics Data System (ADS)

Lyon, S. W.; Walter, M. T.; Jantze, E. J.; Archibald, J. A.

2013-12-01

Structuring an education strategy capable of addressing the various spheres of ecohydrology is difficult due to the inter-disciplinary and cross-disciplinary nature of this emergent field. Clearly, there is a need for such strategies to accommodate more progressive educational concepts while highlighting a skills-based education. To demonstrate a possible way to develop courses that include such concepts, we offer a case-study or a ';how-you-can-do-it' example from an ecohydrology course recently co-taught by teachers from Stockholm University and Cornell University at Stockholm University's Navarino Environmental Observatory (NEO) in Costa Navarino, Greece. This course focused on introducing hydrology Master's students to some of the central concepts of ecohydrology while at the same time supplying process-based understanding relevant for characterizing evapotranspiration. As such, the main goal of the course was to explore central theories in ecohydrology and their connection to plant-water interactions and the water cycle in a semiarid environment. In addition to presenting this roadmap for ecohydrology course development, we explore the utility and effectiveness of adopting active teaching and learning strategies drawing from the suite of learn-by-doing, hands-on, and inquiry-based techniques in such a course. We test a gradient of ';activeness' across a sequence of three teaching and learning activities. Our results indicate that there was a clear advantage for utilizing active learning techniques in place of traditional lecture-based styles. In addition, there was a preference among the student towards the more ';active' techniques. This demonstrates the added value of incorporating even the simplest active learning approaches in our ecohydrology (or general) teaching.

Training hydrologists to be ecohydrologists: A 'how-you-can-do-it' example leveraging an active learning environment

NASA Astrophysics Data System (ADS)

Lyon, Steve W.; Walter, M. Todd; Jantze, Elin J.; Archibald, Josephine A.

2015-04-01

Structuring an education strategy capable of addressing the various spheres of ecohydrology is difficult due to the inter-disciplinary and cross-disciplinary nature of this emergent field. Clearly, there is a need for such strategies to accommodate more progressive educational concepts while highlighting a skills-based education. To demonstrate a possible way to develop courses that include such concepts, we offer a case-study or a 'how-you-can-do-it' example from an ecohydrology course recently co-taught by teachers from Stockholm University and Cornell University at Stockholm University's Navarino Environmental Observatory (NEO) in Costa Navarino, Greece. This course focused on introducing hydrology Master's students to some of the central concepts of ecohydrology while at the same time supplying process-based understanding relevant for characterizing evapotranspiration. As such, the main goal of the course was to explore central theories in ecohydrology and their connection to plant-water interactions and the water cycle in a semiarid environment. In addition to presenting this roadmap for ecohydrology course development, we explore the utility and effectiveness of adopting active teaching and learning strategies drawing from the suite of learn-by-doing, hands-on, and inquiry-based techniques in such a course. We test a gradient of 'activeness' across a sequence of three teaching and learning activities. Our results indicate that there was a clear advantage for utilizing active learning techniques in place of traditional lecture-based styles. In addition, there was a preference among the student towards the more 'active' techniques. This demonstrates the added value of incorporating even the simplest active learning approaches in our ecohydrology (or general) teaching.

Teachers' Everyday Professional Development: Mapping Informal Learning Activities, Antecedents, and Learning Outcomes

ERIC Educational Resources Information Center

Kyndt, Eva; Gijbels, David; Grosemans, Ilke; Donche, Vincent

2016-01-01

Although a lot is known about teacher development by means of formal learning activities, research on teachers' everyday learning is limited. In the current systematic review, we analyzed 74 studies focusing on teachers' informal learning to identify teachers' learning activities, antecedents for informal learning, and learning outcomes. In…

Connecting Family Learning and Active Citizenship

ERIC Educational Resources Information Center

Flanagan, Mary

2009-01-01

In Ireland family learning and active citizenship has not been linked together until 2006. It was while the Clare Family Learning Project was involved in a family learning EU learning network project, that a suggestion to create a new partnership project linking both areas was made and FACE IT! was born (Families and Active Citizenship…

Create a good learning environment and motivate active learning enthusiasm

NASA Astrophysics Data System (ADS)

Bi, Weihong; Fu, Guangwei; Fu, Xinghu; Zhang, Baojun; Liu, Qiang; Jin, Wa

2017-08-01

In view of the current poor learning initiative of undergraduates, the idea of creating a good learning environment and motivating active learning enthusiasm is proposed. In practice, the professional tutor is allocated and professional introduction course is opened for college freshman. It can promote communication between the professional teachers and students as early as possible, and guide students to know and devote the professional knowledge by the preconceived form. Practice results show that these solutions can improve the students interest in learning initiative, so that the active learning and self-learning has become a habit in the classroom.

Analyzing Activity Behavior and Movement in a Naturalistic Environment using Smart Home Techniques

PubMed Central

Cook, Diane J.; Schmitter-Edgecombe, Maureen; Dawadi, Prafulla

2015-01-01

One of the many services that intelligent systems can provide is the ability to analyze the impact of different medical conditions on daily behavior. In this study we use smart home and wearable sensors to collect data while (n=84) older adults perform complex activities of daily living. We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these differences can be automatically recognized. Our machine learning classifiers reach an accuracy of 0.97 with an AUC value of 0.97 in distinguishing these groups. Our permutation-based testing confirms that the sensor-based differences between these groups are statistically significant. PMID:26259225

Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques.

PubMed

Cook, Diane J; Schmitter-Edgecombe, Maureen; Dawadi, Prafulla

2015-11-01

One of the many services that intelligent systems can provide is the ability to analyze the impact of different medical conditions on daily behavior. In this study, we use smart home and wearable sensors to collect data, while ( n = 84) older adults perform complex activities of daily living. We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these differences can be automatically recognized. Our machine learning classifiers reach an accuracy of 0.97 with an area under the ROC curve value of 0.97 in distinguishing these groups. Our permutation-based testing confirms that the sensor-based differences between these groups are statistically significant.

Involving postgraduate's students in undergraduate small group teaching promotes active learning in both

PubMed Central

Kalra, Ruchi; Modi, Jyoti Nath; Vyas, Rashmi

2015-01-01

Background: Lecture is a common traditional method for teaching, but it may not stimulate higher order thinking and students may also be hesitant to express and interact. The postgraduate (PG) students are less involved with undergraduate (UG) teaching. Team based small group active learning method can contribute to better learning experience. Aim: To-promote active learning skills among the UG students using small group teaching methods involving PG students as facilitators to impart hands-on supervised training in teaching and managerial skills. Methodology: After Institutional approval under faculty supervision 92 UGs and 8 PGs participated in 6 small group sessions utilizing the jigsaw technique. Feedback was collected from both. Observations: Undergraduate Feedback (Percentage of Students Agreed): Learning in small groups was a good experience as it helped in better understanding of the subject (72%), students explored multiple reading resources (79%), they were actively involved in self-learning (88%), students reported initial apprehension of performance (71%), identified their learning gaps (86%), team enhanced their learning process (71%), informal learning in place of lecture was a welcome change (86%), it improved their communication skills (82%), small group learning can be useful for future self-learning (75%). Postgraduate Feedback: Majority performed facilitation for first time, perceived their performance as good (75%), it was helpful in self-learning (100%), felt confident of managing students in small groups (100%), as facilitator they improved their teaching skills, found it more useful and better identified own learning gaps (87.5%). Conclusions: Learning in small groups adopting team based approach involving both UGs and PGs promoted active learning in both and enhanced the teaching skills of the PGs. PMID:26380201

Active Self-Paced Learning for Cost-Effective and Progressive Face Identification.

PubMed

Lin, Liang; Wang, Keze; Meng, Deyu; Zuo, Wangmeng; Zhang, Lei

2018-01-01

This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the "instructor-student-collaborative" learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://hcp.sysu.edu.cn/projects/aspl/.

"Bringing Life to Learning": A Study of Active Learning in Hospitality Education

ERIC Educational Resources Information Center

Chau, Salott; Cheung, Catherine

2017-01-01

Active learning connects students to the real life situations they will encounter in their future jobs. In hospitality education, active learning implements various lively, fun activities to introduce practical scenarios students may experience in their hospitality careers. This study identifies 18 essential active-learning items of hospitality…

Learning Activity Package, Physical Science. LAP Numbers 1, 2, 3, and 4.

ERIC Educational Resources Information Center

Williams, G. J.

These four units of the Learning Activity Packages (LAPs) for individualized instruction in physical science cover measuring techniques, operations of instruments, metric system heat, matter, energy, elements, atomic numbers, isotopes, molecules, mixtures, compounds, physical and chemical properties, liquids, solids, and gases. Each unit contains…

Active Learning to Understand Infectious Disease Models and Improve Policy Making

PubMed Central

Vladislavleva, Ekaterina; Broeckhove, Jan; Beutels, Philippe; Hens, Niel

2014-01-01

Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding. We present an active learning approach based on machine learning techniques as iterative surrogate modeling and model-guided experimentation to systematically analyze both common and edge manifestations of complex model runs. Symbolic regression is used for nonlinear response surface modeling with automatic feature selection. First, we illustrate our approach using an individual-based model for influenza vaccination. After optimizing the parameter space, we observe an inverse relationship between vaccination coverage and cumulative attack rate reinforced by herd immunity. Second, we demonstrate the use of surrogate modeling techniques on input-response data from a deterministic dynamic model, which was designed to explore the cost-effectiveness of varicella-zoster virus vaccination. We use symbolic regression to handle high dimensionality and correlated inputs and to identify the most influential variables. Provided insight is used to focus research, reduce dimensionality and decrease decision uncertainty. We conclude that active learning is needed to fully understand complex systems behavior. Surrogate models can be readily explored at no computational expense, and can also be used as emulator to improve rapid policy making in various settings. PMID:24743387

Active learning to understand infectious disease models and improve policy making.

PubMed

Willem, Lander; Stijven, Sean; Vladislavleva, Ekaterina; Broeckhove, Jan; Beutels, Philippe; Hens, Niel

2014-04-01

Modeling plays a major role in policy making, especially for infectious disease interventions but such models can be complex and computationally intensive. A more systematic exploration is needed to gain a thorough systems understanding. We present an active learning approach based on machine learning techniques as iterative surrogate modeling and model-guided experimentation to systematically analyze both common and edge manifestations of complex model runs. Symbolic regression is used for nonlinear response surface modeling with automatic feature selection. First, we illustrate our approach using an individual-based model for influenza vaccination. After optimizing the parameter space, we observe an inverse relationship between vaccination coverage and cumulative attack rate reinforced by herd immunity. Second, we demonstrate the use of surrogate modeling techniques on input-response data from a deterministic dynamic model, which was designed to explore the cost-effectiveness of varicella-zoster virus vaccination. We use symbolic regression to handle high dimensionality and correlated inputs and to identify the most influential variables. Provided insight is used to focus research, reduce dimensionality and decrease decision uncertainty. We conclude that active learning is needed to fully understand complex systems behavior. Surrogate models can be readily explored at no computational expense, and can also be used as emulator to improve rapid policy making in various settings.

Teaching massage to nursing students of geriatrics through active learning.

PubMed

Adler, Patricia A

2009-03-01

The use of massage in nursing practice has declined through the years in favor of high-tech interventions. This article describes a project using active learning to teach nursing students massage with dementia residents in assisted living. Students participated in a workshop to practice basic relaxation massage techniques with the guidance of their clinical instructor and then provided massages to resident volunteers. Afterward, students discussed their experience and completed a resident assessment form. The students requested more such activities, and the residents and facility management invited the students to return for another session. The instructor observed growth in the students' assessment skills and in their confidence. Use of massage to teach nursing students how to care for and relate to older adults with cognitive impairment is recommended. Further research is needed on the use of massage as an active learning method for nursing students in long-term care.

Identifying Key Features of Effective Active Learning: The Effects of Writing and Peer Discussion

PubMed Central

Pangle, Wiline M.; Wyatt, Kevin H.; Powell, Karli N.; Sherwood, Rachel E.

2014-01-01

We investigated some of the key features of effective active learning by comparing the outcomes of three different methods of implementing active-learning exercises in a majors introductory biology course. Students completed activities in one of three treatments: discussion, writing, and discussion + writing. Treatments were rotated weekly between three sections taught by three different instructors in a full factorial design. The data set was analyzed by generalized linear mixed-effect models with three independent variables: student aptitude, treatment, and instructor, and three dependent (assessment) variables: change in score on pre- and postactivity clicker questions, and coding scores on in-class writing and exam essays. All independent variables had significant effects on student performance for at least one of the dependent variables. Students with higher aptitude scored higher on all assessments. Student scores were higher on exam essay questions when the activity was implemented with a writing component compared with peer discussion only. There was a significant effect of instructor, with instructors showing different degrees of effectiveness with active-learning techniques. We suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning. PMID:25185230

Application of machine learning techniques to lepton energy reconstruction in water Cherenkov detectors

NASA Astrophysics Data System (ADS)

Drakopoulou, E.; Cowan, G. A.; Needham, M. D.; Playfer, S.; Taani, M.

2018-04-01

The application of machine learning techniques to the reconstruction of lepton energies in water Cherenkov detectors is discussed and illustrated for TITUS, a proposed intermediate detector for the Hyper-Kamiokande experiment. It is found that applying these techniques leads to an improvement of more than 50% in the energy resolution for all lepton energies compared to an approach based upon lookup tables. Machine learning techniques can be easily applied to different detector configurations and the results are comparable to likelihood-function based techniques that are currently used.

Learning from truth: youth participation in field marketing techniques to counter tobacco advertising.

PubMed

Eisenberg, Merrill; Ringwalt, Chris; Driscoll, David; Vallee, Manuel; Gullette, Gregory

2004-01-01

In 2000, the American Legacy Foundation (Legacy) launched truth, a national, multi-medium tobacco control social marketing campaign targeting youth age 12-17. This paper provides a brief description of one aspect of that campaign, the truth tour, and compares and contrasts the truth tour with commercial field marketing approaches used by the tobacco industry. The methods used for the tour's process evaluation are also described, and two important lessons learned about using field marketing techniques and using youth to implement field marketing techniques in social marketing campaigns are discussed. Social marketing campaigns that target youth may want to launch field marketing activities. The truth tour experience can inform the development of those efforts.

Learning of serial digits leads to frontal activation in functional MR imaging.

PubMed

Karakaş, Hakki Muammer; Karakaş, Sirel

2006-03-01

Clinical studies have shown that performance on the serial digit learning test (SDLT) is dependent upon the mesial temporal lobes, which are responsible for learning and its consolidation. However, an effective SDLT performance is also dependent upon sequencing, temporal ordering, and the utilization of mnemonic strategies. All of these processes are among the functions of the frontal lobes; in spite of this, the relationship between SDLT performance and the frontal lobes has not been demonstrated with previously used mapping techniques. The aim of this study was to investigate the areas of the brain that are activated by SDLT performance. Ten healthy, right handed volunteers (mean age, 20.1 years; SD: 3.3) who had 12 years of education were studied with a 1.0 T MR imaging scanner. BOLD (blood oxygen level dependent) contrast and a modified SDLT were used. Activated loci were automatically mapped using a proportional grid. In learning, the most consistent activation was observed in B-a-7 of the right (80%) and the left hemispheres (50%). In recall, the most consistent activation was observed in B-a-7 of the right hemisphere (60%). Activations were observed in 2.5+/-0.97 Talairach volumes in learning, whereas they encompassed 1.7+/-0.95 volumes in recall. The difference between both phases (learning and recall) regarding total activated volume was significant (p < 0.05). The prefrontal activation during SDLT performance was not related to learning or to recall, but to a function that is common to both of these cognitive processes. A candidate for this common factor may be the executive functions, which also include serial position processing and temporal ordering.

Active cleaning technique device

NASA Technical Reports Server (NTRS)

Shannon, R. L.; Gillette, R. B.

1973-01-01

The objective of this program was to develop a laboratory demonstration model of an active cleaning technique (ACT) device. The principle of this device is based primarily on the technique for removing contaminants from optical surfaces. This active cleaning technique involves exposing contaminated surfaces to a plasma containing atomic oxygen or combinations of other reactive gases. The ACT device laboratory demonstration model incorporates, in addition to plasma cleaning, the means to operate the device as an ion source for sputtering experiments. The overall ACT device includes a plasma generation tube, an ion accelerator, a gas supply system, a RF power supply and a high voltage dc power supply.

E-Learning System Using Segmentation-Based MR Technique for Learning Circuit Construction

ERIC Educational Resources Information Center

Takemura, Atsushi

2016-01-01

This paper proposes a novel e-Learning system using the mixed reality (MR) technique for technical experiments involving the construction of electronic circuits. The proposed system comprises experimenters' mobile computers and a remote analysis system. When constructing circuits, each learner uses a mobile computer to transmit image data from the…

Drama Techniques in Language Learning.

ERIC Educational Resources Information Center

Maley, Alan; Duff, Alan

The drama activities in this teaching guide are designed to develop second language learning skills by constructing situations that require the student to concentrate on the meaning and emotional content of language rather than on its structure. In an attempt to involve the whole personality of the learner in the acquisition of language, the…

Developing Interactive E-Learning Activities

ERIC Educational Resources Information Center

Watkins, Ryan

2005-01-01

Although e-learning can offer interactive and engaging learning experiences, the creative ideas that are necessary to create such environments are not always easy to come up with when designing, developing, or teaching e-learning courses. E-learning activities use online technologies such as chat rooms, discussion boards, or email to facilitate…

An Innovative Teaching Method To Promote Active Learning: Team-Based Learning

NASA Astrophysics Data System (ADS)

Balasubramanian, R.

2007-12-01

Traditional teaching practice based on the textbook-whiteboard- lecture-homework-test paradigm is not very effective in helping students with diverse academic backgrounds achieve higher-order critical thinking skills such as analysis, synthesis, and evaluation. Consequently, there is a critical need for developing a new pedagogical approach to create a collaborative and interactive learning environment in which students with complementary academic backgrounds and learning skills can work together to enhance their learning outcomes. In this presentation, I will discuss an innovative teaching method ('Team-Based Learning (TBL)") which I recently developed at National University of Singapore to promote active learning among students in the environmental engineering program with learning abilities. I implemented this new educational activity in a graduate course. Student feedback indicates that this pedagogical approach is appealing to most students, and promotes active & interactive learning in class. Data will be presented to show that the innovative teaching method has contributed to improved student learning and achievement.

Computer-aided auscultation learning system for nursing technique instruction.

PubMed

Hou, Chun-Ju; Chen, Yen-Ting; Hu, Ling-Chen; Chuang, Chih-Chieh; Chiu, Yu-Hsien; Tsai, Ming-Shih

2008-01-01

Pulmonary auscultation is a physical assessment skill learned by nursing students for examining the respiratory system. Generally, a sound simulator equipped mannequin is used to group teach auscultation techniques via classroom demonstration. However, nursing students cannot readily duplicate this learning environment for self-study. The advancement of electronic and digital signal processing technologies facilitates simulating this learning environment. This study aims to develop a computer-aided auscultation learning system for assisting teachers and nursing students in auscultation teaching and learning. This system provides teachers with signal recording and processing of lung sounds and immediate playback of lung sounds for students. A graphical user interface allows teachers to control the measuring device, draw lung sound waveforms, highlight lung sound segments of interest, and include descriptive text. Effects on learning lung sound auscultation were evaluated for verifying the feasibility of the system. Fifteen nursing students voluntarily participated in the repeated experiment. The results of a paired t test showed that auscultative abilities of the students were significantly improved by using the computer-aided auscultation learning system.

Student Perceptions of Value Added in an Active Learning Experience: Producing, Reviewing and Evaluating a Sales Team Video Presentation

ERIC Educational Resources Information Center

Corbett, James J.; Kezim, Boualem; Stewart, James

2010-01-01

This study investigates the effectiveness of a video team-based activity as a learning experience in a sales management course. Students perceived this learning activity approach as a beneficial and effective instructional technique. The benefits of making a video in a marketing course reinforce the understanding and the use of the sales process…

Debate preparation/participation: an active, effective learning tool.

PubMed

Koklanaris, Nikki; MacKenzie, Andrew P; Fino, M Elizabeth; Arslan, Alan A; Seubert, David E

2008-01-01

Passive educational techniques (such as lectures) are thought to be less productive than active learning. We examined whether preparing for and participating in a debate would be an effective, active way to learn about a controversial topic. We compared quiz performance in residents who attended a lecture to residents who prepared for/participated in a debate. Twelve residents each participated in one lecture session and one debate session. Learning was evaluated via a quiz. Quizzes were given twice: before the debate/lecture and 1 week after the debate/lecture. Quiz scores were compared using repeated measures analysis of variance, with a p value of < .05 considered significant. A survey evaluating the usefulness of debating was given to all participants. There was a statistically significant difference in the pretest mean quiz score between the debate and lecture groups: 78.3% and 52.5%, respectively (p = .02). Similarly, on posttest quizzes, the average debater scored 85.8%, versus 61.7% for the lecture group (p = .003). Although no one in the debate group scored lower on a follow-up quiz, 3 residents in the lecture group did worse on follow-up. When learning about a controversial topic, residents who prepared for/participated in a debate achieved higher quiz scores and were better at retaining information than those who attended a lecture. When faced with teaching a controversial topic, organizing a debate may be more effective than giving a lecture.

Student Perceptions of Active Learning

ERIC Educational Resources Information Center

Lumpkin, Angela; Achen, Rebecca M.; Dodd, Regan K.

2015-01-01

A paradigm shift from lecture-based courses to interactive classes punctuated with engaging, student-centered learning activities has begun to characterize the work of some teachers in higher education. Convinced through the literature of the values of using active learning strategies, we assessed through an action research project in five college…

Active-Learning versus Teacher-Centered Instruction for Learning Acids and Bases

ERIC Educational Resources Information Center

Sesen, Burcin Acar; Tarhan, Leman

2011-01-01

Background and purpose: Active-learning as a student-centered learning process has begun to take more interest in constructing scientific knowledge. For this reason, this study aimed to investigate the effectiveness of active-learning implementation on high-school students' understanding of "acids and bases". Sample: The sample of this…

Current Situation and Analysis of Geography Teachers' Active Learning Knowledge and Usage in Turkey

ERIC Educational Resources Information Center

Tuna, Fikret

2012-01-01

In parallel to the developments in the approach to education, the secondary education geography curriculum in Turkey was renewed in 2005. This new programme encourages the use of active learning methods and techniques in the classroom by adopting the idea that students should construct and interpret knowledge by actively participating in the…

Using Active Learning to Identify Health Information Technology Related Patient Safety Events.

PubMed

Fong, Allan; Howe, Jessica L; Adams, Katharine T; Ratwani, Raj M

2017-01-18

The widespread adoption of health information technology (HIT) has led to new patient safety hazards that are often difficult to identify. Patient safety event reports, which are self-reported descriptions of safety hazards, provide one view of potential HIT-related safety events. However, identifying HIT-related reports can be challenging as they are often categorized under other more predominate clinical categories. This challenge of identifying HIT-related reports is exacerbated by the increasing number and complexity of reports which pose challenges to human annotators that must manually review reports. In this paper, we apply active learning techniques to support classification of patient safety event reports as HIT-related. We evaluated different strategies and demonstrated a 30% increase in average precision of a confirmatory sampling strategy over a baseline no active learning approach after 10 learning iterations.

