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.

Cross-domain active learning for video concept detection

NASA Astrophysics Data System (ADS)

Li, Huan; Li, Chao; Shi, Yuan; Xiong, Zhang; Hauptmann, Alexander G.

2011-08-01

As video data from a variety of different domains (e.g., news, documentaries, entertainment) have distinctive data distributions, cross-domain video concept detection becomes an important task, in which one can reuse the labeled data of one domain to benefit the learning task in another domain with insufficient labeled data. In this paper, we approach this problem by proposing a cross-domain active learning method which iteratively queries labels of the most informative samples in the target domain. Traditional active learning assumes that the training (source domain) and test data (target domain) are from the same distribution. However, it may fail when the two domains have different distributions because querying informative samples according to a base learner that initially learned from source domain may no longer be helpful for the target domain. In our paper, we use the Gaussian random field model as the base learner which has the advantage of exploring the distributions in both domains, and adopt uncertainty sampling as the query strategy. Additionally, we present an instance weighting trick to accelerate the adaptability of the base learner, and develop an efficient model updating method which can significantly speed up the active learning process. Experimental results on TRECVID collections highlight the effectiveness.

Acquisition of STEM Images by Adaptive Compressive Sensing

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

Xie, Weiyi; Feng, Qianli; Srinivasan, Ramprakash

Compressive Sensing (CS) allows a signal to be sparsely measured first and accurately recovered later in software [1]. In scanning transmission electron microscopy (STEM), it is possible to compress an image spatially by reducing the number of measured pixels, which decreases electron dose and increases sensing speed [2,3,4]. The two requirements for CS to work are: (1) sparsity of basis coefficients and (2) incoherence of the sensing system and the representation system. However, when pixels are missing from the image, it is difficult to have an incoherent sensing matrix. Nevertheless, dictionary learning techniques such as Beta-Process Factor Analysis (BPFA) [5]more » are able to simultaneously discover a basis and the sparse coefficients in the case of missing pixels. On top of CS, we would like to apply active learning [6,7] to further reduce the proportion of pixels being measured, while maintaining image reconstruction quality. Suppose we initially sample 10% of random pixels. We wish to select the next 1% of pixels that are most useful in recovering the image. Now, we have 11% of pixels, and we want to decide the next 1% of “most informative” pixels. Active learning methods are online and sequential in nature. Our goal is to adaptively discover the best sensing mask during acquisition using feedback about the structures in the image. In the end, we hope to recover a high quality reconstruction with a dose reduction relative to the non-adaptive (random) sensing scheme. In doing this, we try three metrics applied to the partial reconstructions for selecting the new set of pixels: (1) variance, (2) Kullback-Leibler (KL) divergence using a Radial Basis Function (RBF) kernel, and (3) entropy. Figs. 1 and 2 display the comparison of Peak Signal-to-Noise (PSNR) using these three different active learning methods at different percentages of sampled pixels. At 20% level, all the three active learning methods underperform the original CS without active learning. However, they all beat the original CS as more of the “most informative” pixels are sampled. One can also argue that CS equipped with active learning requires less sampled pixels to achieve the same value of PSNR than CS with pixels randomly sampled, since all the three PSNR curves with active learning grow at a faster pace than that without active learning. For this particular STEM image, by observing the reconstructed images and the sensing masks, we find that while the method based on RBF kernel acquires samples more uniformly, the one on entropy samples more areas of significant change, thus less uniformly. The KL-divergence method performs the best in terms of reconstruction error (PSNR) for this example [8].« less

Active learning based segmentation of Crohns disease from abdominal MRI.

PubMed

Mahapatra, Dwarikanath; Vos, Franciscus M; Buhmann, Joachim M

2016-05-01

This paper proposes a novel active learning (AL) framework, and combines it with semi supervised learning (SSL) for segmenting Crohns disease (CD) tissues from abdominal magnetic resonance (MR) images. Robust fully supervised learning (FSL) based classifiers require lots of labeled data of different disease severities. Obtaining such data is time consuming and requires considerable expertise. SSL methods use a few labeled samples, and leverage the information from many unlabeled samples to train an accurate classifier. AL queries labels of most informative samples and maximizes gain from the labeling effort. Our primary contribution is in designing a query strategy that combines novel context information with classification uncertainty and feature similarity. Combining SSL and AL gives a robust segmentation method that: (1) optimally uses few labeled samples and many unlabeled samples; and (2) requires lower training time. Experimental results show our method achieves higher segmentation accuracy than FSL methods with fewer samples and reduced training effort. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Using Electronic Communication Tools in Online Group Activities to Develop Collaborative Learning Skills

ERIC Educational Resources Information Center

Khalil, Hanan; Ebner, Martin

2017-01-01

The purpose of this study was to investigate the effect of using synchronous and asynchronous communication tools in online group activities to develop collaborative learning skills. An experimental study was implemented on a sample of faculty of education students in Mansoura University. The sample was divided into two groups, a group studied…

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

Smartphone-Based Patients' Activity Recognition by Using a Self-Learning Scheme for Medical Monitoring.

PubMed

Guo, Junqi; Zhou, Xi; Sun, Yunchuan; Ping, Gong; Zhao, Guoxing; Li, Zhuorong

2016-06-01

Smartphone based activity recognition has recently received remarkable attention in various applications of mobile health such as safety monitoring, fitness tracking, and disease prediction. To achieve more accurate and simplified medical monitoring, this paper proposes a self-learning scheme for patients' activity recognition, in which a patient only needs to carry an ordinary smartphone that contains common motion sensors. After the real-time data collection though this smartphone, we preprocess the data using coordinate system transformation to eliminate phone orientation influence. A set of robust and effective features are then extracted from the preprocessed data. Because a patient may inevitably perform various unpredictable activities that have no apriori knowledge in the training dataset, we propose a self-learning activity recognition scheme. The scheme determines whether there are apriori training samples and labeled categories in training pools that well match with unpredictable activity data. If not, it automatically assembles these unpredictable samples into different clusters and gives them new category labels. These clustered samples combined with the acquired new category labels are then merged into the training dataset to reinforce recognition ability of the self-learning model. In experiments, we evaluate our scheme using the data collected from two postoperative patient volunteers, including six labeled daily activities as the initial apriori categories in the training pool. Experimental results demonstrate that the proposed self-learning scheme for activity recognition works very well for most cases. When there exist several types of unseen activities without any apriori information, the accuracy reaches above 80 % after the self-learning process converges.

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

Jamieson, Kevin; Davis, IV, Warren L.

Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algorithms to benchmark datasets, as is the norm in (passive) machine learning research. To facilitate the development, testing and deployment of active learning for real applications, we have built an open-source software system for large-scale active learning research and experimentation. The system, called NEXT, provides a unique platform for realworld, reproducible active learning research. This paper details the challenges of building themore » system and demonstrates its capabilities with several experiments. The results show how experimentation can help expose strengths and weaknesses of active learning algorithms, in sometimes unexpected and enlightening ways.« less

Individualized Instruction in Science, Earth Space Project, Learning Activities Package.

ERIC Educational Resources Information Center

Kuczma, R. M.

Learning Activity Packages (LAP) relating to the earth and space are presented for use in sampling a new type of learning for a whole year. Eighteen topics are incorporated into five units: (1) introduction to individualized learning, (2) observation versus interpretation, (3) chemistry in the space age, (4) the space age interdisciplines, and (5)…

Individualized Instruction in Science, Time-Space-Matter, Learning Activity Packages.

ERIC Educational Resources Information Center

Kuczma, R. M.

Learning Activity Packages (LAP) relating to time, space, and matter are presented for use in sampling a new type of learning for a whole year. Besides the unit on introduction to individualized learning, 11 major topics are incorporated into three other units: (1) observation of the physical world, (2) space and exploration for environmental…

Resource-Bounded Information Gathering for Correlation Clustering

DTIC Science & Technology

2007-01-01

5], budgeted learning, [4], and active learning , for example, [3]. 3 Acknowledgments We thank Avrim Blum, Katrina Ligett, Chris Pal, Sridhar...2007 3. N. Roy, A. McCallum, Toward Optimal Active Learning through Sampling Estima- tion of Error Reduction, Proc. of 18th ICML, 2001 4. A. Kapoor, R

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.

A Large-scale Distributed Indexed Learning Framework for Data that Cannot Fit into Memory

DTIC Science & Technology

2015-03-27

learn a classifier. Integrating three learning techniques (online, semi-supervised and active learning ) together with a selective sampling with minimum communication between the server and the clients solved this problem.

Is Knowledge Random? Introducing Sampling and Bias through Outdoor Inquiry

ERIC Educational Resources Information Center

Stier, Sam

2010-01-01

Sampling, very generally, is the process of learning about something by selecting and assessing representative parts of that population or object. In the inquiry activity described here, students learned about sampling techniques as they estimated the number of trees greater than 12 cm dbh (diameter at breast height) in a wooded, discrete area…

How to Select a Good Training-data Subset for Transcription: Submodular Active Selection for Sequences

DTIC Science & Technology

2009-01-01

selection and uncertainty sampling signif- icantly. Index Terms: Transcription, labeling, submodularity, submod- ular selection, active learning , sequence...name of batch active learning , where a subset of data that is most informative and represen- tative of the whole is selected for labeling. Often...representative subset. Note that our Fisher ker- nel is over an unsupervised generative model, which enables us to bootstrap our active learning approach

Research report: learning styles of biomedical engineering students.

PubMed

Dee, Kay C; Nauman, Eric A; Livesay, Glen A; Rice, Janet

2002-09-01

Examining students' learning styles can yield information useful to the design of learning activities, courses, and curricula. A variety of measures have been used to characterize learning styles, but the literature contains little information specific to biomedical engineering (BMEN) students. We, therefore, utilized Felder's Index of Learning Styles to investigate the learning style preferences of BMEN students at Tulane University. Tulane BMEN students preferred to receive information visually (preferred by 88% of the student sample) rather than verbally, focus on sensory information (55%) instead of intuitive information, process information actively (66%) instead of reflectively, and understand information globally (59%) rather than sequentially. These preferences varied between cohorts (freshman, sophomore, etc.) and a significantly higher percentage of female students preferred active and sensing learning styles. Compared to other engineering student populations, our sample of Tulane BMEN students contained the highest percentage of students preferring the global learning style. Whether this is a general trend for all BMEN students or a trait specific to Tulane engineers requires further investigation. Regardless, this study confirms the existence of a range of learning styles within biomedical engineering students, and provides motivation for instructors to consider how well their teaching style engages multiple learning styles.

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/.

Activity File of Learning Center and Classroom Multi-Cultural Activities.

ERIC Educational Resources Information Center

Riverside Unified School District, CA.

The cards in this file are representative samples of the types of activities developed by teachers involved in a Title I funded learning center of multi-cultural classroom activities for elementary school students. The five cultures that are stuoied are those of blacks, Asian Americans, native Americans, Mexican Americans, and Anglos. A…

An experience sampling study of learning, affect, and the demands control support model.

PubMed

Daniels, Kevin; Boocock, Grahame; Glover, Jane; Holland, Julie; Hartley, Ruth

2009-07-01

The demands control support model (R. A. Karasek & T. Theorell, 1990) indicates that job control and social support enable workers to engage in problem solving. In turn, problem solving is thought to influence learning and well-being (e.g., anxious affect, activated pleasant affect). Two samples (N = 78, N = 106) provided data up to 4 times per day for up to 5 working days. The extent to which job control was used for problem solving was assessed by measuring the extent to which participants changed aspects of their work activities to solve problems. The extent to which social support was used to solve problems was assessed by measuring the extent to which participants discussed problems to solve problems. Learning mediated the relationship between changing aspects of work activities to solve problems and activated pleasant affect. Learning also mediated the relationship between discussing problems to solve problems and activated pleasant affect. The findings indicated that how individuals use control and support to respond to problem-solving demands is associated with organizational and individual phenomena, such as learning and affective well-being.

Reward and punishment learning in daily life: A replication study.

PubMed

Heininga, Vera E; van Roekel, Eeske; Wichers, Marieke; Oldehinkel, Albertine J

2017-01-01

Day-to-day experiences are accompanied by feelings of Positive Affect (PA) and Negative Affect (NA). Implicitly, without conscious processing, individuals learn about the reward and punishment value of each context and activity. These associative learning processes, in turn, affect the probability that individuals will re-engage in such activities or seek out that context. So far, implicit learning processes are almost exclusively investigated in controlled laboratory settings and not in daily life. Here we aimed to replicate the first study that investigated implicit learning processes in real life, by means of the Experience Sampling Method (ESM). That is, using an experience-sampling study with 90 time points (three measurements over 30 days), we prospectively measured time spent in social company and amount of physical activity as well as PA and NA in the daily lives of 18-24-year-old young adults (n = 69 with anhedonia, n = 69 without anhedonia). Multilevel analyses showed a punishment learning effect with regard to time spent in company of friends, but not a reward learning effect. Neither reward nor punishment learning effects were found with regard to physical activity. Our study shows promising results for future research on implicit learning processes in daily life, with the proviso of careful consideration of the timescale used. Short-term retrospective ESM design with beeps approximately six hours apart may suffer from mismatch noise that hampers accurate detection of associative learning effects over time.

Reducing Annotation Effort Using Generalized Expectation Criteria

DTIC Science & Technology

2007-11-30

constraints additionally consider input variables. Active learning is a related problem in which the learner can choose the particular instances to be...labeled. In pool-based active learning [Cohn et al., 1994], the learner has access to a set of unlabeled instances, and can choose the instance that...has the highest expected utility according to some metric. A standard pool- based active learning method is uncertainty sampling [Lewis and Catlett

Active sensing associated with spatial learning reveals memory-based attention in an electric fish

PubMed Central

Longtin, André; Maler, Leonard

2016-01-01

Active sensing behaviors reveal what an animal is attending to and how it changes with learning. Gymnotus sp., a gymnotiform weakly electric fish, generates an electric organ discharge (EOD) as discrete pulses to actively sense its surroundings. We monitored freely behaving gymnotid fish in a large dark “maze” and extracted their trajectories and EOD pulse pattern and rate while they learned to find food with electrically detectable landmarks as cues. After training, they more rapidly found food using shorter, more stereotyped trajectories and spent more time near the food location. We observed three forms of active sensing: sustained high EOD rates per unit distance (sampling density), transient large increases in EOD rate (E-scans) and stereotyped scanning movements (B-scans) were initially strong at landmarks and food, but, after learning, intensified only at the food location. During probe (no food) trials, after learning, the fish's search area and intense active sampling was still centered on the missing food location, but now also increased near landmarks. We hypothesize that active sensing is a behavioral manifestation of attention and essential for spatial learning; the fish use spatial memory of landmarks and path integration to reach the expected food location and confine their attention to this region. PMID:26961107

Active sensing associated with spatial learning reveals memory-based attention in an electric fish.

PubMed

Jun, James J; Longtin, André; Maler, Leonard

2016-05-01

Active sensing behaviors reveal what an animal is attending to and how it changes with learning. Gymnotus sp, a gymnotiform weakly electric fish, generates an electric organ discharge (EOD) as discrete pulses to actively sense its surroundings. We monitored freely behaving gymnotid fish in a large dark "maze" and extracted their trajectories and EOD pulse pattern and rate while they learned to find food with electrically detectable landmarks as cues. After training, they more rapidly found food using shorter, more stereotyped trajectories and spent more time near the food location. We observed three forms of active sensing: sustained high EOD rates per unit distance (sampling density), transient large increases in EOD rate (E-scans) and stereotyped scanning movements (B-scans) were initially strong at landmarks and food, but, after learning, intensified only at the food location. During probe (no food) trials, after learning, the fish's search area and intense active sampling was still centered on the missing food location, but now also increased near landmarks. We hypothesize that active sensing is a behavioral manifestation of attention and essential for spatial learning; the fish use spatial memory of landmarks and path integration to reach the expected food location and confine their attention to this region. Copyright © 2016 the American Physiological Society.

Understanding active sampling strategies: Empirical approaches and implications for attention and decision research.

PubMed

Gottlieb, Jacqueline

2018-05-01

In natural behavior we actively gather information using attention and active sensing behaviors (such as shifts of gaze) to sample relevant cues. However, while attention and decision making are naturally coordinated, in the laboratory they have been dissociated. Attention is studied independently of the actions it serves. Conversely, decision theories make the simplifying assumption that the relevant information is given, and do not attempt to describe how the decision maker may learn and implement active sampling policies. In this paper I review recent studies that address questions of attentional learning, cue validity and information seeking in humans and non-human primates. These studies suggest that learning a sampling policy involves large scale interactions between networks of attention and valuation, which implement these policies based on reward maximization, uncertainty reduction and the intrinsic utility of cognitive states. I discuss the importance of using such paradigms for formalizing the role of attention, as well as devising more realistic theories of decision making that capture a broader range of empirical observations. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Assessment of Service-Learning in 34 US Schools of Pharmacy Follow Up on the 2001 Professional Affairs Committee Report

PubMed Central

Schlesselman, Lauren; Borrego, Matthew; Mehta, Bella; Drobitch, Robert K.; Smith, Thomas

2015-01-01

Objective. To determine if the service-learning components used at a convenience sample of schools and colleges of pharmacy meet the intent of the 2001 AACP Professional Affairs Committee (PAC) report. Methods. An online questionnaire was used to survey faculty members or staff involved with service-learning education at their school of pharmacy. Questions addressed aspects of service-learning including types of activities used, duration of student involvement with community partners, and association of learning objectives with service-learning activities. Results. The majority (85.3%) of respondents reported their institution used service-learning. Activities reported as part of service-learning ranged from working at health fairs to involvement with pharmacy school recruitment. More than half (64.3%) of service-learning activities involved long-term interactions with one community partner, and 74.1% of respondents indicated there was always an opportunity for student reflection on the service-learning activity. Conclusion. There is increasing though inconsistent application of PAC guidelines regarding service-learning. PMID:26688584

Teaching and Learning Activities in Chilean Classrooms: Is ICT Making a Difference?

ERIC Educational Resources Information Center

Hinostroza, J. Enrique; Labbe, Christian; Brun, Mario; Matamala, Carolina

2011-01-01

This paper presents the results of the analysis of teaching and learning activities in state subsidized schools in Chile. The study is based on the data collected through a national survey applied to all state subsidized schools (census) and a sample of private schools and examines teachers' and students' reported teaching and learning activities…

Active learning for clinical text classification: is it better than random sampling?

PubMed

Figueroa, Rosa L; Zeng-Treitler, Qing; Ngo, Long H; Goryachev, Sergey; Wiechmann, Eduardo P

2012-01-01

This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty.

Active learning for clinical text classification: is it better than random sampling?

PubMed Central

Figueroa, Rosa L; Ngo, Long H; Goryachev, Sergey; Wiechmann, Eduardo P

2012-01-01

Objective This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. Design Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. Measurements Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. Results The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. Conclusion For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty. PMID:22707743

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).

Differences in Developmental Experiences for Commonly Used Categories of Organized Youth Activities

ERIC Educational Resources Information Center

Hansen, David M.; Skorupski, William P.; Arrington, Tiffany L.

2010-01-01

The coherence of adolescents' self-reported learning experiences between subgroups of organized youth activities within five commonly used categories was evaluated. Data for the present study come from a representative sample of eleventh grade adolescents' reports on learning experiences in an organized youth activity using the Youth Experience…

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.

The effectiveness of problem-based learning on teaching the first law of thermodynamics

NASA Astrophysics Data System (ADS)

Tatar, Erdal; Oktay, Münir

2011-11-01

Background: Problem-based learning (PBL) is a teaching approach working in cooperation with self-learning and involving research to solve real problems. The first law of thermodynamics states that energy can neither be created nor destroyed, but that energy is conserved. Students had difficulty learning or misconceptions about this law. This study is related to the teaching of the first law of thermodynamics within a PBL environment. Purpose: This study examined the effectiveness of PBL on candidate science teachers' understanding of the first law of thermodynamics and their science process skills. This study also examined their opinions about PBL. Sample: The sample consists of 48 third-grade university students from the Department of Science Education in one of the public universities in Turkey. Design and methods: A one-group pretest-posttest experimental design was used. Data collection tools included the Achievement Test, Science Process Skill Test, Constructivist Learning Environment Survey and an interview with open-ended questions. Paired samples t-test was conducted to examine differences in pre/post tests. Results: The PBL approach has a positive effect on the students' learning abilities and science process skills. The students thought that the PBL environment supports effective and permanent learning, and self-learning planning skills. On the other hand, some students think that the limited time and unfamiliarity of the approach impede learning. Conclusions: The PBL is an active learning approach supporting students in the process of learning. But there are still many practical disadvantages that could reduce the effectiveness of the PBL. To prevent the alienation of the students, simple PBL activities should be applied from the primary school level. In order to overcome time limitations, education researchers should examine short-term and effective PBL activities.

Reward and punishment learning in daily life: A replication study

PubMed Central

van Roekel, Eeske; Wichers, Marieke; Oldehinkel, Albertine J.

2017-01-01

Day-to-day experiences are accompanied by feelings of Positive Affect (PA) and Negative Affect (NA). Implicitly, without conscious processing, individuals learn about the reward and punishment value of each context and activity. These associative learning processes, in turn, affect the probability that individuals will re-engage in such activities or seek out that context. So far, implicit learning processes are almost exclusively investigated in controlled laboratory settings and not in daily life. Here we aimed to replicate the first study that investigated implicit learning processes in real life, by means of the Experience Sampling Method (ESM). That is, using an experience-sampling study with 90 time points (three measurements over 30 days), we prospectively measured time spent in social company and amount of physical activity as well as PA and NA in the daily lives of 18-24-year-old young adults (n = 69 with anhedonia, n = 69 without anhedonia). Multilevel analyses showed a punishment learning effect with regard to time spent in company of friends, but not a reward learning effect. Neither reward nor punishment learning effects were found with regard to physical activity. Our study shows promising results for future research on implicit learning processes in daily life, with the proviso of careful consideration of the timescale used. Short-term retrospective ESM design with beeps approximately six hours apart may suffer from mismatch noise that hampers accurate detection of associative learning effects over time. PMID:28976985

Teaching Research Methodology through Active Learning

ERIC Educational Resources Information Center

Lundahl, Brad W.

2008-01-01

To complement traditional learning activities in a masters-level research methodology course, social work students worked on a formal research project which involved: designing the study, constructing measures, selecting a sampling strategy, collecting data, reducing and analyzing data, and finally interpreting and communicating the results. The…

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.

Levels of Engagement and Barriers to Physical Activity in a Population of Adults with Learning Disabilities

ERIC Educational Resources Information Center

Hawkins, Andrew; Look, Roger

2006-01-01

This study examined levels of, and barriers to, physical activity in a population of 19 adults with learning disabilities living in community supported accommodation, using diary records and semi-structured interviews with staff. The levels of physical activity were higher in the sample population than previous figures for adults with learning…

The Internet: A Learning Environment.

ERIC Educational Resources Information Center

McGreal, Rory

1997-01-01

The Internet environment is suitable for many types of learning activities and teaching and learning styles. Every World Wide Web-based course should provide: home page; introduction; course overview; course requirements, vital information; roles and responsibilities; assignments; schedule; resources; sample tests; teacher biography; course…

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.

Narrative assessment: making mathematics learning visible in early childhood settings

NASA Astrophysics Data System (ADS)

Anthony, Glenda; McLachlan, Claire; Lim Fock Poh, Rachel

2015-09-01

Narratives that capture children's learning as they go about their day-to-day activities are promoted as a powerful assessment tool within early childhood settings. However, in the New Zealand context, there is increasing concern that learning stories—the preferred form of narrative assessment—currently downplay domain knowledge. In this paper, we draw on data from 13 teacher interviews and samples of 18 children's learning stories to examine how mathematics is made visible within learning stories. Despite appreciating that mathematics is embedded in a range of everyday activities within the centres, we found that the nature of a particular activity appeared to influence `how' and `what' the teachers chose to document as mathematics learning. Many of the teachers expressed a preference to document and analyse mathematics learning that occurred within explicit mathematics activities rather than within play that involves mathematics. Our concern is that this restricted documentation of mathematical activity could potentially limit opportunities for mathematics learning both in the centre and home settings.

A Collaborative 20 Questions Model for Target Search with Human-Machine Interaction

DTIC Science & Technology

2013-05-01

optimal policies for entropy loss,” Journal of Applied Probability, vol. 49, pp. 114–136, 2012. [2] R. Castro and R. Nowak, “ Active learning and...vol. 10, pp. 223231, 1974. [8] R. Castro, Active Learning and Adaptive Sampling for Non- parametric Inference, Ph.D. thesis, Rice University, August...2007. [9] R. Castro and R. D. Nowak, “Upper and lower bounds for active learning ,” in 44th Annual Allerton Conference on Communica- tion, Control and Computing, 2006.

Multiple subclasses of Purkinje cells in the primate floccular complex provide similar signals to guide learning in the vestibulo-ocular reflex

NASA Technical Reports Server (NTRS)

Raymond, J. L.; Lisberger, S. G.

1997-01-01

The neural "learning rules" governing the induction of plasticity in the cerebellum were analyzed by recording the patterns of neural activity in awake, behaving animals during stimuli that induce a form of cerebellum-dependent learning. We recorded the simple- and complex-spike responses of a broad sample of Purkinje cells in the floccular complex during a number of stimulus conditions that induce motor learning in the vestibulo-ocular reflex (VOR). Each subclass of Purkinje cells carried essentially the same information about required changes in the gain of the VOR. The correlation of simple-spike activity in Purkinje cells with activity in vestibular pathways could guide learning during low-frequency but not high-frequency stimuli. Climbing fiber activity could guide learning during all stimuli tested but only if compared with the activity present approximately 100 msec earlier in either vestibular pathways or Purkinje cells.

Measurement for Work. Teaching Guide and Sample Learning Activities.

ERIC Educational Resources Information Center

Angel, Margo; Bolton, Chris

This document is intended to help Australian technical and further education instructors in New South Wales (TAFE NSW) identify teaching principles and learning activities that they can use to help adult learners master the mathematics processes, knowledge, and skills needed to perform basic measurement tasks in today's workplace. The materials…

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.

When I Grow Up: The Relationship of "Science Learning Activation" to STEM Career Preferences

ERIC Educational Resources Information Center

Dorph, Rena; Bathgate, Meghan E.; Schunn, Christian D.; Cannady, Matthew A.

2018-01-01

This paper proposes three new measures of components STEM career preferences (affinity, certainty, and goal), and then explores which dimensions of "science learning activation" (fascination, values, competency belief, and scientific sensemaking) are predictive of STEM career preferences. Drawn from the ALES14 dataset, a sample of 2938…

The Effectiveness of Cooperative Learning Activities in Enhancing EFL Learners' Fluency

ERIC Educational Resources Information Center

Alrayah, Hassan

2018-01-01

This research-paper aims at examining the effectiveness of cooperative learning activities in enhancing EFL learners' fluency. The researcher has used the descriptive approach, recorded interviews for testing fluency as tools of data collection and the software program SPSS as a tool for the statistical treatment of data. Research sample consists…

Sources of Variation in Consequences of Everyday Activity Settings on Child and Parent Functioning

ERIC Educational Resources Information Center

Trivette, Carol; Dunst, Carl; Hamby, Deborah

2004-01-01

Relationships between acculturation and enculturation, parent beliefs about child learning methods and parenting roles in children's learning, children's participation in family and community activity settings, and a variety of child, parent and family outcomes were examined in a sample of 203 parents. Information received from these parents…

A machine learning approach to computer-aided molecular design

NASA Astrophysics Data System (ADS)

Bolis, Giorgio; Di Pace, Luigi; Fabrocini, Filippo

1991-12-01

Preliminary results of a machine learning application concerning computer-aided molecular design applied to drug discovery are presented. The artificial intelligence techniques of machine learning use a sample of active and inactive compounds, which is viewed as a set of positive and negative examples, to allow the induction of a molecular model characterizing the interaction between the compounds and a target molecule. The algorithm is based on a twofold phase. In the first one — the specialization step — the program identifies a number of active/inactive pairs of compounds which appear to be the most useful in order to make the learning process as effective as possible and generates a dictionary of molecular fragments, deemed to be responsible for the activity of the compounds. In the second phase — the generalization step — the fragments thus generated are combined and generalized in order to select the most plausible hypothesis with respect to the sample of compounds. A knowledge base concerning physical and chemical properties is utilized during the inductive process.

Lunar and Meteorite Sample Disk for Educators

NASA Technical Reports Server (NTRS)

Foxworth, Suzanne; Luckey, M.; McInturff, B.; Allen, J.; Kascak, A.

2015-01-01

NASA Johnson Space Center (JSC) has the unique responsibility to curate NASA's extraterrestrial samples from past and future missions. Curation includes documentation, preservation, preparation and distribution of samples for research, education and public outreach. Between 1969 and 1972 six Apollo missions brought back 382 kilograms of lunar rocks, core and regolith samples, from the lunar surface. JSC also curates meteorites collected from a US cooperative effort among NASA, the National Science Foundation (NSF) and the Smithsonian Institution that funds expeditions to Antarctica. The meteorites that are collected include rocks from Moon, Mars, and many asteroids including Vesta. The sample disks for educational use include these different samples. Active relevant learning has always been important to teachers and the Lunar and Meteorite Sample Disk Program provides this active style of learning for students and the general public. The Lunar and Meteorite Sample Disks permit students to conduct investigations comparable to actual scientists. The Lunar Sample Disk contains 6 samples; Basalt, Breccia, Highland Regolith, Anorthosite, Mare Regolith and Orange Soil. The Meteorite Sample Disk contains 6 samples; Chondrite L3, Chondrite H5, Carbonaceous Chondrite, Basaltic Achondrite, Iron and Stony-Iron. Teachers are given different activities that adhere to their standards with the disks. During a Sample Disk Certification Workshop, teachers participate in the activities as students gain insight into the history, formation and geologic processes of the moon, asteroids and meteorites.

Explorations in Statistics: the Bootstrap

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2009-01-01

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

Learning to Write and Loving It! Preschool-Kindergarten

ERIC Educational Resources Information Center

Trehearne, Miriam P.

2011-01-01

"Learning to Write and Loving It!" equips teachers of young children with practical strategies, assessment tools, and motivating writing activities that are based on current research and proven practice and are easily applicable to all kinds of learning environments. Included are many authentic writing samples and photos to illustrate effective,…

Integrating the Core Curriculum through Cooperative Learning. Lesson Plans for Teachers.

ERIC Educational Resources Information Center

Winget, Patricia L., Ed.

Cooperative learning strategies are used to facilitate the integration of multicultural and multi-ability level students into California regular education classrooms. This handbook is a sampling of innovative lesson plans using cooperative learning activities developed by teachers to incorporate the core curriculum into their instruction. Three…

An Experience Sampling Study of Learning, Affect, and the Demands Control Support Model

ERIC Educational Resources Information Center

Daniels, Kevin; Boocock, Grahame; Glover, Jane; Hartley, Ruth; Holland, Julie

2009-01-01

The demands control support model (R. A. Karasek & T. Theorell, 1990) indicates that job control and social support enable workers to engage in problem solving. In turn, problem solving is thought to influence learning and well-being (e.g., anxious affect, activated pleasant affect). Two samples (N = 78, N = 106) provided data up to 4 times per…

Cooperative learning model with high order thinking skills questions: an understanding on geometry

NASA Astrophysics Data System (ADS)

Sari, P. P.; Budiyono; Slamet, I.