Using active learning strategies to investigate student learning and attitudes in a large enrollment, introductory geology course

NASA Astrophysics Data System (ADS)

Berry, Stacy Jane

There has been an increased emphasis for college instruction to incorporate more active and collaborative involvement of students in the learning process. These views have been asserted by The Association of American Colleges (AAC), the National Science Foundation (NSF), and The National Research Counsel (NRC), which are advocating for the modification of traditional instructional techniques to allow students the opportunity to be more cooperative (Task Group on General Education, 1988). This has guided educators and facilitators into shifting teaching paradigms from a teacher centered to a more student-centered curriculum. The present study investigated achievement outcomes and attitudes of learners in a large enrollment (n ~ 200), introductory geology course using a student centered learning cycle format of instruction versus another similar section that used a traditional lecture format. Although the course is a recruiting class for majors, over 95% of the students that enroll are non-majors. Measurements of academic evaluation were through four unit exams, classroom communication systems, weekly web-based homework, in-class activities, and a thematic collaborative poster/paper project and presentation. The qualitative methods to investigate the effectiveness of the teaching design included: direct observation, self-reporting about learning, and open-ended interviews. By disaggregating emerging data, we tried to concentrate on patterns and causal relationships between achievement performance and attitudes regarding learning geology. Statistical analyses revealed positive relationships between student engagement in supplemental activities and achievement mean scores within and between the two sections. Completing weekly online homework had the most robust relationship with overall achievement performance. Contrary to expectations, a thematic group project only led to modest gains in achievement performance, although the social and professional gains could be

A Tool for Measuring Active Learning in the Classroom

PubMed Central

Devlin, John W.; Kirwin, Jennifer L.; Qualters, Donna M.

2007-01-01

Objectives To develop a valid and reliable active-learning inventory tool for use in large classrooms and compare faculty perceptions of active-learning using the Active-Learning Inventory Tool. Methods The Active-Learning Inventory Tool was developed using published literature and validated by national experts in educational research. Reliability was established by trained faculty members who used the Active-Learning Inventory Tool to observe 9 pharmacy lectures. Instructors were then interviewed to elicit perceptions regarding active learning and asked to share their perceptions. Results Per lecture, 13 (range: 4-34) episodes of active learning encompassing 3 (range: 2-5) different types of active learning occurred over 2.2 minutes (0.6-16) per episode. Both interobserver (≥87%) and observer-instructor agreement (≥68%) were high for these outcomes. Conclusions The Active-Learning Inventory Tool is a valid and reliable tool to measure active learning in the classroom. Future studies are needed to determine the impact of the Active-Learning Inventory Tool on teaching and its usefulness in other disciplines. PMID:17998982

Are students' impressions of improved learning through active learning methods reflected by improved test scores?

PubMed

Everly, Marcee C

2013-02-01

To report the transformation from lecture to more active learning methods in a maternity nursing course and to evaluate whether student perception of improved learning through active-learning methods is supported by improved test scores. The process of transforming a course into an active-learning model of teaching is described. A voluntary mid-semester survey for student acceptance of the new teaching method was conducted. Course examination results, from both a standardized exam and a cumulative final exam, among students who received lecture in the classroom and students who had active learning activities in the classroom were compared. Active learning activities were very acceptable to students. The majority of students reported learning more from having active-learning activities in the classroom rather than lecture-only and this belief was supported by improved test scores. Students who had active learning activities in the classroom scored significantly higher on a standardized assessment test than students who received lecture only. The findings support the use of student reflection to evaluate the effectiveness of active-learning methods and help validate the use of student reflection of improved learning in other research projects. Copyright © 2011 Elsevier Ltd. All rights reserved.

Linking Mission to Learning Activities for Assurance of Learning

ERIC Educational Resources Information Center

Yeung, Shirley Mo-ching

2011-01-01

Can accreditation-related requirements and mission statements measure learning outcomes? This study focuses on triangulating accreditation-related requirements with mission statements and learning activities to learning outcomes. This topic has not been comprehensively explored in the past. After looking into the requirements of AACSB, ISO, and…

Active Learning in Engineering Education: a (re)introduction

NASA Astrophysics Data System (ADS)

Lima, Rui M.; Andersson, Pernille Hammar; Saalman, Elisabeth

2017-01-01

The informal network 'Active Learning in Engineering Education' (ALE) has been promoting Active Learning since 2001. ALE creates opportunity for practitioners and researchers of engineering education to collaboratively learn how to foster learning of engineering students. The activities in ALE are centred on the vision that learners construct their knowledge based on meaningful activities and knowledge. In 2014, the steering committee of the ALE network reinforced the need to discuss the meaning of Active Learning and that was the base for this proposal for a special issue. More than 40 submissions were reviewed by the European Journal of Engineering Education community and this theme issue ended up with eight contributions, which are different both in their research and Active Learning approaches. These different Active Learning approaches are aligned with the different approaches that can be increasingly found in indexed journals.

Active Learning in the Middle Grades

ERIC Educational Resources Information Center

Edwards, Susan

2015-01-01

What is active learning and what does it look like in the classroom? If students are participating in active learning, they are playing a more engaged role in the learning process and are not overly reliant on the teacher (Bransford, Brown, & Cocking, 2003; Petress, 2008). The purpose of this article is to propose a framework to describe and…

Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses

PubMed Central

Andrews, T. M.; Leonard, M. J.; Colgrove, C. A.; Kalinowski, S. T.

2011-01-01

Previous research has suggested that adding active learning to traditional college science lectures substantially improves student learning. However, this research predominantly studied courses taught by science education researchers, who are likely to have exceptional teaching expertise. The present study investigated introductory biology courses randomly selected from a list of prominent colleges and universities to include instructors representing a broader population. We examined the relationship between active learning and student learning in the subject area of natural selection. We found no association between student learning gains and the use of active-learning instruction. Although active learning has the potential to substantially improve student learning, this research suggests that active learning, as used by typical college biology instructors, is not associated with greater learning gains. We contend that most instructors lack the rich and nuanced understanding of teaching and learning that science education researchers have developed. Therefore, active learning as designed and implemented by typical college biology instructors may superficially resemble active learning used by education researchers, but lacks the constructivist elements necessary for improving learning. PMID:22135373

Novel Breast Imaging and Machine Learning: Predicting Breast Lesion Malignancy at Cone-Beam CT Using Machine Learning Techniques.

PubMed

Uhlig, Johannes; Uhlig, Annemarie; Kunze, Meike; Beissbarth, Tim; Fischer, Uwe; Lotz, Joachim; Wienbeck, Susanne

2018-05-24

The purpose of this study is to evaluate the diagnostic performance of machine learning techniques for malignancy prediction at breast cone-beam CT (CBCT) and to compare them to human readers. Five machine learning techniques, including random forests, back propagation neural networks (BPN), extreme learning machines, support vector machines, and K-nearest neighbors, were used to train diagnostic models on a clinical breast CBCT dataset with internal validation by repeated 10-fold cross-validation. Two independent blinded human readers with profound experience in breast imaging and breast CBCT analyzed the same CBCT dataset. Diagnostic performance was compared using AUC, sensitivity, and specificity. The clinical dataset comprised 35 patients (American College of Radiology density type C and D breasts) with 81 suspicious breast lesions examined with contrast-enhanced breast CBCT. Forty-five lesions were histopathologically proven to be malignant. Among the machine learning techniques, BPNs provided the best diagnostic performance, with AUC of 0.91, sensitivity of 0.85, and specificity of 0.82. The diagnostic performance of the human readers was AUC of 0.84, sensitivity of 0.89, and specificity of 0.72 for reader 1 and AUC of 0.72, sensitivity of 0.71, and specificity of 0.67 for reader 2. AUC was significantly higher for BPN when compared with both reader 1 (p = 0.01) and reader 2 (p < 0.001). Machine learning techniques provide a high and robust diagnostic performance in the prediction of malignancy in breast lesions identified at CBCT. BPNs showed the best diagnostic performance, surpassing human readers in terms of AUC and specificity.

In Defense of Active Learning

ERIC Educational Resources Information Center

Pica, Rae

2008-01-01

Effective early childhood teachers use what they know about and have observed in young children to design programs to meet children's developmental needs. Play and active learning are key tools to address those needs and facilitate children's early education. In this article, the author discusses the benefits of active learning in the education of…

Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments

NASA Astrophysics Data System (ADS)

Badura, Aleksandra; Clopath, Claudia; Schonewille, Martijn; de Zeeuw, Chris I.

2016-11-01

Translating neuronal activity to measurable behavioral changes has been a long-standing goal of systems neuroscience. Recently, we have developed a model of phase-reversal learning of the vestibulo-ocular reflex, a well-established, cerebellar-dependent task. The model, comprising both the cerebellar cortex and vestibular nuclei, reproduces behavioral data and accounts for the changes in neural activity during learning in wild type mice. Here, we used our model to predict Purkinje cell spiking as well as behavior before and after learning of five different lines of mutant mice with distinct cell-specific alterations of the cerebellar cortical circuitry. We tested these predictions by obtaining electrophysiological data depicting changes in neuronal spiking. We show that our data is largely consistent with the model predictions for simple spike modulation of Purkinje cells and concomitant behavioral learning in four of the mutants. In addition, our model accurately predicts a shift in simple spike activity in a mutant mouse with a brainstem specific mutation. This combination of electrophysiological and computational techniques opens a possibility of predicting behavioral impairments from neural activity.

Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments

PubMed Central

Badura, Aleksandra; Clopath, Claudia; Schonewille, Martijn; De Zeeuw, Chris I.

2016-01-01

Translating neuronal activity to measurable behavioral changes has been a long-standing goal of systems neuroscience. Recently, we have developed a model of phase-reversal learning of the vestibulo-ocular reflex, a well-established, cerebellar-dependent task. The model, comprising both the cerebellar cortex and vestibular nuclei, reproduces behavioral data and accounts for the changes in neural activity during learning in wild type mice. Here, we used our model to predict Purkinje cell spiking as well as behavior before and after learning of five different lines of mutant mice with distinct cell-specific alterations of the cerebellar cortical circuitry. We tested these predictions by obtaining electrophysiological data depicting changes in neuronal spiking. We show that our data is largely consistent with the model predictions for simple spike modulation of Purkinje cells and concomitant behavioral learning in four of the mutants. In addition, our model accurately predicts a shift in simple spike activity in a mutant mouse with a brainstem specific mutation. This combination of electrophysiological and computational techniques opens a possibility of predicting behavioral impairments from neural activity. PMID:27805050

Comparing supervised learning techniques on the task of physical activity recognition.

PubMed

Dalton, A; OLaighin, G

2013-01-01

The objective of this study was to compare the performance of base-level and meta-level classifiers on the task of physical activity recognition. Five wireless kinematic sensors were attached to each subject (n = 25) while they completed a range of basic physical activities in a controlled laboratory setting. Subjects were then asked to carry out similar self-annotated physical activities in a random order and in an unsupervised environment. A combination of time-domain and frequency-domain features were extracted from the sensor data including the first four central moments, zero-crossing rate, average magnitude, sensor cross-correlation, sensor auto-correlation, spectral entropy and dominant frequency components. A reduced feature set was generated using a wrapper subset evaluation technique with a linear forward search and this feature set was employed for classifier comparison. The meta-level classifier AdaBoostM1 with C4.5 Graft as its base-level classifier achieved an overall accuracy of 95%. Equal sized datasets of subject independent data and subject dependent data were used to train this classifier and high recognition rates could be achieved without the need for user specific training. Furthermore, it was found that an accuracy of 88% could be achieved using data from the ankle and wrist sensors only.

Instructional Television: Visual Production Techniques and Learning Comprehension.

ERIC Educational Resources Information Center

Silbergleid, Michael Ian

The purpose of this study was to determine if increasing levels of complexity in visual production techniques would increase the viewer's learning comprehension and the degree of likeness expressed for a college level instructional television program. A total of 119 mass communications students at the University of Alabama participated in the…

α1-Adrenoceptors in the hippocampal dentate gyrus involved in learning-dependent long-term potentiation during active-avoidance learning in rats.

PubMed

Lv, Jing; Zhan, Su-Yang; Li, Guang-Xie; Wang, Dan; Li, Ying-Shun; Jin, Qing-Hua

2016-11-09

The hippocampus is the key structure for learning and memory in mammals and long-term potentiation (LTP) is an important cellular mechanism responsible for learning and memory. The influences of norepinephrine (NE) on the modulation of learning and memory, as well as LTP, through β-adrenoceptors are well documented, whereas the role of α1-adrenoceptors in learning-dependent LTP is not yet clear. In the present study, we measured extracellular concentrations of NE in the hippocampal dentate gyrus (DG) region using an in-vivo brain microdialysis and high-performance liquid chromatography techniques during the acquisition and extinction of active-avoidance behavior in freely moving conscious rats. Next, the effects of prazosin (an antagonist of α1-adrenoceptor) and phenylephrine (an agonist of the α1-adrenoceptor) on amplitudes of field excitatory postsynaptic potential were measured in the DG region during the active-avoidance behavior. Our results showed that the extracellular concentration of NE in the DG was significantly increased during the acquisition of active-avoidance behavior and gradually returned to the baseline level following extinction training. A local microinjection of prazosin into the DG significantly accelerated the acquisition of the active-avoidance behavior, whereas a local microinjection of phenylephrine retarded the acquisition of the active-avoidance behavior. Furthermore, in all groups, the changes in field excitatory postsynaptic potential amplitude were accompanied by corresponding changes in active-avoidance behavior. Our results suggest that NE activation of α1-adrenoceptors in the hippocampal DG inhibits active-avoidance learning by modulation of synaptic efficiency in rats.

Active Learning in Engineering Education: A (Re)Introduction

ERIC Educational Resources Information Center

Lima, Rui M.; Andersson, Pernille Hammar; Saalman, Elisabeth

2017-01-01

The informal network "Active Learning in Engineering Education" (ALE) has been promoting Active Learning since 2001. ALE creates opportunity for practitioners and researchers of engineering education to collaboratively learn how to foster learning of engineering students. The activities in ALE are centred on the vision that learners…

Learning Style Differences in the Perceived Effectiveness of Learning Activities

ERIC Educational Resources Information Center

Karns, Gary L.

2006-01-01

The learning style individual difference factor has long been a basis for understanding student preferences for various learning activities. Marketing educators have been advised to heavily invest in tailoring course design based on the learning style groups in their classes. A further exploration of the effects of learning style differences on…

A comparison of professional-level faculty and student perceptions of active learning: its current use, effectiveness, and barriers

PubMed Central

Metz, Michael J.

2014-01-01

Active learning is an instructional method in which students become engaged participants in the classroom through the use of in-class written exercises, games, problem sets, audience-response systems, debates, class discussions, etc. Despite evidence supporting the effectiveness of active learning strategies, minimal adoption of the technique has occurred in many professional programs. The goal of this study was to compare the perceptions of active learning between students who were exposed to active learning in the classroom (n = 116) and professional-level physiology faculty members (n = 9). Faculty members reported a heavy reliance on lectures and minimal use of educational games and activities, whereas students indicated that they learned best via the activities. A majority of faculty members (89%) had observed active learning in the classroom and predicted favorable effects of the method on student performance and motivation. The main reported barriers by faculty members to the adoption of active learning were a lack of necessary class time, a high comfort level with traditional lectures, and insufficient time to develop materials. Students hypothesized similar obstacles for faculty members but also associated many negative qualities with the traditional lecturers. Despite these barriers, a majority of faculty members (78%) were interested in learning more about the alternative teaching strategy. Both faculty members and students indicated that active learning should occupy portions (29% vs. 40%) of face-to-face class time. PMID:25179615

Active learning in capstone design courses.

PubMed

Goldberg, Jay R

2012-01-01

There is a growing trend to encourage students to take a more active role in their own education. Many schools are moving away from the sage on the stage to the guide on the side model where the instructor is a facilitator of learning. In this model, the emphasis is more on learning and less on teaching, and it requires instructors to incorporate more active and student-centered learning methods into their courses. These methods include collaborative, cooperative, problem-based, and project-based learning.

Strategies for active learning in online continuing education.

PubMed

Phillips, Janet M

2005-01-01

Online continuing education and staff development is on the rise as the benefits of access, convenience, and quality learning are continuing to take shape. Strategies to enhance learning call for learner participation that is self-directed and independent, thus changing the educator's role from expert to coach and facilitator. Good planning of active learning strategies promotes optimal learning whether the learning content is presented in a course or a just-in-time short module. Active learning strategies can be used to enhance online learning during all phases of the teaching-learning process and can accommodate a variety of learning styles. Feedback from peers, educators, and technology greatly influences learner satisfaction and must be harnessed to provide effective learning experiences. Outcomes of active learning can be assessed online and implemented conveniently and successfully from the initiation of the course or module planning to the end of the evaluation process. Online learning has become accessible and convenient and allows the educator to track learner participation. The future of online education will continue to grow, and using active learning strategies will ensure that quality learning will occur, appealing to a wide variety of learning needs.

Technology Learning Activities I.

ERIC Educational Resources Information Center

International Technology Education Association, Reston, VA.

This guide contains 30 technology learning activities. Activities may contain all or some of the following: an introduction, objectives, materials and equipment, challenges, limitations, notes and investigations, resources and references used, and evaluation ideas. Activity titles are: (1) Occupations in Construction Technology; (2) Designing a…

Students' Satisfaction on Their Learning Process in Active Learning and Traditional Classrooms

ERIC Educational Resources Information Center

Hyun, Jung; Ediger, Ruth; Lee, Donghun

2017-01-01

Studies have shown Active Learning Classrooms [ALCs] help increase student engagement and improve student performance. However, remodeling all traditional classrooms to ALCs entails substantial financial burdens. Thus, an imperative question for institutions of higher education is whether active learning pedagogies can improve learning outcomes…

Modelling Typical Online Language Learning Activity

ERIC Educational Resources Information Center

Montoro, Carlos; Hampel, Regine; Stickler, Ursula

2014-01-01

This article presents the methods and results of a four-year-long research project focusing on the language learning activity of individual learners using online tasks conducted at the University of Guanajuato (Mexico) in 2009-2013. An activity-theoretical model (Blin, 2010; Engeström, 1987) of the typical language learning activity was used to…

FísicActiva: Applying Active Learning Strategies to a Large Engineering Lecture

ERIC Educational Resources Information Center

Auyuanet, Adriana; Modzelewski, Helena; Loureiro, Silvia; Alessandrini, Daniel; Míguez, Marina

2018-01-01

This paper presents and analyses the results obtained by applying Active Learning techniques in overcrowded Physics lectures at the University of the Republic, Uruguay. The course referred to is Physics 1, the first Physics course that all students of the Faculty of Engineering take in their first semester for all the Engineering-related careers.…

Kinaesthetic Learning Activities and Learning about Solar Cells

ERIC Educational Resources Information Center

Richards, A. J.; Etkina, Eugenia

2013-01-01

Kinaesthetic learning activities (KLAs) can be a valuable pedagogical tool for physics instructors. They have been shown to increase engagement, encourage participation and improve learning outcomes. This paper details several KLAs developed at Rutgers University for inclusion in an instructional unit about semiconductors, p-n junctions and solar…

Group-Based Active Learning of Classification Models.

PubMed

Luo, Zhipeng; Hauskrecht, Milos

2017-05-01

Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labeling groups of examples. Briefly, unlike traditional active learning methods that seek feedback on individual examples, we develop a new group-based active learning framework that solicits label information on groups of multiple examples. In order to describe groups in a user-friendly way, conjunctive patterns are used to compactly represent groups. Our empirical study on 12 UCI data sets demonstrates the advantages and superiority of our approach over both classic instance-based active learning work, as well as existing group-based active-learning methods.

Scene recognition based on integrating active learning with dictionary learning

NASA Astrophysics Data System (ADS)

Wang, Chengxi; Yin, Xueyan; Yang, Lin; Gong, Chengrong; Zheng, Caixia; Yi, Yugen

2018-04-01

Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.

Swarm Intelligence: New Techniques for Adaptive Systems to Provide Learning Support

ERIC Educational Resources Information Center

Wong, Lung-Hsiang; Looi, Chee-Kit

2012-01-01

The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for…

Active Learning Crosses Generations.

ERIC Educational Resources Information Center

Woodard, Diane K.

2002-01-01

Describes the benefits of intergenerational programs, highlighting a child care program that offers age-appropriate and mutually beneficial activities for children and elders within a nearby retirement community. The program has adopted High/Scope's active learning approach to planning and implementing activities that involve both generations. The…

Annotating smart environment sensor data for activity learning.

PubMed

Szewcyzk, S; Dwan, K; Minor, B; Swedlove, B; Cook, D

2009-01-01

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track the activities that people perform at home. Machine learning techniques can perform this task, but the software algorithms rely upon large amounts of sample data that is correctly labeled with the corresponding activity. Labeling, or annotating, sensor data with the corresponding activity can be time consuming, may require input from the smart home resident, and is often inaccurate. Therefore, in this paper we investigate four alternative mechanisms for annotating sensor data with a corresponding activity label. We evaluate the alternative methods along the dimensions of annotation time, resident burden, and accuracy using sensor data collected in a real smart apartment.

Active Learning: The Way Children Construct Knowledge.

ERIC Educational Resources Information Center

Hohmann, Mary; Weikart, David P.

2002-01-01

The High/Scope approach to early childhood education promotes the belief that active learning is fundamental to the development of human potential and occurs most effectively in settings that provide developmentally appropriate learning opportunities. Describes five ingredients of active learning (materials, manipulation, choice, language from…

Accelerated Learning Techniques for the Foreign Language Class: A Personal View.

ERIC Educational Resources Information Center

Bancroft, W. Jane

Foreign language instructors cope with problems of learner anxiety in the classroom, fossilization of language use and language skill loss. Relaxation and concentration techniques can alleviate stress and fatigue and improve students' capabilities. Three categories of accelerated learning techniques are: (1) those that serve as a preliminary to…

Comparative Study on the Different Testing Techniques in Tree Classification for Detecting the Learning Motivation

NASA Astrophysics Data System (ADS)

Juliane, C.; Arman, A. A.; Sastramihardja, H. S.; Supriana, I.

2017-03-01

Having motivation to learn is a successful requirement in a learning process, and needs to be maintained properly. This study aims to measure learning motivation, especially in the process of electronic learning (e-learning). Here, data mining approach was chosen as a research method. For the testing process, the accuracy comparative study on the different testing techniques was conducted, involving Cross Validation and Percentage Split. The best accuracy was generated by J48 algorithm with a percentage split technique reaching at 92.19 %. This study provided an overview on how to detect the presence of learning motivation in the context of e-learning. It is expected to be good contribution for education, and to warn the teachers for whom they have to provide motivation.

Memory-Augmented Active Deep Learning for Identifying Relations Between Distant Medical Concepts in Electroencephalography Reports.

PubMed

Maldonado, Ramon; Goodwin, Travis R; Harabagiu, Sanda M

2018-01-01

The automatic identification of relations between medical concepts in a large corpus of Electroencephalography (EEG) reports is an important step in the development of an EEG-specific patient cohort retrieval system as well as in the acquisition of EEG-specific knowledge from this corpus. EEG-specific relations involve medical concepts that are not typically mentioned in the same sentence or even the same section of a report, thus requiring extraction techniques that can handle such long-distance dependencies. To address this challenge, we present a novel frame work which combines the advantages of a deep learning framework employing Dynamic Relational Memory (DRM) with active learning. While DRM enables the prediction of long-distance relations, active learning provides a mechanism for accurately identifying relations with minimal training data, obtaining an 5-fold cross validationF1 score of 0.7475 on a set of 140 EEG reports selected with active learning. The results obtained with our novel framework show great promise.

Learning Outcomes between Socioscientific Issues-Based Learning and Conventional Learning Activities

ERIC Educational Resources Information Center

Wongsri, Piyaluk; Nuangchalerm, Prasart

2010-01-01

Problem statement: Socioscientific issues-based learning activity is essential for scientific reasoning skills and it could be used for analyzing problems be applied to each situation for more successful and suitable. The purposes of this research aimed to compare learning achievement, analytical thinking and moral reasoning of seventh grade…

[Purity Detection Model Update of Maize Seeds Based on Active Learning].

PubMed

Tang, Jin-ya; Huang, Min; Zhu, Qi-bing

2015-08-01

Seed purity reflects the degree of seed varieties in typical consistent characteristics, so it is great important to improve the reliability and accuracy of seed purity detection to guarantee the quality of seeds. Hyperspectral imaging can reflect the internal and external characteristics of seeds at the same time, which has been widely used in nondestructive detection of agricultural products. The essence of nondestructive detection of agricultural products using hyperspectral imaging technique is to establish the mathematical model between the spectral information and the quality of agricultural products. Since the spectral information is easily affected by the sample growth environment, the stability and generalization of model would weaken when the test samples harvested from different origin and year. Active learning algorithm was investigated to add representative samples to expand the sample space for the original model, so as to implement the rapid update of the model's ability. Random selection (RS) and Kennard-Stone algorithm (KS) were performed to compare the model update effect with active learning algorithm. The experimental results indicated that in the division of different proportion of sample set (1:1, 3:1, 4:1), the updated purity detection model for maize seeds from 2010 year which was added 40 samples selected by active learning algorithm from 2011 year increased the prediction accuracy for 2011 new samples from 47%, 33.75%, 49% to 98.89%, 98.33%, 98.33%. For the updated purity detection model of 2011 year, its prediction accuracy for 2010 new samples increased by 50.83%, 54.58%, 53.75% to 94.57%, 94.02%, 94.57% after adding 56 new samples from 2010 year. Meanwhile the effect of model updated by active learning algorithm was better than that of RS and KS. Therefore, the update for purity detection model of maize seeds is feasible by active learning algorithm.

A Sourcebook of Cooperative Learning Activities for Introductory Undergraduate Astronomy for Non-Science Majors

NASA Astrophysics Data System (ADS)

Deming, Grace L.; Miller, Scott T.; Trasco, John D.

1996-05-01

Students become more interested in learning and retain more in courses that rely on active rather than passive teaching methods. Cooperative learning activities can be structured to engage students toward greater participation in their own education. We have developed a sourcebook containing a variety of cooperative learning methods and activities to aid in the teaching of astronomy at the undergraduate level. Special effort has been made to include activities that can be used within the classroom or as a group homework assignment, in courses with teaching assistants and those without, and in large or small classes. In addition to reinforcing concepts taught in introductory astronomy, the activities are structured to strengthen skills associated with a scientifically literate person. A goal of undergraduate science education is to produce citizens who can understand and share in the excitement of scientific discoveries as well as make informed decisions regarding scientific and technological issues. The sourcebook, available in August, 1996, will contain sections on the advantages/disadvantages of group activities, basic cooperative learning techniques, in class/out of class activities, and how to use peer instruction to expose students to the wonderfaul astronomy resources on the internet. Each activity includes suggestions to the instructor as to how the assignment can be incorporated into an introductory astronomy course. This project funded by NSF DUE-9354503.

A comparison of professional-level faculty and student perceptions of active learning: its current use, effectiveness, and barriers.

PubMed

Miller, Cynthia J; Metz, Michael J

2014-09-01

Active learning is an instructional method in which students become engaged participants in the classroom through the use of in-class written exercises, games, problem sets, audience-response systems, debates, class discussions, etc. Despite evidence supporting the effectiveness of active learning strategies, minimal adoption of the technique has occurred in many professional programs. The goal of this study was to compare the perceptions of active learning between students who were exposed to active learning in the classroom (n = 116) and professional-level physiology faculty members (n = 9). Faculty members reported a heavy reliance on lectures and minimal use of educational games and activities, whereas students indicated that they learned best via the activities. A majority of faculty members (89%) had observed active learning in the classroom and predicted favorable effects of the method on student performance and motivation. The main reported barriers by faculty members to the adoption of active learning were a lack of necessary class time, a high comfort level with traditional lectures, and insufficient time to develop materials. Students hypothesized similar obstacles for faculty members but also associated many negative qualities with the traditional lecturers. Despite these barriers, a majority of faculty members (78%) were interested in learning more about the alternative teaching strategy. Both faculty members and students indicated that active learning should occupy portions (29% vs. 40%) of face-to-face class time. Copyright © 2014 The American Physiological Society.

Research on Mobile Learning Activities Applying Tablets

ERIC Educational Resources Information Center

Kurilovas, Eugenijus; Juskeviciene, Anita; Bireniene, Virginija

2015-01-01

The paper aims to present current research on mobile learning activities in Lithuania while implementing flagship EU-funded CCL project on application of tablet computers in education. In the paper, the quality of modern mobile learning activities based on learning personalisation, problem solving, collaboration, and flipped class methods is…

Active Learning with Irrelevant Examples

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri; Mazzoni, Dominic

2009-01-01

An improved active learning method has been devised for training data classifiers. One example of a data classifier is the algorithm used by the United States Postal Service since the 1960s to recognize scans of handwritten digits for processing zip codes. Active learning algorithms enable rapid training with minimal investment of time on the part of human experts to provide training examples consisting of correctly classified (labeled) input data. They function by identifying which examples would be most profitable for a human expert to label. The goal is to maximize classifier accuracy while minimizing the number of examples the expert must label. Although there are several well-established methods for active learning, they may not operate well when irrelevant examples are present in the data set. That is, they may select an item for labeling that the expert simply cannot assign to any of the valid classes. In the context of classifying handwritten digits, the irrelevant items may include stray marks, smudges, and mis-scans. Querying the expert about these items results in wasted time or erroneous labels, if the expert is forced to assign the item to one of the valid classes. In contrast, the new algorithm provides a specific mechanism for avoiding querying the irrelevant items. This algorithm has two components: an active learner (which could be a conventional active learning algorithm) and a relevance classifier. The combination of these components yields a method, denoted Relevance Bias, that enables the active learner to avoid querying irrelevant data so as to increase its learning rate and efficiency when irrelevant items are present. The algorithm collects irrelevant data in a set of rejected examples, then trains the relevance classifier to distinguish between labeled (relevant) training examples and the rejected ones. The active learner combines its ranking of the items with the probability that they are relevant to yield a final decision about which item

Faculty motivations to use active learning among pharmacy educators.

PubMed

Rockich-Winston, Nicole; Train, Brian C; Rudolph, Michael J; Gillette, Chris

2018-03-01

Faculty motivations to use active learning have been limited to surveys evaluating faculty perceptions within active learning studies. Our objective in this study was to evaluate the relationship between faculty intrinsic motivation, extrinsic motivation, and demographic variables and the extent of active learning use in the classroom. An online survey was administered to individual faculty members at 137 colleges and schools of pharmacy across the United States. The survey assessed intrinsic and extrinsic motivations, active learning strategies, classroom time dedicated to active learning, and faculty development resources. Bivariate associations and multivariable stepwise linear regression were used to analyze the results. In total, 979 faculty members completed the questionnaire (23.6% response rate). All motivation variables were significantly correlated with percent active learning use (p < 0.001). Intrinsic motivation demonstrated the highest correlation (r = 0.447) followed by current extrinsic motivations (r = 0.245) and ideal extrinsic motivations (r = 0.291). Variables associated with higher intrinsic motivation included the number of resources used (r = 0.233, p < 0.001) and the number of active learning methods used in the last year (r = 0.259, p < 0.001). Years of teaching experience was negatively associated with intrinsic motivation (r = -0.177, p < 0.001). Regression analyses confirmed the importance of intrinsic and extrinsic motivations in predicting active learning use. Our results suggest that faculty members who are intrinsically motivated to use active learning are more likely to dedicate additional class time to active learning. Furthermore, intrinsic motivation may be positively associated with encouraging faculty members to attend active learning workshops and supporting faculty to use various active learning strategies in the classroom. Copyright © 2017 Elsevier Inc. All rights reserved.

Creating Stimulating Learning and Thinking Using New Models of Activity-Based Learning and Metacognitive-Based Activities

ERIC Educational Resources Information Center

Pang, Katherine

2010-01-01

The purpose of this paper is to present a novel way to stimulate learning, creativity, and thinking based on a new understanding of activity-based learning (ABL) and two methods for developing metacognitive-based activities for the classroom. ABL, in this model, is based on the premise that teachers are distillers and facilitators of information…

Active Learning by Querying Informative and Representative Examples.

PubMed

Huang, Sheng-Jun; Jin, Rong; Zhou, Zhi-Hua

2014-10-01

Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select either informative or representative unlabeled instances to query their labels, which could significantly limit their performance. Although several active learning algorithms were proposed to combine the two query selection criteria, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this limitation by developing a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance. Further, by incorporating the correlation among labels, we extend the QUIRE approach to multi-label learning by actively querying instance-label pairs. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches in both single-label and multi-label learning.

Learning Activities for the Young Handicapped Child.

ERIC Educational Resources Information Center

Bailey, Don; And Others

Presented is a collection of learning activities for the young handicapped child covering 295 individual learning objectives in six areas of development: gross motor skills, fine motor skills, social skills, self help skills, cognitive skills, and language skills. Provided for each learning activity are the teaching objective, teaching procedures,…

[Effect of 5-HT1A receptors in the hippocampal DG on active avoidance learning in rats].

PubMed

Jiang, Feng-ze; Lv, Jing; Wang, Dan; Jiang, Hai-ying; Li, Ying-shun; Jin, Qing-hua

2015-01-01

To investigate the effects of serotonin (5-HTIA) receptors in the hippocampal dentate gyrus (DG) on active avoidance learning in rats. Totally 36 SD rats were randomly divided into control group, antagonist group and agonist group(n = 12). Active avoidance learning ability of rats was assessed by the shuttle box. The extracellular concentrations of 5-HT in the DG during active avoidance conditioned reflex were measured by microdialysis and high performance liquid chromatography (HPLC) techniques. Then the antagonist (WAY-100635) or agonist (8-OH-DPAT) of the 5-HT1A receptors were microinjected into the DG region, and the active avoidance learning was measured. (1) During the active avoidance learning, the concentration of 5-HT in the hippocampal DG was significantly increased in the extinction but not establishment in the conditioned reflex, which reached 164.90% ± 26.07% (P <0.05) of basal level. (2) The microinjection of WAY-100635 (an antagonist of 5-HT1A receptor) into the DG did not significantly affect the active avoidance learning. (3) The microinjection of 8-OH-DPAT(an agonist of 5-HT1A receptor) into the DG significantly facilitated the establishment process and inhibited the extinction process during active avoidance conditioned reflex. The data suggest that activation of 5-HT1A receptors in hipocampal DG may facilitate active avoidance learning and memory in rats.

The GenTechnique Project: Developing an Open Environment for Learning Molecular Genetics.

ERIC Educational Resources Information Center

Calza, R. E.; Meade, J. T.

1998-01-01

The GenTechnique project at Washington State University uses a networked learning environment for molecular genetics learning. The project is developing courseware featuring animation, hyper-link controls, and interactive self-assessment exercises focusing on fundamental concepts. The first pilot course featured a Web-based module on DNA…

An experimental result of estimating an application volume by machine learning techniques.

PubMed

Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

2015-01-01

In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.

Assessing Student Behaviors and Motivation for Actively Learning Biology

NASA Astrophysics Data System (ADS)

Moore, Michael Edward

Vision and Change states that one of the major changes in the way we design biology courses should be a switch in approach from teacher-centered learning to student-centered learning and identifies active learning as a recommended methods. Studies show performance benefits for students taking courses that use active learning. What is unknown is why active learning is such an effective instructional tool and the limits of this instructional method’s ability to influence performance. This dissertation builds a case in three steps for why active learning is an effective instructional tool. In step one, I assessed the influence of different types of active learning (clickers, group activities, and whole class discussions) on student engagement behavior in one semester of two different introductory biology courses and found that active learning positively influenced student engagement behavior significantly more than lecture. For step two, I examined over four semesters whether student engagement behavior was a predictor of performance and found participation (engagement behavior) in the online (video watching) and in-class course activities (clicker participation) that I measure were significant predictors of performance. In the third, I assessed whether certain active learning satisfied the psychological needs that lead to students’ intrinsic motivation to participate in those activities when compared over two semesters and across two different institutions of higher learning. Findings from this last step show us that student’s perceptions of autonomy, competency, and relatedness in doing various types of active learning are significantly higher than lecture and consistent across two institutions of higher learning. Lastly, I tie everything together, discuss implications of the research, and address future directions for research on biology student motivation and behavior.

3-Dimensional and Interactive Istanbul University Virtual Laboratory Based on Active Learning Methods

ERIC Educational Resources Information Center

Ince, Elif; Kirbaslar, Fatma Gulay; Yolcu, Ergun; Aslan, Ayse Esra; Kayacan, Zeynep Cigdem; Alkan Olsson, Johanna; Akbasli, Ayse Ceylan; Aytekin, Mesut; Bauer, Thomas; Charalambis, Dimitris; Gunes, Zeliha Ozsoy; Kandemir, Ceyhan; Sari, Umit; Turkoglu, Suleyman; Yaman, Yavuz; Yolcu, Ozgu

2014-01-01

The purpose of this study is to develop a 3-dimensional interactive multi-user and multi-admin IUVIRLAB featuring active learning methods and techniques for university students and to introduce the Virtual Laboratory of Istanbul University and to show effects of IUVIRLAB on students' attitudes on communication skills and IUVIRLAB. Although there…

History and Evolution of Active Learning Spaces

ERIC Educational Resources Information Center

Beichner, Robert J.

2014-01-01

This chapter examines active learning spaces as they have developed over the years. Consistently well-designed classrooms can facilitate active learning even though the details of implementing pedagogies may differ.

GeoMapApp Learning Activities: Enabling the democratisation of geoscience learning

NASA Astrophysics Data System (ADS)

Goodwillie, A. M.; Kluge, S.

2011-12-01

GeoMapApp Learning Activities (http://serc.carleton.edu/geomapapp) are step-by-step guided inquiry geoscience education activities that enable students to dictate the pace of learning. They can be used in the classroom or out of class, and their guided nature means that the requirement for teacher intervention is minimised which allows students to spend increased time analysing and understanding a broad range of geoscience data, content and concepts. Based upon GeoMapApp (http://www.geomapapp.org), a free, easy-to-use map-based data exploration and visualisation tool, each activity furnishes the educator with an efficient package of downloadable documents. This includes step-by-step student instructions and answer sheet; a teacher's edition annotated worksheet containing teaching tips, additional content and suggestions for further work; quizzes for use before and after the activity to assess learning; and a multimedia tutorial. The activities can be used by anyone at any time in any place with an internet connection. In essence, GeoMapApp Learning Activities provide students with cutting-edge technology, research-quality geoscience data sets, and inquiry-based learning in a virtual lab-like environment. Examples of activities so far created are student calculation and analysis of the rate of seafloor spreading, and present-day evidence on the seafloor for huge ancient landslides around the Hawaiian islands. The activities are designed primarily for students at the community college, high school and introductory undergraduate levels, exposing students to content and concepts typically found in those settings.

Active Learning Environment with Lenses in Geometric Optics

ERIC Educational Resources Information Center

Tural, Güner

2015-01-01

Geometric optics is one of the difficult topics for students within physics discipline. Students learn better via student-centered active learning environments than the teacher-centered learning environments. So this study aimed to present a guide for middle school teachers to teach lenses in geometric optics via active learning environment…

Identifying key features of effective active learning: the effects of writing and peer discussion.

PubMed

Linton, Debra L; Pangle, Wiline M; Wyatt, Kevin H; Powell, Karli N; Sherwood, Rachel E

2014-01-01

We investigated some of the key features of effective active learning by comparing the outcomes of three different methods of implementing active-learning exercises in a majors introductory biology course. Students completed activities in one of three treatments: discussion, writing, and discussion + writing. Treatments were rotated weekly between three sections taught by three different instructors in a full factorial design. The data set was analyzed by generalized linear mixed-effect models with three independent variables: student aptitude, treatment, and instructor, and three dependent (assessment) variables: change in score on pre- and postactivity clicker questions, and coding scores on in-class writing and exam essays. All independent variables had significant effects on student performance for at least one of the dependent variables. Students with higher aptitude scored higher on all assessments. Student scores were higher on exam essay questions when the activity was implemented with a writing component compared with peer discussion only. There was a significant effect of instructor, with instructors showing different degrees of effectiveness with active-learning techniques. We suggest that individual writing should be implemented as part of active learning whenever possible and that instructors may need training and practice to become effective with active learning. © 2014 D. L. Linton et al. CBE—Life Sciences Education © 2014 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Learning Style Activities for Computer Applications. Field Review.

ERIC Educational Resources Information Center

Patton, Jan

This document contains a composite of learning activities for use in a secondary-level course in business computer applications. The collection is unique in that the individual learning activities constituting it have each been tailored to one or more of the diverse learning styles possessed by individual students. The activities are grouped into…

Predicting adherence of patients with HF through machine learning techniques.

PubMed

Karanasiou, Georgia Spiridon; Tripoliti, Evanthia Eleftherios; Papadopoulos, Theofilos Grigorios; Kalatzis, Fanis Georgios; Goletsis, Yorgos; Naka, Katerina Kyriakos; Bechlioulis, Aris; Errachid, Abdelhamid; Fotiadis, Dimitrios Ioannis

2016-09-01

Heart failure (HF) is a chronic disease characterised by poor quality of life, recurrent hospitalisation and high mortality. Adherence of patient to treatment suggested by the experts has been proven a significant deterrent of the above-mentioned serious consequences. However, the non-adherence rates are significantly high; a fact that highlights the importance of predicting the adherence of the patient and enabling experts to adjust accordingly patient monitoring and management. The aim of this work is to predict the adherence of patients with HF, through the application of machine learning techniques. Specifically, it aims to classify a patient not only as medication adherent or not, but also as adherent or not in terms of medication, nutrition and physical activity (global adherent). Two classification problems are addressed: (i) if the patient is global adherent or not and (ii) if the patient is medication adherent or not. About 11 classification algorithms are employed and combined with feature selection and resampling techniques. The classifiers are evaluated on a dataset of 90 patients. The patients are characterised as medication and global adherent, based on clinician estimation. The highest detection accuracy is 82 and 91% for the first and the second classification problem, respectively.

Semantics of User Interface for Image Retrieval: Possibility Theory and Learning Techniques.

ERIC Educational Resources Information Center

Crehange, M.; And Others

1989-01-01

Discusses the need for a rich semantics for the user interface in interactive image retrieval and presents two methods for building such interfaces: possibility theory applied to fuzzy data retrieval, and a machine learning technique applied to learning the user's deep need. Prototypes developed using videodisks and knowledge-based software are…

Collegewide Promotion of E-Learning/Active Learning and Faculty Development

ERIC Educational Resources Information Center

Ogawa, Nobuyuki; Shimizu, Akira

2016-01-01

Japanese National Institutes of Technology have revealed a plan to strongly promote e-Learning and active learning under the common schematization of education in over 50 campuses nationwide. Our e-Learning and ICT-driven education practiced for more than fifteen years were highly evaluated, and is playing a leading role in promoting e-Learning…

Pedagogical Distance: Explaining Misalignment in Student-Driven Online Learning Activities Using Activity Theory

ERIC Educational Resources Information Center

Westberry, Nicola; Franken, Margaret

2015-01-01

This paper provides an Activity Theory analysis of two online student-driven interactive learning activities to interrogate assumptions that such groups can effectively learn in the absence of the teacher. Such an analysis conceptualises learning tasks as constructed objects that drive pedagogical activity. The analysis shows a disconnect between…

Use of the Dichotic Listening Technique with Learning Disabilities

ERIC Educational Resources Information Center

Obrzut, John E.; Mahoney, Emery B.

2011-01-01

Dichotic listening (DL) techniques have been used extensively as a non-invasive procedure to assess language lateralization among children with and without learning disabilities (LD), and with individuals who have other auditory system related brain disorders. Results of studies using DL have indicated that language is lateralized in children with…

Generating a Spanish Affective Dictionary with Supervised Learning Techniques

ERIC Educational Resources Information Center

Bermudez-Gonzalez, Daniel; Miranda-Jiménez, Sabino; García-Moreno, Raúl-Ulises; Calderón-Nepamuceno, Dora

2016-01-01

Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective…

Manifold Regularized Experimental Design for Active Learning.

PubMed

Zhang, Lining; Shum, Hubert P H; Shao, Ling

2016-12-02

Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized. The problem of insufficient training data in real-world systems limits the potential applications of these approaches. This paper presents a novel method of active learning called manifold regularized experimental design (MRED), which can label multiple informative samples at one time for training. In addition, MRED gives an explicit geometric explanation for the selected samples to be labeled by the user. Different from existing active learning methods, our method avoids the intrinsic problems caused by insufficiently labeled samples in real-world applications. Various experiments on synthetic datasets, the Yale face database and the Corel image database have been carried out to show how MRED outperforms existing methods.

Active learning: a step towards automating medical concept extraction.

PubMed

Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

2016-03-01

This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined. The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional random fields as the supervised method, and least confidence and information density as 2 selection criteria for active learning framework were used. The effect of incremental learning vs standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. The following 2 clinical data sets were used for evaluation: the Informatics for Integrating Biology and the Bedside/Veteran Affairs (i2b2/VA) 2010 natural language processing challenge and the Shared Annotated Resources/Conference and Labs of the Evaluation Forum (ShARe/CLEF) 2013 eHealth Evaluation Lab. The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared with the random sampling baseline, the saving is at least doubled. Incremental active learning is a promising approach for building effective and robust medical concept extraction models while significantly reducing the burden of manual annotation. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

NASA Astrophysics Data System (ADS)

Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

2017-12-01

In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

A Novel Teaching Tool Combined With Active-Learning to Teach Antimicrobial Spectrum Activity.

PubMed

MacDougall, Conan

2017-03-25

Objective. To design instructional methods that would promote long-term retention of knowledge of antimicrobial pharmacology, particularly the spectrum of activity for antimicrobial agents, in pharmacy students. Design. An active-learning approach was used to teach selected sessions in a required antimicrobial pharmacology course. Students were expected to review key concepts from the course reader prior to the in-class sessions. During class, brief concept reviews were followed by active-learning exercises, including a novel schematic method for learning antimicrobial spectrum of activity ("flower diagrams"). Assessment. At the beginning of the next quarter (approximately 10 weeks after the in-class sessions), 360 students (three yearly cohorts) completed a low-stakes multiple-choice examination on the concepts in antimicrobial spectrum of activity. When data for students was pooled across years, the mean number of correct items was 75.3% for the items that tested content delivered with the active-learning method vs 70.4% for items that tested content delivered via traditional lecture (mean difference 4.9%). Instructor ratings on student evaluations of the active-learning approach were high (mean scores 4.5-4.8 on a 5-point scale) and student comments were positive about the active-learning approach and flower diagrams. Conclusion. An active-learning approach led to modestly higher scores in a test of long-term retention of pharmacology knowledge and was well-received by students.