2018-05-01

Geometry, a branch of mathematics, has an important role in mathematics learning. This research aims to find out the effect of learning model, emotional intelligence, and the interaction between learning model and emotional intelligence toward students’ mathematics achievement. This research is quasi-experimental research with 2 × 3 factorial design. The sample in this research included 179 Senior High School students on 11th grade in Sukoharjo Regency, Central Java, Indonesia in academic year of 2016/2017. The sample was taken by using stratified cluster random sampling. The results showed that: the student are taught by Thinking Aloud Pairs Problem-Solving using HOTs questions provides better mathematics learning achievement than Make A Match using HOTs questions. High emotional intelligence students have better mathematics learning achievement than moderate and low emotional intelligence students, and moderate emotional intelligence students have better mathematics learning achievement than low emotional intelligence students. There is an interaction between learning model and emotional intelligence, and these affect mathematics learning achievement. We conclude that appropriate learning model can support learning activities become more meaningful and facilitate students to understand material. For further research, we suggest to explore the contribution of other aspects in cooperative learning modification to mathematics achievement.

Individual values, learning routines and academic procrastination.

PubMed

Dietz, Franziska; Hofer, Manfred; Fries, Stefan

2007-12-01

Academic procrastination, the tendency to postpone learning activities, is regarded as a consequence of postmodern values that are prominent in post-industrialized societies. When students strive for leisure goals and have no structured routines for academic tasks, delaying strenuous learning activities becomes probable. The model tested in this study posits that postmodern value orientations are positively related to procrastination and to a lack of daily routines concerning the performance of academic activities. In contrast, modern values are negatively related to procrastination and positively to learning routines. Academic procrastination, in-turn, should be associated with the tendency to prefer leisure activities to schoolwork in case of conflicts between these two life domains. Seven hundred and four students from 6th and 8th grade with a mean age of 13.5 years participated in the study. The sample included students from all tracks of the German educational system. Students completed a questionnaire containing two value prototypes as well as scales on learning routines and procrastination. Decisions in motivational conflicts were measured using two vignettes. Results from structural equation modelling supported the proposed model for the whole sample as well as for each school track. A planned course of the day can prevent procrastination and foster decisions for academic tasks in case of conflicts. Students' learning takes place within a societal context and reflects the values held in the respective culture.

Teaching Tip: Active Learning via a Sample Database: The Case of Microsoft's Adventure Works

ERIC Educational Resources Information Center

Mitri, Michel

2015-01-01

This paper describes the use and benefits of Microsoft's Adventure Works (AW) database to teach advanced database skills in a hands-on, realistic environment. Database management and querying skills are a key element of a robust information systems curriculum, and active learning is an important way to develop these skills. To facilitate active…

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.

Reinforcement active learning in the vibrissae system: optimal object localization.

PubMed

Gordon, Goren; Dorfman, Nimrod; Ahissar, Ehud

2013-01-01

Rats move their whiskers to acquire information about their environment. It has been observed that they palpate novel objects and objects they are required to localize in space. We analyze whisker-based object localization using two complementary paradigms, namely, active learning and intrinsic-reward reinforcement learning. Active learning algorithms select the next training samples according to the hypothesized solution in order to better discriminate between correct and incorrect labels. Intrinsic-reward reinforcement learning uses prediction errors as the reward to an actor-critic design, such that behavior converges to the one that optimizes the learning process. We show that in the context of object localization, the two paradigms result in palpation whisking as their respective optimal solution. These results suggest that rats may employ principles of active learning and/or intrinsic reward in tactile exploration and can guide future research to seek the underlying neuronal mechanisms that implement them. Furthermore, these paradigms are easily transferable to biomimetic whisker-based artificial sensors and can improve the active exploration of their environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Sparse feature learning for instrument identification: Effects of sampling and pooling methods.

PubMed

Han, Yoonchang; Lee, Subin; Nam, Juhan; Lee, Kyogu

2016-05-01

Feature learning for music applications has recently received considerable attention from many researchers. This paper reports on the sparse feature learning algorithm for musical instrument identification, and in particular, focuses on the effects of the frame sampling techniques for dictionary learning and the pooling methods for feature aggregation. To this end, two frame sampling techniques are examined that are fixed and proportional random sampling. Furthermore, the effect of using onset frame was analyzed for both of proposed sampling methods. Regarding summarization of the feature activation, a standard deviation pooling method is used and compared with the commonly used max- and average-pooling techniques. Using more than 47 000 recordings of 24 instruments from various performers, playing styles, and dynamics, a number of tuning parameters are experimented including the analysis frame size, the dictionary size, and the type of frequency scaling as well as the different sampling and pooling methods. The results show that the combination of proportional sampling and standard deviation pooling achieve the best overall performance of 95.62% while the optimal parameter set varies among the instrument classes.

Deep Recurrent Neural Networks for Human Activity Recognition

PubMed Central

Murad, Abdulmajid

2017-01-01

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs. PMID:29113103

Deep Recurrent Neural Networks for Human Activity Recognition.

PubMed

Murad, Abdulmajid; Pyun, Jae-Young

2017-11-06

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

The Impact of Personality, Goal Orientation and Self-Efficacy on Participation of High School Teachers in Learning Activities in the Workplace

ERIC Educational Resources Information Center

van Daal, Tine; Donche, Vincent; De Maeyer, Sven

2014-01-01

This study examines the influence of personality traits, goal orientation and self-efficacy on high school teachers' participation in learning activities in the workplace (i.e. experimentation, informal interaction with colleagues, self-regulation and avoidance behaviour). A convenience sample of 95 teachers from six high schools in Flanders…

Exploring the Moon: A Teacher's Guide with Activities for Earth and Space Sciences.

ERIC Educational Resources Information Center

National Aeronautics and Space Administration, Washington, DC.

This educational guide concerns exploring the moon. Activities are divided into three units: Pre-Apollo, Learning from Apollo, and The Future. These correspond, at least roughly, to exercises that can be done before the Lunar Sample Disk (available from NASA) arrives to the school (Pre-Apollo), while it is there (Learning from Apollo), and after…

An Examination of Undergraduates' Metacognitive Strategies in Pre-Class Asynchronous Activity in a Flipped Classroom

ERIC Educational Resources Information Center

Yilmaz, Rabia M.; Baydas, Ozlem

2017-01-01

The aim of the study is to examine undergraduate students' awareness of metacognition, the metacognitive strategies they use in their learning and their learning performance in pre-class asynchronous activity in a flipped classroom. The sample consisted of 47 undergraduate students. Eleven students were not included in this study since they did…

Active learning approach for detection of hard exudates, cotton wool spots, and drusen in retinal images

NASA Astrophysics Data System (ADS)

Sánchez, Clara I.; Niemeijer, Meindert; Kockelkorn, Thessa; Abràmoff, Michael D.; van Ginneken, Bram

2009-02-01

Computer-aided Diagnosis (CAD) systems for the automatic identification of abnormalities in retinal images are gaining importance in diabetic retinopathy screening programs. A huge amount of retinal images are collected during these programs and they provide a starting point for the design of machine learning algorithms. However, manual annotations of retinal images are scarce and expensive to obtain. This paper proposes a dynamic CAD system based on active learning for the automatic identification of hard exudates, cotton wool spots and drusen in retinal images. An uncertainty sampling method is applied to select samples that need to be labeled by an expert from an unlabeled set of 4000 retinal images. It reduces the number of training samples needed to obtain an optimum accuracy by dynamically selecting the most informative samples. Results show that the proposed method increases the classification accuracy compared to alternative techniques, achieving an area under the ROC curve of 0.87, 0.82 and 0.78 for the detection of hard exudates, cotton wool spots and drusen, respectively.

Improving semi-automated segmentation by integrating learning with active sampling

NASA Astrophysics Data System (ADS)

Huo, Jing; Okada, Kazunori; Brown, Matthew

2012-02-01

Interactive segmentation algorithms such as GrowCut usually require quite a few user interactions to perform well, and have poor repeatability. In this study, we developed a novel technique to boost the performance of the interactive segmentation method GrowCut involving: 1) a novel "focused sampling" approach for supervised learning, as opposed to conventional random sampling; 2) boosting GrowCut using the machine learned results. We applied the proposed technique to the glioblastoma multiforme (GBM) brain tumor segmentation, and evaluated on a dataset of ten cases from a multiple center pharmaceutical drug trial. The results showed that the proposed system has the potential to reduce user interaction while maintaining similar segmentation accuracy.

Predict-share-observe-explain learning activity for the Torricelli's tank experiment

NASA Astrophysics Data System (ADS)

Panich, Charunya; Puttharugsa, Chokchai; Khemmani, Supitch

2018-01-01

The purpose of this research was to study the students' scientific concept and achievement on fluid mechanics before and after the predict-share-observe-explain (PSOE) learning activity for the Torricelli's tank experiment. The 24 participants, who were selected by purposive sampling, were students at grade 12 at Nannakorn School, Nan province. A one group pre-test/post-test design was employed in the study. The research instruments were 1) the lesson plans using the PSOE learning activity and 2) two-tier multiple choice question and subjective tests. The results indicated that students had better scientific concept about Torricelli's tank experiment and the post-test mean score was significantly higher than the pre-test mean score at a 0.05 level of significance. Moreover, the students had retention of knowledge after the PSOE learning activity for 4 weeks at a 0.05 level of significance. The study showed that the PSOE learning activity is suitable for developing students' scientific concept and achievement.

Active learning strategies for the deduplication of electronic patient data using classification trees.

PubMed

Sariyar, M; Borg, A; Pommerening, K

2012-10-01

Supervised record linkage methods often require a clerical review to gain informative training data. Active learning means to actively prompt the user to label data with special characteristics in order to minimise the review costs. We conducted an empirical evaluation to investigate whether a simple active learning strategy using binary comparison patterns is sufficient or if string metrics together with a more sophisticated algorithm are necessary to achieve high accuracies with a small training set. Based on medical registry data with different numbers of attributes, we used active learning to acquire training sets for classification trees, which were then used to classify the remaining data. Active learning for binary patterns means that every distinct comparison pattern represents a stratum from which one item is sampled. Active learning for patterns consisting of the Levenshtein string metric values uses an iterative process where the most informative and representative examples are added to the training set. In this context, we extended the active learning strategy by Sarawagi and Bhamidipaty (2002). On the original data set, active learning based on binary comparison patterns leads to the best results. When dropping four or six attributes, using string metrics leads to better results. In both cases, not more than 200 manually reviewed training examples are necessary. In record linkage applications where only forename, name and birthday are available as attributes, we suggest the sophisticated active learning strategy based on string metrics in order to achieve highly accurate results. We recommend the simple strategy if more attributes are available, as in our study. In both cases, active learning significantly reduces the amount of manual involvement in training data selection compared to usual record linkage settings. Copyright © 2012 Elsevier Inc. All rights reserved.

The Effect of Mastery Learning Model with Reflective Thinking Activities on Medical Students' Academic Achievement: An Experimental Study

ERIC Educational Resources Information Center

Elaldi, Senel

2016-01-01

This study aimed to determine the effect of mastery learning model supported with reflective thinking activities on the fifth grade medical students' academic achievement. Mixed methods approach was applied in two samples (n = 64 and n = 6). Quantitative part of the study was based on a pre-test-post-test control group design with an experiment…

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.

Learning from an Owl.

ERIC Educational Resources Information Center

Greeves, Adrian

1988-01-01

Describes one creative writing teacher's use of an owl as a focal point for writing activities and how the writing activities aided the students' personal and creative development. Provides samples of student writing. (ARH)

User-Driven Sampling Strategies in Image Exploitation

DOE PAGES

Harvey, Neal R.; Porter, Reid B.

2013-12-23

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-drivenmore » sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. We discovered that in user-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. Furthermore, in preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.« less

User-driven sampling strategies in image exploitation

NASA Astrophysics Data System (ADS)

Harvey, Neal; Porter, Reid

2013-12-01

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. In preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

High School Teachers Use of Writing to Support Students' Learning: A National Survey

ERIC Educational Resources Information Center

Gillespie, Amy; Graham, Steve; Kiuhara, Sharlene; Hebert, Michael

2014-01-01

A random sample of language arts, social studies, science, and math high school teachers from across the United States were surveyed about their use of writing to support student learning. Four out of every five teachers reported they used writing to support student learning, applying on average 24 different writing activities across the school…

Exploring the Icebergs of Adult Learning: Findings of the First Canadian Survey of Informal Learning Practices.

ERIC Educational Resources Information Center

Livingstone, D. W.

The extent and distribution of self-reported learning activities in the current Canadian adult population was estimated on the basis of data collected during a 1998 telephone survey of a sample of 1,562 Canadian adults. Random digital dialing was used to give all provinces, households, and individuals within households an equal chance of…

Learning Environment, Attitudes and Achievement among Middle-School Science Students Using Inquiry-Based Laboratory Activities

ERIC Educational Resources Information Center

Wolf, Stephen J.; Fraser, Barry J.

2008-01-01

This study compared inquiry and non-inquiry laboratory teaching in terms of students' perceptions of the classroom learning environment, attitudes toward science, and achievement among middle-school physical science students. Learning environment and attitude scales were found to be valid and related to each other for a sample of 1,434 students in…

Digital learning objects in nursing consultation: technology assessment by undergraduate students.

PubMed

Silveira, DeniseTolfo; Catalan, Vanessa Menezes; Neutzling, Agnes Ludwig; Martinato, Luísa Helena Machado

2010-01-01

This study followed the teaching-learning process about the nursing consultation, based on digital learning objects developed through the active Problem Based Learning method. The goals were to evaluate the digital learning objects about nursing consultation, develop cognitive skills on the subject using problem based learning and identify the students' opinions on the use of technology. This is an exploratory and descriptive study with a quantitative approach. The sample consisted of 71 students in the sixth period of the nursing program at the Federal University of Rio Grande do Sul. The data was collected through a questionnaire to evaluate the learning objects. The results showed positive agreement (58%) on the content, usability and didactics of the proposed computer-mediated activity regarding the nursing consultation. The application of materials to the students is considered positive.

Reinforcement learning or active inference?

PubMed

Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J

2009-07-29

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

Attention Cueing and Activity Equally Reduce False Alarm Rate in Visual-Auditory Associative Learning through Improving Memory.

PubMed

Nikouei Mahani, Mohammad-Ali; Haghgoo, Hojjat Allah; Azizi, Solmaz; Nili Ahmadabadi, Majid

2016-01-01

In our daily life, we continually exploit already learned multisensory associations and form new ones when facing novel situations. Improving our associative learning results in higher cognitive capabilities. We experimentally and computationally studied the learning performance of healthy subjects in a visual-auditory sensory associative learning task across active learning, attention cueing learning, and passive learning modes. According to our results, the learning mode had no significant effect on learning association of congruent pairs. In addition, subjects' performance in learning congruent samples was not correlated with their vigilance score. Nevertheless, vigilance score was significantly correlated with the learning performance of the non-congruent pairs. Moreover, in the last block of the passive learning mode, subjects significantly made more mistakes in taking non-congruent pairs as associated and consciously reported lower confidence. These results indicate that attention and activity equally enhanced visual-auditory associative learning for non-congruent pairs, while false alarm rate in the passive learning mode did not decrease after the second block. We investigated the cause of higher false alarm rate in the passive learning mode by using a computational model, composed of a reinforcement learning module and a memory-decay module. The results suggest that the higher rate of memory decay is the source of making more mistakes and reporting lower confidence in non-congruent pairs in the passive learning mode.

Perspective transformation: enhancing the development of professionalism in RN-to-BSN students.

PubMed

Morris, Arlene H; Faulk, Debbie

2007-10-01

The purpose of this research was to examine whether there are resultant behavioral changes in professionalism for returning adult RN-to-BSN students and to identify teaching-learning activities that stimulate transformative learning. Mezirow's adult learning theory served as a theoretical guide for the study. A convenience sample of students enrolled in a RN-to-BSN completion program during 2 academic years was surveyed using the core standards from the American Association of Colleges of Nursing's essentials of baccalaureate nursing education. A total of 26 learning activities were identified as creating cognitive dissonance (conflict of values). Changes in professional behavior 3 months postgraduation included increased collaboration with the health care team, increased patient advocacy, and increased confidence in the role as a teacher of patients and families. The findings indicate that planning learning activities in nursing curricula can foster perspective transformation in professionalism.

Active machine learning for rapid landslide inventory mapping with VHR satellite images (Invited)

NASA Astrophysics Data System (ADS)

Stumpf, A.; Lachiche, N.; Malet, J.; Kerle, N.; Puissant, A.

2013-12-01

VHR satellite images have become a primary source for landslide inventory mapping after major triggering events such as earthquakes and heavy rainfalls. Visual image interpretation is still the prevailing standard method for operational purposes but is time-consuming and not well suited to fully exploit the increasingly better supply of remote sensing data. Recent studies have addressed the development of more automated image analysis workflows for landslide inventory mapping. In particular object-oriented approaches that account for spatial and textural image information have been demonstrated to be more adequate than pixel-based classification but manually elaborated rule-based classifiers are difficult to adapt under changing scene characteristics. Machine learning algorithm allow learning classification rules for complex image patterns from labelled examples and can be adapted straightforwardly with available training data. In order to reduce the amount of costly training data active learning (AL) has evolved as a key concept to guide the sampling for many applications. The underlying idea of AL is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and data structure to iteratively select the most valuable samples that should be labelled by the user. With relatively few queries and labelled samples, an AL strategy yields higher accuracies than an equivalent classifier trained with many randomly selected samples. This study addressed the development of an AL method for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. Our approach [1] is based on the Random Forest algorithm and considers the classifier uncertainty as well as the variance of potential sampling regions to guide the user towards the most valuable sampling areas. The algorithm explicitly searches for compact regions and thereby avoids a spatially disperse sampling pattern inherent to most other AL methods. The accuracy, the sampling time and the computational runtime of the algorithm were evaluated on multiple satellite images capturing recent large scale landslide events. Sampling between 1-4% of the study areas the accuracies between 74% and 80% were achieved, whereas standard sampling schemes yielded only accuracies between 28% and 50% with equal sampling costs. Compared to commonly used point-wise AL algorithm the proposed approach significantly reduces the number of iterations and hence the computational runtime. Since the user can focus on relatively few compact areas (rather than on hundreds of distributed points) the overall labeling time is reduced by more than 50% compared to point-wise queries. An experimental evaluation of multiple expert mappings demonstrated strong relationships between the uncertainties of the experts and the machine learning model. It revealed that the achieved accuracies are within the range of the inter-expert disagreement and that it will be indispensable to consider ground truth uncertainties to truly achieve further enhancements in the future. The proposed method is generally applicable to a wide range of optical satellite images and landslide types. [1] A. Stumpf, N. Lachiche, J.-P. Malet, N. Kerle, and A. Puissant, Active learning in the spatial domain for remote sensing image classification, IEEE Transactions on Geosciece and Remote Sensing. 2013, DOI 10.1109/TGRS.2013.2262052.

Environmental Education: Back to Basics.

ERIC Educational Resources Information Center

Warpinski, Robert

1984-01-01

Describes an instructional framework based on concepts of energy, ecosystems, carrying capacity, change, and stewardship. Stresses the importance of determining what is really important (basic) for each student to experience or learn in relation to each concept and grade level. Student-centered learning activities and sample lesson on energy…

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

PubMed Central

Hartmann, Christoph; Lazar, Andreea; Nessler, Bernhard; Triesch, Jochen

2015-01-01

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms. PMID:26714277

Student’s STEM Literacy in Biotechnology Learning at Junior High School

NASA Astrophysics Data System (ADS)

Nurlaely, N.; Permanasari, A.; Riandi, R.

2017-09-01

A considerable study to student’s STEM literacy achievement profile, especially in biotechnology learning, has been conducted to make the innovation of the STEM-based learning. The study aims to find out the STEM literacy. The sample is taken through purposive sampling technique to 45 students of 9th grade of a junior high school in Tasikmalaya district. The instruments are multiple choice questions. Data are analysed by calculating mean score of students’ STEM literacy achievement. The results show that student’s STEM literacy achievement was low. Science literacy aspect was the lowest, while mathematical literacy gained better than another aspect. The low achievement of students’ STEM literacy was because of learning activities that have not been able to integrate science, technology, engineering, and mathematics in science learning. The literacy profile indicates the importance of applying STEM approach to science learning, and it is recommended to improve students’ STEM literacy achievement.

Pharmacists' perceptions of facilitators and barriers to lifelong learning.

PubMed

Hanson, Alan L; Bruskiewitz, Ruth H; Demuth, James E

2007-08-15

To reevaluate facilitators of and barriers to pharmacists' participation in lifelong learning previously examined in a 1990 study. A survey instrument was mailed to 274 pharmacists who volunteered to participate based on a prior random sample survey. Data based on perceptions of facilitators and barriers to lifelong learning, as well as self-perception as a lifelong learner, were analyzed and compared to a similar 1990 survey. The response rate for the survey was 88%. The top 3 facilitators and barriers to lifelong learning from the 2003 and the 1990 samples were: (1) personal desire to learn; (2) requirement to maintain professional licensure; and (3) enjoyment/relaxation provided by learning as change of pace from the "routine." The top 3 barriers were: (1) job constraints; (2) scheduling (location, distance, time) of group learning activities; and (3) family constraints (eg, spouse, children, personal). Respondents' broad self-perception as lifelong learners continued to be highly positive overall, but remained less positive relative to more specific lifelong learning skills such as the ability to identify learning objectives as well as to evaluate learning outcomes. Little has changed in the last decade relative to how pharmacists view themselves as lifelong learners, as well as what they perceive as facilitators and barriers to lifelong learning. To address factors identified as facilitators and barriers, continuing education (CE) providers should focus on pharmacists' time constraints, whether due to employment, family responsibilities, or time invested in the educational activity itself, and pharmacists' internal motivations to learn (personal desire, enjoyment), as well as external forces such as mandatory CE for relicensure.

Pharmacists' Perceptions of Facilitators and Barriers to Lifelong Learning

PubMed Central

Bruskiewitz, Ruth H.; DeMuth, James E.

2007-01-01

Objectives To reevaluate facilitators of and barriers to pharmacists' participation in lifelong learning previously examined in a 1990 study. Methods A survey instrument was mailed to 274 pharmacists who volunteered to participate based on a prior random sample survey. Data based on perceptions of facilitators and barriers to lifelong learning, as well as self-perception as a lifelong learner, were analyzed and compared to a similar 1990 survey. Results The response rate for the survey was 88%. The top 3 facilitators and barriers to lifelong learning from the 2003 and the 1990 samples were: (1) personal desire to learn; (2) requirement to maintain professional licensure; and (3) enjoyment/relaxation provided by learning as change of pace from the “routine.” The top 3 barriers were: (1) job constraints; (2) scheduling (location, distance, time) of group learning activities; and (3) family constraints (eg, spouse, children, personal). Respondents' broad self-perception as lifelong learners continued to be highly positive overall, but remained less positive relative to more specific lifelong learning skills such as the ability to identify learning objectives as well as to evaluate learning outcomes. Conclusions Little has changed in the last decade relative to how pharmacists view themselves as lifelong learners, as well as what they perceive as facilitators and barriers to lifelong learning. To address factors identified as facilitators and barriers, continuing education (CE) providers should focus on pharmacists' time constraints, whether due to employment, family responsibilities, or time invested in the educational activity itself, and pharmacists' internal motivations to learn (personal desire, enjoyment), as well as external forces such as mandatory CE for relicensure. PMID:17786254

Trajectories of the Home Learning Environment across the First 5 Years: Associations with Children's Vocabulary and Literacy Skills at Prekindergarten

ERIC Educational Resources Information Center

Rodriguez, Eileen T.; Tamis-LeMonda, Catherine S.

2011-01-01

Children's home learning environments were examined in a low-income sample of 1,852 children and families when children were 15, 25, 37, and 63 months. During home visits, children's participation in literacy activities, the quality of mothers' engagements with their children, and the availability of learning materials were assessed, yielding a…

Effects of team-based learning on self-regulated online learning.

PubMed

Whittaker, Alice A

2015-04-10

Online learning requires higher levels of self-regulation in order to achieve optimal learning outcomes. As nursing education moves further into the blended and online learning venue, new teaching/learning strategies will be required to develop and enhance self-regulated learning skills in nursing students. The purpose of this study was to compare the effectiveness of team-based learning (TBL) with traditional instructor-led (IL) learning, on self-regulated online learning outcomes, in a blended undergraduate research and evidence-based practice course. The nonrandomized sample consisted of 98 students enrolled in the IL control group and 86 students enrolled in the TBL intervention group. The percentage of total possible online viewing time was used as the measure of self-regulated online learning activity. The TBL group demonstrated a significantly higher percentage (p < 0.001) of self-regulated learning activities than the IL control group. The TBL group scored significantly higher on the course examinations (p = 0.003). The findings indicate that TBL is an effective instructional strategy that can be used to achieve the essential outcomes of baccalaureate nursing education by increasing self-regulated learning capabilities in nursing students.

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…

Effectiveness of Active Learning Strategy in Improving the Acoustic Awareness Skills and Understanding What Is Heard by the Basic Stage Students in Jordan

ERIC Educational Resources Information Center

Al-Odwan, Yaser

2016-01-01

This research aims to get acquainted with the effectiveness of the active learning strategy in improving the acoustic awareness skills and understanding what is heard by the basic stage students in Jordan by answering the two following questions: This research has been applied to a sample of 60 students from the basic third grade in Al-Ahnaf Ben…

Bioethicsing.

PubMed

Arawi, Thalia; Mikati, Diana

2017-04-01

This article describes the components of a unique 9 month required course in bioethics for 3rd year medical students at the American University of Beirut. The blended (hybrid) learning format emphasizes three innovative learning activities: the bioethics documentary, edutainment games, and the bioethics log book. Sample student responses are included as well as an outline of limitations.

Social Emotional Learning: Implementation of Sustainability-Oriented Program in Latvia

ERIC Educational Resources Information Center

Martinsone, Baiba

2016-01-01

This article is focused on the description of the content and the implementation process of an originally developed, culturally appropriate and sustainable social and emotional learning program in Latvia. The article also includes the teachers' self-reflected experience illustrated through the perspective of the program's sample activities. The…

Secondary Social Studies: Alaska Curriculum Guide. Second Edition.

ERIC Educational Resources Information Center

Alaska State Dept. of Education, Juneau. Office of Curriculum Services.

A secondary social studies model curriculum guide for Alaska is presented. The body of the guide lists topics/concepts, learning outcomes/objectives, and sample learning activities in a 3 column format. The first column, topics/concepts, describes the content area, defining the subject broadly and listing subconcepts or associated vocabulary. The…

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 concepts more meaningfully than students in control group. Conclusion The study revealed that active-learning implementation is more effective at improving students' learning achievement and preventing misconceptions.

Effects of biology teachers' professional knowledge and cognitive activation on students' achievement

NASA Astrophysics Data System (ADS)

Förtsch, Christian; Werner, Sonja; von Kotzebue, Lena; Neuhaus, Birgit J.

2016-11-01

This study examined the effects of teachers' biology-specific dimensions of professional knowledge - pedagogical content knowledge (PCK) and content knowledge (CK) - and cognitively activating biology instruction, as a feature of instructional quality, on students' learning. The sample comprised 39 German secondary school teachers whose lessons on the topic neurobiology were videotaped twice. Teachers' instruction was coded with regard to cognitive activation using a rating manual. Multilevel path analysis results showed a positive significant effect of cognitive activation on students' learning and an indirect effect of teachers' PCK on students' learning mediated through cognitive activation. These findings highlight the importance of PCK in preservice biology teachers' education. Items of the rating manual may be used to provide exemplars of concrete teaching situations during university seminars for preservice teacher education or professional development initiatives for in-service teachers.

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.

Factors associated with Spanish older people's membership in political organizations: the role of active aging activities.

PubMed

Serrat, Rodrigo; Villar, Feliciano; Celdrán, Montserrat

2015-09-01

This study explores older people's membership in political organizations by using data from the Survey on older people 2010, carried out by Spain's National Institute for older people and social services. The objectives were to describe the extent of this kind of participation among Spaniards aged 65 and over, and to analyze the factors that are associated with it. Results show that only slightly less than 7 % of the sample belonged to a political organization. To analyze the factors related to this membership, a set of models of multivariate analyses were run, including socioeconomic resources and participation in other types of active aging activity (participation in leisure, learning, and productive activities). Educational level, leisure activities, learning activities, and only volunteering in the case of productive activities were found to be associated with membership in political organizations. Results provide partial support for the socioeconomic resources model and suggest that engagement in leisure activities, learning activities, and volunteering might have an enhancing effect on membership in political organizations.

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.

The strategic use of lecture recordings to facilitate an active and self-directed learning approach.

PubMed

Topale, Luminica

2016-08-12

New learning technologies have the capacity to dramatically impact how students go about learning and to facilitate an active, self-directed learning approach. In U. S. medical education, students encounter a large volume of content, which must be mastered at an accelerated pace. The added pressure to excel on the USMLE Step 1 licensing exam and competition for residency placements, require that students adopt an informed approach to the use of learning technologies so as to enhance rather than to detract from the learning process. The primary aim of this study was to gain a better understanding of how students were using recorded lectures in their learning and how their study habits have been influenced by the technology. Survey research was undertaken using a convenience sample. Students were asked to voluntarily participate in an electronic survey comprised of 27 closed ended, multiple choice questions, and one open ended item. The survey was designed to explore students' perceptions of how recorded lectures affected their choices regarding class participation and impacted their learning and to gain an understanding of how recorded lectures facilitated a strategic, active learning process. Findings revealed that recorded lectures had little influence on students' choices to participate, and that the perceived benefits of integrating recorded lectures into study practices were related to their facilitation of and impact on efficient, active, and self-directed learning. This study was a useful investigation into how the availability of lecture capture technology influenced medical students' study behaviors and how students were making valuable use of the technology as an active learning tool.

Travel without Leaving the Classroom.

ERIC Educational Resources Information Center

Zertuche, Albert A.