A Novel Teaching Tool Combined With Active-Learning to Teach Antimicrobial Spectrum Activity

PubMed Central

2017-01-01

Objective. To design instructional methods that would promote long-term retention of knowledge of antimicrobial pharmacology, particularly the spectrum of activity for antimicrobial agents, in pharmacy students. Design. An active-learning approach was used to teach selected sessions in a required antimicrobial pharmacology course. Students were expected to review key concepts from the course reader prior to the in-class sessions. During class, brief concept reviews were followed by active-learning exercises, including a novel schematic method for learning antimicrobial spectrum of activity (“flower diagrams”). Assessment. At the beginning of the next quarter (approximately 10 weeks after the in-class sessions), 360 students (three yearly cohorts) completed a low-stakes multiple-choice examination on the concepts in antimicrobial spectrum of activity. When data for students was pooled across years, the mean number of correct items was 75.3% for the items that tested content delivered with the active-learning method vs 70.4% for items that tested content delivered via traditional lecture (mean difference 4.9%). Instructor ratings on student evaluations of the active-learning approach were high (mean scores 4.5-4.8 on a 5-point scale) and student comments were positive about the active-learning approach and flower diagrams. Conclusion. An active-learning approach led to modestly higher scores in a test of long-term retention of pharmacology knowledge and was well-received by students. PMID:28381885

Active Learning with Ubiquitous Presenter and Tablet PCs

NASA Astrophysics Data System (ADS)

Price, Edward; Simon, B.

2006-12-01

Ubiquitous Presenter (UP)* is a digital presentation system that facilitates spontaneity and interactivity in the classroom. Using the system, an instructor with a Tablet PC can spontaneously modify prepared slides. Furthermore, students with web-enabled devices can add digital 'ink' or text to the instructor's slides and submit them to the instructor during class. We have used this system to facilitate interactive engagement techniques in an introductory physics class where approximately one-third of the students had access to a Tablet PC during class. Class time was used for Interactive Lecture Demonstrations, Peer Instruction, and group problem solving. We describe the implementation of these active learning activities with UP and Tablet PCs, show examples of student contributions, and describe the impact on the classroom setting. *http://up.ucsd.edu/about/

Using assistive technology adaptations to include students with learning disabilities in cooperative learning activities.

PubMed

Bryant, D P; Bryant, B R

1998-01-01

Cooperative learning (CL) is a common instructional arrangement that is used by classroom teachers to foster academic achievement and social acceptance of students with and without learning disabilities. Cooperative learning is appealing to classroom teachers because it can provide an opportunity for more instruction and feedback by peers than can be provided by teachers to individual students who require extra assistance. Recent studies suggest that students with LD may need adaptations during cooperative learning activities. The use of assistive technology adaptations may be necessary to help some students with LD compensate for their specific learning difficulties so that they can engage more readily in cooperative learning activities. A process for integrating technology adaptations into cooperative learning activities is discussed in terms of three components: selecting adaptations, monitoring the use of the adaptations during cooperative learning activities, and evaluating the adaptations' effectiveness. The article concludes with comments regarding barriers to and support systems for technology integration, technology and effective instructional practices, and the need to consider technology adaptations for students who have learning disabilities.

Using Oceanography to Support Active Learning

NASA Astrophysics Data System (ADS)

Byfield, V.

2012-04-01

Teachers are always on the lookout for material to give their brightest students, in order to keep them occupied, stimulated and challenged, while the teacher gets on with helping the rest. They are also looking for material that can inspire and enthuse those who think that school is 'just boring!' Oceanography, well presented, has the capacity to do both. As a relatively young science, oceanography is not a core curriculum subject (possibly an advantage), but it draws on the traditional sciences of biology, chemistry, physic and geology, and can provide wonderful examples for teaching concepts in school sciences. It can also give good reasons for learning science, maths and technology. Exciting expeditions (research cruises) to far-flung places; opportunities to explore new worlds, a different angle on topical debates such as climate change, pollution, or conservation can bring a new life to old subjects. Access to 'real' data from satellites or Argo floats can be used to develop analytical and problem solving skills. The challenge is to make all this available in a form that can easily be used by teachers and students to enhance the learning experience. We learn by doing. Active teaching methods require students to develop their own concepts of what they are learning. This stimulates new neural connections in the brain - the physical manifestation of learning. There is a large body of evidence to show that active learning is much better remembered and understood. Active learning develops thinking skills through analysis, problem solving, and evaluation. It helps learners to use their knowledge in realistic and useful ways, and see its importance and relevance. Most importantly, properly used, active learning is fun. This paper presents experiences from a number of education outreach projects that have involved the National Oceanography Centre in Southampton, UK. All contain some element of active learning - from quizzes and puzzles to analysis of real data from

Introduction to the Symposium "Leading Students and Faculty to Quantitative Biology through Active Learning".

PubMed

Waldrop, Lindsay D; Miller, Laura A

2015-11-01

The broad aim of this symposium and set of associated papers is to motivate the use of inquiry-based, active-learning teaching techniques in undergraduate quantitative biology courses. Practical information, resources, and ready-to-use classroom exercises relevant to physicists, mathematicians, biologists, and engineers are presented. These resources can be used to address the lack of preparation of college students in STEM fields entering the workforce by providing experience working on interdisciplinary and multidisciplinary problems in mathematical biology in a group setting. Such approaches can also indirectly help attract and retain under-represented students who benefit the most from "non-traditional" learning styles and strategies, including inquiry-based, collaborative, and active learning. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

Learning in a u-Museum: Developing a Context-Aware Ubiquitous Learning Environment

ERIC Educational Resources Information Center

Chen, Chia-Chen; Huang, Tien-Chi

2012-01-01

Context-awareness techniques can support learners in learning without time or location constraints by using mobile devices and associated learning activities in a real learning environment. Enrichment of context-aware technologies has enabled students to learn in an environment that integrates learning resources from both the real world and the…

Active Learning Is Not Enough

ERIC Educational Resources Information Center

Casem, Merri Lynn

2006-01-01

I have examined how frequency of assessment impacts learning in an undergraduate biology course employing a student-centered, active-learning pedagogy. Frequent assessment was associated with better student performance and greater retention of course concepts. Improvement of higher-order thinking skills may require more classroom practice.…

Weekly active-learning activities in a drug information and literature evaluation course.

PubMed

Timpe, Erin M; Motl, Susannah E; Eichner, Samantha F

2006-06-15

To incorporate learning activities into the weekly 2-hour Drug Information and Literature Evaluation class sessions to improve student ability and confidence in performing course objectives, as well as to assess student perception of the value of these activities. In-class activities that emphasized content and skills taught within class periods were created and implemented. Three different surveys assessing student ability and confidence in completing drug information and literature retrieval and evaluation tasks were administered prior to and following the appropriate class sessions. At the completion of the course, an additional evaluation was administered to assess the students' impressions of the value of the learning activities. Students reported increased ability and confidence in all course objectives. The teaching activities were also stated to be useful in students' learning of the material. Incorporation of weekly learning activities resulted in an improvement in student ability and confidence to perform course objectives. Students considered these activities to be beneficial and to contribute to the completion of course objectives.

A review of active learning approaches to experimental design for uncovering biological networks

PubMed Central

2017-01-01

Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area. PMID:28570593

Active Ageing, Active Learning: Policy and Provision in Hong Kong

ERIC Educational Resources Information Center

Tam, M.

2011-01-01

This paper discusses the relationship between ageing and learning, previous literature having confirmed that participation in continued learning in old age contributes to good health, satisfaction with life, independence and self-esteem. Realizing that learning is vital to active ageing, the Hong Kong government has implemented policies and…

Developing an instrument to measure emotional behaviour abilities of meaningful learning through the Delphi technique.

PubMed

Cadorin, Lucia; Bagnasco, Annamaria; Tolotti, Angela; Pagnucci, Nicola; Sasso, Loredana

2017-09-01

To identify items for a new instrument that measures emotional behaviour abilities of meaningful learning, according to Fink's Taxonomy. Meaningful learning is an active process that promotes a wider and deeper understanding of concepts. It is the result of an interaction between new and previous knowledge and produces a long-term change of knowledge and skills. To measure meaningful learning capability, it is very important in the education of health professionals to identify problems or special learning needs. For this reason, it is necessary to create valid instruments. A Delphi Study technique was implemented in four phases by means of e-mail. The study was conducted from April-September 2015. An expert panel consisting of ten researchers with experience in Fink's Taxonomy was established to identify the items of the instrument. Data were analysed for conceptual description and item characteristics and attributes were rated. Expert consensus was sought in each of these phases. An 87·5% consensus cut-off was established. After four rounds, consensus was obtained for validation of the content of the instrument 'Assessment of Meaningful learning Behavioural and Emotional Abilities'. This instrument consists of 56 items evaluated on a 6-point Likert-type scale. Foundational Knowledge, Application, Integration, Human Dimension, Caring and Learning How to Learn were the six major categories explored. This content validated tool can help educators (teachers, trainers and tutors) to identify and improve the strategies to support students' learning capability, which could increase their awareness of and/or responsibility in the learning process. © 2017 John Wiley & Sons Ltd.

Assessing Performance through Informal Techniques. Learning Package No. 10.

ERIC Educational Resources Information Center

Nelson, Carol; Smith, Carl, Comp.

Originally developed for the Department of Defense Schools (DoDDS) system, this learning package on assessing performance through informal techniques is designed for teachers who wish to upgrade or expand their teaching skills on their own. The package includes a comprehensive search of the ERIC database; a lecture giving an overview on the topic;…

Training hydrologists to be ecohydrologists: a "how-you-can-do-it" example leveraging an active learning environment for studying plant-water interaction

NASA Astrophysics Data System (ADS)

Lyon, S. W.; Walter, M. T.; Jantze, E. J.; Archibald, J. A.

2012-08-01

Structuring an education strategy capable of addressing the various spheres of ecohydrology is difficult due to the inter-disciplinary and cross-disciplinary nature of this emergent field. Clearly, there is a need for such strategies to accommodate more progressive educational concepts while highlighting a skills-based education. To demonstrate a possible way to develop courses that include such concepts, we offer a case-study or a "how-you-can-do-it" example from an ecohydrology course recently co-taught by teachers from Stockholm University and Cornell University at the Navarino Environmental Observatory (NEO) in Costa Navarino, Greece. This course focused on introducing hydrology Master's students to some of the central concepts of ecohydrology while at the same time supplying process-based understanding relevant for characterizing evapotranspiration. As such, the main goal of the course was to explore central theories in ecohydrology and their connection to plant-water interactions and the water cycle in a semiarid environment. In addition to presenting this roadmap for ecohydrology course development, we explore the utility and effectiveness of adopting active teaching and learning strategies drawing from the suite of learn-by-doing, hands-on, and inquiry-based techniques in such a course. We test a gradient of "activeness" across a sequence of three teaching and learning activities. Our results indicate that there was a clear advantage for utilizing active learning techniques in place of traditional lecture-based styles. In addition, there was a preference among the student towards the more "active" techniques. This demonstrates the added value of incorporating even the simplest active learning approaches in our ecohydrology (or general) teaching.

Designing for Inquiry-Based Learning with the Learning Activity Management System

ERIC Educational Resources Information Center

Levy, P.; Aiyegbayo, O.; Little, S.

2009-01-01

This paper explores the relationship between practitioners' pedagogical purposes, values and practices in designing for inquiry-based learning in higher education, and the affordances of the Learning Activity Management System (LAMS) as a tool for creating learning designs in this context. Using a qualitative research methodology, variation was…

MO-E-18C-03: Incorporating Active Learning Into A Traditional Graduate Medical Physics Course

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

Burmeister, J

Purpose: To improve the ability of graduate students to learn medical physics concepts through the incorporation of active learning techniques. Methods: A traditional lecture-based radiological physics course was modified such that: (1) traditional (two-hour) lectures were provided online for students to watch prior to class, (2) a student was chosen randomly at the start of each class to give a two minute synopsis of the material and its relevance (two-minute drill), (3) lectures were significantly abbreviated and remaining classroom time used for group problem solving, and (4) videos of the abbreviated lectures were made available online for review. In themore » transition year, students were surveyed about the perceived effects of these changes on learning. Student performance was evaluated for 3 years prior to and 4 years after modification. Results: The survey tool used a five point scale from 1=Not True to 5=Very True. While nearly all students reviewed written materials prior to class (4.3±0.9), a minority watched the lectures (2.1±1.5). A larger number watched the abbreviated lectures for further clarification (3.6±1.6) and found it helpful in learning the content (4.2±1.0). Most felt that the two-minute drill helped them get more out of the lecture (3.9±0.8) and the problem solving contributed to their understanding of the content (4.1±0.8). However, no significant improvement in exam scores resulted from the modifications (mean scores well within 1 SD during study period). Conclusion: Students felt that active learning techniques improved their ability to learn the material in what is considered the most difficult course in the program. They valued the ability to review the abbreviated class lecture more than the opportunity to watch traditional lectures prior to class. While no significant changes in student performance were observed, aptitude variations across the student cohorts make it difficult to draw conclusions about the effectiveness of

Active learning reduces annotation time for clinical concept extraction.

PubMed

Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

2017-10-01

To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.

Using machine learning techniques to automate sky survey catalog generation

NASA Technical Reports Server (NTRS)

Fayyad, Usama M.; Roden, J. C.; Doyle, R. J.; Weir, Nicholas; Djorgovski, S. G.

1993-01-01

We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data.

Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques

NASA Astrophysics Data System (ADS)

Segura, Alejandra; Vidal, Christian; Menendez, Victor; Zapata, Alfredo; Prieto, Manuel

Learning object repositories provide a platform for the sharing of Web-based educational resources. As these repositories evolve independently, it is difficult for users to have a clear picture of the kind of contents they give access to. Metadata can be used to automatically extract a characterization of these resources by using machine learning techniques. This paper presents an exploratory study carried out in the contents of four public repositories that uses clustering and association rule mining algorithms to extract characterizations of repository contents. The results of the analysis include potential relationships between different attributes of learning objects that may be useful to gain an understanding of the kind of resources available and eventually develop search mechanisms that consider repository descriptions as a criteria in federated search.

Combining Active Learning with Service Learning: A Student-Driven Demonstration Project.

ERIC Educational Resources Information Center

Hatcher-Skeers, Mary; Aragon, Ellen

2002-01-01

Describes a project that integrates active learning into service learning targeting both college students and middle schools students wherein college students perform chemical demonstrations for middle school students. (YDS)

Hypermedia in Vocational Learning: A Hypermedia Learning Environment for Training Management Skills

ERIC Educational Resources Information Center

Konradt, Udo

2004-01-01

A learning environment is defined as an arrangement of issues, methods, techniques, and media in a given domain. Besides temporal and spatial features a learning environment considers the social situation in which learning takes place. In (hypermedia) learning environments the concept of exploration and the active role of the learner is…

Attitudes of Face-to-Face and E-Learning Instructors toward "Active Learning"

ERIC Educational Resources Information Center

Pundak, David; Herscovitz, Orit; Shacham, Miri

2010-01-01

Instruction in higher education has developed significantly over the past two decades, influenced by two trends: promotion of active learning methods and integration of web technology in e-Learning. Many studies found that active teaching improves students' success, involvement and thinking skills. Nevertheless, internationally, most instructors…

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review.

PubMed

Dallora, Ana Luiza; Eivazzadeh, Shahryar; Mendes, Emilia; Berglund, Johan; Anderberg, Peter

2017-01-01

Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques. To achieve our goal we carried out a systematic literature review, in which three large databases-Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables. In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer's disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable. Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be taken when interpreting the reported accuracy of ML

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

PubMed

Librenza-Garcia, Diego; Kotzian, Bruno Jaskulski; Yang, Jessica; Mwangi, Benson; Cao, Bo; Pereira Lima, Luiza Nunes; Bermudez, Mariane Bagatin; Boeira, Manuela Vianna; Kapczinski, Flávio; Passos, Ives Cavalcante

2017-09-01

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation.

PubMed

Caravaca, Juan; Soria-Olivas, Emilio; Bataller, Manuel; Serrano, Antonio J; Such-Miquel, Luis; Vila-Francés, Joan; Guerrero, Juan F

2014-02-01

This work presents the application of machine learning techniques to analyse the influence of physical exercise in the physiological properties of the heart, during ventricular fibrillation. To this end, different kinds of classifiers (linear and neural models) are used to classify between trained and sedentary rabbit hearts. The use of those classifiers in combination with a wrapper feature selection algorithm allows to extract knowledge about the most relevant features in the problem. The obtained results show that neural models outperform linear classifiers (better performance indices and a better dimensionality reduction). The most relevant features to describe the benefits of physical exercise are those related to myocardial heterogeneity, mean activation rate and activation complexity. © 2013 Published by Elsevier Ltd.

Application of Learning Engineering Techniques Thinking Aloud Pair Problem Solving in Learning Mathematics Students Class VII SMPN 15 Padang

NASA Astrophysics Data System (ADS)

Widuri, S. Y. S.; Almash, L.; Zuzano, F.

2018-04-01

The students activity and responsible in studying mathematic is still lack. It gives an effect for the bad result in studying mathematic. There is one of learning technic to increase students activity in the classroom and the result of studying mathematic with applying a learning technic. It is “Thinking Aloud Pair Problem Solving (TAPPS)”. The purpose of this research is to recognize the developing of students activity in mathematic subject during applying that technic “TAPPS” in seven grade at SMPN 15 Padang and compare the students proportion in learning mathematic with TAPPS between learning process without it in seven grade at SMPN 15 Padang. Students activity for indicators 1, 2, 3, 4, 5, 6 at each meeting is likely to increase and students activity for indicator 7 at each meeting is likely to decrease. The finding of this research is χ 2 = 9,42 and the value of p is 0,0005 < p < 0,005. Therefore p < 0,05 has means H 0 was rejected and H 1 was accepted. Thus, it was concluded that the activities and result in studying mathematic increased after applying learning technic the TAPPS.

Resource Letter ALIP-1: Active-Learning Instruction in Physics

NASA Astrophysics Data System (ADS)

Meltzer, David E.; Thornton, Ronald K.

2012-06-01

This Resource Letter provides a guide to the literature on research-based active-learning instruction in physics. These are instructional methods that are based on, assessed by, and validated through research on the teaching and learning of physics. They involve students in their own learning more deeply and more intensely than does traditional instruction, particularly during class time. The instructional methods and supporting body of research reviewed here offer potential for significantly improved learning in comparison to traditional lecture-based methods of college and university physics instruction. We begin with an introduction to the history of active learning in physics in the United States, and then discuss some methods for and outcomes of assessing pedagogical effectiveness. We enumerate and describe common characteristics of successful active-learning instructional strategies in physics. We then discuss a range of methods for introducing active-learning instruction in physics and provide references to those methods for which there is published documentation of student learning gains.

E-Collaboration Technologies in Teaching/Learning Activity

ERIC Educational Resources Information Center

Zascerinska, Jelena; Ahrens, Andreas

2009-01-01

A proper use of e-collaboration technologies in the teaching/learning process is provided by varied cooperative networks, which penetrate teachers' and students' activity more thoroughly with the availability of broadband services. However, the successful use of e-collaboration technologies in teaching/learning activity within a multicultural…

Active learning: learning a motor skill without a coach.

PubMed

Huang, Vincent S; Shadmehr, Reza; Diedrichsen, Jörn

2008-08-01

When we learn a new skill (e.g., golf) without a coach, we are "active learners": we have to choose the specific components of the task on which to train (e.g., iron, driver, putter, etc.). What guides our selection of the training sequence? How do choices that people make compare with choices made by machine learning algorithms that attempt to optimize performance? We asked subjects to learn the novel dynamics of a robotic tool while moving it in four directions. They were instructed to choose their practice directions to maximize their performance in subsequent tests. We found that their choices were strongly influenced by motor errors: subjects tended to immediately repeat an action if that action had produced a large error. This strategy was correlated with better performance on test trials. However, even when participants performed perfectly on a movement, they did not avoid repeating that movement. The probability of repeating an action did not drop below chance even when no errors were observed. This behavior led to suboptimal performance. It also violated a strong prediction of current machine learning algorithms, which solve the active learning problem by choosing a training sequence that will maximally reduce the learner's uncertainty about the task. While we show that these algorithms do not provide an adequate description of human behavior, our results suggest ways to improve human motor learning by helping people choose an optimal training sequence.

LEARN: Playful Techniques To Accelerate Learning.

ERIC Educational Resources Information Center

Richards, Regina G.

The methods outlined in this guide offer teachers a variety of ways to stimulate interest, enhance concentration, increase understanding, and improve memory in their students. Chapter 1 discusses the LEARN (Learning Efficiently And Remembering Mnemonics) system, a set of strategies that help students use a variety of processing styles to a greater…

Empathy and feedback processing in active and observational learning.

PubMed

Rak, Natalia; Bellebaum, Christian; Thoma, Patrizia

2013-12-01

The feedback-related negativity (FRN) and the P300 have been related to the processing of one's own and other individuals' feedback during both active and observational learning. The aim of the present study was to elucidate the role of trait-empathic responding with regard to the modulation of the neural correlates of observational learning in particular. Thirty-four healthy participants completed an active and an observational learning task. On both tasks, the participants' aim was to maximize their monetary gain by choosing from two stimuli the one that showed the higher probability of reward. Participants gained insight into the stimulus-reward contingencies according to monetary feedback presented after they had made an active choice or by observing the choices of a virtual partner. Participants showed a general improvement in learning performance on both learning tasks. P200, FRN, and P300 amplitudes were larger during active, as compared with observational, learning. Furthermore, nonreward elicited a significantly more negative FRN than did reward in the active learning task, while only a trend was observed for observational learning. Distinct subcomponents of trait cognitive empathy were related to poorer performance and smaller P300 amplitudes for observational learning only. Taken together, both the learning performance and event-related potentials during observational learning are affected by different aspects of trait cognitive empathy, and certain types of observational learning may actually be disrupted by a higher tendency to understand and adopt other people's perspectives.

Developing Metacognition: A Basis for Active Learning

ERIC Educational Resources Information Center

Vos, Henk; de Graaff, E.

2004-01-01

The reasons to introduce formats of active learning in engineering (ALE) such as project work, problem-based learning, use of cases, etc. are mostly based on practical experience, and sometimes from applied research on teaching and learning. Such research shows that students learn more and different abilities than in traditional formats of…

Perceptions of the use of intelligent information access systems in university level active learning activities among teachers of biomedical subjects.

PubMed

Aparicio, Fernando; Morales-Botello, María Luz; Rubio, Margarita; Hernando, Asunción; Muñoz, Rafael; López-Fernández, Hugo; Glez-Peña, Daniel; Fdez-Riverola, Florentino; de la Villa, Manuel; Maña, Manuel; Gachet, Diego; Buenaga, Manuel de

2018-04-01

Student participation and the use of active methodologies in classroom learning are being increasingly emphasized. The use of intelligent systems can be of great help when designing and developing these types of activities. Recently, emerging disciplines such as 'educational data mining' and 'learning analytics and knowledge' have provided clear examples of the importance of the use of artificial intelligence techniques in education. The main objective of this study was to gather expert opinions regarding the benefits of using complementary methods that are supported by intelligent systems, specifically, by intelligent information access systems, when processing texts written in natural language and the benefits of using these methods as companion tools to the learning activities that are employed by biomedical and health sciences teachers. Eleven teachers of degree courses who belonged to the Faculties of Biomedical Sciences (BS) and Health Sciences (HS) of a Spanish university in Madrid were individually interviewed. These interviews were conducted using a mixed methods questionnaire that included 66 predefined close-ended and open-ended questions. In our study, three intelligent information access systems (i.e., BioAnnote, CLEiM and MedCMap) were successfully used to evaluate the teacher's perceptions regarding the utility of these systems and their different methods in learning activities. All teachers reported using active learning methods in the classroom, most of which were computer programs that were used for initially designing and later executing learning activities. All teachers used case-based learning methods in the classroom, with a specific emphasis on case reports written in Spanish and/or English. In general, few or none of the teachers were familiar with the technical terms related to the technologies used for these activities such as "intelligent systems" or "concept/mental maps". However, they clearly realized the potential applicability of such

A Systematic Characterization of Cognitive Techniques for Learning from Textual and Pictorial Representations

ERIC Educational Resources Information Center

Ploetzner, Rolf; Lowe, Richard; Schlag, Sabine

2013-01-01

Pictorial representations can play a pivotal role in both printed and digital learning material. Although there has been extensive research on cognitive techniques and strategies for learning from text, the same cannot be said for static and dynamic pictorial representations. In this paper we propose a systematic characterization of cognitive…

Alternative Assessment Techniques for Blended and Online Courses

ERIC Educational Resources Information Center

Litchfield, Brenda C.; Dempsey, John V.

2013-01-01

Alternative assessment techniques are essential for increasing student learning in blended and online courses. Rather than simply answer multiple-choice questions, students can choose activities in an academic contract. By using a contract, students will be active participants in their own learning. Contracts add a dimension of authenticity to…

Captivate: Building Blocks for Implementing Active Learning

ERIC Educational Resources Information Center

Kitchens, Brent; Means, Tawnya; Tan, Yinliang

2018-01-01

In this study, the authors propose a set of key elements that impact the success of an active learning implementation: content delivery, active learning methods, physical environment, technology enhancement, incentive alignment, and educator investment. Through a range of metrics the authors present preliminary evidence that students in courses…

Tractor Mechanics: Learning Activity Packages 1-19.

ERIC Educational Resources Information Center

Clemson Univ., SC. Vocational Education Media Center.

Learning activity packages are presented for teaching tractor mechanics. The first of two sections deals with miscellaneous tasks and contains learning activity packages on cleaning the tractor and receiving new tractor parts. Section 2 is concerned with maintaining and servicing the electrical system, and it includes the following learning…

Active Learning and Engagement with the Wireless Indoor Location Device (WILD) Learning System

NASA Astrophysics Data System (ADS)

Moldwin, M.; Samson, P. J.; Ojeda, L.; Miller, T.; Yu, J.

2016-12-01

The Wireless Indoor Location Device (WILD) Learning System being developed at the University of Michigan and the Education Technology company A2 Motus LLC provides a unique platform for social learning by allowing students to become active participants in live simulations of complex systems, like hurricane formation. The WILD Learning System enables teachers to engage students in kinesthetic activities that explore complex models from a wide variety of STEAM (Science, Technology, Engineering, Art and Math) disciplines. The system provides students' location, orientation and motion within the classroom and assigns each student different parameters depending on the activity. For example, students learning about hurricanes could be assigned atmospheric pressure levels and asked to arrange themselves around the room to simulate a hurricane. The Wild Learning System software then takes the students' pressure readings and locations and projects their locations overlaid onto a real-time generated simulated pressure weather map enabling the observation of how their arrangement influences the pressure structure. The teacher then could have the students orient themselves in the direction they think the resulting wind field will be based on the pressure contours as the system can show an arrow originating from each of the students position in the direction that they are facing. The system also could incorporate a student response-type system for the instructor to then directly question students about other concepts and record their response to both the kinesthetic activity and other formative assessment questions. The WILD Learning System consists of a sensor package for each student in the class, beacons to enable precise localization of the students, software to calculate student location information, and educational software for a variety of activities. In addition, a software development kit (SDK) is under development that would allow others to create additional learning

Psychological and Pedagogic Conditions of Activating Creative Activity in Students for Successful Learning

ERIC Educational Resources Information Center

Abykanova, Bakytgul; Bilyalova, Zhupar; Makhatova, Valentina; Idrissov, Salamat; Nugumanov, Samal

2016-01-01

Creative activity of a pedagogic process subject depends on the pedagogue's position, on his faith in the abilities to learn successfully, on encouragement of achievements, stimulating the initiative and activity. Successful learning by activating creative activity is possible with the presence of respectful attitude towards the pedagogic process…

Use of Advanced Machine-Learning Techniques for Non-Invasive Monitoring of Hemorrhage

DTIC Science & Technology

2010-04-01

that state-of-the-art machine learning techniques when integrated with novel non-invasive monitoring technologies could detect subtle, physiological...decompensation. Continuous, non-invasively measured hemodynamic signals (e.g., ECG, blood pressures, stroke volume) were used for the development of machine ... learning algorithms. Accuracy estimates were obtained by building models using 27 subjects and testing on the 28th. This process was repeated 28 times

Faculty Perceptions about Barriers to Active Learning

ERIC Educational Resources Information Center

Michael, Joel

2007-01-01

Faculty may perceive many barriers to active learning in their classrooms. Four groups of participants in a faculty development workshop were asked to list their perceived barriers to active learning. Many of the problems identified were present on more than one list. The barriers fall into three categories: student characteristics, issues…

Assessing and Improving Learning in Business Schools: Direct and Indirect Measures of Learning

ERIC Educational Resources Information Center

Weldy, Teresa G.; Turnipseed, David L.

2010-01-01

Institutions of higher education are scrambling to make program changes to improve the quality of learning and assessment of learning in the face of pressure from multiple constituencies. Business educators are incorporating various active learning techniques to enhance learning and application of skills and knowledge to real-world situations.…

Who is that masked educator? Deconstructing the teaching and learning processes of an innovative humanistic simulation technique.

PubMed

McAllister, Margaret; Searl, Kerry Reid; Davis, Susan

2013-12-01

Simulation learning in nursing has long made use of mannequins, standardized actors and role play to allow students opportunity to practice technical body-care skills and interventions. Even though numerous strategies have been developed to mimic or amplify clinical situations, a common problem that is difficult to overcome in even the most well-executed simulation experiences, is that students may realize the setting is artificial and fail to fully engage, remember or apply the learning. Another problem is that students may learn technical competence but remain uncertain about communicating with the person. Since communication capabilities are imperative in human service work, simulation learning that only achieves technical competence in students is not fully effective for the needs of nursing education. Furthermore, while simulation learning is a burgeoning space for innovative practices, it has been criticized for the absence of a basis in theory. It is within this context that an innovative simulation learning experience named "Mask-Ed (KRS simulation)", has been deconstructed and the active learning components examined. Establishing a theoretical basis for creative teaching and learning practices provides an understanding of how, why and when simulation learning has been effective and it may help to distinguish aspects of the experience that could be improved. Three conceptual theoretical fields help explain the power of this simulation technique: Vygotskian sociocultural learning theory, applied theatre and embodiment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Active Learning: The Importance of Developing a Comprehensive Measure

ERIC Educational Resources Information Center

Carr, Rodney; Palmer, Stuart; Hagel, Pauline

2015-01-01

This article reports on an investigation into the validity of a widely used scale for measuring the extent to which higher education students employ active learning strategies. The scale is the active learning scale in the Australasian Survey of Student Engagement. This scale is based on the Active and Collaborative Learning scale of the National…

Age-related impairments in active learning and strategic visual exploration.

PubMed

Brandstatt, Kelly L; Voss, Joel L

2014-01-01

Old age could impair memory by disrupting learning strategies used by younger individuals. We tested this possibility by manipulating the ability to use visual-exploration strategies during learning. Subjects controlled visual exploration during active learning, thus permitting the use of strategies, whereas strategies were limited during passive learning via predetermined exploration patterns. Performance on tests of object recognition and object-location recall was matched for younger and older subjects for objects studied passively, when learning strategies were restricted. Active learning improved object recognition similarly for younger and older subjects. However, active learning improved object-location recall for younger subjects, but not older subjects. Exploration patterns were used to identify a learning strategy involving repeat viewing. Older subjects used this strategy less frequently and it provided less memory benefit compared to younger subjects. In previous experiments, we linked hippocampal-prefrontal co-activation to improvements in object-location recall from active learning and to the exploration strategy. Collectively, these findings suggest that age-related memory problems result partly from impaired strategies during learning, potentially due to reduced hippocampal-prefrontal co-engagement.

Enhancing Learning Performance and Adaptability for Complex Tasks

DTIC Science & Technology

2005-03-30

development of active learning interventions and techniques that influence the focus and quality of learner regulatory activity (Kozlowski Toney et al...what are the effects of these goal representations on learning strategies, performance, and adaptability? Can active learning inductions, that influence...and mindful process - active learning - are generally associated with improved skill acquisition and adaptability for complex tasks (Smith et al

Active learning machine learns to create new quantum experiments.

PubMed

Melnikov, Alexey A; Poulsen Nautrup, Hendrik; Krenn, Mario; Dunjko, Vedran; Tiersch, Markus; Zeilinger, Anton; Briegel, Hans J

2018-02-06

How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments-a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.

Classroom Activities: Simple Strategies to Incorporate Student-Centered Activities within Undergraduate Science Lectures

PubMed Central

Lom, Barbara

2012-01-01

The traditional science lecture, where an instructor delivers a carefully crafted monolog to a large audience of students who passively receive the information, has been a popular mode of instruction for centuries. Recent evidence on the science of teaching and learning indicates that learner-centered, active teaching strategies can be more effective learning tools than traditional lectures. Yet most colleges and universities retain lectures as their central instructional method. This article highlights several simple collaborative teaching techniques that can be readily deployed within traditional lecture frameworks to promote active learning. Specifically, this article briefly introduces the techniques of: reader’s theatre, think-pair-share, roundtable, jigsaw, in-class quizzes, and minute papers. Each technique is broadly applicable well beyond neuroscience courses and easily modifiable to serve an instructor’s specific pedagogical goals. The benefits of each technique are described along with specific examples of how each technique might be deployed within a traditional lecture to create more active learning experiences. PMID:23494568

Celebrating Service and Learning

ERIC Educational Resources Information Center

Emeagwali, Susan; Berkey, Lisa; Guempel, Martha

2010-01-01

This month's "Techniques" magazine celebrates service-learning and the contributions that it makes to students' learning by fostering civic engagement while students learn in hands-on, real-world contexts. For close to half a century, service-learning has spread throughout schools in the United States as students engage in activities as diverse as…

Assessing Student Behaviors and Motivation for Actively Learning Biology

ERIC Educational Resources Information Center

Moore, Michael Edward

2017-01-01

Vision and Change states that one of the major changes in the way we design biology courses should be a switch in approach from teacher-centered learning to student-centered learning and identifies active learning as a recommended methods. Studies show performance benefits for students taking courses that use active learning. What is unknown is…

TECHNIQUES FOR TEACHING CONSERVATION EDUCATION.

ERIC Educational Resources Information Center

BROWN, ROBERT E.; MOUSER, G.W.

CONSERVATION PRINCIPLES, FIELD METHODS AND TECHNIQUES, AND SPECIFIC FIELD LEARNING ACTIVITIES ARE INCLUDED IN THIS REFERENCE VOLUME FOR TEACHERS. CONSERVATION PRINCIPLES INCLUDE STATEMENTS PERTAINING TO (1) SOIL, (2) WATER, (3) FOREST, AND (4) WILDLIFE. FIELD METHODS AND TECHNIQUES INCLUDE (1) PREPARING FOR A FIELD TRIP, (2) GETTING STUDENT…

Collegial Activity Learning between Heterogeneous Sensors.

PubMed

Feuz, Kyle D; Cook, Diane J

2017-11-01

Activity recognition algorithms have matured and become more ubiquitous in recent years. However, these algorithms are typically customized for a particular sensor platform. In this paper we introduce PECO, a Personalized activity ECOsystem, that transfers learned activity information seamlessly between sensor platforms in real time so that any available sensor can continue to track activities without requiring its own extensive labeled training data. We introduce a multi-view transfer learning algorithm that facilitates this information handoff between sensor platforms and provide theoretical performance bounds for the algorithm. In addition, we empirically evaluate PECO using datasets that utilize heterogeneous sensor platforms to perform activity recognition. These results indicate that not only can activity recognition algorithms transfer important information to new sensor platforms, but any number of platforms can work together as colleagues to boost performance.

Action Research to Improve the Learning Space for Diagnostic Techniques.

PubMed

Ariel, Ellen; Owens, Leigh

2015-12-01

The module described and evaluated here was created in response to perceived learning difficulties in diagnostic test design and interpretation for students in third-year Clinical Microbiology. Previously, the activities in lectures and laboratory classes in the module fell into the lower cognitive operations of "knowledge" and "understanding." The new approach was to exchange part of the traditional activities with elements of interactive learning, where students had the opportunity to engage in deep learning using a variety of learning styles. The effectiveness of the new curriculum was assessed by means of on-course student assessment throughout the module, a final exam, an anonymous questionnaire on student evaluation of the different activities and a focus group of volunteers. Although the new curriculum enabled a major part of the student cohort to achieve higher pass grades (p < 0.001), it did not meet the requirements of the weaker students, and the proportion of the students failing the module remained at 34%. The action research applied here provided a number of valuable suggestions from students on how to improve future curricula from their perspective. Most importantly, an interactive online program that facilitated flexibility in the learning space for the different reagents and their interaction in diagnostic tests was proposed. The methods applied to improve and assess a curriculum refresh by involving students as partners in the process, as well as the outcomes, are discussed. Journal of Microbiology & Biology Education.

Using Machine Learning Techniques in the Analysis of Oceanographic Data

NASA Astrophysics Data System (ADS)

Falcinelli, K. E.; Abuomar, S.

2017-12-01

Acoustic Doppler Current Profilers (ADCPs) are oceanographic tools capable of collecting large amounts of current profile data. Using unsupervised machine learning techniques such as principal component analysis, fuzzy c-means clustering, and self-organizing maps, patterns and trends in an ADCP dataset are found. Cluster validity algorithms such as visual assessment of cluster tendency and clustering index are used to determine the optimal number of clusters in the ADCP dataset. These techniques prove to be useful in analysis of ADCP data and demonstrate potential for future use in other oceanographic applications.

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.

Introducing Social Stratification and Inequality: An Active Learning Technique.

ERIC Educational Resources Information Center

McCammon, Lucy

1999-01-01

Summarizes literature on techniques for teaching social stratification. Describes the three parts of an exercise that enables students to understand economic and political inequality: students are given a family scenario, create household budgets, and finally rework the national budget with their family scenario groups. Discusses student…

An integrative review of in-class activities that enable active learning in college science classroom settings

NASA Astrophysics Data System (ADS)

Arthurs, Leilani A.; Kreager, Bailey Zo

2017-10-01

Engaging students in active learning is linked to positive learning outcomes. This study aims to synthesise the peer-reviewed literature about 'active learning' in college science classroom settings. Using the methodology of an integrative literature review, 337 articles archived in the Educational Resources Information Center (ERIC) are examined. Four categories of in-class activities emerge: (i) individual non-polling activities, (ii) in-class polling activities, (iii) whole-class discussion or activities, and (iv) in-class group activities. Examining the collection of identified in-class activities through the lens of a theoretical framework informed by constructivism and social interdependence theory, we synthesise the reviewed literature to propose the active learning strategies (ALSs) model and the instructional decisions to enable active learning (IDEAL) theory. The ALS model characterises in-class activities in terms of the degrees to which they are designed to promote (i) peer interaction and (ii) social interdependence. The IDEAL theory includes the ALS model and provides a framework for conceptualising different levels of the general concept 'active learning' and how these levels connect to instructional decision-making about using in-class activities. The proposed ALS model and IDEAL theory can be utilised to inform instructional decision-making and future research about active learning in college science courses.

Academic Controversy in Macroeconomics: An Active and Collaborative Method to Increase Student Learning

ERIC Educational Resources Information Center

Santicola, Craig F.

2015-01-01

The literature indicates that there is a lack of learning outcomes in economics that can be attributed to the reliance on traditional lecture and the failure to adopt innovative instructional techniques. This study sought to investigate the student learning effects of academic controversy, a cooperative learning technique that shows promise in the…

Effect of Learning Activity on Students' Motivation, Physical Activity Levels and Effort/Persistence

ERIC Educational Resources Information Center

Gao, Zan; Lee, Amelia M.; Xiang, Ping; Kosma, Maria

2011-01-01

The type of learning activity offered in physical education may influence students' motivational beliefs, physical activity participation and effort/persistence in class. However, most empirical studies have focused on the individual level rather than on the learner-content interactions. Accordingly, the potential effects of learning activities on…

Design and Implementation of an Object Oriented Learning Activity System

ERIC Educational Resources Information Center

Lin, Huan-Yu; Tseng, Shian-Shyong; Weng, Jui-Feng; Su, Jun-Ming

2009-01-01

With the development of e-learning technology, many specifications of instructional design have been proposed to make learning activity sharable and reusable. With the specifications and sufficient learning resources, the researches further focus on how to provide learners more appropriate learning activities to improve their learning performance.…

Student Buy-In to Active Learning in a College Science Course

PubMed Central

Cavanagh, Andrew J.; Aragón, Oriana R.; Chen, Xinnian; Couch, Brian; Durham, Mary; Bobrownicki, Aiyana; Hanauer, David I.; Graham, Mark J.

2016-01-01

The benefits of introducing active learning in college science courses are well established, yet more needs to be understood about student buy-in to active learning and how that process of buy-in might relate to student outcomes. We test the exposure–persuasion–identification–commitment (EPIC) process model of buy-in, here applied to student (n = 245) engagement in an undergraduate science course featuring active learning. Student buy-in to active learning was positively associated with engagement in self-regulated learning and students’ course performance. The positive associations among buy-in, self-regulated learning, and course performance suggest buy-in as a potentially important factor leading to student engagement and other student outcomes. These findings are particularly salient in course contexts featuring active learning, which encourage active student participation in the learning process. PMID:27909026

Exploring Characteristics of Fine-Grained Behaviors of Learning Mathematics in Tablet-Based E-Learning Activities

ERIC Educational Resources Information Center

Yeung, Cheuk Yu; Shum, Kam Hong; Hui, Lucas Chi Kwong; Chu, Samuel Kai Wah; Chan, Tsing Yun; Kuo, Yung Nin; Ng, Yee Ling

2017-01-01

Attributes of teaching and learning contexts provide rich information about how students participate in learning activities. By tracking and analyzing snapshots of these attributes captured continuously throughout the duration of the learning activities, teachers can identify individual students who need special attention and apply different…

Prediction in Health Domain Using Bayesian Networks Optimization Based on Induction Learning Techniques

NASA Astrophysics Data System (ADS)

Felgaer, Pablo; Britos, Paola; García-Martínez, Ramón

A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

Active Learning in the Physics Classroom

NASA Astrophysics Data System (ADS)

Naron, Carol

Many students enter physics classes filled with misconceptions about physics concepts. Students tend to retain these misconceptions into their adult lives, even after physics instruction. Constructivist researchers have found that students gain understanding through their experiences. Researchers have also found that active learning practices increase conceptual understanding of introductory physics students. This project study sought to examine whether incorporating active learning practices in an advanced placement physics classroom increased conceptual understanding as measured by the force concept inventory (FCI). Physics students at the study site were given the FCI as both a pre- and posttest. Test data were analyzed using two different methods---a repeated-measures t test and the Hake gain method. The results of this research project showed that test score gains were statistically significant, as measured by the t test. The Hake gain results indicated a low (22.5%) gain for the class. The resulting project was a curriculum plan for teaching the mechanics portion of Advanced Placement (AP) physics B as well as several active learning classroom practices supported by the research. This project will allow AP physics teachers an opportunity to improve their curricular practices. Locally, the results of this project study showed that research participants gained understanding of physics concepts. Social change may occur as teachers implement active learning strategies, thus creating improved student understanding of physics concepts.

Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review

PubMed Central

Mendes, Emilia; Berglund, Johan; Anderberg, Peter

2017-01-01

Background Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. Objective The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques. Method To achieve our goal we carried out a systematic literature review, in which three large databases—Pubmed, Socups and Web of Science were searched to select studies that employed machine learning or microsimulation techniques for the prognosis of dementia. A single backward snowballing was done to identify further studies. A quality checklist was also employed to assess the quality of the evidence presented by the selected studies, and low quality studies were removed. Finally, data from the final set of studies were extracted in summary tables. Results In total 37 papers were included. The data summary results showed that the current research is focused on the investigation of the patients with mild cognitive impairment that will evolve to Alzheimer’s disease, using machine learning techniques. Microsimulation studies were concerned with cost estimation and had a populational focus. Neuroimaging was the most commonly used variable. Conclusions Prediction of conversion from MCI to AD is the dominant theme in the selected studies. Most studies used ML techniques on Neuroimaging data. Only a few data sources have been recruited by most studies and the ADNI database is the one most commonly used. Only two studies have investigated the prediction of epidemiological aspects of Dementia using either ML or MS techniques. Finally, care should be

Active-learning versus teacher-centered instruction for learning acids and bases

NASA Astrophysics Data System (ADS)

Acar Sesen, Burcin; Tarhan, Leman

2011-07-01

Background and purpose: Active-learning as a student-centered learning process has begun to take more interest in constructing scientific knowledge. For this reason, this study aimed to investigate the effectiveness of active-learning implementation on high-school students' understanding of 'acids and bases'. Sample The sample of this study was 45 high-school students (average age 17 years) from two different classes, which were randomly assigned to the experimental (n = 21) and control groups (n = 25), in a high school in Turkey. Design and methods A pre-test consisting of 25 items was applied to both experimental and control groups before the treatment in order to identify student prerequisite knowledge about their proficiency for learning 'acids and bases'. A one-way analysis of variance (ANOVA) was conducted to compare the pre-test scores for groups and no significant difference was found between experimental (ME = 40.14) and control groups (MC = 41.92) in terms of mean scores (F 1,43 = 2.66, p > 0.05). The experimental group was taught using an active-learning curriculum developed by the authors and the control group was taught using traditional course content based on teacher-centered instruction. After the implementation, 'Acids and Bases Achievement Test' scores were collected for both groups. Results ANOVA results showed that students' 'Acids and Bases Achievement Test' post-test scores differed significantly in terms of groups (F 1,43 = 102.53; p < 0.05). Additionally, in this study 54 misconceptions, 14 of them not reported in the literature before, were observed in the following terms: 'acid and base theories'; 'metal and non-metal oxides'; 'acid and base strengths'; 'neutralization'; 'pH and pOH'; 'hydrolysis'; 'acid-base equilibrium'; 'buffers'; 'indicators'; and 'titration'. Based on the achievement test and individual interview results, it was found that high-school students in the experimental group had fewer misconceptions and understood the

Diverse expected gradient active learning for relative attributes.