2002-01-01

Describes a lesson on different world ecosystems in which activities are based on the constructivist approach to teaching that encourages learners to control their own learning. Includes a sample grading rubric and national science education standards related to these activities. (KHR)

Inclusive jet cross sections in proton-proton collisions at 7.0 TeV center-of-mass energy with the ATLAS detector at the Large Hadron Collider

NASA Astrophysics Data System (ADS)

Park-Martinez, Jayne Irene

The purpose of this study was to assess the effects of node-link mapping on students' meaningful learning and conceptual change in a 1-semester introductory life-science course. This study used node-link mapping to integrate and apply the National Research Council's (NRC, 2005) three principles of human learning: engaging students' prior knowledge, fostering their metacognition, and supporting their formulation of a scientific conceptual framework. The study was a quasi-experimental, pretest-posttest, control group design. The sample consisted of 68 primarily freshmen non-science majors enrolled in two intact sections of the targeted course. Both groups received the same teacher-centered instruction and student-centered activities designed to promote meaningful learning and conceptual change; however, the activity format differed. Control group activities were written; treatment group activities were node-link mapped. Prior to instruction, both groups demonstrated equivalent knowledge and misconceptions associated with genetics and evolution (GE), and ecology and environmental science (EE). Mean differences, pre-to-post instruction, on the GE and EE meaningful learning exam scores and the EE conceptual change inventory scores between the writing group (control) and the node-link mapping group (treatment) were analyzed using repeated measures MANOVAs. There were no significant mean pre-to-post differences between groups with respect to meaningful learning in the GE or EE units, or conceptual change in the EE unit. However, independent of group membership, the overall mean pre-to-post increases in meaningful learning and conceptual change were significant. These findings suggest that both node-link mapping and writing, when used in conjunction with the National Research Council's (NRC, 2005) three principles of human learning, can promote meaningful learning and conceptual change. The only significant interaction found with respect to meaningful learning, conceptual change, and learning styles (Kolb, 2005) was a positive effect of node-link mapping on converger's meaningful learning. However, that result was probably an artifact of small sample size rather than a true treatment effect. No other significant interactions were found. These results suggest that all students, regardless of their learning style, can benefit from either node-link mapping or writing to promote meaningful learning and conceptual change in general life-science courses.

Stimulating Participation and Learning in Microbiology: Presence and Identification of Bacteria from Student's Hands

ERIC Educational Resources Information Center

Antunes, Patrícia

2016-01-01

We proposed in the Basic Microbiology Subject for food science and nutrition students, a "hands-on" activity consisting on sampling student's hands for bacterial presence and identification. This is a project to be implemented in multiple laboratory classes throughout the semester, allowing students to learn, and apply general…

Generative Learning Strategy Use and Self-Regulatory Prompting in Digital Text

ERIC Educational Resources Information Center

Reid, Alan J.; Morrison, Gary M.

2014-01-01

The digital revolution is shifting print-based textbooks to digital text, and it has afforded the opportunity to incorporate meaningful learning strategies and otherwise separate metacognitive activities directly into these texts as embedded support. A sample of 89 undergraduates read a digital, expository text on the basics of photography. The…

Recollections of Sexual Socialisation among Marginalised Heterosexual Black Men

ERIC Educational Resources Information Center

Dunlap, Eloise; Benoit, Ellen; Graves, Jennifer L.

2013-01-01

This paper describes the sexual socialisation process of marginalised, drug-using heterosexual black men, focusing primarily on the sources and content of sexual information. Analysing qualitative interview data, we discovered that the men in our sample both learn about sex and become sexually active at an early age. They most often learn about…

Effect of Writing-to-Learn Strategy on Undergraduates' Conceptual Understanding of Electrostatics

ERIC Educational Resources Information Center

Atasoy, Sengül

2013-01-01

The purpose of this study is to explore the effect of Writing-to-Learn (WTL) strategy on undergraduates' conceptual understanding of electrostatics. The sample of the study was 54 university students registered at elementary school mathematics education department. While the experimental group was asked to conduct WTL activities like explanatory…

Students' Motivations for Voluntary Remedial Learning in High School

ERIC Educational Resources Information Center

Pelletier, Daniel; Green-Demers, Isabelle; Lafleur, Karine

2013-01-01

Most high schools offer remedial learning sessions to their students; however, very little is known about the perception of these activities, especially with regards to the students' motivations. In order to gain insights into both topics, an exploratory study was conducted within a sample of 1388 high school students in the Province of Quebec…

Students' Reflections Using Visualized Learning Outcomes and E-Portfolios

ERIC Educational Resources Information Center

Narumi, Takatsune; Gotoh, Yasushi

2014-01-01

How to guarantee graduate attributes has become an urgent challenge amid the increasing progress in scientific and technological development and the globalization of economic activity. In order to solve these problems, a system is required which can visualize learning outcomes in relation to attainment targets, and store and sample records of the…

Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.

PubMed

Davila, Juan Carlos; Cretu, Ana-Maria; Zaremba, Marek

2017-06-07

The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample.

PubMed

Freedson, Patty S; Lyden, Kate; Kozey-Keadle, Sarah; Staudenmayer, John

2011-12-01

Previous work from our laboratory provided a "proof of concept" for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330-1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type.

Sample Energy Conservation Education Activities for Elementary School Students.

ERIC Educational Resources Information Center

Allen, Rodney F., Ed.; LaHart, David E., Ed.

The booklet contains learning activities for introducing energy and conservation concepts into the existing elementary school curriculum. The activities were developed by Palm Beach County teachers during a one-week workshop. A framework of ideas is divided into three functional categories: universe of energy, living systems and energy, and social…

The role of picture of process (pp) on senior high school students’ collision concept learning activities and multirepresentation ability

NASA Astrophysics Data System (ADS)

Sutarto; Indrawati; Wicaksono, I.

2018-04-01

The objectives of the study are to describe the effect of PP collision concepts to high school students’ learning activities and multirepresentation abilities. This study was a quasi experimental with non- equivalent post-test only control group design. The population of this study were students who will learn the concept of collision in three state Senior High Schools in Indonesia, with a sample of each school 70 students, 35 students as an experimental group and 35 students as a control group. Technique of data collection were observation and test. The data were analized by descriptive and inferensial statistic. Student learning activities were: group discussions, describing vectors of collision events, and formulating problem-related issues of impact. Multirepresentation capabilities were student ability on image representation, verbal, mathematics, and graph. The results showed that the learning activities in the three aspects for the three high school average categorized good. The impact of using PP on students’ ability on image and graph representation were a significant impact, but for verbal and mathematical skills there are differences but not significant.

Integrated method of teaching in Web Quest activity and its impact on undergraduate students' cognition and learning behaviors: a future trend in medical education.

PubMed

Badiyepeymaie Jahromi, Zohreh; Mosalanejad, Leili

2015-01-14

Web Quest is one of the new ways of teaching and learning that is based on research, and includes the principles of learning and cognitive activities, such as collaborative learning, social and cognitive learning, and active learning, and increases motivation. The aim of this study is to evaluate the Web Quest influence on students' learning behaviors. In this quasi-experimental study, which was performed on undergraduates taking a psychiatric course at Jahrom University of Medical Sciences, simple sampling was used to select the cases to be studied; the students entered the study through census and were trained according to Web Quest methodology. The procedure was to present the course as a case study and team work. Each topic included discussing concepts and then patient's treatment and the communicative principles for two weeks. Active participation of the students in response to the scenario and introduced problem was equal to preparing scientific videos about the disease and collecting the latest medical treatment for the disease from the Internet.Three questionnaires, including the self-directed learning Questionnaire, teamwork evaluation Questionnaire (value of team), and Buffard self-regulated Questionnaire, were the data gathering tools. The results showed that the average of self-regulated learning and self-directed learning (SDL) increased after the educational intervention. However, the increase was not significant. On the other hand, problem solving (P=0.001) and the value of teamwork (P=0.002), apart from increasing the average, had significant statistical values. In view of Web Quest's positive impacts on students' learning behaviors, problem solving and teamwork, the effective use of active learning and teaching practices and use of technology in medical education are recommended.

Integrated Method of Teaching in Web Quest Activity and Its Impact on Undergraduate Students’ Cognition and Learning Behaviors: A Future Trend in Medical Education

PubMed Central

Jahromi, Zohreh Badiyepeymaie; Mosalanejad, Leili

2015-01-01

Introduction: Web Quest is one of the new ways of teaching and learning that is based on research, and includes the principles of learning and cognitive activities, such as collaborative learning, social and cognitive learning, and active learning, and increases motivation. The aim of this study is to evaluate the Web Quest influence on students’ learning behaviors. Materials and Methods: In this quasi-experimental study, which was performed on undergraduates taking a psychiatric course at Jahrom University of Medical Sciences, simple sampling was used to select the cases to be studied; the students entered the study through census and were trained according toWeb Quest methodology. The procedure was to present the course as a case study and team work. Each topic included discussing concepts and then patient’s treatment and the communicative principles for two weeks. Active participation of the students in response to the scenario and introduced problem was equal to preparing scientific videos about the disease and collecting the latest medical treatment for the disease from the Internet. Three questionnaires, including the self-directed learning Questionnaire, teamwork evaluation Questionnaire (value of team), and Buffard self-regulated Questionnaire, were the data gathering tools. Results: The results showed that the average of self-regulated learning and self-directed learning (SDL) increased after the educational intervention. However, the increase was not significant. On the other hand, problem solving (P=0.001) and the value of teamwork (P=0.002), apart from increasing the average, had significant statistical values. Conclusions: In view of Web Quest’s positive impacts on students’ learning behaviors, problem solving and teamwork, the effective use of active learning and teaching practices and use of technology in medical education are recommended. PMID:25946931

Active and observational reward learning in adults with autism spectrum disorder: relationship with empathy in an atypical sample.

PubMed

Bellebaum, Christian; Brodmann, Katja; Thoma, Patrizia

2014-01-01

Autism spectrum disorders (ASDs) are characterised by disturbances in social behaviour. A prevailing hypothesis suggests that these problems are related to deficits in assigning rewarding value to social stimuli. The present study aimed to examine monetary reward processing in adults with ASDs by means of event-related potentials (ERPs). Ten individuals with mild ASDs (Asperger's syndrome and high-functioning autism) and 12 healthy control subjects performed an active and an observational probabilistic reward-learning task. Both groups showed similar overall learning performance. With respect to reward processing, subjects with ASDs exhibited a general reduction in feedback-related negativity (FRN) amplitude, irrespective of feedback valence and type of learning (active or observational). Individuals with ASDs showed lower scores for cognitive empathy, while affective empathy did not differ between groups. Correlation analyses revealed that higher empathy (both cognitive and affective) negatively affected performance in observational learning in controls and in active learning in ASDs (only cognitive empathy). No relationships were seen between empathy and ERPs. Reduced FRN amplitudes are discussed in terms of a deficit in fast reward processing in ASDs, which may indicate altered reward system functioning.

Hierarchical prediction errors in midbrain and septum during social learning.

PubMed

Diaconescu, Andreea O; Mathys, Christoph; Weber, Lilian A E; Kasper, Lars; Mauer, Jan; Stephan, Klaas E

2017-04-01

Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling and genetics to address this question in two separate samples (N = 35, N = 47). Participants played a game requiring inference on an adviser's intentions whose motivation to help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates about current advice validity and the adviser's trustworthiness, respectively, depend on different neuromodulatory systems. Low-level prediction errors (PEs) about advice accuracy not only activated regions known to support 'theory of mind', but also the dopaminergic midbrain. Furthermore, PE responses in ventral striatum were influenced by the Met/Val polymorphism of the Catechol-O-Methyltransferase (COMT) gene. By contrast, high-level PEs ('expected uncertainty') about the adviser's fidelity activated the cholinergic septum. These findings, replicated in both samples, have important implications: They suggest that social learning rests on hierarchically related PEs encoded by midbrain and septum activity, respectively, in the same manner as other forms of learning under volatility. Furthermore, these hierarchical PEs may be broadcast by dopaminergic and cholinergic projections to induce plasticity specifically in cortical areas known to represent beliefs about others. © The Author (2017). Published by Oxford University Press.

Hierarchical prediction errors in midbrain and septum during social learning

PubMed Central

Mathys, Christoph; Weber, Lilian A. E.; Kasper, Lars; Mauer, Jan; Stephan, Klaas E.

2017-01-01

Abstract Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling and genetics to address this question in two separate samples (N = 35, N = 47). Participants played a game requiring inference on an adviser’s intentions whose motivation to help or mislead changed over time. Our analyses suggest that hierarchically structured belief updates about current advice validity and the adviser’s trustworthiness, respectively, depend on different neuromodulatory systems. Low-level prediction errors (PEs) about advice accuracy not only activated regions known to support ‘theory of mind’, but also the dopaminergic midbrain. Furthermore, PE responses in ventral striatum were influenced by the Met/Val polymorphism of the Catechol-O-Methyltransferase (COMT) gene. By contrast, high-level PEs (‘expected uncertainty’) about the adviser’s fidelity activated the cholinergic septum. These findings, replicated in both samples, have important implications: They suggest that social learning rests on hierarchically related PEs encoded by midbrain and septum activity, respectively, in the same manner as other forms of learning under volatility. Furthermore, these hierarchical PEs may be broadcast by dopaminergic and cholinergic projections to induce plasticity specifically in cortical areas known to represent beliefs about others. PMID:28119508

Problem Posing with Realistic Mathematics Education Approach in Geometry Learning

NASA Astrophysics Data System (ADS)

Mahendra, R.; Slamet, I.; Budiyono

2017-09-01

One of the difficulties of students in the learning of geometry is on the subject of plane that requires students to understand the abstract matter. The aim of this research is to determine the effect of Problem Posing learning model with Realistic Mathematics Education Approach in geometry learning. This quasi experimental research was conducted in one of the junior high schools in Karanganyar, Indonesia. The sample was taken using stratified cluster random sampling technique. The results of this research indicate that the model of Problem Posing learning with Realistic Mathematics Education Approach can improve students’ conceptual understanding significantly in geometry learning especially on plane topics. It is because students on the application of Problem Posing with Realistic Mathematics Education Approach are become to be active in constructing their knowledge, proposing, and problem solving in realistic, so it easier for students to understand concepts and solve the problems. Therefore, the model of Problem Posing learning with Realistic Mathematics Education Approach is appropriately applied in mathematics learning especially on geometry material. Furthermore, the impact can improve student achievement.

Application of Student Book Based On Integrated Learning Model Of Networked Type With Heart Electrical Activity Theme For Junior High School

NASA Astrophysics Data System (ADS)

Gusnedi, G.; Ratnawulan, R.; Triana, L.

2018-04-01

The purpose of this study is to determine the effect of the use of Integrated Science IPA books Using Networked Learning Model of knowledge competence through improved learning outcomes obtained. The experimental design used is one group pre test post test design to know the results before and after being treated. The number of samples used is one class that is divided into two categories of initial ability to see the improvement of knowledge competence. The sample used was taken from the students of grade VIII SMPN 2 Sawahlunto, Indonesia. The results of this study indicate that most students have increased knowledge competence.

Active learning for solving the incomplete data problem in facial age classification by the furthest nearest-neighbor criterion.

PubMed

Wang, Jian-Gang; Sung, Eric; Yau, Wei-Yun

2011-07-01

Facial age classification is an approach to classify face images into one of several predefined age groups. One of the difficulties in applying learning techniques to the age classification problem is the large amount of labeled training data required. Acquiring such training data is very costly in terms of age progress, privacy, human time, and effort. Although unlabeled face images can be obtained easily, it would be expensive to manually label them on a large scale and getting the ground truth. The frugal selection of the unlabeled data for labeling to quickly reach high classification performance with minimal labeling efforts is a challenging problem. In this paper, we present an active learning approach based on an online incremental bilateral two-dimension linear discriminant analysis (IB2DLDA) which initially learns from a small pool of labeled data and then iteratively selects the most informative samples from the unlabeled set to increasingly improve the classifier. Specifically, we propose a novel data selection criterion called the furthest nearest-neighbor (FNN) that generalizes the margin-based uncertainty to the multiclass case and which is easy to compute, so that the proposed active learning algorithm can handle a large number of classes and large data sizes efficiently. Empirical experiments on FG-NET and Morph databases together with a large unlabeled data set for age categorization problems show that the proposed approach can achieve results comparable or even outperform a conventionally trained active classifier that requires much more labeling effort. Our IB2DLDA-FNN algorithm can achieve similar results much faster than random selection and with fewer samples for age categorization. It also can achieve comparable results with active SVM but is much faster than active SVM in terms of training because kernel methods are not needed. The results on the face recognition database and palmprint/palm vein database showed that our approach can handle problems with large number of classes. Our contributions in this paper are twofold. First, we proposed the IB2DLDA-FNN, the FNN being our novel idea, as a generic on-line or active learning paradigm. Second, we showed that it can be another viable tool for active learning of facial age range classification.

Instructional Package of Development of Skill in Using Fine Motor of Children for Children with Intellectual Disabilities

NASA Astrophysics Data System (ADS)

Sangsawang, T.

2018-02-01

This research has the following purposes: 1) to find the efficiency of the self-learning activity set on development of skill in using fine motor of children with intellectual disabilities., 2) to compare the abilities to use the small muscles after the study more than before the study of children with intellectual disabilities, who made study with the self-learning activity on development of small muscles use., 3) to study the satisfaction of the children with intellectual disabilities using the self-learning activity on development of small muscles use. The sample groups on the research are the children with intellectual disabilities of the special education Maha Chakri Sirindhorn Provincial Nakhon Nayok Center in the school year 2016, for 7 children. The tools used on the research consist of the self-learning activity on development of small muscles use for the children with intellectual disabilities of the special, the observation form of abilities of small muscles before and after using the activity set and the observation form of satisfaction of the children with intellectual disabilities of the special towards the self-learning activity set on development of small muscles for the children with intellectual disabilities of the special. The statistics used on the research include the percentage, mean value, standard deviation and the t-test for dependent sample. From the research, it was found that the self-learning activity set on development of small muscles use for children with intellectual disabilities of the special is efficient based on the criteria in average equal to 77.78/76.51, the educational coefficient of the student after the study higher than before the study with average points before the study equal to 55.14 and S.D. value equal to 3.72. The average points after the study equal to 68.86, S.D. value equal to 2.73, t-test value before and after the study equal to 7.94, which are different significantly on statistics at the level 0.05 and the satisfaction observation form of the student towards the self-learning activity on small muscles use for he down syndrome children with average value equal to 4.58 in the considerable level.

Using the mixed media according to internet-based on the instructional multimedia for developing students' learning achievements in biology course on foundational cell issue of secondary students at the 10th grade level in Rangsit University demonstration school

NASA Astrophysics Data System (ADS)

Kangloan, Pichet; Chayaburakul, Kanokporn; Santiboon, Toansakul

2018-01-01

The aims of this research study were 1) to develop students' learning achievements in biology course on foundational cell issue, 2) to examine students' satisfactions of their learning activities through the mixed media according to internet-based multi-instruction in biology on foundational cell issue at the 10th grade level were used in the first semester in the academic year 2014, which a sample size of 17 students in Rangsit University Demonstration School with cluster random sampling was selected. Students' learning administrations were instructed with the 3-instructional lesson plans according to the 5-Step Ladder Learning Management Plan (LLMP) namely; the maintaining lesson plan on the equilibrium of cell issue, a lesson plan for learning how to communicate between cell and cell division. Students' learning achievements were assessed with the 30-item Assessment of Learning Biology Test (ALBT), students' perceptions of their satisfactions were satisfied with the 20-item Questionnaire on Students Satisfaction (QSS), and students' learning activities were assessed with the Mixed Media Internet-Based Instruction (MMIBI) on foundational cell issue was designed. The results of this research study have found that: statistically significant of students' post-learning achievements were higher than their pre-learning outcomes and indicated that the differences were significant at the .05 level. Students' performances of their satisfaction to their perceptions toward biology class with the mixed media according to internet-based multi instruction in biology on foundational cell issue were the highest level and evidence of average mean score as 4.59.

The Effect of Carrying out Writing to Learn Activities on Academic Success of Fifth Grade Students in Secondary School on the Subject of "Force and Motion"

ERIC Educational Resources Information Center

Seven, Sabriye; Koksal, Asiye Pinar; Kocak, Gulsen

2017-01-01

The aim of this study is to investigate the impact of writing poems and keeping a journal as writing-to-learn activities on the academic achievement of students in teaching the Force and Motion unit in the Science class of fifth grade students in secondary school. Sample of the study consists of 50 students who study in the fifth grade of two…

Learning With E-books and Project-based Strategy in a Community Health Nursing Course.

PubMed

Sung, Tien-Wen; Wu, Ting-Ting

2018-03-01

With advances in information technology, "information-assisted instruction" has been gradually introduced to nursing education curricula. Specifically, the integration of an e-book system can effectively enhance nursing students' attention and interest. Most studies on nursing education that incorporated e-books have focused on the advantages of convenience and assistance provided by e-books. Few studies have addressed community health nursing and off-campus practice activities in relation to suitable teaching strategies for learning activities. This study involved designing and planning a multimedia e-book learning system with a project-based learning activity that conforms to the curriculum and practical requirements of a community health nursing course. The purpose was to reduce the gap between theory and practice and realize an effective learning process. For learning evaluations, a final examination analysis with an independent sample t test; a scoring scheme with intrateam, interteam, and expert ratings; and Bloom's taxonomy-based analysis were conducted. The evaluation results indicated that the comprehension and learning abilities of the experimental group using the e-book system with a mobile device were effectively improved. In addition, the exploratory process involved in project-based learning can develop multiple cognitive skills and problem-solving ability, thereby realizing effective learning.

My Super Bowl of Favorite Foods.

ERIC Educational Resources Information Center

Trede, Mildred

1992-01-01

Various learning activities in language arts, mathematics, social studies, and science are presented, using the theme of favorite foods. Sample activities include thinking of similes and metaphors related to food, calculating calories eaten in a day, and listing foods associated with specific countries. (JDD)

Studying the Impact of Technology-Infused Activities among Low-Income Spanish-Speaking Immigrant Families

ERIC Educational Resources Information Center

Rivera, Héctor H.

2014-01-01

This study examines the impact of an intervention technology program--Community Learning Centers--designed to assist low-income Spanish-speaking parents in learning and using technology for family advancement. The study is based on a sample of 408 participants who completed pre- and post-surveys. Data collection was conducted across 2 years in…

Career Exploration in Agribusiness, Renewable Natural Resources, and Environmental Protection: A Curriculum Guide for Grades 7-9.

ERIC Educational Resources Information Center

Lawson, Dorothy; And Others

This guide contains eight learning modules which are designed as samples which fuse the career development concepts, subject matter, and occupational information into learning activities using occupations as the nucleus. There is one module for each of the eight occupational areas: agricultural equipment and mechanics, agricultural products (food…

Catholic Social Teaching in Their Own Words: Oral Histories of College Students Learning CST

ERIC Educational Resources Information Center

Nickerson, Michelle; Dammer, Harry

2018-01-01

This research offers insight into what undergraduates at five Catholic colleges and universities learned about Catholic Social Teaching (CST) during their college experience. The study used a purposive sample of twenty-six personal interviews with students who were exposed to CST either in the classroom or through some co-curricular activity. The…

Academic Self-Efficacy in the College Classroom: An Examination of Undergraduate Students' Reported Efficacy for Learning Tasks

ERIC Educational Resources Information Center

Conner, Timothy W., II; Aagaard, Lola; Skidmore, Ronald L.

2011-01-01

Self-efficacy is a personal belief in one's ability to accomplish particular tasks. Academic self-efficacy relates to one's belief in ability to accomplish learning activities. A convenient cluster sample (n = 105) of undergraduate students at a regional university in the midsouth was administered a survey that measured student academic…

Engaging Experiential Service Learning through a Co-Curricular Club: The Chase Charlie Races

ERIC Educational Resources Information Center

Judge, Lawrence W.; Pierce, David; Petersen, Jeffrey; Bellar, David; Wanless, Elizabeth; Gilreath, Erin; Simon, Laura

2011-01-01

The efficacy of the "Chase Charlie Races" (an experiential learning activity) was demonstrated via program assessment. This was achieved via post-event evaluations of race participants and student club members, and with fitness assessments of 76 elementary students who participated in an eight-week training program. Paired sample t-tests revealed…

Deep imitation learning for 3D navigation tasks.

PubMed

Hussein, Ahmed; Elyan, Eyad; Gaber, Mohamed Medhat; Jayne, Chrisina

2018-01-01

Deep learning techniques have shown success in learning from raw high-dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: deep-Q-networks and Asynchronous actor-critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.

Experience-Sampling Research Methods and Their Potential for Education Research

ERIC Educational Resources Information Center

Zirkel, Sabrina; Garcia, Julie A.; Murphy, Mary C.

2015-01-01

Experience-sampling methods (ESM) enable us to learn about individuals' lives in context by measuring participants' feelings, thoughts, actions, context, and/or activities as they go about their daily lives. By capturing experience, affect, and action "in the moment" and with repeated measures, ESM approaches allow researchers…

Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers

PubMed Central

2018-01-01

Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods. PMID:29304512

Chemical Characterization and Source Apportionment of Size Fractionated Atmospheric Aerosols, and, Evaluating Student Attitudes and Learning in Large Lecture General Chemistry Classes

NASA Astrophysics Data System (ADS)

Allen, Gregory Harold

Chemical speciation and source apportionment of size fractionated atmospheric aerosols were investigated using laser desorption time-of-flight mass spectrometry (LD TOF-MS) and source apportionment was carried out using carbon-14 accelerator mass spectrometry (14C AMS). Sample collection was carried out using the Davis Rotating-drum Unit for Monitoring impact analyzer in Davis, Colfax, and Yosemite, CA. Ambient atmospheric aerosols collected during the winter of 2010/11 and 2011/12 showed a significant difference in the types of compounds found in the small and large sized particles. The difference was due to the increase number of oxidized carbon species that were found in the small particles size ranges, but not in the large particles size ranges. Overall, the ambient atmospheric aerosols collected during the winter in Davis, CA had and average fraction modern of F14C = 0.753 +/- 0.006, indicating that the majority of the size fractionated particles originated from biogenic sources. Samples collected during the King Fire in Colfax, CA were used to determine the contribution of biomass burning (wildfire) aerosols. Factor analysis was used to reduce the ions found in the LD TOF-MS analysis of the King Fire samples. The final factor analysis generated a total of four factors that explained an overall 83% of the variance in the data set. Two of the factors correlated heavily with increased smoke events during the sample period. The increased smoke events produced a large number of highly oxidized organic aerosols (OOA2) and aromatic compounds that are indicative of biomass burning organic aerosols (WBOA). The signal intensities of the factors generated in the King Fire data were investigated in samples collected in Yosemite and Davis, CA to look at the impact of biomass burning on ambient atmospheric aerosols. In both comparison sample collections the OOA2 and WBOA factors both increased during biomass burning events located near the sampling sites. The correlation between the OOA2 and WBOA factors and smoke levels indicates that these factors can be used to identify the influence of biomass burning on ambient aerosols. The effectiveness of using the ChemWiki instead of a traditional textbook was investigated during the spring quarter of 2014. Student performance was measured using common midterms, a final, and a pre/post content exams. We also employed surveys, the Colorado Learning Attitudes about Science Survey (CLASS) for Chemistry, and a weekly time-on-task survey to quantify students' attitudes and study habits. The effectiveness of the ChemWiki compared to a traditional textbook was examined using multiple linear regression analysis with a standard non-inferiority testing framework. Results show that the performance of students in the section who were assigned readings from the ChemWiki was non-inferior to the performance of students in the section who were assigned readings from the traditional textbook, indicating that the ChemWiki does not substantially differ from the standard textbook in terms of student learning outcomes. The results from the surveys also suggest that the two classes were similar in their beliefs about chemistry and overall average time spent studying. These results indicate that the ChemWiki is a viable cost-saving alternative to traditional textbooks. The impact of using active learning techniques in a large lecture general chemistry class was investigated by assessing student performance and attitudes during the fall 2014 and winter 2015 quarters. One instructor applied active learning strategies while the remaining instructors employed more traditional lecture styles. Student performance, learning, learning environments, and attitudes were measured using a standardized pre/post exams, common final exams, classroom observations, and the CLASS chemistry instrument in large lecture general chemistry courses. Classroom observation data showed that the active learning class was the most student centered and of the other classes two instructors were transitional in their teaching style and the remaining two primarily employed traditional lecture techniques. The active learning class had the highest student performance but the difference was only statistically significant when compared to the two traditional lecture classes. Overall, our data showed a trend that student performance increased as the instructional style became more student centered. Student attitudes didn't seem to correlate with any specific instructional style and the students in the active learning class had similar attitudes to the other general students. The active learning class was successful in increasing the average time students spent studying outside of the class, a statistically significant difference of about 1.5 to 3.0 hrs/week.

Distribution-Preserving Stratified Sampling for Learning Problems.

PubMed

Cervellera, Cristiano; Maccio, Danilo

2017-06-09

The need for extracting a small sample from a large amount of real data, possibly streaming, arises routinely in learning problems, e.g., for storage, to cope with computational limitations, obtain good training/test/validation sets, and select minibatches for stochastic gradient neural network training. Unless we have reasons to select the samples in an active way dictated by the specific task and/or model at hand, it is important that the distribution of the selected points is as similar as possible to the original data. This is obvious for unsupervised learning problems, where the goal is to gain insights on the distribution of the data, but it is also relevant for supervised problems, where the theory explains how the training set distribution influences the generalization error. In this paper, we analyze the technique of stratified sampling from the point of view of distances between probabilities. This allows us to introduce an algorithm, based on recursive binary partition of the input space, aimed at obtaining samples that are distributed as much as possible as the original data. A theoretical analysis is proposed, proving the (greedy) optimality of the procedure together with explicit error bounds. An adaptive version of the algorithm is also introduced to cope with streaming data. Simulation tests on various data sets and different learning tasks are also provided.

Body Mass Index, Physical Activity, and Working Memory in a Sample of Children with Down Syndrome: Can Physical Activity Improve Learning in Children with Intellectual Disabilities?

ERIC Educational Resources Information Center

Ellis, Geertina Houthuijzen

2013-01-01

Research has suggested that in typical developing children a positive relationship exists between physical activity level and cognitive functioning. For some children, academic performance may increase when levels of physical activity are increased. Moreover, some studies have supported the idea that physical activity seems to improve attention.…

Lufkin_Exercise_ Setup

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

Milbrath, Brian; Sussman, Aviva Joy

As part of this training course, we have created a scenario at a location that will provide you with an opportunity to practice the techniques you have learned during the week. For the first hour, you will have the opportunity to conduct a Visual Observation and use VOB to determine ideal locations for RN soil sampling, swipe sampling, and in situ measurements. After the VOB and sampling locating, you will rotate between soil sample, swipe sample, and two in situ activities.

Reading Clinic. A New Use for Dr. Seuss: Rhymes Help Children Learn About Words.

ERIC Educational Resources Information Center

Cunningham, Patricia

1998-01-01

This activity for K-3 students helps them learn to decode and spell words using rhyme, noting that hearing and creating rhyme helps children hear similarities among words. Books with the Dr. Seuss imprint are recommended because they appeal to children. A sample poem entitled March, by Solveig Paulson Russell, is included on a reproducible sheet.…

Get on Board the Underground Railroad: A Sample Unit for Fifth-Grade History Students.

ERIC Educational Resources Information Center

Ferguson, Phyllis M.; Young, Terrell A.

1996-01-01

Reviews the materials and procedures used in a fifth-grade history unit on the Underground Railroad. The unit integrated a variety of teaching methods and materials making extensive use of historical literature, K-W-L (what we Know, what we Want to find out, what we Learned) charts, and activities aimed at different learning styles. (MJP)

Understanding the Increase in Parents' Involvement in Organized Youth Sports

ERIC Educational Resources Information Center

Stefansen, Kari; Smette, Ingrid; Strandbu, Åse

2018-01-01

As part of an ethnographic study on young people and learning (the knowledge in motion across contexts of learning project, set in Norway), we interviewed a diverse sample of parents of young teenagers, many of whom were active in organized sports. The parents described their level of involvement in sport in a way that contrasted sharply to our…

Plasma homovanillic acid correlates inversely with history of learning problems in healthy volunteer and personality disordered subjects.