PubMed

You, Xinge; Wang, Ruxin; Tao, Dacheng

2014-07-01

The use of relative attributes for semantic understanding of images and videos is a promising way to improve communication between humans and machines. However, it is extremely labor- and time-consuming to define multiple attributes for each instance in large amount of data. One option is to incorporate active learning, so that the informative samples can be actively discovered and then labeled. However, most existing active-learning methods select samples one at a time (serial mode), and may therefore lose efficiency when learning multiple attributes. In this paper, we propose a batch-mode active-learning method, called diverse expected gradient active learning. This method integrates an informativeness analysis and a diversity analysis to form a diverse batch of queries. Specifically, the informativeness analysis employs the expected pairwise gradient length as a measure of informativeness, while the diversity analysis forces a constraint on the proposed diverse gradient angle. Since simultaneous optimization of these two parts is intractable, we utilize a two-step procedure to obtain the diverse batch of queries. A heuristic method is also introduced to suppress imbalanced multiclass distributions. Empirical evaluations of three different databases demonstrate the effectiveness and efficiency of the proposed approach.

Diverse Expected Gradient Active Learning for Relative Attributes.

PubMed

You, Xinge; Wang, Ruxin; Tao, Dacheng

2014-06-02

The use of relative attributes for semantic understanding of images and videos is a promising way to improve communication between humans and machines. However, it is extremely labor- and time-consuming to define multiple attributes for each instance in large amount of data. One option is to incorporate active learning, so that the informative samples can be actively discovered and then labeled. However, most existing active-learning methods select samples one at a time (serial mode), and may therefore lose efficiency when learning multiple attributes. In this paper, we propose a batch-mode active-learning method, called Diverse Expected Gradient Active Learning (DEGAL). This method integrates an informativeness analysis and a diversity analysis to form a diverse batch of queries. Specifically, the informativeness analysis employs the expected pairwise gradient length as a measure of informativeness, while the diversity analysis forces a constraint on the proposed diverse gradient angle. Since simultaneous optimization of these two parts is intractable, we utilize a two-step procedure to obtain the diverse batch of queries. A heuristic method is also introduced to suppress imbalanced multi-class distributions. Empirical evaluations of three different databases demonstrate the effectiveness and efficiency of the proposed approach.

Active-learning strategies: the use of a game to reinforce learning in nursing education. A case study.

PubMed

Boctor, Lisa

2013-03-01

The majority of nursing students are kinesthetic learners, preferring a hands-on, active approach to education. Research shows that active-learning strategies can increase student learning and satisfaction. This study looks at the use of one active-learning strategy, a Jeopardy-style game, 'Nursopardy', to reinforce Fundamentals of Nursing material, aiding in students' preparation for a standardized final exam. The game was created keeping students varied learning styles and the NCLEX blueprint in mind. The blueprint was used to create 5 categories, with 26 total questions. Student survey results, using a five-point Likert scale showed that they did find this learning method enjoyable and beneficial to learning. More research is recommended regarding learning outcomes, when using active-learning strategies, such as games. Copyright © 2012 Elsevier Ltd. All rights reserved.

Telling Active Learning Pedagogies Apart: From Theory to Practice

ERIC Educational Resources Information Center

Cattaneo, Kelsey Hood

2017-01-01

Designing learning environments to incorporate active learning pedagogies is difficult as definitions are often contested and intertwined. This article seeks to determine whether classification of active learning pedagogies (i.e., project-based, problem-based, inquiry-based, case-based, and discovery-based), through theoretical and practical…

Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

NASA Astrophysics Data System (ADS)

Tsakmalis, Anestis; Chatzinotas, Symeon; Ottersten, Bjorn

2018-02-01

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) accessing the frequency band of a Primary User (PU) in an underlay cognitive scenario with a designed PU protection specification. The main idea is that the CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback indicates whether the probing-induced interference is harmful or not and can be used to learn the PU interference constraint. The cognitive part of this sequential probing process is the selection of the power levels of the Secondary Users (SUs) which aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window. This constrained design problem is studied within the Active Learning (AL) framework and an optimal solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP). The performance of this solution is also demonstrated through numerical simulations and compared with modified versions of AL techniques we developed in earlier work.

Active-learning implementation in an advanced elective course on infectious diseases.

PubMed

Hidayat, Levita; Patel, Shreya; Veltri, Keith

2012-06-18

To describe the development, implementation, and assessment of an advanced elective course on infectious diseases using active-learning strategies. Pedagogy for active learning was incorporated by means of mini-lecture, journal club, and debate with follow-up discussion. Forty-eight students were enrolled in this 4-week elective course, in which 30% of course time was allocated for active-learning exercises. All activities were fundamentally designed as a stepwise approach in complementing each active-learning exercise. Achievement of the course learning objectives was assessed using a 5-point Likert scale survey instrument. Students' awareness of the significance of antimicrobial resistance was improved (p ≤ 0.05). Students' ability to critically evaluate the infectious-disease literature and its application in informed clinical judgments was also enhanced through these active-learning exercises (p ≤ 0.05). Students agreed that active learning should be part of the pharmacy curriculum and that active-learning exercises improved their critical-thinking, literature-evaluation, and self-learning skills. An elective course using active-learning strategies allowed students to combine information gained from the evaluation of infectious-disease literature, critical thinking, and informed clinical judgment. This blended approach ultimately resulted in an increased knowledge and awareness of infectious diseases.

Active-Learning Implementation in an Advanced Elective Course on Infectious Diseases

PubMed Central

Patel, Shreya; Veltri, Keith

2012-01-01

Objectives. To describe the development, implementation, and assessment of an advanced elective course on infectious diseases using active-learning strategies. Design. Pedagogy for active learning was incorporated by means of mini-lecture, journal club, and debate with follow-up discussion. Forty-eight students were enrolled in this 4-week elective course, in which 30% of course time was allocated for active-learning exercises. All activities were fundamentally designed as a stepwise approach in complementing each active-learning exercise. Assessment. Achievement of the course learning objectives was assessed using a 5-point Likert scale survey instrument. Students’ awareness of the significance of antimicrobial resistance was improved (p ≤ 0.05). Students’ ability to critically evaluate the infectious-disease literature and its application in informed clinical judgments was also enhanced through these active-learning exercises (p ≤ 0.05). Students agreed that active learning should be part of the pharmacy curriculum and that active-learning exercises improved their critical-thinking, literature-evaluation, and self-learning skills. Conclusion. An elective course using active-learning strategies allowed students to combine information gained from the evaluation of infectious-disease literature, critical thinking, and informed clinical judgment. This blended approach ultimately resulted in an increased knowledge and awareness of infectious diseases. PMID:22761528

Student Activity and Learning Outcomes in a Virtual Learning Environment

ERIC Educational Resources Information Center

Romanov, Kalle; Nevgi, Anne

2008-01-01

The aim of the study was to explore the relationship between degree of participation and learning outcomes in an e-learning course on medical informatics. Overall activity in using course materials and degree of participation in the discussion forums of an online course were studied among 39 medical students. Students were able to utilise the…

Active inference and learning.

PubMed

Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O Doherty, John; Pezzulo, Giovanni

2016-09-01

This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation

PubMed Central

O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-01-01

Background Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. Objective The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. Methods We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was

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.

Improving Student Engagement in Learning Activities.

ERIC Educational Resources Information Center

Adams, Nancy; And Others

Engaging students seriously in their own academic learning is a persistent difficulty for teachers. The goal of this action research project was to actively involve elementary school students in their learning. The program was implemented at three elementary schools in northern Illinois serving multicultural populations; special education…

DASL-Data and Activities for Solar Learning

NASA Technical Reports Server (NTRS)

Jones, Harrison P.; Henney, Carl; Hill, Frank; Gearen, Michael; Pompca, Stephen; Stagg, Travis; Stefaniak, Linda; Walker, Connie

2004-01-01

DASL-Data and Activities for Solar Learning Data and Activities for Solar Learning (DASL) provides a classroom learning environment based on a twenty-five year record of solar magnetograms from the National Solar Observatory (NSO) at Kitt Peak, AZ. The data, together with image processing software for Macs or PCs, can be used to learn basic facts about the Sun and astronomy at the middle school level. At the high school level, students can study properties of the Sun's magnetic cycle with classroom exercises emphasizing data and error analysis and can participate in a new scientific study, Research in Active Solar Longitudes (RASL), in collaboration with classrooms throughout the country and scientists at NSO and NASA. We present a half-day course to train teachers in the scientific content of the project and its classroom use. We will provide a compact disc with the data and software and will demonstrate software installation and use, classroom exercises, and participation in RASL with computer projection.

Moments of movement: active learning and practice development.

PubMed

Dewing, Jan

2010-01-01

As our understanding of practice development becomes more sophisticated, we enhance our understanding of how the facilitation of learning in and from practice, can be more effectively achieved. This paper outlines an approach for enabling and maximizing learning within practice development known as 'Active Learning'. It considers how, given establishing a learning culture is a prerequisite for the sustainability of PD within organisations, practice developers can do more to maximize learning for practitioners and other stakeholders. Active Learning requires that more attention be given by organisations committed to PD, at a corporate and strategic level for how learning strategies are developed in the workplace. Specifically, a move away from a heavy reliance on training may be required. Practice development facilitators also need to review: how they organise and offer learning, so that learning strategies are consistent with the vision, aims and processes of PD; have skills in the planning, delivery and evaluation of learning as part of their role and influence others who provide more traditional methods of training and education.

Active learning in the space engineering education at Technical University of Madrid

NASA Astrophysics Data System (ADS)

Rodríguez, Jacobo; Laverón-Simavilla, Ana; Lapuerta, Victoria; Ezquerro Navarro, Jose Miguel; Cordero-Gracia, Marta

This work describes the innovative activities performed in the field of space education at the Technical University of Madrid (UPM), in collaboration with the center engaged by the European Space Agency (ESA) in Spain to support the operations for scientific experiments on board the International Space Station (E-USOC). These activities have been integrated along the last academic year of the Aerospatiale Engineering degree. A laboratory has been created, where the students have to validate and integrate the subsystems of a microsatellite by using demonstrator satellites. With the acquired skills, the students participate in a training process centered on Project Based Learning, where the students work in groups to perform the conceptual design of a space mission, being each student responsible for the design of a subsystem of the satellite and another one responsible of the mission design. In parallel, the students perform a training using a ground station, installed at the E-USOC building, which allow them to learn how to communicate with satellites, how to download telemetry and how to process the data. This also allows students to learn how the E-USOC works. Two surveys have been conducted to evaluate the impact of these techniques in the student engineering skills and to know the degree of satisfaction of students with respect to the use of these learning methodologies.

Semantic-gap-oriented active learning for multilabel image annotation.

PubMed

Tang, Jinhui; Zha, Zheng-Jun; Tao, Dacheng; Chua, Tat-Seng

2012-04-01

User interaction is an effective way to handle the semantic gap problem in image annotation. To minimize user effort in the interactions, many active learning methods were proposed. These methods treat the semantic concepts individually or correlatively. However, they still neglect the key motivation of user feedback: to tackle the semantic gap. The size of the semantic gap of each concept is an important factor that affects the performance of user feedback. User should pay more efforts to the concepts with large semantic gaps, and vice versa. In this paper, we propose a semantic-gap-oriented active learning method, which incorporates the semantic gap measure into the information-minimization-based sample selection strategy. The basic learning model used in the active learning framework is an extended multilabel version of the sparse-graph-based semisupervised learning method that incorporates the semantic correlation. Extensive experiments conducted on two benchmark image data sets demonstrated the importance of bringing the semantic gap measure into the active learning process.

Active cycle of breathing technique for cystic fibrosis.

PubMed

Mckoy, Naomi A; Wilson, Lisa M; Saldanha, Ian J; Odelola, Olaide A; Robinson, Karen A

2016-07-05

People with cystic fibrosis experience chronic airway infections as a result of mucus build up within the lungs. Repeated infections often cause lung damage and disease. Airway clearance therapies aim to improve mucus clearance, increase sputum production, and improve airway function. The active cycle of breathing technique (also known as ACBT) is an airway clearance method that uses a cycle of techniques to loosen airway secretions including breathing control, thoracic expansion exercises, and the forced expiration technique. This is an update of a previously published review. To compare the clinical effectiveness of the active cycle of breathing technique with other airway clearance therapies in cystic fibrosis. We searched the Cochrane Cystic Fibrosis Trials Register, compiled from electronic database searches and handsearching of journals and conference abstract books. We also searched the reference lists of relevant articles and reviews.Date of last search: 25 April 2016. Randomised or quasi-randomised controlled clinical studies, including cross-over studies, comparing the active cycle of breathing technique with other airway clearance therapies in cystic fibrosis. Two review authors independently screened each article, abstracted data and assessed the risk of bias of each study. Our search identified 62 studies, of which 19 (440 participants) met the inclusion criteria. Five randomised controlled studies (192 participants) were included in the meta-analysis; three were of cross-over design. The 14 remaining studies were cross-over studies with inadequate reports for complete assessment. The study size ranged from seven to 65 participants. The age of the participants ranged from six to 63 years (mean age 22.33 years). In 13 studies, follow up lasted a single day. However, there were two long-term randomised controlled studies with follow up of one to three years. Most of the studies did not report on key quality items, and therefore, have an unclear risk of

It takes biking to learn: Physical activity improves learning a second language.

PubMed

Liu, Fengqin; Sulpizio, Simone; Kornpetpanee, Suchada; Job, Remo

2017-01-01

Recent studies have shown that concurrent physical activity enhances learning a completely unfamiliar L2 vocabulary as compared to learning it in a static condition. In this paper we report a study whose aim is twofold: to test for possible positive effects of physical activity when L2 learning has already reached some level of proficiency, and to test whether the assumed better performance when engaged in physical activity is limited to the linguistic level probed at training (i.e. L2 vocabulary tested by means of a Word-Picture Verification task), or whether it extends also to the sentence level (which was tested by means of a Sentence Semantic Judgment Task). The results show that Chinese speakers with basic knowledge of English benefited from physical activity while learning a set of new words. Furthermore, their better performance emerged also at the sentential level, as shown by their performance in a Semantic Judgment task. Finally, an interesting temporal asymmetry between the lexical and the sentential level emerges, with the difference between the experimental and control group emerging from the 1st testing session at the lexical level but after several weeks at the sentential level.

It takes biking to learn: Physical activity improves learning a second language.

PubMed Central

Liu, Fengqin; Sulpizio, Simone; Kornpetpanee, Suchada; Job, Remo

2017-01-01

Recent studies have shown that concurrent physical activity enhances learning a completely unfamiliar L2 vocabulary as compared to learning it in a static condition. In this paper we report a study whose aim is twofold: to test for possible positive effects of physical activity when L2 learning has already reached some level of proficiency, and to test whether the assumed better performance when engaged in physical activity is limited to the linguistic level probed at training (i.e. L2 vocabulary tested by means of a Word-Picture Verification task), or whether it extends also to the sentence level (which was tested by means of a Sentence Semantic Judgment Task). The results show that Chinese speakers with basic knowledge of English benefited from physical activity while learning a set of new words. Furthermore, their better performance emerged also at the sentential level, as shown by their performance in a Semantic Judgment task. Finally, an interesting temporal asymmetry between the lexical and the sentential level emerges, with the difference between the experimental and control group emerging from the 1st testing session at the lexical level but after several weeks at the sentential level. PMID:28542333

Enhanced Quality Control in Pharmaceutical Applications by Combining Raman Spectroscopy and Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Martinez, J. C.; Guzmán-Sepúlveda, J. R.; Bolañoz Evia, G. R.; Córdova, T.; Guzmán-Cabrera, R.

2018-06-01

In this work, we applied machine learning techniques to Raman spectra for the characterization and classification of manufactured pharmaceutical products. Our measurements were taken with commercial equipment, for accurate assessment of variations with respect to one calibrated control sample. Unlike the typical use of Raman spectroscopy in pharmaceutical applications, in our approach the principal components of the Raman spectrum are used concurrently as attributes in machine learning algorithms. This permits an efficient comparison and classification of the spectra measured from the samples under study. This also allows for accurate quality control as all relevant spectral components are considered simultaneously. We demonstrate our approach with respect to the specific case of acetaminophen, which is one of the most widely used analgesics in the market. In the experiments, commercial samples from thirteen different laboratories were analyzed and compared against a control sample. The raw data were analyzed based on an arithmetic difference between the nominal active substance and the measured values in each commercial sample. The principal component analysis was applied to the data for quantitative verification (i.e., without considering the actual concentration of the active substance) of the difference in the calibrated sample. Our results show that by following this approach adulterations in pharmaceutical compositions can be clearly identified and accurately quantified.

Medical Student Perspectives of Active Learning: A Focus Group Study.

PubMed

Walling, Anne; Istas, Kathryn; Bonaminio, Giulia A; Paolo, Anthony M; Fontes, Joseph D; Davis, Nancy; Berardo, Benito A

2017-01-01

Phenomenon: Medical student perspectives were sought about active learning, including concerns, challenges, perceived advantages and disadvantages, and appropriate role in the educational process. Focus groups were conducted with students from all years and campuses of a large U.S. state medical school. Students had considerable experience with active learning prior to medical school and conveyed accurate understanding of the concept and its major strategies. They appreciated the potential of active learning to deepen and broaden learning and its value for long-term professional development but had significant concerns about the efficiency of the process, the clarity of expectations provided, and the importance of receiving preparatory materials. Most significantly, active learning experiences were perceived as disconnected from grading and even as impeding preparation for school and national examinations. Insights: Medical students understand the concepts of active learning and have considerable experience in several formats prior to medical school. They are generally supportive of active learning concepts but frustrated by perceived inefficiencies and lack of contribution to the urgencies of achieving optimal grades and passing United States Medical Licensing Examinations, especially Step 1.

Teacher feedback during active learning: current practices in primary schools.

PubMed

van den Bergh, Linda; Ros, Anje; Beijaard, Douwe

2013-06-01

Feedback is one of the most powerful tools, which teachers can use to enhance student learning. It appears difficult for teachers to give qualitatively good feedback, especially during active learning. In this context, teachers should provide facilitative feedback that is focused on the development of meta-cognition and social learning. The purpose of the present study is to contribute to the existing knowledge about feedback and to give directions to improve teacher feedback in the context of active learning. The participants comprised 32 teachers who practiced active learning in the domain of environmental studies in the sixth, seventh, or eighth grade of 13 Dutch primary schools. A total of 1,465 teacher-student interactions were examined. Video observations were made of active learning lessons in the domain of environmental studies. A category system was developed based on the literature and empirical data. Teacher-student interactions were assessed using this system. Results. About half of the teacher-student interactions contained feedback. This feedback was usually focused on the tasks that were being performed by the students and on the ways in which these tasks were processed. Only 5% of the feedback was explicitly related to a learning goal. In their feedback, the teachers were directing (rather than facilitating) the learning processes. During active learning, feedback on meta-cognition and social learning is important. Feedback should be explicitly related to learning goals. In practice, these kinds of feedback appear to be scarce. Therefore, giving feedback during active learning seems to be an important topic for teachers' professional development. © 2012 The British Psychological Society.

Towards Online Delivery of Process-Oriented Guided Inquiry Learning Techniques in Information Technology Courses

ERIC Educational Resources Information Center

Trevathan, Jarrod; Myers, Trina

2013-01-01

Process-Oriented Guided Inquiry Learning (POGIL) is a technique used to teach in large lectures and tutorials. It invokes interaction, team building, learning and interest through highly structured group work. Currently, POGIL has only been implemented in traditional classroom settings where all participants are physically present. However,…

Child Development: An Active Learning Approach

ERIC Educational Resources Information Center

Levine, Laura E.; Munsch, Joyce

2010-01-01

Within each chapter of this innovative topical text, the authors engage students by demonstrating the wide range of real-world applications of psychological research connected to child development. In particular, the distinctive Active Learning features incorporated throughout the book foster a dynamic and personal learning process for students.…

Active Learning in an Introductory Meteorology Class

NASA Astrophysics Data System (ADS)

Marchese, P. J.; Bluestone, C.

2007-12-01

Active learning modules were introduced to the primarily minority population in the introductory meteorology class at Queensborough Community College (QCC). These activities were developed at QCC and other 4 year colleges and designed to reinforce basic meteorological concepts. The modules consisted of either Interactive Lecture Demonstrations (ILD) or discovery-based activities. During the ILD the instructor would describe an experiment that would be demonstrated in class. Students would predict what the outcome would be and compare their expected results to the actual outcome of the experiment. In the discovery-based activities students would learn about physical concepts by performing basic experiments. These activities differed from the traditional lab in that it avoided "cookbook" procedures and emphasized having the students learn about the concept using the scientific method. As a result of these activities student scores measuring conceptual understanding, as well as factual knowledge, increased as compared to student scores in a more affluent community college. Students also had higher self- efficacy scores. Lower scoring students demonstrated the greatest benefit, while the better students had little (or no) changes.

Activating Metacognition through Online Learning Log (OLL)

ERIC Educational Resources Information Center

Kurt, Mustafa

2007-01-01

This study aims to investigate the activation process of metacognition of learners who systematically reflect on their learning using Online Learning Logs (OLL) which were designed to encourage them to think about learning. The study is qualitative and attempts to identify the metacognitive strategies of learners and their attitudes towards OLL.…

Using Stable Carbon and Nitrogen Isotopes of Hair to Teach about Sustainable Agriculture through Active Learning

ERIC Educational Resources Information Center

Cotton, Jennifer M.; Sheldon, Nathan D.

2013-01-01

The call for reform of science education is nearly three decades old (National Commission on Excellence in Education, 1983), but the implementation of such education improvements in the form of active learning techniques in large enrollment classes remains difficult. Here we present a class project designed to increase student involvement and…

Generation of Tutorial Dialogues: Discourse Strategies for Active Learning

DTIC Science & Technology

1998-05-29

AND SUBTITLE Generation of Tutorial Dialogues: Discourse Strategies for active Learning AUTHORS Dr. Martha Evens 7. PERFORMING ORGANI2ATION NAME...time the student starts in on a new topic. Michael and Rovick constantly attempt to promote active learning . They regularly use hints and only resort...Controlling active learning : How tutors decide when to generate hints. Proceedings of FLAIRS 󈨣. Melbourne Beach, FL. 157-161. Hume, G., Michael

Teacher’s Perception about the Use of E-Learning/Edmodo in Educational Activities

NASA Astrophysics Data System (ADS)

Yanti, H.; Setiawan, A.; Nurhabibah; Yannuar

2018-02-01

This study examined the perception of the teachers about the use of e- learning/Edmodo in their educational activities. The teachers consist of diverse subject. Their perceptions were investigated in terms of three aspects: effects of the use of this technology on their perceived motivation, the perceived usefulness and the perceived ease of use of this technology. Edmodo was set up a Learning Management System (LMS) in an online discussion group of subject. The study was conducted in descriptive method. The data were collected by using a questionnaire, interview, and documentation technique. The findings of the study indicated that the teachers perceived that e-learning/Edmodo is a useful and also easy to use technology. It was found out that the teachers are satisfied with advantages of the use of this new technology in their LMS.

[Supporting an Academic Society with the Active Learning Tool Clica].

PubMed

Arai, Kensuke; Mitsubori, Masahiro

2018-01-01

　Within school classrooms, Active Learning has been receiving unprecedented attention. Indeed, Active Learning's popularity does not stop in the classroom. As more and more people argue that the Japanese government needs to renew guidelines for education, Active Learning has surfaced as a method capable of providing the necessary knowledge and training for people in all areas of society, helping them reach their full potential. It has become accepted that Active Learning is more effective over the passive listening of lectures, where there is little to no interaction. Active Learning emphasizes that learners explain their thoughts, ask questions, and express their opinions, resulting in a better retention rate of the subject at hand. In this review, I introduce an Active Learning support tool developed at Digital Knowledge, "Clica". This tool is currently being used at many educational institutions. I will also introduce an online questionnaire that Digital Knowledge provided at the 10th Annual Meeting of the Japanese Society for Pharmaceutical Palliative Care and Sciences.

Cultivating ICT Students' Interpersonal Soft Skills in Online Learning Environments Using Traditional Active Learning Techniques

ERIC Educational Resources Information Center

Myers, Trina S.; Blackman, Anna; Andersen, Trevor; Hay, Rachel; Lee, Ickjai; Gray, Heather

2014-01-01

Flexible online delivery of tertiary ICT programs is experiencing rapid growth. Creating an online environment that develops team building and interpersonal skills is difficult due to factors such as student isolation and the individual-centric model of online learning that encourages discrete study rather than teamwork. Incorporating teamwork…

MLS student active learning within a "cloud" technology program.

PubMed

Tille, Patricia M; Hall, Heather

2011-01-01

In November 2009, the MLS program in a large public university serving a geographically large, sparsely populated state instituted an initiative for the integration of technology enhanced teaching and learning within the curriculum. This paper is intended to provide an introduction to the system requirements and sample instructional exercises used to create an active learning technology-based classroom. Discussion includes the following: 1.) define active learning and the essential components, 2.) summarize teaching methods, technology and exercises utilized within a "cloud" technology program, 3.) describe a "cloud" enhanced classroom and programming 4.) identify active learning tools and exercises that can be implemented into laboratory science programs, and 5.) describe the evaluation and assessment of curriculum changes and student outcomes. The integration of technology in the MLS program is a continual process and is intended to provide student-driven active learning experiences.

Graduate Faculty Perceptions of Experiential Learning Activities in Multicultural Classrooms

ERIC Educational Resources Information Center

Su, Yu-Han

2012-01-01

Current graduate programs employ many effective teaching methods. One of these methods, using experiential learning activities (Lee & Caffarella, 1994) in class, includes the subcomponents of cooperative learning, self-directed learning, and active learning. While these methods are commonly used, not much scholarly literature has examined the…

An Active Learning Activity to Reinforce the Design Components of the Corticosteroids

PubMed Central

Mandela, Prashant

2018-01-01

Despite the popularity of active learning applications over the past few decades, few activities have been reported for the field of medicinal chemistry. The purpose of this study is to report a new active learning activity, describe participant contributions, and examine participant performance on the assessment questions mapped to the objective covered by the activity. In this particular activity, students are asked to design two novel corticosteroids as a group (6–8 students per group) based on the design characteristics of marketed corticosteroids covered in lecture coupled with their pharmaceutics knowledge from the previous semester and then defend their design to the class through an interactive presentation model. Although class performance on the objective mapped to this material on the assessment did not reach statistical significance, use of this activity has allowed fruitful discussion of misunderstood concepts and facilitated multiple changes to the lecture presentation. As pharmacy schools continue to emphasize alternative learning pedagogies, publication of previously implemented activities demonstrating their use will help others apply similar methodologies. PMID:29401733

An Active Learning Activity to Reinforce the Design Components of the Corticosteroids.

PubMed

Slauson, Stephen R; Mandela, Prashant

2018-02-05

Despite the popularity of active learning applications over the past few decades, few activities have been reported for the field of medicinal chemistry. The purpose of this study is to report a new active learning activity, describe participant contributions, and examine participant performance on the assessment questions mapped to the objective covered by the activity. In this particular activity, students are asked to design two novel corticosteroids as a group (6-8 students per group) based on the design characteristics of marketed corticosteroids covered in lecture coupled with their pharmaceutics knowledge from the previous semester and then defend their design to the class through an interactive presentation model. Although class performance on the objective mapped to this material on the assessment did not reach statistical significance, use of this activity has allowed fruitful discussion of misunderstood concepts and facilitated multiple changes to the lecture presentation. As pharmacy schools continue to emphasize alternative learning pedagogies, publication of previously implemented activities demonstrating their use will help others apply similar methodologies.

Changing University Students' Alternative Conceptions of Optics by Active Learning

ERIC Educational Resources Information Center

Hadžibegovic, Zalkida; Sliško, Josip

2013-01-01

Active learning is individual and group participation in effective activities such as in-class observing, writing, experimenting, discussion, solving problems, and talking about to-be-learned topics. Some instructors believe that active learning is impossible, or at least extremely difficult to achieve in large lecture sessions. Nevertheless, the…

An active-learning laboratory on immunizations.

PubMed

Donohoe, Krista L; Mawyer, Tonya M; Stevens, J Tyler; Morgan, Laura A; Harpe, Spencer E

2012-12-12

To implement and evaluate an active-learning laboratory activity to teach pharmacy students about influenza, pneumococcal, and shingles vaccines. The laboratory session was divided into 6 immunization stations: 3 stations on influenza including a pediatrics station, and 1 station each for pneumococcal, shingles, and anaphylaxis. Although 118 of 123 (95.9%) students had completed an immunization training certificate prior to attending the laboratory, the average score on a pre-assessment to measure immunization knowledge and confidence was 56%. The post-assessment score was 87.4%. Students' confidence improved by 18.7% to 51.2% in each of the 5 areas assessed. Most respondents rated the activity overall as good or excellent on a post-activity evaluation. An active-learning approach to teaching immunizations allowed students to gain knowledge in simulated real-world experiences and reinforced key concepts on influenza, pneumococcal, and shingles vaccines.

The Teacher's Sourcebook for Cooperative Learning: Practical Techniques, Basic Principles, and Frequently Asked Questions.

ERIC Educational Resources Information Center

Jacobs, George M.; Power, Michael A.; Inn, Loh Wan

This book demonstrates how classroom teachers can use cooperative learning techniques for lesson planning and classroom management. It emphasizes that cooperation among students is powerful, and it notes that just because students are in a group does not mean that they are cooperating. Part 1, "Getting Started with Cooperative Learning," includes…

Active Learning Strategies in Face-to-Face Courses. IDEA Paper #53

ERIC Educational Resources Information Center

Millis, Barbara J.

2012-01-01

As numerous research studies suggest, teachers who desire increased student learning should adopt active learning. This article explores the research, defines active learning, discusses its value, offers suggestions for implementing it, and provides six concrete examples of active learning approaches: Thinking-Aloud Pair Problem-Solving;…

Predicting reading and mathematics from neural activity for feedback learning.

PubMed

Peters, Sabine; Van der Meulen, Mara; Zanolie, Kiki; Crone, Eveline A

2017-01-01

Although many studies use feedback learning paradigms to study the process of learning in laboratory settings, little is known about their relevance for real-world learning settings such as school. In a large developmental sample (N = 228, 8-25 years), we investigated whether performance and neural activity during a feedback learning task predicted reading and mathematics performance 2 years later. The results indicated that feedback learning performance predicted both reading and mathematics performance. Activity during feedback learning in left superior dorsolateral prefrontal cortex (DLPFC) predicted reading performance, whereas activity in presupplementary motor area/anterior cingulate cortex (pre-SMA/ACC) predicted mathematical performance. Moreover, left superior DLPFC and pre-SMA/ACC activity predicted unique variance in reading and mathematics ability over behavioral testing of feedback learning performance alone. These results provide valuable insights into the relationship between laboratory-based learning tasks and learning in school settings, and the value of neural assessments for prediction of school performance over behavioral testing alone. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Workshop on active learning: two examples

NASA Astrophysics Data System (ADS)

Ben Lakhdar, Zohra; Lahmar, Souad; Lakshminarayanan, Vasudevan

2014-07-01

Optics is an enabling science that has far ranging importance in many diverse fields. However, many students do not find it to be of great interest. A solution to this problem is to train teachers in active learning methodologies so that the subject matter can be presented to generate student interest. We describe a workshop to present an example of an active learning process in Optics developed for training of teachers in developing countries (a UNESCO project) and will focus on 2 two different activities: 1. Interference and diffraction is considered by students as being very hard to understand and is taught in most developing countries as purely theoretical with almost no experiments. Simple experiments to enhance the conceptual understanding of these wave phenomena will be presented and 2. Image formation by the eye. Here we will discuss myopia, hyperopia and astigmatism as well as accommodation. In this module we will discuss image. The objective of the workshop will be to provide an experience of the use of the active learning method in optics including the use of experiments, mind's on and hands-on exercises, group and class discussions

Teaching Engineering with Autonomous Learning Activities

ERIC Educational Resources Information Center

Otero, Beatriz; Rodríguez, Eva; Royo, Pablo

2015-01-01

This paper proposes several activities that encourage self-learning in engineering courses. For each activity, the context and the pedagogical issues addressed are described emphasizing strengths and weaknesses. Specifically, this work describes and implements five activities, which are: questionnaires, conceptual maps, videos, jigsaw and…

Mobile-Assisted Seamless Learning Activities in Higher Distance Education

ERIC Educational Resources Information Center

Amhag, Lisbeth

2017-01-01

Among online learning factors stated in the research literature, it is argued that online activities is the strongest factor which contributes to online learning. This article illuminates mobile-assisted seamless learning activities by using laptops, tablets, or smart phones. Two conditions are compared, a) face-to-face (F2F) online webinars…

Active Learning in Secondary Schools: Educational Media and Technology.

ERIC Educational Resources Information Center

Page, Marilyn

The incorporation of media and technology into the classroom does not ensure the enhancement of student learning. Research has shown that students learn more through active participation in their own learning process. From 1984 to 1987, a teacher's secondary social studies students were actively involved in the National History Day Program (NHDP),…

Prediction of lung cancer patient survival via supervised machine learning classification techniques.

PubMed

Lynch, Chip M; Abdollahi, Behnaz; Fuqua, Joshua D; de Carlo, Alexandra R; Bartholomai, James A; Balgemann, Rayeanne N; van Berkel, Victor H; Frieboes, Hermann B

2017-12-01

Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble. Key data attributes in applying these methods include tumor grade, tumor size, gender, age, stage, and number of primaries, with the goal to enable comparison of predictive power between the various methods The prediction is treated like a continuous target, rather than a classification into categories, as a first step towards improving survival prediction. The results show that the predicted values agree with actual values for low to moderate survival times, which constitute the majority of the data. The best performing technique was the custom ensemble with a Root Mean Square Error (RMSE) value of 15.05. The most influential model within the custom ensemble was GBM, while Decision Trees may be inapplicable as it had too few discrete outputs. The results further show that among the five individual models generated, the most accurate was GBM with an RMSE value of 15.32. Although SVM underperformed with an RMSE value of 15.82, statistical analysis singles the SVM as the only model that generated a distinctive output. The results of the models are consistent with a classical Cox proportional hazards model used as a reference technique. We conclude that application of these supervised learning techniques to lung cancer data in the SEER database may be of use to estimate patient survival time

The Effect of Cooperative Learning Techniques on College Students' Reading Comprehension

ERIC Educational Resources Information Center

Jalilifar, Alireza

2010-01-01

This study investigated the impact of Student Team Achievement Divisions (STAD) and Group Investigation (GI), which are two techniques of Cooperative Learning, on students' reading comprehension achievement of English as a Foreign Language (EFL). After administering an English Language Proficiency test (Fowler and Coe, 1976), 90 homogeneous…

New Techniques for Deep Learning with Geospatial Data using TensorFlow, Earth Engine, and Google Cloud Platform

NASA Astrophysics Data System (ADS)

Hancher, M.

2017-12-01

Recent years have seen promising results from many research teams applying deep learning techniques to geospatial data processing. In that same timeframe, TensorFlow has emerged as the most popular framework for deep learning in general, and Google has assembled petabytes of Earth observation data from a wide variety of sources and made them available in analysis-ready form in the cloud through Google Earth Engine. Nevertheless, developing and applying deep learning to geospatial data at scale has been somewhat cumbersome to date. We present a new set of tools and techniques that simplify this process. Our approach combines the strengths of several underlying tools: TensorFlow for its expressive deep learning framework; Earth Engine for data management, preprocessing, postprocessing, and visualization; and other tools in Google Cloud Platform to train TensorFlow models at scale, perform additional custom parallel data processing, and drive the entire process from a single familiar Python development environment. These tools can be used to easily apply standard deep neural networks, convolutional neural networks, and other custom model architectures to a variety of geospatial data structures. We discuss our experiences applying these and related tools to a range of machine learning problems, including classic problems like cloud detection, building detection, land cover classification, as well as more novel problems like illegal fishing detection. Our improved tools will make it easier for geospatial data scientists to apply modern deep learning techniques to their own problems, and will also make it easier for machine learning researchers to advance the state of the art of those techniques.

Musical Peddy-Paper: A Collaborative Learning Activity Suported by Augmented Reality

ERIC Educational Resources Information Center

Gomes, José Duarte Cardoso; Figueiredo, Mauro Jorge Guerreiro; Amante, Lúcia da Graça Cruz Domingues; Gomes, Cristina Maria Cardoso

2014-01-01

Gaming activities are an integral part of the human learning process, in particular for children. Game-based learning focuses on motivation and children's engagement towards learning. Educational game-based activities are becoming effective strategies to enhance the learning process. This paper presents an educational activity focusing to merge…

Active Learning to Improve Presentation Skills: The Use of Pecha Kucha in Undergraduate Sales Management Classes

ERIC Educational Resources Information Center

McDonald, Robert E.; Derby, Joseph M.

2015-01-01

Recruiters seek candidates with certain business skills that are not developed in the typical lecture-based classroom. Instead, active-learning techniques have been shown to be effective in honing these skills. One skill that is particularly important in sales careers is the ability to make a powerful and effective presentation. To help students…

The Validation of the Active Learning in Health Professions Scale

ERIC Educational Resources Information Center

Kammer, Rebecca; Schreiner, Laurie; Kim, Young K.; Denial, Aurora

2015-01-01

There is a need for an assessment tool for evaluating the effectiveness of active learning strategies such as problem-based learning in promoting deep learning and clinical reasoning skills within the dual environments of didactic and clinical settings in health professions education. The Active Learning in Health Professions Scale (ALPHS)…

Enhancing Learning Outcomes through Application Driven Activities in Marketing

ERIC Educational Resources Information Center

Stegemann, Nicole; Sutton-Brady, Catherine

2013-01-01

This paper introduces an activity used in class to allow students to apply previously acquired information to a hands-on task. As the authors have previously shown active learning is a way to effectively facilitate and improve students' learning outcomes. As a result to improve learning outcomes we have overtime developed a series of learning…

Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

PubMed

Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

2015-08-01

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Quantum Speedup for Active Learning Agents

NASA Astrophysics Data System (ADS)

Paparo, Giuseppe Davide; Dunjko, Vedran; Makmal, Adi; Martin-Delgado, Miguel Angel; Briegel, Hans J.

2014-07-01

Can quantum mechanics help us build intelligent learning agents? A defining signature of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in real-life situations is the size and complexity of the corresponding task environment. Even in a moderately realistic environment, it may simply take too long to rationally respond to a given situation. If the environment is impatient, allowing only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all. Here, we show that quantum physics can help and provide a quadratic speedup for active learning as a genuine problem of artificial intelligence. This result will be particularly relevant for applications involving complex task environments.

Accelerated Learning: Madness with a Method.

ERIC Educational Resources Information Center

Zemke, Ron

1995-01-01

Accelerated learning methods have evolved into a variety of holistic techniques that involve participants in the learning process and overcome negative attitudes about learning. These components are part of the mix: the brain, learning environment, music, imaginative activities, suggestion, positive mental state, the arts, multiple intelligences,…

Active and Reflective Learning to Engage All Students

ERIC Educational Resources Information Center

McCoy, Bryan

2013-01-01

This article describes how teachers effectively manage learning through active engagement of all students throughout each class period. A case study is presented which demonstrates how students learn through active and reflective engagement with ideas, the environment, and other learners (National Middle School Association, 2010). The case study…

Learning Microbiology through Cooperation: Designing Cooperative Learning Activities That Promote Interdependence, Interaction, and Accountability.

ERIC Educational Resources Information Center

Trempy, Janine E.; Skinner, Monica M.; Siebold, William A.

2002-01-01

Describes the course "The World According to Microbes" which puts science, mathematics, engineering, and technology majors into teams of students charged with problem solving activities that are microbial in origin. Describes the development of learning activities that utilize key components of cooperative learning including positive…

Exploring the Earth Using Deep Learning Techniques

NASA Astrophysics Data System (ADS)

Larraondo, P. R.; Evans, B. J. K.; Antony, J.

2016-12-01

Research using deep neural networks have significantly matured in recent times, and there is now a surge in interest to apply such methods to Earth systems science and the geosciences. When combined with Big Data, we believe there are opportunities for significantly transforming a number of areas relevant to researchers and policy makers. In particular, by using a combination of data from a range of satellite Earth observations as well as computer simulations from climate models and reanalysis, we can gain new insights into the information that is locked within the data. Global geospatial datasets describe a wide range of physical and chemical parameters, which are mostly available using regular grids covering large spatial and temporal extents. This makes them perfect candidates to apply deep learning methods. So far, these techniques have been successfully applied to image analysis through the use of convolutional neural networks. However, this is only one field of interest, and there is potential for many more use cases to be explored. The deep learning algorithms require fast access to large amounts of data in the form of tensors and make intensive use of CPU in order to train its models. The Australian National Computational Infrastructure (NCI) has recently augmented its Raijin 1.2 PFlop supercomputer with hardware accelerators. Together with NCI's 3000 core high performance OpenStack cloud, these computational systems have direct access to NCI's 10+ PBytes of datasets and associated Big Data software technologies (see http://geonetwork.nci.org.au/ and http://nci.org.au/systems-services/national-facility/nerdip/). To effectively use these computing infrastructures requires that both the data and software are organised in a way that readily supports the deep learning software ecosystem. Deep learning software, such as the open source TensorFlow library, has allowed us to demonstrate the possibility of generating geospatial models by combining information from

Machine-Learning Techniques Applied to Antibacterial Drug Discovery

PubMed Central

Durrant, Jacob D.; Amaro, Rommie E.

2014-01-01

The emergence of drug-resistant bacteria threatens to catapult humanity back to the pre-antibiotic era. Even now, multi-drug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the resulting vacuum. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics in an academic setting, leading to improved hit rates and faster transitions to pre-clinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field. PMID:25521642

Distributed and Problem-based Learning Techniques for the Family Communication Course.

ERIC Educational Resources Information Center

LeBlanc, H. Paul, III

Current technological advances have made possible teaching techniques which were previously impossible. Distance and distributed learning technologies have made it possible to instruct outside of the classroom setting. An advantage to this advance includes that ability to reach students who are unable to relocate to the university. However, there…

Effects of Sharing Clickers in an Active Learning Environment

ERIC Educational Resources Information Center

Daniel, Todd; Tivener, Kristin

2016-01-01

Scientific research into learning enhancement gained by the use of clickers in active classrooms has largely focused on the use of individual clickers. In this study, we compared the learning experiences of participants in active learning groups in which an entire small group shared a single clicker to groups in which each member of the group had…

Learning Setting-Generalized Activity Models for Smart Spaces

PubMed Central

Cook, Diane J.

2011-01-01

The data mining and pervasive computing technologies found in smart homes offer unprecedented opportunities for providing context-aware services, including health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to provide these services, smart environment algorithms need to recognize and track activities that people normally perform as part of their daily routines. However, activity recognition has typically involved gathering and labeling large amounts of data in each setting to learn a model for activities in that setting. We hypothesize that generalized models can be learned for common activities that span multiple environment settings and resident types. We describe our approach to learning these models and demonstrate the approach using eleven CASAS datasets collected in seven environments. PMID:21461133

Towards large-scale FAME-based bacterial species identification using machine learning techniques.

PubMed

Slabbinck, Bram; De Baets, Bernard; Dawyndt, Peter; De Vos, Paul

2009-05-01

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species

RoboResource Technology Learning Activities.

ERIC Educational Resources Information Center

Keck, Tom, Comp.; Frye, Ellen, Ed.

Preparing students to be successful in a rapidly changing world means showing them how to use the tools of technology and how to integrate those tools into all areas of learning. This booklet is divided into three sections: Design Activities, Experiments, and Resources. The design activities ask students to collaborate on design projects. In these…

Environmental Monitoring Networks Optimization Using Advanced Active Learning Algorithms

NASA Astrophysics Data System (ADS)

Kanevski, Mikhail; Volpi, Michele; Copa, Loris

2010-05-01

The problem of environmental monitoring networks optimization (MNO) belongs to one of the basic and fundamental tasks in spatio-temporal data collection, analysis, and modeling. There are several approaches to this problem, which can be considered as a design or redesign of monitoring network by applying some optimization criteria. The most developed and widespread methods are based on geostatistics (family of kriging models, conditional stochastic simulations). In geostatistics the variance is mainly used as an optimization criterion which has some advantages and drawbacks. In the present research we study an application of advanced techniques following from the statistical learning theory (SLT) - support vector machines (SVM) and the optimization of monitoring networks when dealing with a classification problem (data are discrete values/classes: hydrogeological units, soil types, pollution decision levels, etc.) is considered. SVM is a universal nonlinear modeling tool for classification problems in high dimensional spaces. The SVM solution is maximizing the decision boundary between classes and has a good generalization property for noisy data. The sparse solution of SVM is based on support vectors - data which contribute to the solution with nonzero weights. Fundamentally the MNO for classification problems can be considered as a task of selecting new measurement points which increase the quality of spatial classification and reduce the testing error (error on new independent measurements). In SLT this is a typical problem of active learning - a selection of the new unlabelled points which efficiently reduce the testing error. A classical approach (margin sampling) to active learning is to sample the points closest to the classification boundary. This solution is suboptimal when points (or generally the dataset) are redundant for the same class. In the present research we propose and study two new advanced methods of active learning adapted to the solution of

Postnatal TLR2 activation impairs learning and memory in adulthood.

PubMed

Madar, Ravit; Rotter, Aviva; Waldman Ben-Asher, Hiba; Mughal, Mohamed R; Arumugam, Thiruma V; Wood, W H; Becker, K G; Mattson, Mark P; Okun, Eitan

2015-08-01

Neuroinflammation in the central nervous system is detrimental for learning and memory, as evident form epidemiological studies linking developmental defects and maternal exposure to harmful pathogens. Postnatal infections can also induce neuroinflammatory responses with long-term consequences. These inflammatory responses can lead to motor deficits and/or behavioral disabilities. Toll like receptors (TLRs) are a family of innate immune receptors best known as sensors of microbial-associated molecular patterns, and are the first responders to infection. TLR2 forms heterodimers with either TLR1 or TLR6, is activated in response to gram-positive bacterial infections, and is expressed in the brain during embryonic development. We hypothesized that early postnatal TLR2-mediated neuroinflammation would adversely affect cognitive behavior in the adult. Our data indicate that postnatal TLR2 activation affects learning and memory in adult mice in a heterodimer-dependent manner. TLR2/6 activation improved motor function and fear learning, while TLR2/1 activation impaired spatial learning and enhanced fear learning. Moreover, developmental TLR2 deficiency significantly impairs spatial learning and enhances fear learning, stressing the involvement of the TLR2 pathway in learning and memory. Analysis of the transcriptional effects of TLR2 activation reveals both common and unique transcriptional programs following heterodimer-specific TLR2 activation. These results imply that adult cognitive behavior could be influenced in part, by activation or alterations in the TLR2 pathway at birth. Copyright © 2015 Elsevier Inc. All rights reserved.