PubMed

Coccaro, Emil F; Hirsch, Sharon L; Stein, Mark A

2007-01-15

Central dopaminergic activity is critical to the functioning of both motor and cognitive systems. Based on the therapeutic action of dopaminergic agents in treating attention deficit hyperactivity disorder (ADHD), ADHD symptoms may be related to a reduction in central dopaminergic activity. We tested the hypothesis that dopaminergic activity, as reflected by plasma homovanillic acid (pHVA), may be related to dimensional aspects of ADHD in adults. Subjects were 30 healthy volunteer and 39 personality disordered subjects, in whom morning basal pHVA concentration and a dimensional measure of childhood ADHD symptoms (Wender Utah Rating Scale: WURS) were obtained. A significant inverse correlation was found between WURS Total score and pHVA concentration in the total sample. Among WURS factor scores, a significant inverse relationship was noted between pHVA and history of "childhood learning problems". Consistent with the dopaminergic dysfunction hypothesis of ADHD and of cognitive function, pHVA concentrations were correlated with childhood history of ADHD symptoms in general and with history of "learning problems" in non-ADHD psychiatric patients and controls. Replication is needed in treated and untreated ADHD samples to confirm these initial results.

Learning induces the translin/trax RNase complex to express activin receptors for persistent memory.

PubMed

Park, Alan Jung; Havekes, Robbert; Fu, Xiuping; Hansen, Rolf; Tudor, Jennifer C; Peixoto, Lucia; Li, Zhi; Wu, Yen-Ching; Poplawski, Shane G; Baraban, Jay M; Abel, Ted

2017-09-20

Long-lasting forms of synaptic plasticity and memory require de novo protein synthesis. Yet, how learning triggers this process to form memory is unclear. Translin/trax is a candidate to drive this learning-induced memory mechanism by suppressing microRNA-mediated translational silencing at activated synapses. We find that mice lacking translin/trax display defects in synaptic tagging, which requires protein synthesis at activated synapses, and long-term memory. Hippocampal samples harvested from these mice following learning show increases in several disease-related microRNAs targeting the activin A receptor type 1C (ACVR1C), a component of the transforming growth factor-β receptor superfamily. Furthermore, the absence of translin/trax abolishes synaptic upregulation of ACVR1C protein after learning. Finally, synaptic tagging and long-term memory deficits in mice lacking translin/trax are mimicked by ACVR1C inhibition. Thus, we define a new memory mechanism by which learning reverses microRNA-mediated silencing of the novel plasticity protein ACVR1C via translin/trax.

Drought Management Activities of the National Drought Mitigation Center (NDMC): Contributions Toward a Global Drought Early Warning System (GDEWS)

NASA Astrophysics Data System (ADS)

Stumpf, A.; Lachiche, N.; Malet, J.; Kerle, N.; Puissant, A.

2011-12-01

VHR satellite images have become a primary source for landslide inventory mapping after major triggering events such as earthquakes and heavy rainfalls. Visual image interpretation is still the prevailing standard method for operational purposes but is time-consuming and not well suited to fully exploit the increasingly better supply of remote sensing data. Recent studies have addressed the development of more automated image analysis workflows for landslide inventory mapping. In particular object-oriented approaches that account for spatial and textural image information have been demonstrated to be more adequate than pixel-based classification but manually elaborated rule-based classifiers are difficult to adapt under changing scene characteristics. Machine learning algorithm allow learning classification rules for complex image patterns from labelled examples and can be adapted straightforwardly with available training data. In order to reduce the amount of costly training data active learning (AL) has evolved as a key concept to guide the sampling for many applications. The underlying idea of AL is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and data structure to iteratively select the most valuable samples that should be labelled by the user. With relatively few queries and labelled samples, an AL strategy yields higher accuracies than an equivalent classifier trained with many randomly selected samples. This study addressed the development of an AL method for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. Our approach [1] is based on the Random Forest algorithm and considers the classifier uncertainty as well as the variance of potential sampling regions to guide the user towards the most valuable sampling areas. The algorithm explicitly searches for compact regions and thereby avoids a spatially disperse sampling pattern inherent to most other AL methods. The accuracy, the sampling time and the computational runtime of the algorithm were evaluated on multiple satellite images capturing recent large scale landslide events. Sampling between 1-4% of the study areas the accuracies between 74% and 80% were achieved, whereas standard sampling schemes yielded only accuracies between 28% and 50% with equal sampling costs. Compared to commonly used point-wise AL algorithm the proposed approach significantly reduces the number of iterations and hence the computational runtime. Since the user can focus on relatively few compact areas (rather than on hundreds of distributed points) the overall labeling time is reduced by more than 50% compared to point-wise queries. An experimental evaluation of multiple expert mappings demonstrated strong relationships between the uncertainties of the experts and the machine learning model. It revealed that the achieved accuracies are within the range of the inter-expert disagreement and that it will be indispensable to consider ground truth uncertainties to truly achieve further enhancements in the future. The proposed method is generally applicable to a wide range of optical satellite images and landslide types. [1] A. Stumpf, N. Lachiche, J.-P. Malet, N. Kerle, and A. Puissant, Active learning in the spatial domain for remote sensing image classification, IEEE Transactions on Geosciece and Remote Sensing. 2013, DOI 10.1109/TGRS.2013.2262052.

Project BETA: Biological Education Through Animals.

ERIC Educational Resources Information Center

Abramson, Charles I.; Wallisch, Kristy; Huss, Jeanine M.; Payne, Delissa

1999-01-01

Describes a program in which biology students study animals in pet shops to learn about animal behavior. Lists general guidelines for starting a partnership and presents two sample student activities. (WRM)

Neural principles of memory and a neural theory of analogical insight

NASA Astrophysics Data System (ADS)

Lawson, David I.; Lawson, Anton E.

1993-12-01

Grossberg's principles of neural modeling are reviewed and extended to provide a neural level theory to explain how analogies greatly increase the rate of learning and can, in fact, make learning and retention possible. In terms of memory, the key point is that the mind is able to recognize and recall when it is able to match sensory input from new objects, events, or situations with past memory records of similar objects, events, or situations. When a match occurs, an adaptive resonance is set up in which the synaptic strengths of neurons are increased; thus a long term record of the new input is formed in memory. Systems of neurons called outstars and instars are presumably the underlying units that enable this to occur. Analogies can greatly facilitate learning and retention because they activate the outstars (i.e., the cells that are sampling the to-be-learned pattern) and cause the neural activity to grow exponentially by forming feedback loops. This increased activity insures the boost in synaptic strengths of neurons, thus causing storage and retention in long-term memory (i.e., learning).

Enhancing students' learning in problem based learning: validation of a self-assessment scale for active learning and critical thinking.

PubMed

Khoiriyah, Umatul; Roberts, Chris; Jorm, Christine; Van der Vleuten, C P M

2015-08-26

Problem based learning (PBL) is a powerful learning activity but fidelity to intended models may slip and student engagement wane, negatively impacting learning processes, and outcomes. One potential solution to solve this degradation is by encouraging self-assessment in the PBL tutorial. Self-assessment is a central component of the self-regulation of student learning behaviours. There are few measures to investigate self-assessment relevant to PBL processes. We developed a Self-assessment Scale on Active Learning and Critical Thinking (SSACT) to address this gap. We wished to demonstrated evidence of its validity in the context of PBL by exploring its internal structure. We used a mixed methods approach to scale development. We developed scale items from a qualitative investigation, literature review, and consideration of previous existing tools used for study of the PBL process. Expert review panels evaluated its content; a process of validation subsequently reduced the pool of items. We used structural equation modelling to undertake a confirmatory factor analysis (CFA) of the SSACT and coefficient alpha. The 14 item SSACT consisted of two domains "active learning" and "critical thinking." The factorial validity of SSACT was evidenced by all items loading significantly on their expected factors, a good model fit for the data, and good stability across two independent samples. Each subscale had good internal reliability (>0.8) and strongly correlated with each other. The SSACT has sufficient evidence of its validity to support its use in the PBL process to encourage students to self-assess. The implementation of the SSACT may assist students to improve the quality of their learning in achieving PBL goals such as critical thinking and self-directed learning.

Gel Electrophoresis--The Easy Way for Students

ERIC Educational Resources Information Center

VanRooy, Wilhelmina; Sultana, Khalida

2010-01-01

This article describes a simple, inexpensive, easy to conduct gel-electrophoresis activity using food dyes. It is an alternative to the more expensive counterparts which require agarose gel, DNA samples, purchased chamber and Tris-borate-EDTA buffer. We suggest some learning activities for senior biology students along with comments on several…

Effects of Biology Teachers' Professional Knowledge and Cognitive Activation on Students' Achievement

ERIC Educational Resources Information Center

Förtsch, Christian; Werner, Sonja; von Kotzebue, Lena; Neuhaus, Birgit J.

2016-01-01

This study examined the effects of teachers' biology-specific dimensions of professional knowledge--pedagogical content knowledge (PCK) and content knowledge (CK)--and cognitively activating biology instruction, as a feature of instructional quality, on students' learning. The sample comprised 39 German secondary school teachers whose lessons on…

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults

PubMed Central

Verschueren, Sabine M. P.; Degens, Hans; Morse, Christopher I.; Onambélé, Gladys L.

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual’s physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry. PMID:29155839

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.

PubMed

Wullems, Jorgen A; Verschueren, Sabine M P; Degens, Hans; Morse, Christopher I; Onambélé, Gladys L

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual's physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry.

Dental Students' Study Habits in Flipped/Blended Classrooms and Their Association with Active Learning Practices.

PubMed

Gadbury-Amyot, Cynthia C; Redford, Gloria J; Bohaty, Brenda S

2017-12-01

In recognition of the importance for dental education programs to take a student-centered approach in which students are encouraged to take responsibility for their learning, a pediatric dentistry course redesign aimed at promoting greater active and self-directed learning was implemented at one U.S. dental school. The aim of this study was to examine the association between the students' self-reported study habits and active learning practices necessary for meaningful learning in the flipped/blended classroom. A convenience sample of two classes of second-year dental students in spring 2014 (SP14, n=106) and spring 2015 (SP15, n=106) was invited to participate in the study. Of the SP14 students, 84 participated, for a response rate of 79%; of the SP15 students, 94 participated, for a response rate of 87%. Students' self-reported responses to questions about study strategies with the prerecorded lecture materials and assigned reading materials were examined. Non-parametric analyses resulted in a cohort effect, so data are reported by class. In the SP15 class, 72% reported watching all/more than half of the prerecorded lectures versus 62% of the SP14 class, with a majority watching more than one lecture per week. In the SP15 cohort, 68% used active learning strategies when watching the lectures versus 58.3% of the SP14 cohort. The time of day preferred by the majority of both cohorts for interacting with course materials was 7-11 pm. Both SP14 and SP15 students reported being unlikely to read assigned materials prior to coming to class. Overall, the course redesign appeared to engage students in self-directed active learning. However, the degree to which active learning practices were taking place to achieve meaningful learning was questionable given students' self-reported study strategies. More work is needed to examine strategies for promoting study practices that will lead to meaningful learning.

Physical Activity Is Associated with Reduced Implicit Learning but Enhanced Relational Memory and Executive Functioning in Young Adults

PubMed Central

Watt, Jennifer C.; Grove, George A.; Wollam, Mariegold E.; Uyar, Fatma; Mataro, Maria; Cohen, Neal J.; Howard, Darlene V.; Howard, James H.; Erickson, Kirk I.

2016-01-01

Accumulating evidence suggests that physical activity improves explicit memory and executive cognitive functioning at the extreme ends of the lifespan (i.e., in older adults and children). However, it is unknown whether these associations hold for younger adults who are considered to be in their cognitive prime, or for implicit cognitive functions that do not depend on motor sequencing. Here we report the results of a study in which we examine the relationship between objectively measured physical activity and (1) explicit relational memory, (2) executive control, and (3) implicit probabilistic sequence learning in a sample of healthy, college-aged adults. The main finding was that physical activity was positively associated with explicit relational memory and executive control (replicating previous research), but negatively associated with implicit learning, particularly in females. These results raise the intriguing possibility that physical activity upregulates some cognitive processes, but downregulates others. Possible implications of this pattern of results for physical health and health habits are discussed. PMID:27584059

Effects of digital game-based learning on student engagement and academic achievement

NASA Astrophysics Data System (ADS)

Little, Timothy W.

This experimental study was designed to determine the effect of digital game-based learning on student engagement and academic achievement. The sample was comprised of 34 students enrolled in a secondary Biology class in a rural public school. The study utilized an experimental pretest-posttest design with switching replications. After random assignment, students participated in one of two supplemental learning activities: playing a digital game designed to review science concepts or participating in a lab to review the same concepts. Students subsequently switched activities. Student achievement data were collected on mastery of science concepts, and student engagement data were collected utilizing self- and teacher-reported measures. Data were analyzed using analysis of variance (ANOVA) with repeated measures. Results demonstrated that the digital game was as effective as the lab activity at increasing teacher-reported student engagement and academic achievement. These findings may be of interest to school administrators or directors of teacher preparation programs on the potential effectiveness of digital games as a learning tool.

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.

Evaluation of social interaction, task management, and trust among dental hygiene students in a collaborative learning environment.

PubMed

Saylor, Catherine D; Keselyak, Nancy T; Simmer-Beck, Melanie; Tira, Daniel

2011-02-01

The purpose of this study was to evaluate the impact of collaborative learning on the development of social interaction, task management, and trust in dental hygiene students. These three traits were assessed with the Teamwork Assessment Scale in two different learning environments (traditional lecture/lab and collaborative learning environment). A convenience sample of fifty-six entry-level dental hygiene students taking an introductory/preclinic course at two metropolitan area dental hygiene programs provided comparable experimental and control groups. Factor scores were computed for the three traits, and comparisons were conducted using the Ryan-Einot-Gabriel-Welsh multiple comparison procedure among specific cell comparisons generated from a two-factor repeated measures ANOVA. The results indicate that the collaborative learning environment influenced dental hygiene students positively regarding the traits of social interaction, task management, and trust. However, comparing dental hygiene students to undergraduate students overall indicates that dental hygiene students already possess somewhat higher levels of these traits. Future studies on active learning strategies should examine factors such as student achievement and explore other possible active learning methodologies.

Learning about Lady Liberty.

ERIC Educational Resources Information Center

Instructor, 1984

1984-01-01

The history and importance of the Statue of Liberty is developed into a teaching unit. Background information on how and why the statue was built is given. Sample activities for classroom use are included. (DF)

Minimization of annotation work: diagnosis of mammographic masses via active learning

NASA Astrophysics Data System (ADS)

Zhao, Yu; Zhang, Jingyang; Xie, Hongzhi; Zhang, Shuyang; Gu, Lixu

2018-06-01

The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In this article, we propose a novel active learning algorithm that can adequately address this problem, leading to the minimization of the labeling costs on the premise of guaranteed performance. Our proposed method is different from the existing active learning methods designed for the general problem as it is specifically designed for mammographic images. Through its modified discriminant functions and improved sample query criteria, the proposed method can fully utilize the pairing of mammographic images and select the most valuable images from both the mediolateral and craniocaudal views. Moreover, in order to extend active learning to the ordinal regression problem, which has no precedent in existing studies, but is essential for mammographic diagnosis (mammographic diagnosis is not only a classification task, but also an ordinal regression task for predicting an ordinal variable, viz. the malignancy risk of lesions), multiple sample query criteria need to be taken into consideration simultaneously. We formulate it as a criteria integration problem and further present an algorithm based on self-adaptive weighted rank aggregation to achieve a good solution. The efficacy of the proposed method was demonstrated on thousands of mammographic images from the digital database for screening mammography. The labeling costs of obtaining optimal performance in the classification and ordinal regression task respectively fell to 33.8 and 19.8 percent of their original costs. The proposed method also generated 1228 wins, 369 ties and 47 losses for the classification task, and 1933 wins, 258 ties and 185 losses for the ordinal regression task compared to the other state-of-the-art active learning algorithms. By taking the particularities of mammographic images, the proposed AL method can indeed reduce the manual annotation work to a great extent without sacrificing the performance of the prediction system for mammographic diagnosis.

Minimization of annotation work: diagnosis of mammographic masses via active learning.

PubMed

Zhao, Yu; Zhang, Jingyang; Xie, Hongzhi; Zhang, Shuyang; Gu, Lixu

2018-05-22

The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In this article, we propose a novel active learning algorithm that can adequately address this problem, leading to the minimization of the labeling costs on the premise of guaranteed performance. Our proposed method is different from the existing active learning methods designed for the general problem as it is specifically designed for mammographic images. Through its modified discriminant functions and improved sample query criteria, the proposed method can fully utilize the pairing of mammographic images and select the most valuable images from both the mediolateral and craniocaudal views. Moreover, in order to extend active learning to the ordinal regression problem, which has no precedent in existing studies, but is essential for mammographic diagnosis (mammographic diagnosis is not only a classification task, but also an ordinal regression task for predicting an ordinal variable, viz. the malignancy risk of lesions), multiple sample query criteria need to be taken into consideration simultaneously. We formulate it as a criteria integration problem and further present an algorithm based on self-adaptive weighted rank aggregation to achieve a good solution. The efficacy of the proposed method was demonstrated on thousands of mammographic images from the digital database for screening mammography. The labeling costs of obtaining optimal performance in the classification and ordinal regression task respectively fell to 33.8 and 19.8 percent of their original costs. The proposed method also generated 1228 wins, 369 ties and 47 losses for the classification task, and 1933 wins, 258 ties and 185 losses for the ordinal regression task compared to the other state-of-the-art active learning algorithms. By taking the particularities of mammographic images, the proposed AL method can indeed reduce the manual annotation work to a great extent without sacrificing the performance of the prediction system for mammographic diagnosis.

Active Learning in the Classroom: A Muscle Identification Game in a Kinesiology Course

ERIC Educational Resources Information Center

McCarroll, Michele L.; Pohle-Krauza, Rachael J.; Martin, Jennifer L.

2009-01-01

It is often difficult for educators to teach a kinesiology and applied anatomy (KAA) course due to the vast amount of information that students are required to learn. In this study, a convenient sample of students ("class A") from one section of a KAA course played the speed muscle introduction and matching game, which is loosely based off the…

The Power of Doing: A Learning Exercise That Brings the Central Limit Theorem to Life

ERIC Educational Resources Information Center

Price, Barbara A.; Zhang, Xiaolong

2007-01-01

This article demonstrates an active learning technique for teaching the Central Limit Theorem (CLT) in an introductory undergraduate business statistics class. Groups of students carry out one of two experiments in the lab, tossing a die in sets of 5 rolls or tossing a die in sets of 10 rolls. They are asked to calculate the sample average of each…

The contribution of parent-child numeracy activities to young Chinese children's mathematical ability.

PubMed

Huang, Qi; Zhang, Xiao; Liu, Yingyi; Yang, Wen; Song, Zhanmei

2017-09-01

A growing body of recent research has shown that parent-child mathematical activities have a strong effect on children's mathematical learning. However, this research was conducted predominantly in Western societies and focused mainly on mothers' involvement in such activities. This study aimed to examine both mother-child and father-child numeracy activities in Hong Kong Chinese families and both parents' unique roles in predicting young Chinese children's mathematics ability. A sample of 104 Hong Kong Chinese children aged approximately 5 years and their mothers and fathers participated in this study. Mothers and fathers independently reported the frequency of their own numeracy activities with their children. Children were assessed individually using two measures of mathematical ability. Hierarchical regression models were used to investigate the contribution of parent-child numeracy activities to children's mathematical ability. Mothers' participation in number skill activities and fathers' participation in number game and application activities significantly predicted their children's mathematical performance even after controlling for background variables and children's language ability. This study extends previous research with a sample of Chinese kindergarten children and shows that parent-child numeracy activities are related to young children's mathematical ability. The findings highlight the important roles that mothers and fathers play in their young children's mathematical learning. © 2017 The British Psychological Society.

Interactive E-learning module in pharmacology: a pilot project at a rural medical college in India.

PubMed

Gaikwad, Nitin; Tankhiwale, Suresh

2014-01-01

Many medical educators are experimenting with innovative ways of E-learning. E-learning provides opportunities to students for self-directed learning in addition to other advantages. In this study, we designed and evaluated an interactive E-learning module in pharmacology for effectiveness, acceptability and feasibility, with the aim of promoting active learning in this fact-filled subject. A quasi-experimental single-group pre-test/post-test study was conducted with fourth-semester students of the second professionals course (II MBBS), selected using non-probability convenience sampling method. An E-learning module in endocrine pharmacology was designed to comprise three units of interactive PowerPoint presentations. The pre-validated presentations were uploaded on the website according to a predefined schedule and the 42 registered students were encouraged to self-learning using these interactive presentations. Cognitive gain was assessed using an online pre- and post-test for each unit. Students' perceptions were recorded using an online feedback questionnaire on a 5-point Likert scale. Finally, focused group discussion was conducted to further explore students' views on E-learning activity. Significant attrition was observed during the E-learning activity. Of the 42 registered students, only 16 students completed the entire E-learning module. The summed average score of all three units (entire module) was increased significantly from 38.42 % (summed average pre-test score: 11.56/30 ± 2.90) to 66.46 % (summed average post-test score: 19.94/30 ± 6.13). The class-average normalized gain for the entire module was 0.4542 (45.42). The students accepted this E-learning activity well as they perceived it to be innovative, convenient, flexible and useful. The average rating was between 4 (agree) and 5 (strongly agree). The interactive E-learning module in pharmacology was moderately effective and well perceived by the students. The simple, cost-effective and readily available Microsoft PowerPoint tool appealed to medical educators to use this kind of simple E-learning technology blended with traditional teaching to encourage active learning among students especially in a rural setup is attractive.

In real time: exploring nursing students' learning during an international experience.

PubMed

Afriyie Asenso, Barbara; Reimer-Kirkham, Sheryl; Astle, Barbara

2013-10-11

Abstract Nursing education has increasingly turned to international learning experiences to educate students who are globally minded and aware of social injustices in local and global communities. To date, research with international learning experiences has focused on the benefits for the students participating, after they have completed the international experience. The purpose of this qualitative study was to explore how nursing students learn during the international experience. The sample consisted of eight nursing students who enrolled in an international learning experience, and data were collected in "real time" in Zambia. The students were observed during learning activities and were interviewed three times. Three major themes emerged from the thematic analysis: expectations shaped students' learning, engagement facilitated learning, and critical reflection enhanced learning. Implications are discussed, related to disrupting media representations of Africa that shape students' expectations, and educational strategies for transformative learning and global citizenship.

Effects of web-based electrocardiography simulation on strategies and learning styles.

PubMed

Granero-Molina, José; Fernández-Sola, Cayetano; López-Domene, Esperanza; Hernández-Padilla, José Manuel; Preto, Leonel São Romão; Castro-Sánchez, Adelaida María

2015-08-01

To identify the association between the use of web simulation electrocardiography and the learning approaches, strategies and styles of nursing degree students. A descriptive and correlational design with a one-group pretest-posttest measurement was used. The study sample included 246 students in a Basic and Advanced Cardiac Life Support nursing class of nursing degree. No significant differences between genders were found in any dimension of learning styles and approaches to learning. After the introduction of web simulation electrocardiography, significant differences were found in some item scores of learning styles: theorist (p < 0.040), pragmatic (p < 0.010) and approaches to learning. The use of a web electrocardiogram (ECG) simulation is associated with the development of active and reflexive learning styles, improving motivation and a deep approach in nursing students.

Social competence and collaborative guided inquiry science activities: Experiences of students with learning disabilities

NASA Astrophysics Data System (ADS)

Taylor, Jennifer Anne

This thesis presents a qualitative investigation of the effects of social competence on the participation of students with learning disabilities (LD) in the science learning processes associated with collaborative, guided inquiry learning. An inclusive Grade 2 classroom provided the setting for the study. Detailed classroom observations were the primary source of data. In addition, the researcher conducted two interviews with the teacher, and collected samples of students' written work. The purpose of the research was to investigate: (a) How do teachers and peers mediate the participation of students with LD in collaborative, guided inquiry science activities, (b) What learning processes do students with LD participate in during collaborative, guided inquiry science activities, and (c) What components of social competence support and constrain the participation of students with LD during collaborative, guided inquiry science activities? The findings of the study suggest five key ideas for research and teaching in collaborative, guided inquiry science in inclusive classrooms. First, using a variety of collaborative learning formats (whole-class, small-group, and pairs) creates more opportunities for the successful participation of diverse students with LD. Second, creating an inclusive community where students feel accepted and valued may enhance the academic and social success of students with LD. Third, careful selection of partners for students with LD is important for a positive learning experience. Students with LD should be partnered with academically successful, socially competent peers; also, this study suggested that students with LD experience more success working collaboratively in pairs rather than in small groups. Fourth, a variety of strategies are needed to promote active participation and positive social interactions for students with and without LD during collaborative, guided inquiry learning. Fifth, adopting a general approach to teaching collaborative inquiry that crosses curriculum borders may enhance success of inclusive teaching practices.

What Is the Key for Older People to Show Interest in Playing Digital Learning Games? Initial Qualitative Findings from the LEAGE Project on a Multicultural European Sample.

PubMed

Diaz-Orueta, Unai; Facal, David; Nap, Henk Herman; Ranga, Myrto-Maria

2012-04-01

Learning digital games can influence both older adults' health condition and their capacity to carry on activities in their actual environment. The goal of the current study was to explore and define the user requirements for developing digital learning games for older Europeans, focusing on types of learning games, motivational and social aspects, and preferences on game controllers. For this initial stage, a qualitative focus group study was performed in three participating countries (Spain, The Netherlands, and Greece) where both games existing in the market and others developed in other European Commission projects like HERMES were presented to them, both on video presentations and also with the possibility to actually test some of them. Challenge, socialization, fun, providing learning opportunities, and escape from daily routine were extracted as the main keys why older people would be interested in playing digital games. Users described themselves as active and participating in many leisure activities, and this level of activity appeared to be related with the contents proposed for digital games, such as physical activity, culture, arts, and other human sciences (history, geography, traveling, foreign languages, music), and daily life skills (cooking, computer use, first aid). The knowledge gathered from the focus groups will be used as input for the design of a learning game that will be largely compatible with the needs and abilities of a wide range of older Europeans.

Neural Signatures of the Reading-Writing Connection: Greater Involvement of Writing in Chinese Reading than English Reading.

PubMed

Cao, Fan; Perfetti, Charles A

2016-01-01

Research on cross-linguistic comparisons of the neural correlates of reading has consistently found that the left middle frontal gyrus (MFG) is more involved in Chinese than in English. However, there is a lack of consensus on the interpretation of the language difference. Because this region has been found to be involved in writing, we hypothesize that reading Chinese characters involves this writing region to a greater degree because Chinese speakers learn to read by repeatedly writing the characters. To test this hypothesis, we recruited English L1 learners of Chinese, who performed a reading task and a writing task in each language. The English L1 sample had learned some Chinese characters through character-writing and others through phonological learning, allowing a test of writing-on-reading effect. We found that the left MFG was more activated in Chinese than English regardless of task, and more activated in writing than in reading regardless of language. Furthermore, we found that this region was more activated for reading Chinese characters learned by character-writing than those learned by phonological learning. A major conclusion is that writing regions are also activated in reading, and that this reading-writing connection is modulated by the learning experience. We replicated the main findings in a group of native Chinese speakers, which excluded the possibility that the language differences observed in the English L1 participants were due to different language proficiency level.

Neural Signatures of the Reading-Writing Connection: Greater Involvement of Writing in Chinese Reading than English Reading

PubMed Central

Cao, Fan; Perfetti, Charles A.

2016-01-01

Research on cross-linguistic comparisons of the neural correlates of reading has consistently found that the left middle frontal gyrus (MFG) is more involved in Chinese than in English. However, there is a lack of consensus on the interpretation of the language difference. Because this region has been found to be involved in writing, we hypothesize that reading Chinese characters involves this writing region to a greater degree because Chinese speakers learn to read by repeatedly writing the characters. To test this hypothesis, we recruited English L1 learners of Chinese, who performed a reading task and a writing task in each language. The English L1 sample had learned some Chinese characters through character-writing and others through phonological learning, allowing a test of writing-on-reading effect. We found that the left MFG was more activated in Chinese than English regardless of task, and more activated in writing than in reading regardless of language. Furthermore, we found that this region was more activated for reading Chinese characters learned by character-writing than those learned by phonological learning. A major conclusion is that writing regions are also activated in reading, and that this reading-writing connection is modulated by the learning experience. We replicated the main findings in a group of native Chinese speakers, which excluded the possibility that the language differences observed in the English L1 participants were due to different language proficiency level. PMID:27992505

Kindergarten to 1st Grade: Classroom Characteristics and the Stability and Change of Children's Classroom Experiences

ERIC Educational Resources Information Center

La Paro, Karen M.; Rimm-Kaufman, Sara E.; Pianta, Robert C.

2006-01-01

This study examines the classroom experiences of 192 children followed longitudinally from kindergarten to 1st grade. Time-sampled observations of children were conducted to compare learning formats, teaching activities, and children's engagement in activities between kindergarten and 1st grade. Classroom observations also were conducted to…

Parent-Child Mathematical Interactions: Examining Self-Report and Direct Observation

ERIC Educational Resources Information Center

Missall, Kristen N.; Hojnoski, Robin L.; Moreano, Ginna

2017-01-01

Variability in children's early-learning home environments points to the need to better understand specific mechanisms of early mathematical development. We used a sample of 66 parent-preschool child dyads to describe parent-reported mathematical activities in the home and observed parent-child mathematical activities in a semi-structured play…

Processes and Outcomes of Joint Activity with E-Books for Promoting Kindergarteners' Emergent Literacy

ERIC Educational Resources Information Center

Shamir, Adina

2009-01-01

This research investigated the effects of an educational electronic book (e-book) on low socioeconomic status (SES) kindergarteners' emergent literacy while focusing on the relationship between process and outcomes during joint learning. The sample (96 kindergarteners, aged five to six) was randomly assigned to experimental (e-book activation) and…

Exploring the opinions of registered nurses working in a clinical transfusion environment on the contribution of e-learning to personal learning and clinical practice: results of a small scale educational research study.

PubMed

Cottrell, Susan; Donaldson, Jayne H

2013-05-01

To explore the opinions of registered nurses on the Learnbloodtransfusion Module 1: Safe Transfusion Practice e-learning programme to meeting personal learning styles and learning needs. A qualitative research methodology was applied based on the principles of phenomenology. Adopting a convenience sampling plan supported the recruitment of participants who had successfully completed the e-learning course. Thematic analysis from the semi-structured interviews identified common emerging themes through application of Colaizzis framework. Seven participants of total sample population (89) volunteered to participate in the study. Five themes emerged which included learning preferences, interactive learning, course design, patient safety and future learning needs. Findings positively show the e-learning programme captures the learning styles and needs of learners. In particular, learning styles of a reflector, theorist and activist as well as a visual learner can actively engage in the online learning experience. In an attempt to bridge the knowledge practice gap, further opinions are offered on the course design and the application of knowledge to practice following completion of the course. The findings of the small scale research study have shown that the e-learning course does meet the diverse learning styles and needs of nurses working in a clinical transfusion environment. However, technology alone is not sufficient and a blended approach to learning must be adopted to meet bridging the theory practice gap supporting the integration of knowledge to clinical practice. Copyright © 2013 Elsevier Ltd. All rights reserved.