Learning from the experts: exploring playground experience and activities using a write and draw technique.

PubMed

Knowles, Zoe Rebecca; Parnell, Daniel; Stratton, Gareth; Ridgers, Nicola Diane

2013-03-01

Qualitative research into the effect of school recess on children's physical activity is currently limited. This study used a write and draw technique to explore children's perceptions of physical activity opportunities during recess. 299 children age 7-11 years from 3 primary schools were enlisted. Children were grouped into Years 3 & 4 and Years 5 & 6 and completed a write and draw task focusing on likes and dislikes. Pen profiles were used to analyze the data. Results indicated 'likes' focused on play, positive social interaction, and games across both age groups but showed an increasing dominance of games with an appreciation for being outdoors with age. 'Dislikes' focused on dysfunctional interactions linked with bullying, membership, equipment, and conflict for playground space. Football was a dominant feature across both age groups and 'likes/dislikes' that caused conflict and dominated the physically active games undertaken. Recess was important for the development of conflict management and social skills and contributed to physical activity engagement. The findings contradict suggestions that time spent in recess should be reduced because of behavioral issues.

Brain Gym. Simple Activities for Whole Brain Learning.

ERIC Educational Resources Information Center

Dennison, Paul E.; Dennison, Gail E.

This booklet contains simple movements and activities that are used with students in Educational Kinesiology to enhance their experience of whole brain learning. Whole brain learning through movement repatterning and Brain Gym activities enable students to access those parts of the brain previously unavailable to them. These movements of body and…

Active Learning of Classification Models with Likert-Scale Feedback.

PubMed

Xue, Yanbing; Hauskrecht, Milos

2017-01-01

Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone.

Active Learning of Classification Models with Likert-Scale Feedback

PubMed Central

Xue, Yanbing; Hauskrecht, Milos

2017-01-01

Annotation of classification data by humans can be a time-consuming and tedious process. Finding ways of reducing the annotation effort is critical for building the classification models in practice and for applying them to a variety of classification tasks. In this paper, we develop a new active learning framework that combines two strategies to reduce the annotation effort. First, it relies on label uncertainty information obtained from the human in terms of the Likert-scale feedback. Second, it uses active learning to annotate examples with the greatest expected change. We propose a Bayesian approach to calculate the expectation and an incremental SVM solver to reduce the time complexity of the solvers. We show the combination of our active learning strategy and the Likert-scale feedback can learn classification models more rapidly and with a smaller number of labeled instances than methods that rely on either Likert-scale labels or active learning alone. PMID:28979827

Humorous Materials to Enhance Active Learning

ERIC Educational Resources Information Center

Miller, J. L.; Wilson, K.; Miller, J.; Enomoto, K.

2017-01-01

The use of humour in teaching and learning can be contentious, with some authors suggesting that the efficacy of humorous materials is mediated by the culture of the student. Nevertheless, humour represents a potential vehicle for the introduction of active learning in a classroom setting, as judicious use of humour may lead to a more relaxed…

Active Learning Increases Children's Physical Activity across Demographic Subgroups.

PubMed

Bartholomew, John B; Jowers, Esbelle M; Roberts, Gregory; Fall, Anna-Mária; Errisuriz, Vanessa L; Vaughn, Sharon

2018-01-01

Given the need to find more opportunities for physical activity within the elementary school day, this study was designed to asses the impact of I-CAN!, active lessons on: 1) student physical activity (PA) outcomes via accelerometry; and 2) socioeconomic status (SES), race, sex, body mass index (BMI), or fitness as moderators of this impact. Participants were 2,493 fourth grade students (45.9% male, 45.8% white, 21.7% low SES) from 28 central Texas elementary schools randomly assigned to intervention (n=19) or control (n=9). Multilevel regression models evaluated the effect of I-CAN! on PA and effect sizes were calculated. The moderating effects of SES, race, sex, BMI, and fitness were examined in separate models. Students in treatment schools took significantly more steps than those in control schools (β = 125.267, SE = 41.327, p = .002, d = .44). I-CAN! had a significant effect on MVPA with treatment schools realizing 80% (β = 0.796, SE =0.251, p = .001; d = .38) more MVPA than the control schools. There were no significant school-level differences on sedentary behavior (β = -0.177, SE = 0.824, p = .83). SES, race, sex, BMI, and fitness level did not moderate the impact of active learning on step count and MVPA. Active learning increases PA within elementary students, and does so consistently across demographic sub-groups. This is important as these sub-groups represent harder to reach populations for PA interventions. While these lessons may not be enough to help children reach daily recommendations of PA, they can supplement other opportunities for PA. This speaks to the potential of schools to adopt policy change to require active learning.

Detecting falls with wearable sensors using machine learning techniques.

PubMed

Özdemir, Ahmet Turan; Barshan, Billur

2014-06-18

Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded.

Learning Activity Package, Algebra.

ERIC Educational Resources Information Center

Evans, Diane

A set of ten teacher-prepared Learning Activity Packages (LAPs) in beginning algebra and nine in intermediate algebra, these units cover sets, properties of operations, number systems, open expressions, solution sets of equations and inequalities in one and two variables, exponents, factoring and polynomials, relations and functions, radicals,…

An active-learning strategies primer for achieving ability-based educational outcomes.

PubMed

Gleason, Brenda L; Peeters, Michael J; Resman-Targoff, Beth H; Karr, Samantha; McBane, Sarah; Kelley, Kristi; Thomas, Tyan; Denetclaw, Tina H

2011-11-10

Active learning is an important component of pharmacy education. By engaging students in the learning process, they are better able to apply the knowledge they gain. This paper describes evidence supporting the use of active-learning strategies in pharmacy education and also offers strategies for implementing active learning in pharmacy curricula in the classroom and during pharmacy practice experiences.

Writing-to-Learn Activities to Provoke Deeper Learning in Calculus

ERIC Educational Resources Information Center

Jaafar, Reem

2016-01-01

For students with little experience in mathematical thinking and conceptualization, writing-to-learn activities (WTL) can be particularly effective in promoting discovery and understanding. For community college students embarking on a first calculus course in particular, writing activities can help facilitate the transition from an "apply…

Debates as a Pedagogical Learning Technique: Empirical Research with Business Students

ERIC Educational Resources Information Center

Rao, Pramila

2010-01-01

Purpose: The purpose of this paper is to enhance knowledge on debates as a pedagogical learning technique. Design/methodology/approach: This empirical research was conducted in a northeastern university in the USA on graduate and undergraduate business students taking human resource management (HRM) classes. This research was conducted in the…

Determining the Deacetylation Degree of Chitosan: Opportunities to Learn Instrumental Techniques

ERIC Educational Resources Information Center

Pérez-Álvarez, Leyre; Ruiz-Rubio, Leire; Vilas-Vilela, Jose Luis

2018-01-01

To enhance critical thinking and problem-solving skills, a project-based learning (PBL) approach for "Instrumental Techniques" courses in undergraduate physical chemistry was specifically developed for a pharmacy bachelor degree program. The starting point of this PBL was an open-ended question that is close to the student scientist's…

The philosophical and pedagogical underpinnings of Active Learning in Engineering Education

NASA Astrophysics Data System (ADS)

Christie, Michael; de Graaff, Erik

2017-01-01

In this paper the authors draw on three sequential keynote addresses that they gave at Active Learning in Engineering Education (ALE) workshops in Copenhagen (2012), Caxias do Sol (2014) and San Sebastian (2015). Active Learning in Engineering Education is an informal international network of engineering educators dedicated to improving engineering education through active learning (http://www.ale-net.org/). The paper reiterates themes from those keynotes, namely, the philosophical and pedagogical underpinnings of Active Learning in Engineering Education, the scholarly questions that inspire engineering educators to go on improving their practice and exemplary models designed to activate the learning of engineering students. This paper aims to uncover the bedrock of established educational philosophies and theories that define and support active learning. The paper does not claim to present any new or innovative educational theory. There is already a surfeit of them. Rather, the aim is to assist Engineering Educators who wish to research how they can best activate the learning of their students by providing a readable, reasonable and solid underpinning for best practice in this field.

Endoscopic Evacuation of Basal Ganglia Hematoma: Surgical Technique, Outcome, and Learning Curve.

PubMed

Ma, Lichao; Hou, Yuanzheng; Zhu, Ruyuan; Chen, Xiaolei

2017-05-01

Minimally invasive endoscopic hematoma evacuation is a promising treatment option for intracerebral hemorrhage. However, the technique still needs improvement. We report our clinical experience of using this technique to evacuate deep-seated basal ganglia hematomas. The frontal approach was used in most patients. The preoperative and postoperative hematoma volumes, Glasgow Coma Scale, hematoma evacuation rate, 30-day mortality, and long-term outcome defined by the modified Rankin Scale were analyzed retrospectively. The surgical duration per milliliter of clot (DPM) was calculated. The learning curve for this technique was determined based on the relation between the DPM and evacuation rate per the number of cases experienced. A total of 24 patients were enrolled. The evacuation rate was 87% ± 10%. The average Glasgow Coma Scale score recovered from 8 to 13 after surgery. Twenty-one patients had follow-up data. The follow-up time was 13 ± 6 months. The 30-day mortality after surgery was zero. Forty-eight percent of patients (10/21) achieved a favorable outcome. The DPM (P = 0.92) and evacuation rate (P = 0.64) did not change substantially with the number of cases experienced. Endoscopic port surgery for hematoma evacuation via the frontal approach is a safe surgical option for deep-seated basal ganglia hematomas. This technique is minimally invasive and may be helpful to provide better long-term outcomes for selected patients. For neurosurgeons, the learning curve for this technique is steep, which implies that the skills needed for our technique can be easily acquired. Copyright © 2017 Elsevier Inc. All rights reserved.

Machine Learning Techniques for Global Sensitivity Analysis in Climate Models

NASA Astrophysics Data System (ADS)

Safta, C.; Sargsyan, K.; Ricciuto, D. M.

2017-12-01

Climate models studies are not only challenged by the compute intensive nature of these models but also by the high-dimensionality of the input parameter space. In our previous work with the land model components (Sargsyan et al., 2014) we identified subsets of 10 to 20 parameters relevant for each QoI via Bayesian compressive sensing and variance-based decomposition. Nevertheless the algorithms were challenged by the nonlinear input-output dependencies for some of the relevant QoIs. In this work we will explore a combination of techniques to extract relevant parameters for each QoI and subsequently construct surrogate models with quantified uncertainty necessary to future developments, e.g. model calibration and prediction studies. In the first step, we will compare the skill of machine-learning models (e.g. neural networks, support vector machine) to identify the optimal number of classes in selected QoIs and construct robust multi-class classifiers that will partition the parameter space in regions with smooth input-output dependencies. These classifiers will be coupled with techniques aimed at building sparse and/or low-rank surrogate models tailored to each class. Specifically we will explore and compare sparse learning techniques with low-rank tensor decompositions. These models will be used to identify parameters that are important for each QoI. Surrogate accuracy requirements are higher for subsequent model calibration studies and we will ascertain the performance of this workflow for multi-site ALM simulation ensembles.

Dopamine, reward learning, and active inference.

PubMed

FitzGerald, Thomas H B; Dolan, Raymond J; Friston, Karl

2015-01-01

Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

PubMed

Dominguez Veiga, Jose Juan; O'Reilly, Martin; Whelan, Darragh; Caulfield, Brian; Ward, Tomas E

2017-08-04

Inertial sensors are one of the most commonly used sources of data for human activity recognition (HAR) and exercise detection (ED) tasks. The time series produced by these sensors are generally analyzed through numerical methods. Machine learning techniques such as random forests or support vector machines are popular in this field for classification efforts, but they need to be supported through the isolation of a potentially large number of additionally crafted features derived from the raw data. This feature preprocessing step can involve nontrivial digital signal processing (DSP) techniques. However, in many cases, the researchers interested in this type of activity recognition problems do not possess the necessary technical background for this feature-set development. The study aimed to present a novel application of established machine vision methods to provide interested researchers with an easier entry path into the HAR and ED fields. This can be achieved by removing the need for deep DSP skills through the use of transfer learning. This can be done by using a pretrained convolutional neural network (CNN) developed for machine vision purposes for exercise classification effort. The new method should simply require researchers to generate plots of the signals that they would like to build classifiers with, store them as images, and then place them in folders according to their training label before retraining the network. We applied a CNN, an established machine vision technique, to the task of ED. Tensorflow, a high-level framework for machine learning, was used to facilitate infrastructure needs. Simple time series plots generated directly from accelerometer and gyroscope signals are used to retrain an openly available neural network (Inception), originally developed for machine vision tasks. Data from 82 healthy volunteers, performing 5 different exercises while wearing a lumbar-worn inertial measurement unit (IMU), was collected. The ability of the

Improving face image extraction by using deep learning technique

NASA Astrophysics Data System (ADS)

Xue, Zhiyun; Antani, Sameer; Long, L. R.; Demner-Fushman, Dina; Thoma, George R.

2016-03-01

The National Library of Medicine (NLM) has made a collection of over a 1.2 million research articles containing 3.2 million figure images searchable using the Open-iSM multimodal (text+image) search engine. Many images are visible light photographs, some of which are images containing faces ("face images"). Some of these face images are acquired in unconstrained settings, while others are studio photos. To extract the face regions in the images, we first applied one of the most widely-used face detectors, a pre-trained Viola-Jones detector implemented in Matlab and OpenCV. The Viola-Jones detector was trained for unconstrained face image detection, but the results for the NLM database included many false positives, which resulted in a very low precision. To improve this performance, we applied a deep learning technique, which reduced the number of false positives and as a result, the detection precision was improved significantly. (For example, the classification accuracy for identifying whether the face regions output by this Viola- Jones detector are true positives or not in a test set is about 96%.) By combining these two techniques (Viola-Jones and deep learning) we were able to increase the system precision considerably, while avoiding the need to manually construct a large training set by manual delineation of the face regions.

Exploring Representativeness and Informativeness for Active Learning.

PubMed

Du, Bo; Wang, Zengmao; Zhang, Lefei; Zhang, Liangpei; Liu, Wei; Shen, Jialie; Tao, Dacheng

2017-01-01

How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified best-versus-second-best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms.

Competency and an active learning program in undergraduate nursing education.

PubMed

Shin, Hyunsook; Sok, Sohyune; Hyun, Kyung Sun; Kim, Mi Ja

2015-03-01

To evaluate the effect of an active learning program on competency of senior students. Active learning strategies have been used to help students achieve desired nursing competency, but their effectiveness has not been systematically examined. A descriptive, cross-sectional comparative design was used. Two cohort group comparisons using t-test were made: one in an active learning group and the other in a traditional learning group. A total of 147 senior nursing students near graduation participated in this study: 73 in 2010 and 74 in 2013. The active learning program incorporated high-fidelity simulation, situation-based case studies, standardized patients, audio-video playback, reflective activities and technology such as a SmartPad-based program. The overall scores of the nursing competency in the active group were significantly higher than those in the traditional group. Of five overall subdomains, the scores of the special and general clinical performance competency, critical thinking and human understanding were significantly higher in the active group than in the traditional group. Importance-performance analysis showed that all five subdomains of the active group clustered in the high importance and high performance quadrant, indicating significantly better achievements. In contrast, the students in the traditional group showed scattered patterns in three quadrants, excluding the low importance and low performance quadrants. This pattern indicates that the traditional learning method did not yield the high performance in most important areas. The findings of this study suggest that an active learning strategy is useful for helping undergraduate students to gain competency. © 2014 John Wiley & Sons Ltd.

An Active-Learning Strategies Primer for Achieving Ability-Based Educational Outcomes

PubMed Central

Gleason, Brenda L.; Peeters, Michael J.; Resman-Targoff, Beth H.; Karr, Samantha; McBane, Sarah; Kelley, Kristi; Thomas, Tyan

2011-01-01

Active learning is an important component of pharmacy education. By engaging students in the learning process, they are better able to apply the knowledge they gain. This paper describes evidence supporting the use of active-learning strategies in pharmacy education and also offers strategies for implementing active learning in pharmacy curricula in the classroom and during pharmacy practice experiences. PMID:22171114

Questions, Questioning Techniques, and Effective Teaching.

ERIC Educational Resources Information Center

Wilen, William W., Ed.

This book focuses on questioning techniques and strategies teachers may employ to make the difference between active and passive learning in the classroom. There are nine chapters: (1) Why Questions? (Ambrose A. Clegg, Jr.); (2) Review of Research on Questioning Techniques (Meredith D. Gall and Tom Rhody); (3) The Multidisciplinary World of…

Active learning increases student performance in science, engineering, and mathematics.

PubMed

Freeman, Scott; Eddy, Sarah L; McDonough, Miles; Smith, Michelle K; Okoroafor, Nnadozie; Jordt, Hannah; Wenderoth, Mary Pat

2014-06-10

To test the hypothesis that lecturing maximizes learning and course performance, we metaanalyzed 225 studies that reported data on examination scores or failure rates when comparing student performance in undergraduate science, technology, engineering, and mathematics (STEM) courses under traditional lecturing versus active learning. The effect sizes indicate that on average, student performance on examinations and concept inventories increased by 0.47 SDs under active learning (n = 158 studies), and that the odds ratio for failing was 1.95 under traditional lecturing (n = 67 studies). These results indicate that average examination scores improved by about 6% in active learning sections, and that students in classes with traditional lecturing were 1.5 times more likely to fail than were students in classes with active learning. Heterogeneity analyses indicated that both results hold across the STEM disciplines, that active learning increases scores on concept inventories more than on course examinations, and that active learning appears effective across all class sizes--although the greatest effects are in small (n ≤ 50) classes. Trim and fill analyses and fail-safe n calculations suggest that the results are not due to publication bias. The results also appear robust to variation in the methodological rigor of the included studies, based on the quality of controls over student quality and instructor identity. This is the largest and most comprehensive metaanalysis of undergraduate STEM education published to date. The results raise questions about the continued use of traditional lecturing as a control in research studies, and support active learning as the preferred, empirically validated teaching practice in regular classrooms.

Active learning increases student performance in science, engineering, and mathematics

PubMed Central

Freeman, Scott; Eddy, Sarah L.; McDonough, Miles; Smith, Michelle K.; Okoroafor, Nnadozie; Jordt, Hannah; Wenderoth, Mary Pat

2014-01-01

To test the hypothesis that lecturing maximizes learning and course performance, we metaanalyzed 225 studies that reported data on examination scores or failure rates when comparing student performance in undergraduate science, technology, engineering, and mathematics (STEM) courses under traditional lecturing versus active learning. The effect sizes indicate that on average, student performance on examinations and concept inventories increased by 0.47 SDs under active learning (n = 158 studies), and that the odds ratio for failing was 1.95 under traditional lecturing (n = 67 studies). These results indicate that average examination scores improved by about 6% in active learning sections, and that students in classes with traditional lecturing were 1.5 times more likely to fail than were students in classes with active learning. Heterogeneity analyses indicated that both results hold across the STEM disciplines, that active learning increases scores on concept inventories more than on course examinations, and that active learning appears effective across all class sizes—although the greatest effects are in small (n ≤ 50) classes. Trim and fill analyses and fail-safe n calculations suggest that the results are not due to publication bias. The results also appear robust to variation in the methodological rigor of the included studies, based on the quality of controls over student quality and instructor identity. This is the largest and most comprehensive metaanalysis of undergraduate STEM education published to date. The results raise questions about the continued use of traditional lecturing as a control in research studies, and support active learning as the preferred, empirically validated teaching practice in regular classrooms. PMID:24821756

Students´ Perspectives on eLearning Activities in Person-Centered, Blended Learning Settings

ERIC Educational Resources Information Center

Haselberger, David; Motsching, Renate

2016-01-01

Blended or hybrid learning has become a frequent practice in higher education. In this article our primary research interest was to find out how students perceived eLearning activities in blended learning courses based on the person-centered paradigm. Through analyzing the content of a series of semi-structured interviews we found out that…

Implementing active-learning strategies to improve physics learning in Latin America

NASA Astrophysics Data System (ADS)

Alarcon, Hugo; Zavala, G.; Fernandez, R.; Benegas, J.

2006-12-01

It is evident that the most effective active-learning strategies to improve physics learning at the college level have been developed in the United States. Recently, some universities in Latin America have begun adopting such methods as a part of institutional projects, or motivated by national projects led by education authorities. In this work we will present two cases, a large-scale implementation of Tutorials in Introductory Physics (1) in Mexico supported by the institution as a part of a change in its educational model, and a medium-scale implementation of this method in Chile supported by the national government. In both experiences, the professors involved in the educational experience have previously participated in a training workshop that prepared them for implementing this strategy in the classroom. The training workshop, described elsewhere (2), was designed also under active learning premises, so teachers completed the proposed activities in the same way as their students will do. We will present the first results of these two projects. References: (1) McDermott, L. C., Shaffer, P. S., & PER (1998). "Tutorials in Introductory Physics", Prentice Hall, translated as "Tutoriales para Física Introductoria" (2001) Prentice Hall, Buenos Aires.. (2) Zavala, G., Alarcón, H. and Benegas, J. (2005). "Innovative training of in-service teachers for active learning: A short teacher development course based on Physics Education Research", accepted for publication, J. of Sc. Teach. Ed. This work has been partially supported by Tecnológico de Monterrey through the Chair in Physics Education Research and by MECE Educación Superior Program (Chile).

Student Buy-In to Active Learning in a College Science Course.

PubMed

Cavanagh, Andrew J; Aragón, Oriana R; Chen, Xinnian; Couch, Brian; Durham, Mary; Bobrownicki, Aiyana; Hanauer, David I; Graham, Mark J

2016-01-01

The benefits of introducing active learning in college science courses are well established, yet more needs to be understood about student buy-in to active learning and how that process of buy-in might relate to student outcomes. We test the exposure-persuasion-identification-commitment (EPIC) process model of buy-in, here applied to student (n = 245) engagement in an undergraduate science course featuring active learning. Student buy-in to active learning was positively associated with engagement in self-regulated learning and students' course performance. The positive associations among buy-in, self-regulated learning, and course performance suggest buy-in as a potentially important factor leading to student engagement and other student outcomes. These findings are particularly salient in course contexts featuring active learning, which encourage active student participation in the learning process. © 2016 A. J. Cavanagh et al. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Unsupervised active learning based on hierarchical graph-theoretic clustering.

PubMed

Hu, Weiming; Hu, Wei; Xie, Nianhua; Maybank, Steve

2009-10-01

Most existing active learning approaches are supervised. Supervised active learning has the following problems: inefficiency in dealing with the semantic gap between the distribution of samples in the feature space and their labels, lack of ability in selecting new samples that belong to new categories that have not yet appeared in the training samples, and lack of adaptability to changes in the semantic interpretation of sample categories. To tackle these problems, we propose an unsupervised active learning framework based on hierarchical graph-theoretic clustering. In the framework, two promising graph-theoretic clustering algorithms, namely, dominant-set clustering and spectral clustering, are combined in a hierarchical fashion. Our framework has some advantages, such as ease of implementation, flexibility in architecture, and adaptability to changes in the labeling. Evaluations on data sets for network intrusion detection, image classification, and video classification have demonstrated that our active learning framework can effectively reduce the workload of manual classification while maintaining a high accuracy of automatic classification. It is shown that, overall, our framework outperforms the support-vector-machine-based supervised active learning, particularly in terms of dealing much more efficiently with new samples whose categories have not yet appeared in the training samples.