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

PubMed

Holzinger, Andreas

2016-06-01

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

Online Student Learning and Earth System Processes

NASA Astrophysics Data System (ADS)

Mackay, R. M.

2002-12-01

Many students have difficulty understanding dynamical processes related to Earth's climate system. This is particularly true in Earth System Science courses designed for non-majors. It is often tempting to gloss over these conceptually difficult topics and have students spend more study time learning factual information or ideas that require rather simple linear thought processes. Even when the professor is ambitious and tackles the more difficult ideas of system dynamics in such courses, they are typically greeted with frustration and limited success. However, an understanding of generic system concepts and processes is quite arguably an essential component of any quality liberal arts education. We present online student-centered learning modules that are designed to help students explore different aspects of Earth's climate system (see http://www.cs.clark.edu/mac/physlets/GlobalPollution/maintrace.htm for a sample activity). The JAVA based learning activities are designed to: be assessable to anyone with Web access; be self-paced, engaging, and hands-on; and make use of past results from science education research. Professors can use module activities to supplement lecture, as controlled-learning-lab activities, or as stand-alone homework assignments. Acknowledgement This work was supported by NASA Office of Space Science contract NASW-98037, Atmospheric and Environmental Research Inc. of Lexington, MA., and Clark College.

The analysis of student’s critical thinking ability on discovery learning by using hand on activity based on the curiosity

NASA Astrophysics Data System (ADS)

Sulistiani, E.; Waluya, S. B.; Masrukan

2018-03-01

This study aims to determine (1) the effectiveness of Discovery Learning model by using Hand on Activity toward critical thinking abilities, and (2) to describe students’ critical thinking abilities in Discovery Learning by Hand on Activity based on curiosity. This study is mixed method research with concurrent embedded design. Sample of this study are students of VII A and VII B of SMP Daarul Qur’an Ungaran. While the subject in this study is based on the curiosity of the students groups are classified Epistemic Curiosity (EC) and Perceptual Curiosity (PC). The results showed that the learning of Discovery Learning by using Hand on Activity is effective toward mathematics critical thinking abilities. Students of the EC type are able to complete six indicators of mathematics critical thinking abilities, although there are still two indicators that the result is less than the maximum. While students of PC type have not fully been able to complete the indicator of mathematics critical thinking abilities. They are only strong on indicators formulating questions, while on the other five indicators they are still weak. The critical thinking abilities of EC’s students is better than the critical thinking abilities of the PC’s students.

The Effect of Teaching Model ‘Learning Cycles 5E’ toward Students’ Achievement in Learning Mathematic at X Years Class SMA Negeri 1 Banuhampu 2013/2014 Academic Year

NASA Astrophysics Data System (ADS)

Yeni, N.; Suryabayu, E. P.; Handayani, T.

2017-02-01

Based on the survey showed that mathematics teacher still dominated in teaching and learning process. The process of learning is centered on the teacher while the students only work based on instructions provided by the teacher without any creativity and activities that stimulate students to explore their potential. Realized the problem above the writer interested in finding the solution by applying teaching model ‘Learning Cycles 5E’. The purpose of his research is to know whether teaching model ‘Learning Cycles 5E’ is better than conventional teaching in teaching mathematic. The type of the research is quasi experiment by Randomized Control test Group Only Design. The population in this research were all X years class students. The sample is chosen randomly after doing normality, homogeneity test and average level of students’ achievement. As the sample of this research was X.7’s class as experiment class used teaching model learning cycles 5E and X.8’s class as control class used conventional teaching. The result showed us that the students achievement in the class that used teaching model ‘Learning Cycles 5E’ is better than the class which did not use the model.

A Case Study of Using Peer Feedback in Face-to-Face and Distance Learning Classes among Pre-Service Teachers

ERIC Educational Resources Information Center

Vásquez-Colina, María D.; Russo, Marianne Robin; Lieberman, Mary; Morris, John D.

2017-01-01

This study investigated a feedback exchange activity for engaging pre-service teachers and the nature of such feedback in two undergraduate classes, a distance learning (DL) and a face-to-face (F2F) class. The research question asked if the nature of peer feedback was different between F2F and DL class formats. Students' work samples were…

Toward Understanding the Role of Web 2.0 Technology in Self-Directed Learning and Job Performance in a Single Organizational Setting: A Qualitative Case Study

ERIC Educational Resources Information Center

Caruso, Shirley J.

2016-01-01

This single instrumental qualitative case study explores and thickly describes job performance outcomes based upon the manner in which self-directed learning activities of a purposefully selected sample of 3 construction managers are conducted, mediated by the use of Web 2.0 technology. The data collected revealed that construction managers are…

Assessing deep and shallow learning methods for quantitative prediction of acute chemical toxicity.

PubMed

Liu, Ruifeng; Madore, Michael; Glover, Kyle P; Feasel, Michael G; Wallqvist, Anders

2018-05-02

Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to outperform other machine-learning methods for quantitative structure-activity relationship modeling of molecular properties. However, most of the reported performance evaluations relied on global performance metrics, such as the root mean squared error (RMSE) between the predicted and experimental values of all samples, without considering the impact of sample distribution across the activity spectrum. Here, we carried out an in-depth analysis of DNN performance for quantitative prediction of acute chemical toxicity using several datasets. We found that the overall performance of DNN models on datasets of up to 30,000 compounds was similar to that of random forest (RF) models, as measured by the RMSE and correlation coefficients between the predicted and experimental results. However, our detailed analyses demonstrated that global performance metrics are inappropriate for datasets with a highly uneven sample distribution, because they show a strong bias for the most populous compounds along the toxicity spectrum. For highly toxic compounds, DNN and RF models trained on all samples performed much worse than the global performance metrics indicated. Surprisingly, our variable nearest neighbor method, which utilizes only structurally similar compounds to make predictions, performed reasonably well, suggesting that information of close near neighbors in the training sets is a key determinant of acute toxicity predictions.

Instructional designing the STEM education model for fostering creative thinking abilities in physics laboratory environment classes

NASA Astrophysics Data System (ADS)

Chanthala, Chumpon; Santiboon, Toansakul; Ponkham, Kamon

2018-01-01

To investigate the effects of students' activity-based on learning approaching management through the STEM Education Instructional Model for fostering their creative thinking abilities of their learning achievements in physics laboratory classroom environments with the sample size consisted of 48 students at the 10th grade level in two classes in Mahasarakham University Demonstration School(Secondary Division) in Thailand. Students' creative thinking abilities were assessed with the with the 24-item GuilfordCreative Thinking Questionnaire (GCTQ). Students' perceptions of their physics classroom learning environments were obtained using the 35-item Physics Laboratory Environment Inventory (PLEI). Associations between students' learning achievements of their post-test assessment indicated that 26% of the coefficient predictive value (R2) of the variance in students' creative thinking abilities was attributable to their perceptions for the GCTQ. Students' learning outcomes of their post-test assessment, the R2value indicated that 35% of the variances for the PLEI, the R2value indicated that 63% of the variances for their creative thinking abilities were attributable to theiraffecting the activity-based on learning for fostering their creative thinking are provided.

The (kinetic) theory of active particles applied to learning dynamics. Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

NASA Astrophysics Data System (ADS)

Nieto, J.

2016-03-01

The learning phenomena, their complexity, concepts, structure, suitable theories and models, have been extensively treated in the mathematical literature in the last century, and [4] contains a very good introduction to the literature describing the many approaches and lines of research developed about them. Two main schools have to be pointed out [5] in order to understand the two -not exclusive- kinds of existing models: the stimulus sampling models and the stochastic learning models. Also [6] should be mentioned as a survey where two methods of learning are pointed out, the cognitive and the social, and where the knowledge looks like a mathematical unknown. Finally, as the authors do, we refer to the works [9,10], where the concept of population thinking was introduced and which motivate the game theory rules as a tool (both included in [4] to develop their theory) and [7], where the ideas of developing a mathematical kinetic theory of perception and learning were proposed.

Construction and Validation of a Measurement Instrument for Attitudes towards Teamwork.

PubMed

Mendo-Lázaro, Santiago; Polo-Del-Río, María I; Iglesias-Gallego, Damián; Felipe-Castaño, Elena; León-Del-Barco, Benito

2017-01-01

Cooperative, collaborative learning and other forms of group learning methods are increasingly used in classrooms. Knowing students' attitudes toward teamwork has great value since they influence the students' learning results as well as their social development. So it is necessary to have robust instruments to provide a better understanding of these attitudes and preferences concerning teamwork. Such instruments also help to identify the factors that promote positive or negative attitudes within the context of group activities. Using a sample of 750 first and second year university students studying a degree in Kindergarten, Primary and Social Education, an instrument measuring attitudes toward team learning has been developed. Two distinct factors were obtained through various factorial analyses and structural equations: Academic attitudes and Social and emotional attitudes . Our study reveals that the instrument is both valid and reliable. Its application is both simple and fast and it has important implications for planning teaching and learning activities that contribute to an improvement in attitudes as well as the practice of teaching in the context of learning through teamwork.

Construction and Validation of a Measurement Instrument for Attitudes towards Teamwork

PubMed Central

Mendo-Lázaro, Santiago; Polo-del-Río, María I.; Iglesias-Gallego, Damián; Felipe-Castaño, Elena; León-del-Barco, Benito

2017-01-01

Cooperative, collaborative learning and other forms of group learning methods are increasingly used in classrooms. Knowing students’ attitudes toward teamwork has great value since they influence the students’ learning results as well as their social development. So it is necessary to have robust instruments to provide a better understanding of these attitudes and preferences concerning teamwork. Such instruments also help to identify the factors that promote positive or negative attitudes within the context of group activities. Using a sample of 750 first and second year university students studying a degree in Kindergarten, Primary and Social Education, an instrument measuring attitudes toward team learning has been developed. Two distinct factors were obtained through various factorial analyses and structural equations: Academic attitudes and Social and emotional attitudes. Our study reveals that the instrument is both valid and reliable. Its application is both simple and fast and it has important implications for planning teaching and learning activities that contribute to an improvement in attitudes as well as the practice of teaching in the context of learning through teamwork. PMID:28676775

Is it better to select or to receive? Learning via active and passive hypothesis testing.

PubMed

Markant, Douglas B; Gureckis, Todd M

2014-02-01

People can test hypotheses through either selection or reception. In a selection task, the learner actively chooses observations to test his or her beliefs, whereas in reception tasks data are passively encountered. People routinely use both forms of testing in everyday life, but the critical psychological differences between selection and reception learning remain poorly understood. One hypothesis is that selection learning improves learning performance by enhancing generic cognitive processes related to motivation, attention, and engagement. Alternatively, we suggest that differences between these 2 learning modes derives from a hypothesis-dependent sampling bias that is introduced when a person collects data to test his or her own individual hypothesis. Drawing on influential models of sequential hypothesis-testing behavior, we show that such a bias (a) can lead to the collection of data that facilitates learning compared with reception learning and (b) can be more effective than observing the selections of another person. We then report a novel experiment based on a popular category learning paradigm that compares reception and selection learning. We additionally compare selection learners to a set of "yoked" participants who viewed the exact same sequence of observations under reception conditions. The results revealed systematic differences in performance that depended on the learner's role in collecting information and the abstract structure of the problem.

Development of models for classification of action between heat-clearing herbs and blood-activating stasis-resolving herbs based on theory of traditional Chinese medicine.

PubMed

Chen, Zhao; Cao, Yanfeng; He, Shuaibing; Qiao, Yanjiang

2018-01-01

Action (" gongxiao " in Chinese) of traditional Chinese medicine (TCM) is the high recapitulation for therapeutic and health-preserving effects under the guidance of TCM theory. TCM-defined herbal properties (" yaoxing " in Chinese) had been used in this research. TCM herbal property (TCM-HP) is the high generalization and summary for actions, both of which come from long-term effective clinical practice in two thousands of years in China. However, the specific relationship between TCM-HP and action of TCM is complex and unclear from a scientific perspective. The research about this is conducive to expound the connotation of TCM-HP theory and is of important significance for the development of the TCM-HP theory. One hundred and thirty-three herbs including 88 heat-clearing herbs (HCHs) and 45 blood-activating stasis-resolving herbs (BAHRHs) were collected from reputable TCM literatures, and their corresponding TCM-HPs/actions information were collected from Chinese pharmacopoeia (2015 edition). The Kennard-Stone (K-S) algorithm was used to split 133 herbs into 100 calibration samples and 33 validation samples. Then, machine learning methods including supported vector machine (SVM), k-nearest neighbor (kNN) and deep learning methods including deep belief network (DBN), convolutional neutral network (CNN) were adopted to develop action classification models based on TCM-HP theory, respectively. In order to ensure robustness, these four classification methods were evaluated by using the method of tenfold cross validation and 20 external validation samples for prediction. As results, 72.7-100% of 33 validation samples including 17 HCHs and 16 BASRHs were correctly predicted by these four types of methods. Both of the DBN and CNN methods gave out the best results and their sensitivity, specificity, precision, accuracy were all 100.00%. Especially, the predicted results of external validation set showed that the performance of deep learning methods (DBN, CNN) were better than traditional machine learning methods (kNN, SVM) in terms of their sensitivity, specificity, precision, accuracy. Moreover, the distribution patterns of TCM-HPs of HCHs and BASRHs were also analyzed to detect the featured TCM-HPs of these two types of herbs. The result showed that the featured TCM-HPs of HCHs were cold, bitter, liver and stomach meridians entered, while those of BASRHs were warm, bitter and pungent, liver meridian entered. The performance on validation set and external validation set of deep learning methods (DBN, CNN) were better than machine learning models (kNN, SVM) in sensitivity, specificity, precision, accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. The deep learning classification methods owned better generalization ability and accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. Besides, the methods of deep learning would help us to improve our understanding about the relationship between herbal property and action, as well as to enrich and develop the theory of TCM-HP scientifically.

Recollections of sexual socialisation among marginalised heterosexual black men

PubMed Central

Dunlap, Eloise; Benoit, Ellen; Graves, Jennifer L.

2013-01-01

This paper describes the sexual socialisation process of marginalised, drug-using heterosexual black men, focusing primarily on the sources and content of sexual information. Analysing qualitative interview data, we discovered that the men in our sample both learn about sex and become sexually active at an early age. They most often learn about sex from the media and least often learn about sex from family members. The content of sexual information varies in specifics, but overall tends to equate sex with pleasure, encourage sexual activity with multiple partners, and emphasise using protection. Our goal is to use this data to better understand how sexual socialisation contributes to the prevalence of multiple sexual partners and high rates of HIV among heterosexual black men in order to inform future risk-reduction intervention programmes. PMID:24482611

An Incremental Type-2 Meta-Cognitive Extreme Learning Machine.

PubMed

Pratama, Mahardhika; Zhang, Guangquan; Er, Meng Joo; Anavatti, Sreenatha

2017-02-01

Existing extreme learning algorithm have not taken into account four issues: 1) complexity; 2) uncertainty; 3) concept drift; and 4) high dimensionality. A novel incremental type-2 meta-cognitive extreme learning machine (ELM) called evolving type-2 ELM (eT2ELM) is proposed to cope with the four issues in this paper. The eT2ELM presents three main pillars of human meta-cognition: 1) what-to-learn; 2) how-to-learn; and 3) when-to-learn. The what-to-learn component selects important training samples for model updates by virtue of the online certainty-based active learning method, which renders eT2ELM as a semi-supervised classifier. The how-to-learn element develops a synergy between extreme learning theory and the evolving concept, whereby the hidden nodes can be generated and pruned automatically from data streams with no tuning of hidden nodes. The when-to-learn constituent makes use of the standard sample reserved strategy. A generalized interval type-2 fuzzy neural network is also put forward as a cognitive component, in which a hidden node is built upon the interval type-2 multivariate Gaussian function while exploiting a subset of Chebyshev series in the output node. The efficacy of the proposed eT2ELM is numerically validated in 12 data streams containing various concept drifts. The numerical results are confirmed by thorough statistical tests, where the eT2ELM demonstrates the most encouraging numerical results in delivering reliable prediction, while sustaining low complexity.

Statistically optimal perception and learning: from behavior to neural representations

PubMed Central

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

2010-01-01

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

Informal learning.

PubMed

Callanan, Maureen; Cervantes, Christi; Loomis, Molly

2011-11-01

We consider research and theory relevant to the notion of informal learning. Beginning with historical and definitional issues, we argue that learning happens not just in schools or in school-aged children. Many theorists have contrasted informal learning with formal learning. Moving beyond this dichotomy, and away from a focus on where learning occurs, we discuss five dimensions of informal learning that are drawn from the literature: (1) non-didactive, (2) highly socially collaborative, (3) embedded in meaningful activity, (4) initiated by learner's interest or choice, and (5) removed from external assessment. We consider these dimensions in the context of four sample domains: learning a first language, learning about the mind and emotions within families and communities, learning about science in family conversations and museum settings, and workplace learning. Finally, we conclude by considering convergences and divergences across the different literatures and suggesting areas for future research. WIREs Cogni Sci 2011 2 646-655 DOI: 10.1002/wcs.143 For further resources related to this article, please visit the WIREs website. Copyright © 2011 John Wiley & Sons, Ltd.

Fit, Healthy, and Ready To Learn: A School Health Policy Guide. Part I: Physical Activity, Health Eating, and Tobacco-Use Prevention.

ERIC Educational Resources Information Center

Bogden, James F.

This policy guide reflects the concerns and priorities of education policymakers and administrators, addressing broad policy issues and focusing on physical activity, healthy eating, and tobacco use prevention. Section 1, "Overview," reviews the issue and presents sample policies. Section 2, "The Art of Policymaking," discusses…

The Dinosaurs Didn't See It Coming, But Historians Had Better: Computer Aided Activities in the History Classroom.

ERIC Educational Resources Information Center

Phillips, Julieanne

2001-01-01

States that in ninety percent of colleges across the United States, some or most classrooms are wired for technology integration. Posits that to facilitate student learning and prepare students for future technological advances, instructors must use effective teaching activities that include computers. Provides a sample computer assisted history…

Automated assessment of cognitive health using smart home technologies.

PubMed

Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen; Parsey, Carolyn

2013-01-01

The goal of this work is to develop intelligent systems to monitor the wellbeing of individuals in their home environments. This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve=0.80, g-mean=0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained.

Automated Assessment of Cognitive Health Using Smart Home Technologies

PubMed Central

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

2014-01-01

BACKGROUND The goal of this work is to develop intelligent systems to monitor the well being of individuals in their home environments. OBJECTIVE This paper introduces a machine learning-based method to automatically predict activity quality in smart homes and automatically assess cognitive health based on activity quality. METHODS This paper describes an automated framework to extract set of features from smart home sensors data that reflects the activity performance or ability of an individual to complete an activity which can be input to machine learning algorithms. Output from learning algorithms including principal component analysis, support vector machine, and logistic regression algorithms are used to quantify activity quality for a complex set of smart home activities and predict cognitive health of participants. RESULTS Smart home activity data was gathered from volunteer participants (n=263) who performed a complex set of activities in our smart home testbed. We compare our automated activity quality prediction and cognitive health prediction with direct observation scores and health assessment obtained from neuropsychologists. With all samples included, we obtained statistically significant correlation (r=0.54) between direct observation scores and predicted activity quality. Similarly, using a support vector machine classifier, we obtained reasonable classification accuracy (area under the ROC curve = 0.80, g-mean = 0.73) in classifying participants into two different cognitive classes, dementia and cognitive healthy. CONCLUSIONS The results suggest that it is possible to automatically quantify the task quality of smart home activities and perform limited assessment of the cognitive health of individual if smart home activities are properly chosen and learning algorithms are appropriately trained. PMID:23949177

Active and Cooperative Learning Using Web-Based Simulations.

ERIC Educational Resources Information Center

Schmidt, Stephen J.

2003-01-01

Cites advantages of using computers and the World Wide Web in classroom simulations. Provides a sample simulation that teaches the basic economic principles of trade, investment, and public goods in the context of U.S. economic history. (JEH)

Measuring strategies for learning regulation in medical education: scale reliability and dimensionality in a Swedish sample.

PubMed

Edelbring, Samuel

2012-08-15

The degree of learners' self-regulated learning and dependence on external regulation influence learning processes in higher education. These regulation strategies are commonly measured by questionnaires developed in other settings than in which they are being used, thereby requiring renewed validation. The aim of this study was to psychometrically evaluate the learning regulation strategy scales from the Inventory of Learning Styles with Swedish medical students (N = 206). The regulation scales were evaluated regarding their reliability, scale dimensionality and interrelations. The primary evaluation focused on dimensionality and was performed with Mokken scale analysis. To assist future scale refinement, additional item analysis, such as item-to-scale correlations, was performed. Scale scores in the Swedish sample displayed good reliability in relation to published results: Cronbach's alpha: 0.82, 0.72, and 0.65 for self-regulation, external regulation and lack of regulation scales respectively. The dimensionalities in scales were adequate for self-regulation and its subscales, whereas external regulation and lack of regulation displayed less unidimensionality. The established theoretical scales were largely replicated in the exploratory analysis. The item analysis identified two items that contributed little to their respective scales. The results indicate that these scales have an adequate capacity for detecting the three theoretically proposed learning regulation strategies in the medical education sample. Further construct validity should be sought by interpreting scale scores in relation to specific learning activities. Using established scales for measuring students' regulation strategies enables a broad empirical base for increasing knowledge on regulation strategies in relation to different disciplinary settings and contributes to theoretical development.

Spatial working memory in Wistar rats: brain sex differences in metabolic activity.

PubMed

Méndez-López, Magdalena; Méndez, Marta; López, Laudino; Arias, Jorge L

2009-05-29

Several works have shown that males and females differ in the ability to learn spatial locations in mazes. In this study, we used the Morris water maze to assess the acquisition of a spatial working memory (WM) task in adult male and female Wistar rats. The task consisted of a paired sample procedure made up of two daily identical trials, sample and retention. To study the oxidative metabolic activity of some brain limbic system regions after the WM task, we applied the cytochrome oxidase (COx) histochemistry. In addition to the experimental groups, free swimming control groups and untrained naïve groups were added to explore the COx changes not specific to the learning process. Similar spatial performances were found between sexes as only one more sample and retention trials were needed in males to reduce the escape latencies significantly. Males showed decreased COx activity as compared to control groups in the medial prefrontal cortex (prelimbic and infralimbic regions) as well as in the lateral septum and dentate gyrus. Regarding females, an increase in COx activity was found in nucleus accumbens, ventral tegmental area and supramammillary region in relation to control groups. Overall, these findings suggest that the acquisition of the spatial WM task is mediated by different subsystems in a sex-dependent manner that points to the hippocampus as the central structure in males whereas other structures would be central in females.

Assessment of Learning Style in a Sample of Saudi Medical Students

PubMed Central

BuAli, Waleed Hamad Al; Muhaidab, Nouria Saab Al

2013-01-01

CONFLICT OF INTEREST: NONE DECLARED Background By knowing the different students’ learning styles, teachers can plan their instruction carefully in ways that are capitalized on student preferences. The current research is done to determine specific learning styles of students. Method This cross sectional study was conducted in Al Ahsa College of Medicine from 2011 to 2012. A sample of 518 students completed a questionnaire based on Kolb inventory (LSI 2) to determine their learning style. A spreadsheet was prepared to compute all the information to get the cumulative scores of learning abilities and identify the learning styles. Results The mean values of the learning abilities; active experimentation (AE), reflective observation (RO), abstract conceptualizing (AC) or concrete experience (CE) for male students were 35, 28, 30 and 26 respectively while they were 31, 30, 31 and 29 respectively for female students. There were significant difference between male and female students regarding the mean values of AE-RO (6.7 vs 1.5) and AC-CE (4.1 vs 2.1). This indicated that the style of male students were more convergent and accommodating than those of female students. The female had more assimilating and divergent styles. Conclusion Learning style in Saudi medical students showed difference between males and females in the early college years. Most male students had convergent and accommodating learning styles, while the female dominant learning styles were divergent and assimilating. Planning and implementation of instruction need to consider these findings. PMID:24058248

Collaborative filtering for brain-computer interaction using transfer learning and active class selection.

PubMed

Wu, Dongrui; Lance, Brent J; Parsons, Thomas D

2013-01-01

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.

Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection

PubMed Central

Wu, Dongrui; Lance, Brent J.; Parsons, Thomas D.

2013-01-01

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing. PMID:23437188

Metabolic Profiling and Classification of Propolis Samples from Southern Brazil: An NMR-Based Platform Coupled with Machine Learning.

PubMed

Maraschin, Marcelo; Somensi-Zeggio, Amélia; Oliveira, Simone K; Kuhnen, Shirley; Tomazzoli, Maíra M; Raguzzoni, Josiane C; Zeri, Ana C M; Carreira, Rafael; Correia, Sara; Costa, Christopher; Rocha, Miguel

2016-01-22

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching ∼90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Explorations in statistics: the log transformation.

PubMed

Curran-Everett, Douglas

2018-06-01

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

Strategies influence neural activity for feedback learning across child and adolescent development.

PubMed

Peters, Sabine; Koolschijn, P Cédric M P; Crone, Eveline A; Van Duijvenvoorde, Anna C K; Raijmakers, Maartje E J

2014-09-01

Learning from feedback is an important aspect of executive functioning that shows profound improvements during childhood and adolescence. This is accompanied by neural changes in the feedback-learning network, which includes pre-supplementary motor area (pre- SMA)/anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), superior parietal cortex (SPC), and the basal ganglia. However, there can be considerable differences within age ranges in performance that are ascribed to differences in strategy use. This is problematic for traditional approaches of analyzing developmental data, in which age groups are assumed to be homogenous in strategy use. In this study, we used latent variable models to investigate if underlying strategy groups could be detected for a feedback-learning task and whether there were differences in neural activation patterns between strategies. In a sample of 268 participants between ages 8 to 25 years, we observed four underlying strategy groups, which were cut across age groups and varied in the optimality of executive functioning. These strategy groups also differed in neural activity during learning; especially the most optimal performing group showed more activity in DLPFC, SPC and pre-SMA/ACC compared to the other groups. However, age differences remained an important contributor to neural activation, even when correcting for strategy. These findings contribute to the debate of age versus performance predictors of neural development, and highlight the importance of studying individual differences in strategy use when studying development. Copyright © 2014 Elsevier Ltd. All rights reserved.

Trajectories of the home learning environment across the first 5 years: associations with children's vocabulary and literacy skills at prekindergarten.

PubMed

Rodriguez, Eileen T; Tamis-LeMonda, Catherine S

2011-01-01

Children's home learning environments were examined in a low-income sample of 1,852 children and families when children were 15, 25, 37, and 63 months. During home visits, children's participation in literacy activities, the quality of mothers' engagements with their children, and the availability of learning materials were assessed, yielding a total learning environment score at each age. At 63 months, children's vocabulary and literacy skills were assessed. Six learning environment trajectories were identified, including environments that were consistently low, environments that were consistently high, and environments characterized by varying patterns of change. The skills of children at the extremes of learning environment trajectories differed by more than 1 SD and the timing of learning experiences related to specific emerging skills. © 2011 The Authors. Child Development © 2011 Society for Research in Child Development, Inc.

Development and Validation of the Elder Learning Barriers Scale Among Older Chinese Adults.

PubMed

Wang, Renfeng; De Donder, Liesbeth; De Backer, Free; He, Tao; Van Regenmortel, Sofie; Li, Shihua; Lombaerts, Koen

2017-12-01

This study describes the development and validation of the Elder Learning Barriers (ELB) scale, which seeks to identify the obstacles that affect the level of educational participation of older adults. The process of item pool design and scale development is presented, as well as the testing and scale refinement procedure. The data were collected from a sample of 579 older Chinese adults (aged over 55) in the Xi'an region of China. After randomly splitting the sample for cross-validation purposes, the construct validity of the ELB scale was confirmed containing five dimensions: dispositional, informational, physical, situational, and institutional barriers. Furthermore, developmental differences in factor structure have been examined among older age groups. The results indicated that the scale demonstrated good reliability and validity. We conclude in general that the ELB scale appears to be a valuable instrument for examining the learning barriers that older Chinese citizens experience for participating in organized educational activities.

The Effects of Conceptual Understanding Procedures (CUPs) Towards Critical Thinking Skills of Senior High School Students

NASA Astrophysics Data System (ADS)

Sukaesih, S.; Sutrisno

2017-04-01

The aim of the study was to analyse the effect of the application of Conceptual Understanding Procedures (CUPs) learning to the students’ critical thinking skills in the matter of categorisaed in SMA Negeri 1 Larangan. This study was quasi-experimental design using nonequivalent control group design. The population in this study was entire class X. The samples that were taken by convenience sampling were class X MIA 1 and X MIA 2. Primary data in the study was the student’s critical thinking skills, which was supported by student activity, the level of adherence to the CUPs learning model, student opinion and teacher opinion. N-gain test results showed that the students’ critical thinking skills of experimental class increased by 89.32%, while the control group increased by 57.14%. Activity grade of experimental class with an average value of 72.37 was better than that of the control class with an average of only 22.69 student and teacher opinions to the learning were excellegoodnt. Based on this study concluded that the model of Conceptual Understanding Procedures (CUPs) had an effect on the student’s critical thinking skills in the matter of protest in SMA Negeri 1 Larangan.

Chesapeake Bay Climate Study Partnership: Undergraduate Student Experiential Learning on Microclimates at the University of Hawai'i, Hilo

NASA Astrophysics Data System (ADS)

Ozbay, G.; Sriharan, S.; Fan, C.; Adolf, J.

2015-12-01

Undergraduate student experiential learning activities focused on microclimates of Hawai'i Island, Hawai'i. Six students from Virginia State University, three students from Delaware State University and faculty advisors were hosted by the University of Hawai'i at Hilo (UHH) Department of Marine Science. This partnership provided integrated, cohesive, and innovative education and research capabilities to minority students on climate change science. Activities included a summer course, instrumentation training, field and laboratory research training, sampling, data collection, logging, analysis, interpretation, report preparation, and research presentation. Most training activities used samples collected during students' field sampling in Hilo Bay. Water quality and phytoplankton data were collected along a 220 degree line transect from the mouth of the Wailuku River to the pelagic zone outside of Hilo Bay into the Pacific Ocean to a distance of 15.5 km. Water clarity, turbidity, chlorophyll, physical water quality parameters, and atmospheric CO2 levels were measured along the transect. Phytoplankton samples were collected for analysis by Scanning Electron Microscopy and Flow Cytometry. Data showed the extent of anthropogenic activity on water quality, with implications for food web dynamics. In addition, atmospheric CO2 concentration, island vegetation, and GPS points were recorded throughout the island of Hawai'i to investigate how variations in microclimate, elevation, and land development affect the amount of CO2 in the atmosphere, vegetation, and water quality. Water quality results at locations near rivers were completely different from other study sites, requiring students' critical thinking skills to find possible reasons for the difference. Our data show a correlation between population density and CO2 concentrations. Anthropogenic activities affecting CO2 and ocean conditions in Hawaiian microclimates can potentially have deleterious effects on the life that call these areas home.

Neurofeedback Training Effects on Inhibitory Brain Activation in ADHD: A Matter of Learning?

PubMed

Baumeister, Sarah; Wolf, Isabella; Holz, Nathalie; Boecker-Schlier, Regina; Adamo, Nicoletta; Holtmann, Martin; Ruf, Matthias; Banaschewski, Tobias; Hohmann, Sarah; Brandeis, Daniel

2018-05-15

Neurofeedback training (NF) is a promising non-pharmacological treatment for ADHD that has been associated with improvement of attention-deficit/hyperactivity disorder (ADHD)-related symptoms as well as changes in electrophysiological measures. However, the functional localization of neural changes following NF compared to an active control condition, and of successful learning during training (considered to be the critical mechanism for improvement), remains largely unstudied. Children with ADHD (N=16, mean age: 11.81, SD: 1.47) were randomly assigned to either slow cortical potential (SCP, n=8) based NF or biofeedback control training (electromyogram feedback, n=8) and performed a combined Flanker/NoGo task pre- and post-training. Effects of NF, compared to the active control, and of learning in transfer trials (approximating successful transfer to everyday life) were examined with respect to clinical outcome and functional magnetic resonance imaging (fMRI) changes during inhibitory control. After 20 sessions of training, children in the NF group presented reduced ADHD symptoms and increased activation in areas associated with inhibitory control compared to baseline. Subjects who were successful learners (n=9) also showed increased activation in an extensive inhibitory network irrespective of the type of training. Activation increased in an extensive inhibitory network following NF training, and following successful learning through NF and control biofeedback. Although this study was only powered to detect large effects and clearly requires replication in larger samples, the results suggest a crucial role for learning effects in biofeedback trainings. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Measuring Teaching Quality in Several European Countries

ERIC Educational Resources Information Center

van de Grift, Wim J. C. M.

2014-01-01

Teaching quality has been observed in large representative samples from Flanders (Belgium), Lower Saxony (Germany), the Slovak Republic, and The Netherlands. This study reveals that measures of "creating a safe and stimulating climate", "clear and activating instruction", and "teaching learning strategies" were…

Roundhouse Diagrams.

ERIC Educational Resources Information Center

Ward, Robin E.; Wandersee, James

2000-01-01

Students must understand key concepts through reasoning, searching out related concepts, and making connections within multiple systems to learn science. The Roundhouse diagram was developed to be a concise, holistic, graphic representation of a science topic, process, or activity. Includes sample Roundhouse diagrams, a diagram checklist, and…

Activity Settings and Daily Routines in Preschool Classrooms: Diverse Experiences in Early Learning Settings for Low-Income Children

ERIC Educational Resources Information Center

Fuligni, Allison Sidle; Howes, Carollee; Huang, Yiching; Hong, Sandra Soliday; Lara-Cinisomo, Sandraluz

2012-01-01

This paper examines activity settings and daily classroom routines experienced by 3- and 4-year-old low-income children in public center-based preschool programs, private center-based programs, and family child care homes. Two daily routine profiles were identified using a time-sampling coding procedure: a High Free-Choice pattern in which…

Prion Protein M129V Polymorphism Affects Retrieval-Related Brain Activity

ERIC Educational Resources Information Center

Buchmann, Andreas; Mondadori, Christian R. A.; Hanggi, Jurgen; Aerni, Amanda; Vrticka, Pascal; Luechinger, Roger; Boesiger, Peter; Hock, Christoph; Nitsch, Roger M.; de Quervain, Dominique J.-F.; Papassotiropoulos, Andreas; Henke, Katharina

2008-01-01

The prion protein Met129Val polymorphism has recently been related to human long-term memory with carriers of either the 129[superscript MM] or the 129[superscript MV] genotype recalling 17% more words than 129[superscript VV] carriers at 24 h following learning. Here, we sampled genotype differences in retrieval-related brain activity at 30 min…

The Effective of Using 5 Simple Steps (QSCCS) Learning Activities on Facebook to Promote Self-Learning in the 21st Century in Technology Printing and Advertising Course for Undergraduate Students in Education Technology and Communications

ERIC Educational Resources Information Center

Sittiwong, Tipparat; Wongnam, Thanet

2015-01-01

The objectives of this study were to: 1) study the result of implementing QSCCS with Facebook; 2) study students' opinions concerning the implementation of QSCCS with Facebook. The samples were 38 Technology and Communications undergraduates who attended Printing and Advertising Technology course in academic year of 2013. The information was…

Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition.

PubMed

Wan, Jun; Guo, Guodong; Li, Stan Z

2016-08-01

Availability of handy RGB-D sensors has brought about a surge of gesture recognition research and applications. Among various approaches, one shot learning approach is advantageous because it requires minimum amount of data. Here, we provide a thorough review about one-shot learning gesture recognition from RGB-D data and propose a novel spatiotemporal feature extracted from RGB-D data, namely mixed features around sparse keypoints (MFSK). In the review, we analyze the challenges that we are facing, and point out some future research directions which may enlighten researchers in this field. The proposed MFSK feature is robust and invariant to scale, rotation and partial occlusions. To alleviate the insufficiency of one shot training samples, we augment the training samples by artificially synthesizing versions of various temporal scales, which is beneficial for coping with gestures performed at varying speed. We evaluate the proposed method on the Chalearn gesture dataset (CGD). The results show that our approach outperforms all currently published approaches on the challenging data of CGD, such as translated, scaled and occluded subsets. When applied to the RGB-D datasets that are not one-shot (e.g., the Cornell Activity Dataset-60 and MSR Daily Activity 3D dataset), the proposed feature also produces very promising results under leave-one-out cross validation or one-shot learning.

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.

Older Australians: Structural barriers to learning in later life.

PubMed

Boulton-Lewis, Gillian; Aird, Rosemary; Buys, Laurie

2016-01-01

Learning in older age is associated with benefits including increases in skills, social interactions, self-satisfaction, coping ability, enjoyment, and resilience to age-related changes in the brain. It is also a fundamental component of active ageing and if active ageing objectives are to be met for the growing ageing population, barriers to learning need to be understood and addressed. This study aimed at determining the degree that structural factors deter people aged 55 years and older from engaging in learning activities. The data were obtained from survey (n=421) with a purposive sample of Australian Seniors aged 55 to 75+, and open ended follow up interviews (n=40). The survey responses to the 22 barriers to learning questions were ranked and quantified. The issues identified in the interviews shed further light on the survey data. The analyses revealed that factors related to educational institutions as well as infrastructure were commonly cited as barriers to participation in learning. In particular expense of educational programmes (55.1%), long travelling time (45.6%) other transportation difficulties (38.9%), lack of interest in offered programmes ((36.4) and lack of information about courses (31.1%) were seen as barriers. The interviews revealed and confirmed five main barriers; money, offerings of interest/availability, travel/transport, information, computer skills and being employed. The findings should provide policy makers, institutions, organizations and government with a list of areas where changes might be made so as to improve older people's opportunities for learning as they proceed through older age.

The relation between Assessment for Learning and elementary students' cognitive and metacognitive strategy use.

PubMed

Baas, Diana; Castelijns, Jos; Vermeulen, Marjan; Martens, Rob; Segers, Mien

2015-03-01

Assessment for Learning (AfL) is believed to create a rich learning environment in which students develop their cognitive and metacognitive strategies. Monitoring student growth and providing scaffolds that shed light on the next step in the learning process are hypothesized to be essential elements of AfL that enhance cognitive and metacognitive strategies. However, empirical evidence for the relation between AfL and students' strategy use is scarce. This study investigates the relation between AfL and elementary school students' use of cognitive and metacognitive strategies. The sample comprised 528 grade four to six students (9- to 12-year-olds) from seven Dutch elementary schools. Students' perceptions of AfL and their cognitive and metacognitive strategy use were measured by means of questionnaires. Structural equation modelling was used to investigate the relations among the variables. The results reveal that monitoring activities that provide students an understanding of where they are in their learning process predict Students' task orientation and planning. Scaffolding activities that support students in taking the next step in their learning are positively related to the use of both surface and deep-level learning strategies and the extent to which they evaluate their learning process after performing tasks. The results underline the importance of assessment practices in ceding responsibility to students in taking control of their own learning. © 2014 The British Psychological Society.

One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM.

PubMed

Rodriguez, Mario; Orrite, Carlos; Medrano, Carlos; Makris, Dimitrios

2016-05-10

This paper presents a novel activity class representation using a single sequence for training. The contribution of this representation lays on the ability to train an one-shot learning recognition system, useful in new scenarios where capturing and labeling sequences is expensive or impractical. The method uses a universal background model of local descriptors obtained from source databases available on-line and adapts it to a new sequence in the target scenario through a maximum a posteriori adaptation. Each activity sample is encoded in a sequence of normalized bag of features and modeled by a new hidden Markov model formulation, where the expectation-maximization algorithm for training is modified to deal with observations consisting in vectors in a unit simplex. Extensive experiments in recognition have been performed using one-shot learning over the public datasets Weizmann, KTH, and IXMAS. These experiments demonstrate the discriminative properties of the representation and the validity of application in recognition systems, achieving state-of-the-art results.

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

PubMed

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

NASA Astrophysics Data System (ADS)

Richards, Joseph W.; Starr, Dan L.; Brink, Henrik; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; James, J. Berian; Long, James P.; Rice, John

2012-01-01

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data often manifest as strongly biased predictions on the data of interest. Typically, training sets are derived from historical surveys of brighter, more nearby objects than those from more extensive, deeper surveys (testing data). This sample selection bias can cause catastrophic errors in predictions on the testing data because (1) standard assumptions for machine-learned model selection procedures break down and (2) dense regions of testing space might be completely devoid of training data. We explore possible remedies to sample selection bias, including importance weighting, co-training, and active learning (AL). We argue that AL—where the data whose inclusion in the training set would most improve predictions on the testing set are queried for manual follow-up—is an effective approach and is appropriate for many astronomical applications. For a variable star classification problem on a well-studied set of stars from Hipparcos and Optical Gravitational Lensing Experiment, AL is the optimal method in terms of error rate on the testing data, beating the off-the-shelf classifier by 3.4% and the other proposed methods by at least 3.0%. To aid with manual labeling of variable stars, we developed a Web interface which allows for easy light curve visualization and querying of external databases. Finally, we apply AL to classify variable stars in the All Sky Automated Survey, finding dramatic improvement in our agreement with the ASAS Catalog of Variable Stars, from 65.5% to 79.5%, and a significant increase in the classifier's average confidence for the testing set, from 14.6% to 42.9%, after a few AL iterations.

[Sample German LAPS.

ERIC Educational Resources Information Center

Rosenthal, Bianca

Four learning activity packages (LAPS) for use in secondary school German programs contain instructional materials which enable students to improve their basic linguistic skills. The units include: (1) "Grusse," (2) "Ich Heisse...Namen," (3) "Tune into Your Career: Business Correspondence 'Auf Deutch'," and (4) "Understanding German Culture."…

Spreadsheets Answer "What If...?

ERIC Educational Resources Information Center

Pogge, Alfred F.; Lunetta, Vincent N.

1987-01-01

Demonstrates how a spreadsheet program can do calculations, freeing students to question, analyze data and learn science. Notes several popular spreadsheet programs. Gives an example using Lotus 1-2-3 spreadsheets for a sampling experiment in Biology. Shows other examples of spreadsheet use in laboratory activities. (CW)

A fast learning method for large scale and multi-class samples of SVM

NASA Astrophysics Data System (ADS)

Fan, Yu; Guo, Huiming

2017-06-01

A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

Statistical Methods in Ai: Rare Event Learning Using Associative Rules and Higher-Order Statistics

NASA Astrophysics Data System (ADS)

Iyer, V.; Shetty, S.; Iyengar, S. S.

2015-07-01

Rare event learning has not been actively researched since lately due to the unavailability of algorithms which deal with big samples. The research addresses spatio-temporal streams from multi-resolution sensors to find actionable items from a perspective of real-time algorithms. This computing framework is independent of the number of input samples, application domain, labelled or label-less streams. A sampling overlap algorithm such as Brooks-Iyengar is used for dealing with noisy sensor streams. We extend the existing noise pre-processing algorithms using Data-Cleaning trees. Pre-processing using ensemble of trees using bagging and multi-target regression showed robustness to random noise and missing data. As spatio-temporal streams are highly statistically correlated, we prove that a temporal window based sampling from sensor data streams converges after n samples using Hoeffding bounds. Which can be used for fast prediction of new samples in real-time. The Data-cleaning tree model uses a nonparametric node splitting technique, which can be learned in an iterative way which scales linearly in memory consumption for any size input stream. The improved task based ensemble extraction is compared with non-linear computation models using various SVM kernels for speed and accuracy. We show using empirical datasets the explicit rule learning computation is linear in time and is only dependent on the number of leafs present in the tree ensemble. The use of unpruned trees (t) in our proposed ensemble always yields minimum number (m) of leafs keeping pre-processing computation to n × t log m compared to N2 for Gram Matrix. We also show that the task based feature induction yields higher Qualify of Data (QoD) in the feature space compared to kernel methods using Gram Matrix.

Teaching for quality learning in chemistry

NASA Astrophysics Data System (ADS)

Teixeira-Dias, José J. C.; Pedrosa de Jesus, Helena; Neri de Souza, Francislê; Watts, Mike

2005-09-01

In Portugal, the number of students in higher education increased from 80,000 in 1975 to 381,000 in 2000 (a change from 11% to 53% in the age group 18 22), meaning a major change in the diversity of student population with consequences well known and studied in other countries. The teaching of chemistry at the University of Aveiro, for the first-year students of science and engineering, has been subjected to continuous attention to implement quality and student-centred approaches. The work devoted to excellence and deep learning by several authors has been carefully followed and considered. This communication reports research work on chemistry teaching, associated with those developments for first-year students. The work included the design of strategies and the adoption of teaching and learning activities exploring ways to stimulate active learning by improving the quality of classroom interactions. In addition to regular lectures, large classes' teaching based on student-generated questions was explored. In order to improve students' motivation and stimulate their curiosity, conference-lectures were adopted to deal with selected topics of wide scientific, technological and social interest. Quantitative analysis and discussion of selected case studies, together with the organization of laboratory classes based on selected enquiry-based experiments, planned and executed by students, stimulated deep learning processes. A sample of 32 students was followed in the academic year of 2000/01 and the results obtained are here discussed in comparison with those of a sample of 100 students followed in 2001/02. Particular attention was paid to the quality of classroom interactions, the use of questions by students and their views about the course design.

Exploring creativity and critical thinking in traditional and innovative problem-based learning groups.

PubMed

Chan, Zenobia C Y

2013-08-01

To explore students' attitude towards problem-based learning, creativity and critical thinking, and the relevance to nursing education and clinical practice. Critical thinking and creativity are crucial in nursing education. The teaching approach of problem-based learning can help to reduce the difficulties of nurturing problem-solving skills. However, there is little in the literature on how to improve the effectiveness of a problem-based learning lesson by designing appropriate and innovative activities such as composing songs, writing poems and using role plays. Exploratory qualitative study. A sample of 100 students participated in seven semi-structured focus groups, of which two were innovative groups and five were standard groups, adopting three activities in problem-based learning, namely composing songs, writing poems and performing role plays. The data were analysed using thematic analysis. There are three themes extracted from the conversations: 'students' perceptions of problem-based learning', 'students' perceptions of creative thinking' and 'students' perceptions of critical thinking'. Participants generally agreed that critical thinking is more important than creativity in problem-based learning and clinical practice. Participants in the innovative groups perceived a significantly closer relationship between critical thinking and nursing care, and between creativity and nursing care than the standard groups. Both standard and innovative groups agreed that problem-based learning could significantly increase their critical thinking and problem-solving skills. Further, by composing songs, writing poems and using role plays, the innovative groups had significantly increased their awareness of the relationship among critical thinking, creativity and nursing care. Nursing educators should include more types of creative activities than it often does in conventional problem-based learning classes. The results could help nurse educators design an appropriate curriculum for preparing professional and ethical nurses for future clinical practice. © 2013 Blackwell Publishing Ltd.

Generalized SMO algorithm for SVM-based multitask learning.

PubMed

Cai, Feng; Cherkassky, Vladimir

2012-06-01

Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as "learning with structured data" and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+MTL for classification. Training the SVM+MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+MTL setting. Empirical results show that, for typical SVM+MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.

Multinomial Bayesian learning for modeling classical and nonclassical receptive field properties.

PubMed

Hosoya, Haruo

2012-08-01

We study the interplay of Bayesian inference and natural image learning in a hierarchical vision system, in relation to the response properties of early visual cortex. We particularly focus on a Bayesian network with multinomial variables that can represent discrete feature spaces similar to hypercolumns combining minicolumns, enforce sparsity of activation to learn efficient representations, and explain divisive normalization. We demonstrate that maximal-likelihood learning using sampling-based Bayesian inference gives rise to classical receptive field properties similar to V1 simple cells and V2 cells, while inference performed on the trained network yields nonclassical context-dependent response properties such as cross-orientation suppression and filling in. Comparison with known physiological properties reveals some qualitative and quantitative similarities.

Learning classification with auxiliary probabilistic information

PubMed Central

Nguyen, Quang; Valizadegan, Hamed; Hauskrecht, Milos

2012-01-01

Finding ways of incorporating auxiliary information or auxiliary data into the learning process has been the topic of active data mining and machine learning research in recent years. In this work we study and develop a new framework for classification learning problem in which, in addition to class labels, the learner is provided with an auxiliary (probabilistic) information that reflects how strong the expert feels about the class label. This approach can be extremely useful for many practical classification tasks that rely on subjective label assessment and where the cost of acquiring additional auxiliary information is negligible when compared to the cost of the example analysis and labelling. We develop classification algorithms capable of using the auxiliary information to make the learning process more efficient in terms of the sample complexity. We demonstrate the benefit of the approach on a number of synthetic and real world data sets by comparing it to the learning with class labels only. PMID:25309141

Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning.

PubMed

Hsu, Anne; Griffiths, Thomas L

2016-01-01

A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning.

Sampling Assumptions Affect Use of Indirect Negative Evidence in Language Learning

PubMed Central

2016-01-01

A classic debate in cognitive science revolves around understanding how children learn complex linguistic patterns, such as restrictions on verb alternations and contractions, without negative evidence. Recently, probabilistic models of language learning have been applied to this problem, framing it as a statistical inference from a random sample of sentences. These probabilistic models predict that learners should be sensitive to the way in which sentences are sampled. There are two main types of sampling assumptions that can operate in language learning: strong and weak sampling. Strong sampling, as assumed by probabilistic models, assumes the learning input is drawn from a distribution of grammatical samples from the underlying language and aims to learn this distribution. Thus, under strong sampling, the absence of a sentence construction from the input provides evidence that it has low or zero probability of grammaticality. Weak sampling does not make assumptions about the distribution from which the input is drawn, and thus the absence of a construction from the input as not used as evidence of its ungrammaticality. We demonstrate in a series of artificial language learning experiments that adults can produce behavior consistent with both sets of sampling assumptions, depending on how the learning problem is presented. These results suggest that people use information about the way in which linguistic input is sampled to guide their learning. PMID:27310576

A Study of Work Based Learning For Construction Building Workers

NASA Astrophysics Data System (ADS)

Siregar, Syafiatun

2018-03-01

Work-based learning (WBL) is designed to improve the competence of participants. This study aims to apply the WBL and to develop attitudes, knowledge, skills, behaviors, and habits, which in turn can improve the competence of construction workers in the field to be sampled. This research was conducted on building construction workers in Medan City with 30 research subjects. The results showed that the evaluation of learning increased in phase I obtained the difference of the average score of 20.9 (the meeting I) and 25.50 (meeting II). The final result shows that the level of activity and competence increased significantly after WBL

Millennial Students' Preferred Methods for Learning Concepts in Psychiatric Nursing.

PubMed

Garwood, Janet K

2015-09-01

The current longitudinal, descriptive, and correlational study explored which traditional teaching strategies can engage Millennial students and adequately prepare them for the ultimate test of nursing competence: the National Council Licensure Examination. The study comprised a convenience sample of 40 baccalaureate nursing students enrolled in a psychiatric nursing course. The students were exposed to a variety of traditional (e.g., PowerPoint(®)-guided lectures) and nontraditional (e.g., concept maps, group activities) teaching and learning strategies, and rated their effectiveness. The students' scores on the final examination demonstrated that student learning outcomes met or exceeded national benchmarks. Copyright 2015, SLACK Incorporated.

Comparisons between students' learning achievements of their conventional instruction and the science, technology and social conceptual instructional design on digestion system issue of secondary students at the 10th grade level

NASA Astrophysics Data System (ADS)

Wichalek, Supattra; Chayaburakul, Kanokporn; Santiboon, Toansakul

2018-01-01

The purposes of this action research study were 1) to develop learning activities according to the instructional designing model of science, technology, and social (STS) on Digestion Issue, 2) to compare students' learning achievements between their learning activities with the conventional instructional (CIM) and conceptual instructional designing methods of science, technology, and social (STS) on digestion system of secondary students at the 10th grade level with a sample size of 35 experimental student group of their STS learning method, and 91 controlling group in two classes in the first semester in academic year 2016. Using the 4-Instructional Innovative Lesson Plans, the Students' Learning Behaviour Observing Form, the Questionnaire on Teacher Behaviour Interaction (QTBI), the Researcher's Recording Form, the Learning Activity Form, and the Parallel Learning Achievement Test (LAT) were assessed. The results of this research have found that; the Index of Item Objective Congruence (IOC) value ranged from 0.67 to 1.00; the difficulty values were 0.47 and 0.79 for the CIM and STS methods, respectively, the discriminative validity for the LAT was ranged from 0.20 to 0.75. The reliability of the QTBI was 0.79. Students' responses of their learning achievements with the average means scores indicted of the normalized gain values of 0.79 for the STS group, and 0.50 and 0.36 for the CIM groups, respectively. Students' learning achievements of their post-test indicated that of a higher than pre-test, the pre-test and post-test assessments were also differentiated evidence at the 0.05 levels for the STS and CIM groups, significantly. The 22-students' learning outcomes from the STS group evidences at a high level, only of the 9-students' responses in a moderate level were developed of their learning achievements, responsibility.

Validation of an instrument to measure students' motivation and self-regulation towards technology learning

NASA Astrophysics Data System (ADS)

Liou, Pey-Yan; Kuo, Pei-Jung

2014-05-01

Background:Few studies have examined students' attitudinal perceptions of technology. There is no appropriate instrument to measure senior high school students' motivation and self-regulation toward technology learning among the current existing instruments in the field of technology education. Purpose:The present study is to validate an instrument for assessing senior high school students' motivation and self-regulation towards technology learning. Sample:A total of 1822 Taiwanese senior high school students (1020 males and 802 females) responded to the newly developed instrument. Design and method:The Motivation and Self-regulation towards Technology Learning (MSRTL) instrument was developed based on the previous instruments measuring students' motivation and self-regulation towards science learning. Exploratory and confirmatory factor analyses were utilized to investigate the structure of the items. Cronbach's alpha was applied for measuring the internal consistency of each scale. Furthermore, multivariate analysis of variance was used to examine gender differences. Results:Seven scales, including 'Technology learning self-efficacy,' 'Technology learning value,' 'Technology active learning strategies,' 'Technology learning environment stimulation,' 'Technology learning goal-orientation,' 'Technology learning self-regulation-triggering,' and 'Technology learning self-regulation-implementing' were confirmed for the MSRTL instrument. Moreover, the results also showed that male and female students did not present the same degree of preference in all of the scales. Conclusions:The MSRTL instrument composed of seven scales corresponding to 39 items was shown to be valid based on validity and reliability analyses. While male students tended to express more positive and active performance in the motivation scales, no gender differences were found in the self-regulation scales.

Inter-level Scaffolding and Sequences of Representational Activities in Teaching a Chemical System with Graphical Simulations

NASA Astrophysics Data System (ADS)

Li, Na; Black, John B.

2016-10-01

Chemistry knowledge can be represented at macro-, micro- and symbolic levels, and learning a chemistry topic requires students to engage in multiple representational activities. This study focused on scaffolding for inter-level connection-making in learning chemistry knowledge with graphical simulations. We also tested whether different sequences of representational activities produced different student learning outcomes in learning a chemistry topic. A sample of 129 seventh graders participated in this study. In a simulation-based environment, participants completed three representational activities to learn several ideal gas law concepts. We conducted a 2 × 3 factorial design experiment. We compared two scaffolding conditions: (1) the inter- level scaffolding condition in which participants received inter-level questions and experienced the dynamic link function in the simulation-based environment and (2) the intra- level scaffolding condition in which participants received intra-level questions and did not experience the dynamic link function. We also compared three different sequences of representational activities: macro-symbolic-micro, micro-symbolic-macro and symbolic-micro-macro. For the scaffolding variable, we found that the inter- level scaffolding condition produced significantly better performance in both knowledge comprehension and application, compared to the intra- level scaffolding condition. For the sequence variable, we found that the macro-symbolic-micro sequence produced significantly better knowledge comprehension performance than the other two sequences; however, it did not benefit knowledge application performance. There was a trend that the treatment group who experienced inter- level scaffolding and the micro-symbolic-macro sequence achieved the best knowledge application performance.

Beyond Homework: Science and Mathematics Backpacks.

ERIC Educational Resources Information Center

Kokoski, Teresa M.; Patton, Mary Martin

1997-01-01

Describes classroom-developed science and mathematics backpacks, self-contained educational packets developed around a theme or concept and designed to be completed at home. Presents generalized contents, a sample backpack on colors, and the backpack's advantages, including promotion of active learning, family involvement, curriculum integration,…

Bacteriophage: A Model System for Active Learning.

ERIC Educational Resources Information Center

Luciano, Carl S.; Young, Matthew W.; Patterson, Robin R.

2002-01-01

Describes a student-centered laboratory course in which student teams select phage from sewage samples and characterize the phage in a semester-long project that models real-life scientific research. Results of student evaluations indicate a high level of satisfaction with the course. (Author/MM)

In vitro detection of androgenic and estrogenic activity in complex environmental effluent samples: Lessions learned

EPA Science Inventory

Fish living in ecosystems contaminated with effluents from human or domestic animal wastes display reproductive alterations suggesting hormone disruption. Recent research with effluent from cattle feeding operations in the US have associated morphological alterations in fish col...

Developing Deep Learning Applications for Life Science and Pharma Industry.

PubMed

Siegismund, Daniel; Tolkachev, Vasily; Heyse, Stephan; Sick, Beate; Duerr, Oliver; Steigele, Stephan

2018-06-01

Deep Learning has boosted artificial intelligence over the past 5 years and is seen now as one of the major technological innovation areas, predicted to replace lots of repetitive, but complex tasks of human labor within the next decade. It is also expected to be 'game changing' for research activities in pharma and life sciences, where large sets of similar yet complex data samples are systematically analyzed. Deep learning is currently conquering formerly expert domains especially in areas requiring perception, previously not amenable to standard machine learning. A typical example is the automated analysis of images which are typically produced en-masse in many domains, e. g., in high-content screening or digital pathology. Deep learning enables to create competitive applications in so-far defined core domains of 'human intelligence'. Applications of artificial intelligence have been enabled in recent years by (i) the massive availability of data samples, collected in pharma driven drug programs (='big data') as well as (ii) deep learning algorithmic advancements and (iii) increase in compute power. Such applications are based on software frameworks with specific strengths and weaknesses. Here, we introduce typical applications and underlying frameworks for deep learning with a set of practical criteria for developing production ready solutions in life science and pharma research. Based on our own experience in successfully developing deep learning applications we provide suggestions and a baseline for selecting the most suited frameworks for a future-proof and cost-effective development. © Georg Thieme Verlag KG Stuttgart · New York.

Student Engagement as a General Factor of Classroom Experience: Associations with Student Practices and Educational Outcomes in a University Gateway Course

PubMed Central

Shernof, David J.; Ruzek, Erik A.; Sannella, Alexander J.; Schorr, Roberta Y.; Sanchez-Wall, Lina; Bressler, Denise M.

2017-01-01

The purpose of this study was to evaluate a model for considering general and specific elements of student experience in a gateway course in undergraduate Financial Accounting in a large university on the East Coast, USA. Specifically, the study evaluated a bifactor analytic strategy including a general factor of student classroom experience, conceptualized as student engagement as rooted in flow theory, as well as factors representing specific dimensions of experience. The study further evaluated the association between these general and specific factors and both student classroom practices and educational outcomes. The sample of students (N = 407) in two cohorts of the undergraduate financial accounting course participated in the Experience Sampling Method (ESM) measuring students' classroom practices, perceptions, engagement, and perceived learning throughout the one-semester course. Course grade information was also collected. Results showed that a two-level bifactor model fit the data better than two traditional (i.e., non-bifactor) models and also avoided significant multicollinearity of the traditional models. In addition to student engagement (general factor), specific dimensions of classroom experience in the bifactor model at the within-student level included intrinsic motivation, academic intensity, salience, and classroom self-esteem. At the between-student level, specific aspects included work orientation, learning orientation, classroom self-esteem, and disengagement. Multilevel Structural Equation Modeling (MSEM) demonstrated that sitting in the front of the classroom (compared to the sitting in the back), taking notes, active listening, and working on problems during class had a positive effect on within-student variation in student engagement and attention. Engagement, in turn, predicted perceived learning. With respect to between-student effects, the tendency to sit in front seats had a significant effect on student engagement, which in turn had a significant effect on perceived learning and course grades. A significant indirect relationship of seating and active learning strategies on learning and course grade as mediated by student engagement was found. Support for the general aspect of student classroom experience was interpreted with flow theory and suggested the need for additional research. Findings also suggested that active learning strategies are associated with positive learning outcomes even in educational environments where possibilities for action are relatively constrained. PMID:28663733

Student Engagement as a General Factor of Classroom Experience: Associations with Student Practices and Educational Outcomes in a University Gateway Course.

PubMed

Shernof, David J; Ruzek, Erik A; Sannella, Alexander J; Schorr, Roberta Y; Sanchez-Wall, Lina; Bressler, Denise M

2017-01-01

The purpose of this study was to evaluate a model for considering general and specific elements of student experience in a gateway course in undergraduate Financial Accounting in a large university on the East Coast, USA. Specifically, the study evaluated a bifactor analytic strategy including a general factor of student classroom experience, conceptualized as student engagement as rooted in flow theory, as well as factors representing specific dimensions of experience. The study further evaluated the association between these general and specific factors and both student classroom practices and educational outcomes. The sample of students ( N = 407) in two cohorts of the undergraduate financial accounting course participated in the Experience Sampling Method (ESM) measuring students' classroom practices, perceptions, engagement, and perceived learning throughout the one-semester course. Course grade information was also collected. Results showed that a two-level bifactor model fit the data better than two traditional (i.e., non-bifactor) models and also avoided significant multicollinearity of the traditional models. In addition to student engagement (general factor), specific dimensions of classroom experience in the bifactor model at the within-student level included intrinsic motivation, academic intensity, salience, and classroom self-esteem. At the between-student level, specific aspects included work orientation, learning orientation, classroom self-esteem, and disengagement. Multilevel Structural Equation Modeling (MSEM) demonstrated that sitting in the front of the classroom (compared to the sitting in the back), taking notes, active listening, and working on problems during class had a positive effect on within-student variation in student engagement and attention. Engagement, in turn, predicted perceived learning. With respect to between-student effects, the tendency to sit in front seats had a significant effect on student engagement, which in turn had a significant effect on perceived learning and course grades. A significant indirect relationship of seating and active learning strategies on learning and course grade as mediated by student engagement was found. Support for the general aspect of student classroom experience was interpreted with flow theory and suggested the need for additional research. Findings also suggested that active learning strategies are associated with positive learning outcomes even in educational environments where possibilities for action are relatively constrained.

Doping Among Professional Athletes in Iran: A Test of Akers's Social Learning Theory.

PubMed

Kabiri, Saeed; Cochran, John K; Stewart, Bernadette J; Sharepour, Mahmoud; Rahmati, Mohammad Mahdi; Shadmanfaat, Syede Massomeh

2018-04-01

The use of performance-enhancing drugs (PED) is common among Iranian professional athletes. As this phenomenon is a social problem, the main purpose of this research is to explain why athletes engage in "doping" activity, using social learning theory. For this purpose, a sample of 589 professional athletes from Rasht, Iran, was used to test assumptions related to social learning theory. The results showed that there are positive and significant relationships between the components of social learning theory (differential association, differential reinforcement, imitation, and definitions) and doping behavior (past, present, and future use of PED). The structural modeling analysis indicated that the components of social learning theory accounts for 36% of the variance in past doping behavior, 35% of the variance in current doping behavior, and 32% of the variance in future use of PED.

When I grow up: the relationship of science learning activation to STEM career preferences

NASA Astrophysics Data System (ADS)

Dorph, Rena; Bathgate, Meghan E.; Schunn, Christian D.; Cannady, Matthew A.

2018-06-01

This paper proposes three new measures of components STEM career preferences (affinity, certainty, and goal), and then explores which dimensions of science learning activation (fascination, values, competency belief, and scientific sensemaking) are predictive of STEM career preferences. Drawn from the ALES14 dataset, a sample of 2938 sixth and eighth grade middle-school students from 11 schools in two purposefully selected diverse areas (Western Pennsylvania & the Bay Area of California) was used for the analyses presented in this paper. These schools were chosen to represent socio-economic and ethnic diversity. Findings indicate that, overall, youth who are activated towards science learning are more likely to have affinity towards STEM careers, certainty about their future career goals, and have identified a specific STEM career goal. However, different dimensions of science learning activation are more strongly correlated with different aspects career preference across different STEM career foci (e.g. science, engineering, technology, health, etc.). Gender, age, minority status, and home resources also have explanatory power. While many results are consistent with prior research, there are also novel results that offer important fodder for future research. Critically, our strategy of measuring affinity towards the specific disciplines that make up STEM, measuring STEM and health career goals separately, and looking at career affinity and career goals separately, offers interesting results and underscores the value of disentangling the conceptual melting pot of what has previously been known as 'career interest.' Study findings also have implications for design of science learning opportunities for youth.

Functionally segregated neural substrates for arbitrary audiovisual paired-association learning.

PubMed

Tanabe, Hiroki C; Honda, Manabu; Sadato, Norihiro

2005-07-06

To clarify the neural substrates and their dynamics during crossmodal association learning, we conducted functional magnetic resonance imaging (MRI) during audiovisual paired-association learning of delayed matching-to-sample tasks. Thirty subjects were involved in the study; 15 performed an audiovisual paired-association learning task, and the remainder completed a control visuo-visual task. Each trial consisted of the successive presentation of a pair of stimuli. Subjects were asked to identify predefined audiovisual or visuo-visual pairs by trial and error. Feedback for each trial was given regardless of whether the response was correct or incorrect. During the delay period, several areas showed an increase in the MRI signal as learning proceeded: crossmodal activity increased in unimodal areas corresponding to visual or auditory areas, and polymodal responses increased in the occipitotemporal junction and parahippocampal gyrus. This pattern was not observed in the visuo-visual intramodal paired-association learning task, suggesting that crossmodal associations might be formed by binding unimodal sensory areas via polymodal regions. In both the audiovisual and visuo-visual tasks, the MRI signal in the superior temporal sulcus (STS) in response to the second stimulus and feedback peaked during the early phase of learning and then decreased, indicating that the STS might be key to the creation of paired associations, regardless of stimulus type. In contrast to the activity changes in the regions discussed above, there was constant activity in the frontoparietal circuit during the delay period in both tasks, implying that the neural substrates for the formation and storage of paired associates are distinct from working memory circuits.

Ethics Issues Arising in the Transition to Learning Health Care Systems: Results from Interviews with Leaders from 25 Health Systems.

PubMed

Morain, Stephanie R; Kass, Nancy E

2016-01-01

There is increased interest in transitioning to a "learning health care system" (LHCS). While this transition brings the potential for significant benefits, it also presents several ethical considerations. Identifying the ethical issues faced by institutions in this transition is critical for realizing the goals of learning health care so that these issues can be anticipated and, where possible, resolved. 29 semi-structured telephone interviews were conducted with leaders within 25 health care institutions. Respondents were recruiting using purposive sampling, targeting institutions considered as LHCS leaders. All interviews were audiorecorded and transcribed. NVIVO10 software was used to support qualitative analysis. Respondents described seven ethical challenges: (1) ethical oversight of learning activities; (2) transparency of learning activities to patients; (3) potential tensions between improving quality and reducing costs; (4) data sharing and data management; (5) lag time between discovery and implementation; (6) transparency to patients about quality; and (7) randomization for quality improvement initiatives. To move towards LHCS, several ethical considerations require further attention, including: the continued appropriateness of the research-treatment distinction; policy frameworks for privacy and data sharing; informing patients about learning activities; obligations to share data on quality; and the potential for trade-offs between quality improvement and cost control. To our knowledge, this is the first project to ask leaders from health care systems committed to ongoing learning about the ethical issues they have faced in this effort. Their experiences can provide guidance on relevant ethical issues, and what might be done to resolve them.

Learning rate and temperament in a high predation risk environment

USGS Publications Warehouse

DePasquale, C.; Wagner, Tyler; Archard, G.A.; Ferguson, B.; Braithwaite, V.A.

2014-01-01

Living in challenging environments can influence the behavior of animals in a number of ways. For instance, populations of prey fish that experience frequent, nonlethal interactions with predators have a high proportion of individuals that express greater reaction to risk and increased activity and exploration—collectively known as temperament traits. Temperament traits are often correlated, such that individuals that are risk-prone also tend to be active and explore more. Spatial learning, which requires the integration of many sensory cues, has also been shown to vary in fish exposed to different levels of predation threat. Fish from areas of low predation risk learn to solve spatial tasks faster than fish from high predation areas. However, it is not yet known whether simpler forms of learning, such as learning associations between two events, are similarly influenced. Simple forms of associative learning are likely to be affected by temperament because a willingness to approach and explore novel situations could provide animals with a learning advantage. However, it is possible that routine-forming and inflexible traits associated with risk-prone and increased exploratory behavior may act in the opposite way and make risk-prone individuals poorer at learning associations. To investigate this, we measured temperament in Panamanian bishop fish (Brachyrhaphis episcopi) sampled from a site known to contain many predators. The B. episcopi were then tested with an associative learning task. Within this population, fish that explored more were faster at learning a cue that predicted access to food, indicating a link between temperament and basic learning abilities.

Analysis student self efficacy in terms of using Discovery Learning model with SAVI approach

NASA Astrophysics Data System (ADS)

Sahara, Rifki; Mardiyana, S., Dewi Retno Sari

2017-12-01

Often students are unable to prove their academic achievement optimally according to their abilities. One reason is that they often feel unsure that they are capable of completing the tasks assigned to them. For students, such beliefs are necessary. The term belief has called self efficacy. Self efficacy is not something that has brought about by birth or something with permanent quality of an individual, but is the result of cognitive processes, the meaning one's self efficacy will be stimulated through learning activities. Self efficacy has developed and enhanced by a learning model that can stimulate students to foster confidence in their capabilities. One of them is by using Discovery Learning model with SAVI approach. Discovery Learning model with SAVI approach is one of learning models that involves the active participation of students in exploring and discovering their own knowledge and using it in problem solving by utilizing all the sensory devices they have. This naturalistic qualitative research aims to analyze student self efficacy in terms of use the Discovery Learning model with SAVI approach. The subjects of this study are 30 students focused on eight students who have high, medium, and low self efficacy obtained through purposive sampling technique. The data analysis of this research used three stages, that were reducing, displaying, and getting conclusion of the data. Based on the results of data analysis, it was concluded that the self efficacy appeared dominantly on the learning by using Discovery Learning model with SAVI approach is magnitude dimension.

Masters of adaptation: learning in late life adjustments.

PubMed

Roberson, Donald N

2005-01-01

The purpose of this research is to understand the relationship between human development in older adults and personal learning. Personal or self-directed learning (SDL) refers to a style of learning where the individual directs, controls, and evaluates what is learned. It may occur with formal classes, but most often takes place in non-formal situations. This study employed a descriptive qualitative design incorporating in-depth, semistructured interviews for data collection. The sample of 10 purposefully selected older adults from a rural area reflected diversity in gender, race, education, and employment. Data analysis was guided by the constant comparative method. The primary late life adjustments of these older adults were in response to having extra time, changes in family, and social and physical loss. This research also indicated that late life adjustments are a primary incentive for self-directed learning. The results of this study indicated that older adults become masters of adaptation through the use of self-directed learning activities.

Have we met before? Neural correlates of emotional learning in women with social phobia.

PubMed

Laeger, Inga; Keuper, Kati; Heitmann, Carina; Kugel, Harald; Dobel, Christian; Eden, Annuschka; Arolt, Volker; Zwitserlood, Pienie; Dannlowski, Udo; Zwanzger, Peter

2014-05-01

Altered memory processes are thought to be a key mechanism in the etiology of anxiety disorders, but little is known about the neural correlates of fear learning and memory biases in patients with social phobia. The present study therefore examined whether patients with social phobia exhibit different patterns of neural activation when confronted with recently acquired emotional stimuli. Patients with social phobia and a group of healthy controls learned to associate pseudonames with pictures of persons displaying either a fearful or a neutral expression. The next day, participants read the pseudonames in the magnetic resonance imaging scanner. Afterwards, 2 memory tests were carried out. We enrolled 21 patients and 21 controls in our study. There were no group differences for learning performance, and results of the memory tests were mixed. On a neural level, patients showed weaker amygdala activation than controls for the contrast of names previously associated with fearful versus neutral faces. Social phobia severity was negatively related to amygdala activation. Moreover, a detailed psychophysiological interaction analysis revealed an inverse correlation between disorder severity and frontolimbic connectivity for the emotional > neutral pseudonames contrast. Our sample included only women. Our results support the theory of a disturbed cortico limbic interplay, even for recently learned emotional stimuli. We discuss the findings with regard to the vigilance-avoidance theory and contrast them to results indicating an oversensitive limbic system in patients with social phobia.

Understanding Mathematic Concept in Relation and Function Method through Active Learning Type Group to Group Distributed LKS

NASA Astrophysics Data System (ADS)

Kudri, F.; Rahmi, R.; Haryono, Y.

2018-04-01

This research is motivated by the lack of understanding of mathematical concepts students and teachers have not familiarize students discussed in groups. This researchaims to determine whether an understanding of mathematical concepts junior class VIII SMPN 2 in Ranah Batahan Kabupaten Pasaman Barat by applying active learning strategy group to group types with LKS better than conventional learning. The type of research is experimental the design of randomized trials on the subject. The population in the study were all students VIII SMPN 2 Ranah Batahan Kabupaten Pasaman Barat in year 2012/2013 which consists of our class room experiment to determine the grade and control class with do nerandomly, so that classes VIII1 elected as a experiment class and class VIII4 as a control class. The instruments used in the test empirically understanding mathematical concepts are shaped by the essay with rt=0,82 greater than rt=0,468 means reliable tests used. The data analysis technique used is the test with the help of MINITAB. Based on the results of the data analisis known that both of the sample are normal and homogenity in real rate α = 0,05, so the hypothesis of this research is received. So, it can be concluded students’ understanding mathematical concept applied the active Group to Group learning strategy with LKS is better than the students’ understanding mathematical concept with Conventional Learning.

Common Neural Mechanisms Underlying Reversal Learning by Reward and Punishment

PubMed Central

Xue, Gui; Xue, Feng; Droutman, Vita; Lu, Zhong-Lin; Bechara, Antoine; Read, Stephen

2013-01-01

Impairments in flexible goal-directed decisions, often examined by reversal learning, are associated with behavioral abnormalities characterized by impulsiveness and disinhibition. Although the lateral orbital frontal cortex (OFC) has been consistently implicated in reversal learning, it is still unclear whether this region is involved in negative feedback processing, behavioral control, or both, and whether reward and punishment might have different effects on lateral OFC involvement. Using a relatively large sample (N = 47), and a categorical learning task with either monetary reward or moderate electric shock as feedback, we found overlapping activations in the right lateral OFC (and adjacent insula) for reward and punishment reversal learning when comparing correct reversal trials with correct acquisition trials, whereas we found overlapping activations in the right dorsolateral prefrontal cortex (DLPFC) when negative feedback signaled contingency change. The right lateral OFC and DLPFC also showed greater sensitivity to punishment than did their left homologues, indicating an asymmetry in how punishment is processed. We propose that the right lateral OFC and anterior insula are important for transforming affective feedback to behavioral adjustment, whereas the right DLPFC is involved in higher level attention control. These results provide insight into the neural mechanisms of reversal learning and behavioral flexibility, which can be leveraged to understand risky behaviors among vulnerable populations. PMID:24349211

Common neural mechanisms underlying reversal learning by reward and punishment.

PubMed

Xue, Gui; Xue, Feng; Droutman, Vita; Lu, Zhong-Lin; Bechara, Antoine; Read, Stephen

2013-01-01

Impairments in flexible goal-directed decisions, often examined by reversal learning, are associated with behavioral abnormalities characterized by impulsiveness and disinhibition. Although the lateral orbital frontal cortex (OFC) has been consistently implicated in reversal learning, it is still unclear whether this region is involved in negative feedback processing, behavioral control, or both, and whether reward and punishment might have different effects on lateral OFC involvement. Using a relatively large sample (N = 47), and a categorical learning task with either monetary reward or moderate electric shock as feedback, we found overlapping activations in the right lateral OFC (and adjacent insula) for reward and punishment reversal learning when comparing correct reversal trials with correct acquisition trials, whereas we found overlapping activations in the right dorsolateral prefrontal cortex (DLPFC) when negative feedback signaled contingency change. The right lateral OFC and DLPFC also showed greater sensitivity to punishment than did their left homologues, indicating an asymmetry in how punishment is processed. We propose that the right lateral OFC and anterior insula are important for transforming affective feedback to behavioral adjustment, whereas the right DLPFC is involved in higher level attention control. These results provide insight into the neural mechanisms of reversal learning and behavioral flexibility, which can be leveraged to understand risky behaviors among vulnerable populations.

Self-paced model learning for robust visual tracking

NASA Astrophysics Data System (ADS)

Huang, Wenhui; Gu, Jason; Ma, Xin; Li, Yibin

2017-01-01

In visual tracking, learning a robust and efficient appearance model is a challenging task. Model learning determines both the strategy and the frequency of model updating, which contains many details that could affect the tracking results. Self-paced learning (SPL) has recently been attracting considerable interest in the fields of machine learning and computer vision. SPL is inspired by the learning principle underlying the cognitive process of humans, whose learning process is generally from easier samples to more complex aspects of a task. We propose a tracking method that integrates the learning paradigm of SPL into visual tracking, so reliable samples can be automatically selected for model learning. In contrast to many existing model learning strategies in visual tracking, we discover the missing link between sample selection and model learning, which are combined into a single objective function in our approach. Sample weights and model parameters can be learned by minimizing this single objective function. Additionally, to solve the real-valued learning weight of samples, an error-tolerant self-paced function that considers the characteristics of visual tracking is proposed. We demonstrate the robustness and efficiency of our tracker on a recent tracking benchmark data set with 50 video sequences.

Measuring emotions during epistemic activities: the Epistemically-Related Emotion Scales.

PubMed

Pekrun, Reinhard; Vogl, Elisabeth; Muis, Krista R; Sinatra, Gale M

2017-09-01

Measurement instruments assessing multiple emotions during epistemic activities are largely lacking. We describe the construction and validation of the Epistemically-Related Emotion Scales, which measure surprise, curiosity, enjoyment, confusion, anxiety, frustration, and boredom occurring during epistemic cognitive activities. The instrument was tested in a multinational study of emotions during learning from conflicting texts (N = 438 university students from the United States, Canada, and Germany). The findings document the reliability, internal validity, and external validity of the instrument. A seven-factor model best fit the data, suggesting that epistemically-related emotions should be conceptualised in terms of discrete emotion categories, and the scales showed metric invariance across the North American and German samples. Furthermore, emotion scores changed over time as a function of conflicting task information and related significantly to perceived task value and use of cognitive and metacognitive learning strategies.

"Who Has the Same Substance that I Have?": A Blueprint for Collaborative Learning Activities

NASA Astrophysics Data System (ADS)

Coppola, Brian P.; Lawton, Richard G.

1995-12-01

Differential classification and categorization are core activities in all disciplines. Although the methods used to collect and identify information vary widely, the fundamental sameness of or difference between many types of samples is a common objective. We have developed this idea in a set of activities we call "Who Has the Same Substance that I Have?", which not only serves as a design for chemistry laboratory tasks, but also as a generic blueprint for any discipline. In our first-term chemistry laboratory course, students learn about chromatographic, spectroscopic, and chemical techniques as tools for collecting information. They work collaboratively to answer the "Who Has the Same Substance that I Have?" question for groups of powdered white solids and again for clear colorless liquids. A number of others have adapted this idea to their own context.

Conative aptitudes in science learning

NASA Astrophysics Data System (ADS)

Jackson, Douglas Northrop, III

2000-09-01

The conative domain of aptitude constructs spans the domains of individual differences in motivation and volition. This research sampled a broad range of conative constructs, including achievement motivation, anxiety, goal orientations, and interest, among others. The purpose was threefold: (a) to explore relationships among conative constructs hypothesized to affect student commitment to learning and subsequent performance, (b) to determine whether or not individual differences in conative constructs were associated with the learning activities and time-on-task of students learning science, and (c) to ascertain whether or not the conative constructs and the time and activity variables were associated with performance differences in a paper-and-pencil science recall measure. This research consisted of three separate studies. Study I involved 60 U.S. college students. In Study II, 234 Canadian high school students participated. These two studies investigated the construct validity of a selection of conative constructs. A principal components analysis of the measures was undertaken and yielded seven components: Pursuit of Excellence, Evaluation Anxiety, Self-Reported Grades, Science Confidence, Science Interest vs. Science Ambivalence, Performance Orientation, and Verbal Ability. For Study III, 82 Canadian high school students completed the same conative questionnaires as were administered in Study II. A computerized environment patterned after an internet browser allowed students to learn about disease-causing microbes. The environment yielded aggregate measures of the time spent learning science, the time spent playing games, the number of games played, and the number of science-related learning activities engaged in by each student. Following administration of the computerized learning environment, students were administered a paper-and pencil science recall measure. Study III found support for the educational importance of the conative variables. Among the principal components, the strongest positive relationship was found between Science Interest vs. Science Ambivalence and performance on the recall measure. Scores on the conative variables were also correlated with both the time and activity variables from the computerized learning task. The implications of the findings are discussed with regard to the construct validation of conative constructs, the use of conative constructs for future educational research, and the design of computerized learning environments for both educational research and applied use.

Integrating Field-Centered, Project Based Activities with Academic Year Coursework: A Curriculum Wide Approach

NASA Astrophysics Data System (ADS)

Kelso, P. R.; Brown, L. M.

2015-12-01

Based upon constructivist principles and the recognition that many students are motivated by hands-on activities and field experiences, we designed a new undergraduate curriculum at Lake Superior State University. One of our major goals was to develop stand-alone field projects in most of the academic year courses. Examples of courses impacted include structural geology, geophysics, and geotectonics, Students learn geophysical concepts in the context of near surface field-based geophysical studies while students in structural geology learn about structural processes through outcrop study of fractures, folds and faults. In geotectonics students learn about collisional and rifting processes through on-site field studies of specific geologic provinces. Another goal was to integrate data and samples collected by students in our sophomore level introductory field course along with stand-alone field projects in our clastic systems and sequence stratigraphy courses. Our emphasis on active learning helps students develop a meaningful geoscience knowledge base and complex reasoning skills in authentic contexts. We simulate the activities of practicing geoscientists by engaging students in all aspects of a project, for example: field-oriented project planning and design; acquiring, analyzing, and interpreting data; incorporating supplemental material and background data; and preparing oral and written project reports. We find through anecdotal evidence including student comments and personal observation that the projects stimulate interest, provide motivation for learning new concepts, integrate skill and concept acquisition vertically through the curriculum, apply concepts from multiple geoscience subdisiplines, and develop soft skills such as team work, problem solving, critical thinking and communication skills. Through this projected-centered Lake Superior State University geology curriculum students practice our motto of "learn geology by doing geology."

[Learning in clinical simulation: observational study on satisfaction perceived by students of nursing].

PubMed

Rubbi, Ivan; Ferri, Paola; Andreina, Giulia; Cremonini, Valeria

2016-01-01

Simulation in the context of the educational workshop is becoming an important learning method, as it allows to play realistic clinical-care situations. These vocational training activities promote the development of cognitive, affective and psychomotor skills in a pedagogical context safe and risk-free, but need to be accounted for using by valid and reliable instruments. To inspect the level of satisfaction of the students of a Degree in Nursing in northern Italy about static and high-fidelity exercises with simulators and clinical cases. A prospective observational study has been conducted involving a non-probabili- stic sample of 51 third-year students throughout the academic year 2013/14. The data collection instrument consists of three questionnaires Student Satisfaction and Self-confidence in Learning Scale, Educational Practices Questionnaire, Simulation Design Scale and 3 questions on overall satisfaction. Statistical analysis was performed with SPSS 20.0 and Office 2003 Excel. The response rate of 89.5% is obtained. The Cronbach Alfa showed a good internal reliability (α = .982). The students were generally satisfied with the activities carried out in the teaching laboratory, showing more enthusiasm for the simulation with static mannequins (71%) and with high-fidelity simulators (60%), activities for which they have experienced a significant involvement and active learning. The teaching with clinical cases scored a lesser degree of satisfaction (38%) and for this method it was found the largest number of elements of weakness.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning.

PubMed

Brincat, Scott L; Miller, Earl K

2016-09-14

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with "internal" memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)-regions critical for sensory associations-of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11-27 Hz) oscillatory power and synchrony associated with "top-down" or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired "top-down" knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. Copyright © 2016 the authors 0270-6474/16/369739-16$15.00/0.

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning

PubMed Central

Brincat, Scott L.

2016-01-01

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with “internal” memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)—regions critical for sensory associations—of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11–27 Hz) oscillatory power and synchrony associated with “top-down” or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing. SIGNIFICANCE STATEMENT As we learn about items in our environment, their representations in our brain become increasingly enriched with our acquired “top-down” knowledge. We found that in the prefrontal cortex, but not the hippocampus, processing of external sensory inputs decreased while internal network dynamics related to top-down processing increased. The results suggest that during learning, prefrontal cortex networks shift their resources from external (sensory) to internal (memory) processing. PMID:27629722

Roadmapping towards Sustainability Proficiency in Engineering Education

ERIC Educational Resources Information Center

Rodriguez-Andara, Alejandro; Río-Belver, Rosa María; Rodríguez-Salvador, Marisela; Lezama-Nicolás, René

2018-01-01

Purpose: The purpose of this paper is to deliver a roadmap that displays pathways to develop sustainability skills in the engineering curricula. Design/methodology/approach: The selected approach to enrich engineering students with sustainability skills was active learning methodologies. First, a survey was carried out on a sample of 189 students…

The Role of Teachers in Facilitating Situational Interest in an Active-Learning Classroom

ERIC Educational Resources Information Center

Rotgans, Jerome I.; Schmidt, Henk G.

2011-01-01

The study sought to explore whether interactional teacher characteristics such as social congruence, subject-matter expertise, and cognitive congruence increase situational interest in students. Correlational and path analyses were conducted on a sample of 498 polytechnic students to assess potential differences in situational interest based on…

Creating a Technologically Literate Classroom: Professional's Guide. Teacher Created Materials No. 887.

ERIC Educational Resources Information Center

Garfield, Gary M.; McDonough, Suzanne

This book discusses how to effectively integrate technology into the classroom. It examines the benefits of curriculum development utilizing technology and presents sample learning activities. Highlights include: technology's past and present role in education; access to computers; the roles of teacher and learner; professional development;…

Interactive Response Systems (IRS) Socrative Application Sample

ERIC Educational Resources Information Center

Aslan, Bilge; Seker, Hasan

2017-01-01

In globally developing education system, technology has made instructional improved in many ways. One of these improvements is the Interactive Response Systems (IRS) that are applied in classroom activities. Therefore, it is "smart" to focus on interactive response systems in learning environment. This study was conducted aiming to focus…

Teamwork through Team Building: Face-to-Face to Online

ERIC Educational Resources Information Center

Staggers, Julie; Garcia, Susan; Nagelhout, Ed

2008-01-01

This article describes the ways the authors incorporated team-building activities into our online business writing courses by interrogating the ways that kinesthetic learning translates into the electronic realm. The authors review foundational theories of team building, including Cog's Ladder and Tuckman's Stages, and offer sample exercises they…

FHA/HERO and the Older Generation.

ERIC Educational Resources Information Center

Mower, Pauline G., Ed.

The booklet describes a sampling of projects and activities carried out by Future Homemakers of America (FHA) and Home Economics Related Occupations (HERO) chapters to indicate how they are concerned for, working with, and learning from older citizens in their respective communities. Projects range from a simple act of friendship to learning…

More than Good Intentions: Building a Network of Collaboratives.

ERIC Educational Resources Information Center

Bailey, Adrienne, Y.

1986-01-01

College Board's national network of school-college collaborative projects to increase the number of high school students prepared to attend college is described: (1) College Board's role; (2) sample conferences on pertinent issues; (3) range of support activities provided by College Board; and (4) lessons learned about both local collaboratives…

Digital Learning Compass: Distance Education State Almanac 2017. Delaware

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Delaware. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Kansas

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Kansas. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Minnesota

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Minnesota. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Utah

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Utah. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Connecticut

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Connecticut. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Wyoming

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Wyoming. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Montana

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Montana. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Iowa

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Iowa. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Alabama

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Alabama. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Nevada

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Nevada. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Mississippi

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Mississippi. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Kentucky

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Kentucky. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Ohio

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Ohio. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Oklahoma

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Oklahoma. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Texas

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Texas. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Vermont

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Vermont. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Colorado

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Colorado. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Arizona

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Arizona . The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Missouri

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Missouri. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Idaho

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Idaho. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Massachusetts

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Massachusetts. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Tennessee

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Tennessee. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Virginia

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Virginia. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Indiana

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Indiana. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Alaska

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Alaska. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Louisiana

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Louisiana. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Nebraska

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Nebraska. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Maine

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Maine. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Wisconsin

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Wisconsin. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Michigan

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Michigan. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Arkansas

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Arkansas . The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Illinois

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Illinois. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Florida

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Florida. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Maryland

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Maryland. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Oregon

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Oregon. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Washington

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Washington. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Hawaii

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Hawaii. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. California

ERIC Educational Resources Information Center

Seaman, Julia A.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of California. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Georgia

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Georgia. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Pennsylvania

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Pennsylvania. The sample for this analysis is comprised of all active, degree-granting…

Learning in Their Own Style.

ERIC Educational Resources Information Center

Cambell, Melvin; Burton, VirLynn

1994-01-01

Presents a unit approach that utilizes Howard Gardner's seven intelligences (Linguistic, Logical/mathematical, Spatial, Music, Kinesthetic/body, Intrapersonal, and Interpersonal). Students are given a list of activities and make a contract to complete a certain number on the list. A sample unit for fifth graders on the bodily systems is provided.…

Adolescent School Experiences and Dropout, Adolescent Pregnancy, and Young Adult Deviant Behavior.

ERIC Educational Resources Information Center

Kasen, Stephanie; Cohen, Patricia; Brook, Judith S.

1998-01-01

This study examined predictability of inappropriate behavior in a random sample of 452 adolescents. Behaviors examined included dropping out, teen pregnancy, criminal activities and conviction, antisocial personality disorder, and alcohol abuse. Found that academic achievement and aspirations, and learning-focused school settings related to…

Annotated Bibliography for 6th Grade Science and Social Studies.

ERIC Educational Resources Information Center

Randolph, Margo

Designed to support curriculum and to facilitate instruction and learning at the sixth grade level, this annotated bibliography contains materials found in the library at the Brawley Middle School in Scotland Neck, North Carolina. To foster cooperative planning between teacher and librarian, the bibliography provides sample activities and lessons…

Fourth Graders Make Inventions Using SCAMPER and Animal Adaptation Ideas

ERIC Educational Resources Information Center

Hussain, Mahjabeen; Carignan, Anastasia

2016-01-01

This study explores to what extent the SCAMPER (Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, and Rearrange) technique combined with animal adaptation ideas learned through form and function analogy activities can help fourth graders generate creative ideas while augmenting their inventiveness. The sample consisted of 24…

Medical students' experiences of their own professional development during three clinical terms: a prospective follow-up study.

PubMed

Kalén, Susanne; Lachmann, Hanna; Varttinen, Maria; Möller, Riitta; Bexelius, Tomas S; Ponzer, Sari

2017-02-27

A modern competency-based medical education is well implemented globally, but less is known about how the included learning activities contribute to medical students' professional development. The aim of this study was to explore Swedish medical students' perceptions of the offered learning activities and their experiences of how these activities were connected to their professional development as defined by the CanMEDS framework. A prospective mixed method questionnaire study during three terms (internal medicine, scientific project, and surgery) in which data were collected by using contextual activity sampling system, i.e., the students were sent a questionnaire via their mobile phones every third week. All 136 medical students in the 6th of 11 terms in the autumn of 2012 were invited to participate. Seventy-four students (54%) filled in all of the required questionnaires (4 per term) for inclusion, the total number of questionnaires being 1335. The questionnaires focused on the students' experiences of learning activities, especially in relation to the CanMEDS Roles, collaboration with others and emotions (positive, negative, optimal experiences, i.e., "flow") related to the studies. The quantitative data was analysed statistically and, for the open-ended questions, manifest inductive content analysis was used. Three of the CanMEDs Roles, Medical Expert, Scholar, and Communicator, were most frequently reported while the four others, e.g., the role Health Advocate, were less common. Collaboration with students from other professions was most usual during the 8th term. Positive emotions and experience of "flow" were most often reported during clinical learning activities while the scientific project term was connected with more negative emotions. Our results showed that it is possible, even during clinical courses, to visualise the different areas of professional competence defined in the curriculum and connect these competences to the actual learning activities. Students halfway through their medical education considered the most important learning activities for their professional development to be connected with the Roles of Medical Expert, Scholar, and Communicator. Given that each of the CanMEDS Roles is at least moderately important during undergraduate medical education, the entire spectrum of the Roles should be emphasised and developed during the clinical years.

Tying knots: an activity theory analysis of student learning goals in clinical education.

PubMed

Larsen, Douglas P; Wesevich, Austin; Lichtenfeld, Jana; Artino, Antony R; Brydges, Ryan; Varpio, Lara

2017-07-01

Learning goal programmes are often created to help students develop self-regulated learning skills; however, these programmes do not necessarily consider the social contexts surrounding learning goals or how they fit into daily educational practice. We investigated a high-frequency learning goal programme in which students generated and shared weekly learning goals with their clinical teams in core Year 3 clerkships. Our study explores: (i) how learning goals were incorporated into the clinical work, and (ii) the factors that influenced the use of students' learning goals in work-based learning. We conducted semi-structured interviews with 14 students and 14 supervisors (attending physicians and residents) sampled from all participating core clerkships. Interviews were coded for emerging themes. Using cultural historical activity theory and knotworking as theoretical lenses, we developed a model of the factors that influenced students' learning goal usage in a work-based learning context. Students and supervisors often faced the challenge of reconciling contradictions that arose when the desired outcomes of student skill development, grading and patient care were not aligned. Learning goals could function as tools for developing new ways of acting that overcame those contradictions by facilitating collaborative effort between students and their supervisors. However, for new collaborations to take place, both students and supervisors had to engage with the goals, and the necessary patients needed to be present. When any one part of the system did not converge around the learning goals, the impact of the learning goals programme was limited. Learning goals are potentially powerful tools to mediate interactions between students, supervisors and patients, and to reconcile contradictions in work-based learning environments. Learning goals provide a means to develop not only learners, but also learning systems. © 2017 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

The New South Wales Allied Health Workplace Learning Study: barriers and enablers to learning in the workplace.

PubMed

Lloyd, Bradley; Pfeiffer, Daniella; Dominish, Jacqueline; Heading, Gaynor; Schmidt, David; McCluskey, Annie

2014-03-25

Workplace learning refers to continuing professional development that is stimulated by and occurs through participation in workplace activities. Workplace learning is essential for staff development and high quality clinical care. The purpose of this study was to explore the barriers to and enablers of workplace learning for allied health professionals within NSW Health. A qualitative study was conducted with a purposively selected maximum variation sample (n =46) including 19 managers, 19 clinicians and eight educators from 10 allied health professions. Seven semi-structured interviews and nine focus groups were audio-recorded and transcribed. The 'framework approach' was used to guide the interviews and analysis. Textual data were coded and charted using an evolving thematic framework. Key enablers of workplace learning included having access to peers, expertise and 'learning networks', protected learning time, supportive management and positive staff attitudes. The absence of these key enablers including heavy workload and insufficient staffing were important barriers to workplace learning. Attention to these barriers and enablers may help organisations to more effectively optimise allied health workplace learning. Ultimately better workplace learning may lead to improved patient, staff and organisational outcomes.

The New South Wales Allied Health Workplace Learning Study: barriers and enablers to learning in the workplace

PubMed Central

2014-01-01

Background Workplace learning refers to continuing professional development that is stimulated by and occurs through participation in workplace activities. Workplace learning is essential for staff development and high quality clinical care. The purpose of this study was to explore the barriers to and enablers of workplace learning for allied health professionals within NSW Health. Methods A qualitative study was conducted with a purposively selected maximum variation sample (n = 46) including 19 managers, 19 clinicians and eight educators from 10 allied health professions. Seven semi-structured interviews and nine focus groups were audio-recorded and transcribed. The ‘framework approach’ was used to guide the interviews and analysis. Textual data were coded and charted using an evolving thematic framework. Results Key enablers of workplace learning included having access to peers, expertise and ‘learning networks’, protected learning time, supportive management and positive staff attitudes. The absence of these key enablers including heavy workload and insufficient staffing were important barriers to workplace learning. Conclusion Attention to these barriers and enablers may help organisations to more effectively optimise allied health workplace learning. Ultimately better workplace learning may lead to improved patient, staff and organisational outcomes. PMID:24661614

Nursing students' attitudes to biomedical science lectures.

PubMed

Al-Modhefer, A K; Roe, S

To explore what first-year nursing students believe to be the preferred characteristics of common foundation programme biomedical science lecturers, and to investigate whether students prefer active or passive learning. Survey and interview methodologies were used to explore the attitudes of a cohort of first-year nursing students at Queen's University Belfast. Questionnaires were distributed among 300 students. Individuals were asked to select five of a list of 14 criteria that they believed characterised the qualities of an effective lecturer. Informal interviews were carried out with five participants who were randomly selected from the sample to investigate which teaching methods were most beneficial in assisting their learning. Nursing students favoured didactic teaching and found interactivity in lectures intimidating. Students preferred to learn biomedical science passively and depended heavily on their instructors. In response to the survey, the authors propose a set of recommendations to enhance the learning process in large classes. This guidance includes giving clear objectives and requirements to students, encouraging active participation, and sustaining student interest through the use of improved teaching aids and innovative techniques.

Bounds on the sample complexity for private learning and private data release

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

Kasiviswanathan, Shiva; Beime, Amos; Nissim, Kobbi

2009-01-01

Learning is a task that generalizes many of the analyses that are applied to collections of data, and in particular, collections of sensitive individual information. Hence, it is natural to ask what can be learned while preserving individual privacy. [Kasiviswanathan, Lee, Nissim, Raskhodnikova, and Smith; FOCS 2008] initiated such a discussion. They formalized the notion of private learning, as a combination of PAC learning and differential privacy, and investigated what concept classes can be learned privately. Somewhat surprisingly, they showed that, ignoring time complexity, every PAC learning task could be performed privately with polynomially many samples, and in many naturalmore » cases this could even be done in polynomial time. While these results seem to equate non-private and private learning, there is still a significant gap: the sample complexity of (non-private) PAC learning is crisply characterized in terms of the VC-dimension of the concept class, whereas this relationship is lost in the constructions of private learners, which exhibit, generally, a higher sample complexity. Looking into this gap, we examine several private learning tasks and give tight bounds on their sample complexity. In particular, we show strong separations between sample complexities of proper and improper private learners (such separation does not exist for non-private learners), and between sample complexities of efficient and inefficient proper private learners. Our results show that VC-dimension is not the right measure for characterizing the sample complexity of proper private learning. We also examine the task of private data release (as initiated by [Blum, Ligett, and Roth; STOC 2008]), and give new lower bounds on the sample complexity. Our results show that the logarithmic dependence on size of the instance space is essential for private data release.« less

A novel method for preparing histology slides to integrate the teaching of gross and microscopic anatomy.

PubMed

Provo-Klimek, Judy A; Troyer, Deryl L

2002-01-01

The authors have previously reported the development of a novel technique for sampling and preparing tissue slides for routine microscopic examination, without the use of a microtome. Termed "RAMP" (Rapid Adhesive Mediated Procedure), this simple, albeit somewhat crude, technique holds promise as a method that can be used in the field by veterinary practitioners for rapid microscopic evaluations to obtain early preliminary estimates of the nature of a mass or lesion. We incorporated the use of this method into a gross anatomy course in an attempt to gauge its utility for novices in tissue sampling and histology slide preparation. By having each group of students take a tissue sample from their cadaver, the activity simulated an actual necropsy situation in which practitioners in the field might use the technique. Because students were able to follow their specimen from sampling to microscopic examination, the activity provided a valuable integration of their learning of gross and microscopic anatomy. We conducted an evaluation of the process and the resulting slides with two successive classes of students. We conclude that the RAMP method is reasonably successful in the hands of individuals not trained in tissue preparation; was well received by the students as a valuable learning tool; and could potentially yield useful histological information for practicing veterinarians. Limitations of the method are also discussed.

Active learning improves on-task behaviors in 4th grade children.

PubMed

Bartholomew, J B; Golaszewski, N M; Jowers, E; Korinek, E; Roberts, G; Fall, A; Vaughn, S

2018-06-01

While increased opportunities for physical activity (PA) are a critical, public health need for children, school-based interventions often place teachers in the position to choose between PA and time spent on academic lessons. Active learning is designed to overcome this by combining PA with academic material. Moreover, teachers are likely to be more responsive to change in academic-related outcomes than in PA. This study utilizes a large, cluster randomized control trial in which student attention, or time on task (TOT) and accelerometer-based PA is assessed in conjunction with active learning. Participants were 2716 children (46% male, 46% white) from 28 elementary schools in Central Texas that were assigned to either: 1) active learning (math n = 10; spelling n = 9); or 2) traditional, sedentary academic lessons (n = 9). PA was measured with accelerometers. TOT was measured through a momentary time sampling protocol. A series of three-level (student, classroom, school) regression models estimated the effect of the intervention. The intervention lead to significantly increased TOT. Moreover, the dose of PA (steps) during the intervention was positively associated with the increase in TOT. In contrast, a greater dose of PA was associated with reduced TOT for students in control schools. Race, gender, and SES did not moderate these effects. Planned PA - as a part of an active, academic lesson - positively impacted TOT. In contrast, a traditional, sedentary lesson was associated with lower TOT. This differential impact offers intriguing possibilities to better understand the relationship between PA and academic performance. Copyright © 2018 Elsevier Inc. All rights reserved.

Comparison of individual answer and group answer with and without structured peer assessment

NASA Astrophysics Data System (ADS)

Kablan, Zeynel

2014-09-01

Background:Cooperative learning activities provide active participation of students leading to better learning. The literature suggests that cooperative learning activities need to be structured for a more effective and productive interaction. Purpose: This study aimed to test the differences among three instructional conditions in terms of science achievement. Sample:A total of 79 fifth-grade students, 42 males (53%) and 37 females (47%), participated in the study. Design and Methods:In the first condition, students answered the teacher's questions individually by raising hands. In the second condition, students discussed the answer in groups and came up with a single group answer. In this condition, the teacher provided only verbal directions to the groups without using any strategy or material. In the third condition, students used a 'peer assessment form' before giving the group answer. A pre-/post-test experimental design was used. Multiple-choice and open-ended tests were used for data collection. One-way analysis of variance (ANOVA) was conducted to test the differences in the test scores between the three groups (individual answer, unstructured group answer and structured group answer). Results:Results showed that there were no significant differences among the three learning conditions in terms of their multiple-choice test scores. In terms of the open-ended test scores, students in the structured group answer condition scored significantly higher than the students in the individual answer condition. Conclusions:Structuring the group work through peer assessment helped to monitor the group discussion, provided a better learning compared to the individual answer condition, and helped students to participate in the activity equally.

Antimicrobial and Antibiofilm Activity and Machine Learning Classification Analysis of Essential Oils from Different Mediterranean Plants against Pseudomonas aeruginosa.

PubMed

Artini, Marco; Patsilinakos, Alexandros; Papa, Rosanna; Božović, Mijat; Sabatino, Manuela; Garzoli, Stefania; Vrenna, Gianluca; Tilotta, Marco; Pepi, Federico; Ragno, Rino; Selan, Laura

2018-02-23

Pseudomonas aeruginosa is a ubiquitous organism and opportunistic pathogen that can cause persistent infections due to its peculiar antibiotic resistance mechanisms and to its ability to adhere and form biofilm. The interest in the development of new approaches for the prevention and treatment of biofilm formation has recently increased. The aim of this study was to seek new non-biocidal agents able to inhibit biofilm formation, in order to counteract virulence rather than bacterial growth and avoid the selection of escape mutants. Herein, different essential oils extracted from Mediterranean plants were analyzed for their activity against P. aeruginosa . Results show that they were able to destabilize biofilm at very low concentration without impairing bacterial viability. Since the action is not related to a bacteriostatic/bactericidal activity on P. aeruginosa , the biofilm change of growth in presence of the essential oils was possibly due to a modulation of the phenotype. To this aim, application of machine learning algorithms led to the development of quantitative activity-composition relationships classification models that allowed to direct point out those essential oil chemical components more involved in the inhibition of biofilm production. The action of selected essential oils on sessile phenotype make them particularly interesting for possible applications such as prevention of bacterial contamination in the community and in healthcare environments in order to prevent human infections. We assayed 89 samples of different essential oils as P. aeruginosa anti-biofilm. Many samples inhibited P. aeruginosa biofilm at concentrations as low as 48.8 µg/mL. Classification of the models was developed through machine learning algorithms.

Effect of data self-collection as an activating teaching method in a statistical software course in medical biometry - a pilot study.

PubMed

Mayer, Benjamin; Braisch, Ulrike; Meule, Marianne; Allgoewer, Andreas; Richter, Silvia; Muche, Rainer

2018-01-01

Background: Biostatistics is an integral part of the studies of human medicine. Students learn the basics of analyzing and interpreting study results. It is important to demonstrate the subject's relevance by means of appropriate measures to maximize learning success. We investigated whether an active involvement of students in the process of data collection may improve test performance and motivation among medical students. Methods: We conducted a pilot study comparing active involvement of students (n1=45) in the process of data collection and standard education (n2=26). All students of this pilot study participated in an observational study assessing their preferences regarding sweets or salty munchies, and students of the experimental group subsequently used this data set during the exercises throughout the semester. Primary and secondary endpoints were examination success and motivation respectively. Results: Superiority of the activating teaching method could not be demonstrated (intervention: 109.0 points (SD 8.8), control: 113.8 points (SD 6.5)). The course ratings were superior in the intervention group (median grade 1 vs. median grade 2 in the control group), although this was not a significant improvement (p=0.487). Conclusions: Biostatistics education should incorporate approaches contributing to a better understanding of learning contents. Possible reasons why this pilot study failed to prove superiority of the intervention were a lack of sample size as well as the good grades in the control group. The presented teaching concept has to be evaluated by means of a larger sample enabling more valid conclusions. Furthermore, the considered research question in the experimental group may be changed to a more relevant one for medical practice.

Effectiveness of interactive, online games in learning neuroscience and students' perception of the games as learning tools. A pre-experimental study.

PubMed

Thompson, Marilyn E; Ford, Ruth; Webster, Andrew

2011-01-01

Neurological concepts applicable to a doctorate in occupational therapy are often challenging to comprehend, and students are required to demonstrate critical reasoning skills beyond simply recalling the information. To achieve this, various learning and teaching strategies are used, including the use of technology in the classroom. The availability of technology in academic settings has allowed for diverse and active teaching approaches. This includes videos, web-based instruction, and interactive online games. In this quantitative pre-experimental analysis, the learning and retention of neuroscience concepts by 30 occupational therapy doctoral students, who participated in an interactive online learning experience, were assessed. The results suggest that student use of these tools may enhance their learning of neuroscience. Furthermore, the students felt that the sites were appropriate, beneficial to them, and easy to use. Thus, the use of online, interactive neuroscience games may be effective in reinforcing lecture materials. This needs to be further assessed in a larger sample size.

Cross-Domain Semi-Supervised Learning Using Feature Formulation.

PubMed

Xingquan Zhu

2011-12-01

Semi-Supervised Learning (SSL) traditionally makes use of unlabeled samples by including them into the training set through an automated labeling process. Such a primitive Semi-Supervised Learning (pSSL) approach suffers from a number of disadvantages including false labeling and incapable of utilizing out-of-domain samples. In this paper, we propose a formative Semi-Supervised Learning (fSSL) framework which explores hidden features between labeled and unlabeled samples to achieve semi-supervised learning. fSSL regards that both labeled and unlabeled samples are generated from some hidden concepts with labeling information partially observable for some samples. The key of the fSSL is to recover the hidden concepts, and take them as new features to link labeled and unlabeled samples for semi-supervised learning. Because unlabeled samples are only used to generate new features, but not to be explicitly included in the training set like pSSL does, fSSL overcomes the inherent disadvantages of the traditional pSSL methods, especially for samples not within the same domain as the labeled instances. Experimental results and comparisons demonstrate that fSSL significantly outperforms pSSL-based methods for both within-domain and cross-domain semi-supervised learning.

Mapping epistemic cultures and learning potential of participants in citizen science projects.

PubMed

Vallabh, Priya; Lotz-Sisitka, Heila; O'Donoghue, Rob; Schudel, Ingrid

2016-06-01

The ever-widening scope and range of global change and interconnected systemic risks arising from people-environment relationships (social-ecological risks) appears to be increasing concern among, and involvement of, citizens in an increasingly diversified number of citizen science projects responding to these risks. We examined the relationship between epistemic cultures in citizen science projects and learning potential related to matters of concern. We then developed a typology of purposes and a citizen science epistemic-cultures heuristic and mapped 56 projects in southern Africa using this framework. The purpose typology represents the range of knowledge-production purposes, ranging from laboratory science to social learning, whereas the epistemic-cultures typology is a relational representation of scientist and citizen participation and their approach to knowledge production. Results showed an iterative relationship between matters of fact and matters of concern across the projects; the nexus of citizens' engagement in knowledge-production activities varied. The knowledge-production purposes informed and shaped the epistemic cultures of all the sampled citizen science projects, which in turn influenced the potential for learning within each project. Through a historical review of 3 phases in a long-term river health-monitoring project, we found that it is possible to evolve the learning curve of citizen science projects. This evolution involved the development of scientific water monitoring tools, the parallel development of pedagogic practices supporting monitoring activities, and situated engagement around matters of concern within social activism leading to learning-led change. We conclude that such evolutionary processes serve to increase potential for learning and are necessary if citizen science is to contribute to wider restructuring of the epistemic culture of science under conditions of expanding social-ecological risk. © 2016 Society for Conservation Biology.

Using Expectancy Value Theory as a Framework to Reduce Student Resistance to Active Learning: A Proof of Concept.

PubMed

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

2017-01-01

There has been a national movement to transition college science courses from passive lectures to active learning environments. Active learning has been shown to be a more effective way for students to learn, yet there is concern that some students are resistant to active learning approaches. Although there is much discussion about student resistance to active learning, few studies have explored this topic. Furthermore, a limited number of studies have applied theoretical frameworks to student engagement in active learning. We propose using a theoretical lens of expectancy value theory to understand student resistance to active learning. In this study, we examined student perceptions of active learning after participating in 40 hours of active learning. We used the principal components of expectancy value theory to probe student experience in active learning: student perceived self-efficacy in active learning, value of active learning, and potential cost of participating in active learning. We found that students showed positive changes in the components of expectancy value theory and reported high levels of engagement in active learning, which provide proof of concept that expectancy value theory can be used to boost student perceptions of active learning and their engagement in active learning classrooms. From these findings, we have built a theoretical framework of expectancy value theory applied to active learning.

Using Expectancy Value Theory as a Framework to Reduce Student Resistance to Active Learning: A Proof of Concept

PubMed Central

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

2017-01-01

There has been a national movement to transition college science courses from passive lectures to active learning environments. Active learning has been shown to be a more effective way for students to learn, yet there is concern that some students are resistant to active learning approaches. Although there is much discussion about student resistance to active learning, few studies have explored this topic. Furthermore, a limited number of studies have applied theoretical frameworks to student engagement in active learning. We propose using a theoretical lens of expectancy value theory to understand student resistance to active learning. In this study, we examined student perceptions of active learning after participating in 40 hours of active learning. We used the principal components of expectancy value theory to probe student experience in active learning: student perceived self-efficacy in active learning, value of active learning, and potential cost of participating in active learning. We found that students showed positive changes in the components of expectancy value theory and reported high levels of engagement in active learning, which provide proof of concept that expectancy value theory can be used to boost student perceptions of active learning and their engagement in active learning classrooms. From these findings, we have built a theoretical framework of expectancy value theory applied to active learning. PMID:28861130

Emotionalized learning experiences: Tapping into the affective domain.

PubMed

Green, Zane Asher; Batool, Sadia

2017-06-01

The experimental study was undertaken to examine the effect of emotionalized learning experiences on the academic achievement of students at Preston University. The major objectives of the study were to identify the effect of teaching methods on students' academic achievement and to evaluate the relationship between affective learning conditions and students' academic achievement. Based on four intact semesters, the population of the study comprised 140 students from the Bachelors of Business Administration Program. The whole population was considered as the sample. The control group (28 students) was taught through the interactive lecture method, whereas, the experimental group 1 (35 students), experimental group 2 (46 students) and experimental group 3 (31 students) were taught through the activity method, reflective learning method and cooperative learning method respectively. Results indicated a significant difference between the pretest and posttest scores obtained in the achievement test as a result of the effect of teaching methods used for offering the emotionalized learning experiences. There was also a significant relationship between affective leaning conditions and students' academic achievement. Furthermore, it was found that students' academic achievement in the affective domain was highest with regard to workshops 1, 2 and 3. It was concluded that the emotionalized learning experiences offered to the students via the four teaching methods helped students in enhancing their knowledge, changing their attitudes and developing their skills with regard to living a happy, healthy and meaningful life. However, the reflective learning method proved to be the most suitable followed by the interactive lecture method, the cooperative learning method and the activity method. Copyright © 2017 Elsevier Ltd. All rights reserved.

Let the Dogs Out: Using Bobble Head Toys to Explore Force and Motion.

ERIC Educational Resources Information Center

Foster, Andrea S.

2003-01-01

Introduces an activity in which students learn principles of force and motion, systems, and simple machines by exploring the best position of the dogs on the dashboard. Includes a sample lesson plan written in the five instructional models: (1) engagement; (2) exploration; (3) explanation; (4) elaboration; and (5) evaluation. (KHR)

Value Orientations and Studying in School-Leisure Conflict: A Study with Samples from Five Countries

ERIC Educational Resources Information Center

Hofer, Manfred; Schmid, Sebastian; Fries, Stefan; Zivkovic, Ilija; Dietz, Franziska

2009-01-01

The relations between students' value orientations and experiences of motivational interference during studying following conflicts between learning and leisure activities were investigated in a self-report study. Overall, 1075 adolescents, mostly from Catholic schools, in Bosnia-Herzegovina (n = 203), India (n = 200), Paraguay (n = 96), Spain (n…

Life Skills in Solitude and Silence in the School.

ERIC Educational Resources Information Center

Byrnes, Deborah A.

1983-01-01

Addresses the rationale for fostering the positive use of silent and alone time in school and notes how silence can be a useful setting for learning and thinking. Explains four sample activities to help acquaint children with the positive use of silence and solitude and suggests references for future reading. (SB)

Welder's Helper. Coordinator's Guide. Individualized Study Guide. General Metal Trades.

ERIC Educational Resources Information Center

Dean, James W.

This guide provides information to enable coordinators to direct learning activities for students using an individualized study guide on being a welder's helper. The study material is designed for students enrolled in cooperative part-time training and employed, or desiring to be employed, as welders' helpers. Contents include a sample progress…

English Education and Aging.

ERIC Educational Resources Information Center

Gillis, Candida

1983-01-01

Suggests that English teachers are in an excellent position to help students learn about the aged and aging because they know literature that treats the joys and pains of later life and they understand how language shapes and reflects cultural attitudes. Proposes objectives and presents samples of activities to be used in an aging unit. (MM)

Effects of Professional Development on Teachers' Instruction: Results from a Three-year Longitudinal Study.

ERIC Educational Resources Information Center

Desimone, Laura M.; Porter, Andrew C.; Garet, Michael S.; Yoon, Kwang Suk; Birman, Beatrice F.

2002-01-01

Examined the effects of professional development on teachers' instruction using a purposeful sample of about 207 teachers across 5 states for 1996-1999. Professional development focused on specific instructional practices increased teachers' use of those practices in the classroom, and specific features, such as active learning opportunities,…

Digital Learning Compass: Distance Education State Almanac 2017. North Dakota

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of North Dakota. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. West Virginia

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of West Virginia. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. South Dakota

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of South Dakota. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. North Carolina

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of North Carolina. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. Rhode Island

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Rhode Island. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. New Hampshire

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of New Hampshire. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. New Jersey

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of New Jersey. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. New Mexico

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of New Mexico. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. New York

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of New York. The sample for this analysis is comprised of all active, degree-granting…

Digital Learning Compass: Distance Education State Almanac 2017. South Carolina

ERIC Educational Resources Information Center

Seaman, Julia E.; Seaman, Jeff

2017-01-01

This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of South Carolina. The sample for this analysis is comprised of all active, degree-granting…

Students' Value Orientations, Intuitive Decision Making, and Motivational Interference, and Their Relations to Regret

ERIC Educational Resources Information Center

Kuhnle, Claudia; Sinclair, Marta; Hofer, Manfred; Kilian, Britta

2014-01-01

Students' learning activities frequently compete with their leisure options, leading to regret after decisions to study. Using a sample of 233 German and 194 Australian undergraduate students, the authors explored possible determinants of the personality construct regret. They investigated whether the level to which students rely on intuition in…

Lathe Operator. Coordinator's Guide. Individualized Study Guide. General Metal Trades.

ERIC Educational Resources Information Center

East Texas State Univ., Commerce. Occupational Curriculum Lab.

This guide provides information to enable coordinators to direct learning activities for students using an individualized study guide on operating a lathe. The study material is designed for students enrolled in cooperative part-time training and employed, or desiring to be employed, as lathe operators. Contents include a sample progress chart,…

Have we met before? Neural correlates of emotional learning in women with social phobia

PubMed Central

Laeger, Inga; Keuper, Kati; Heitmann, Carina; Kugel, Harald; Dobel, Christian; Eden, Annuschka; Arolt, Volker; Zwitserlood, Pienie; Dannlowski, Udo; Zwanzger, Peter

2014-01-01

Background Altered memory processes are thought to be a key mechanism in the etiology of anxiety disorders, but little is known about the neural correlates of fear learning and memory biases in patients with social phobia. The present study therefore examined whether patients with social phobia exhibit different patterns of neural activation when confronted with recently acquired emotional stimuli. Methods Patients with social phobia and a group of healthy controls learned to associate pseudonames with pictures of persons displaying either a fearful or a neutral expression. The next day, participants read the pseudonames in the magnetic resonance imaging scanner. Afterwards, 2 memory tests were carried out. Results We enrolled 21 patients and 21 controls in our study. There were no group differences for learning performance, and results of the memory tests were mixed. On a neural level, patients showed weaker amygdala activation than controls for the contrast of names previously associated with fearful versus neutral faces. Social phobia severity was negatively related to amygdala activation. Moreover, a detailed psychophysiological interaction analysis revealed an inverse correlation between disorder severity and frontolimbic connectivity for the emotional > neutral pseudonames contrast. Limitations Our sample included only women. Conclusion Our results support the theory of a disturbed corticolimbic interplay, even for recently learned emotional stimuli. We discuss the findings with regard to the vigilance–avoidance theory and contrast them to results indicating an oversensitive limbic system in patients with social phobia. PMID:24758944

Do job demands and job control affect problem-solving?

PubMed

Bergman, Peter N; Ahlberg, Gunnel; Johansson, Gun; Stoetzer, Ulrich; Aborg, Carl; Hallsten, Lennart; Lundberg, Ingvar

2012-01-01

The Job Demand Control model presents combinations of working conditions that may facilitate learning, the active learning hypothesis, or have detrimental effects on health, the strain hypothesis. To test the active learning hypothesis, this study analysed the effects of job demands and job control on general problem-solving strategies. A population-based sample of 4,636 individuals (55% women, 45% men) with the same job characteristics measured at two times with a three year time lag was used. Main effects of demands, skill discretion, task authority and control, and the combined effects of demands and control were analysed in logistic regressions, on four outcomes representing general problem-solving strategies. Those reporting high on skill discretion, task authority and control, as well as those reporting high demand/high control and low demand/high control job characteristics were more likely to state using problem solving strategies. Results suggest that working conditions including high levels of control may affect how individuals cope with problems and that workplace characteristics may affect behaviour in the non-work domain.

Learning to apply models of materials while explaining their properties

NASA Astrophysics Data System (ADS)

Karpin, Tiia; Juuti, Kalle; Lavonen, Jari

2014-09-01

Background:Applying structural models is important to chemistry education at the upper secondary level, but it is considered one of the most difficult topics to learn. Purpose:This study analyses to what extent in designed lessons students learned to apply structural models in explaining the properties and behaviours of various materials. Sample:An experimental group is 27 Finnish upper secondary school students and control group included 18 students from the same school. Design and methods:In quasi-experimental setting, students were guided through predict, observe, explain activities in four practical work situations. It was intended that the structural models would encourage students to learn how to identify and apply appropriate models when predicting and explaining situations. The lessons, organised over a one-week period, began with a teacher's demonstration and continued with student experiments in which they described the properties and behaviours of six household products representing three different materials. Results:Most students in the experimental group learned to apply the models correctly, as demonstrated by post-test scores that were significantly higher than pre-test scores. The control group showed no significant difference between pre- and post-test scores. Conclusions:The findings indicate that the intervention where students engage in predict, observe, explain activities while several materials and models are confronted at the same time, had a positive effect on learning outcomes.

Conceptual Learning Outcomes of Virtual Experiential Learning: Results of Google Earth Exploration in Introductory Geoscience Courses

NASA Astrophysics Data System (ADS)

Bitting, Kelsey S.; McCartney, Marsha J.; Denning, Kathy R.; Roberts, Jennifer A.

2018-06-01

Virtual globe programs such as Google Earth replicate real-world experiential learning of spatial and geographic concepts by allowing students to navigate across our planet without ever leaving campus. However, empirical evidence for the learning value of these technological tools and the experience students gain by exploration assignments framed within them remains to be quantified and compared by student demographics. This study examines the impact of a Google Earth-based exploration assignment on conceptual understanding in introductory geoscience courses at a research university in the US Midwest using predominantly traditional college-age students from a range of majors. Using repeated-measures ANOVA and paired-samples t tests, we test the significance of the activity using pretest and posttest scores on a subset of items from the Geoscience Concept Inventory, and the interactive effects of student gender and ethnicity on student score improvement. Analyses show that learning from the Google Earth exploration activity is highly significant overall and for all but one of the concept inventory items. Furthermore, we find no significant interactive effects of class format, student gender, or student ethnicity on the magnitude of the score increases. These results provide strong support for the use of experiential learning in virtual globe environments for students in introductory geoscience and perhaps other disciplines for which direct observation of our planet's surface is conceptually relevant.

Dynamic shaping of dopamine signals during probabilistic Pavlovian conditioning.

PubMed

Hart, Andrew S; Clark, Jeremy J; Phillips, Paul E M

2015-01-01

Cue- and reward-evoked phasic dopamine activity during Pavlovian and operant conditioning paradigms is well correlated with reward-prediction errors from formal reinforcement learning models, which feature teaching signals in the form of discrepancies between actual and expected reward outcomes. Additionally, in learning tasks where conditioned cues probabilistically predict rewards, dopamine neurons show sustained cue-evoked responses that are correlated with the variance of reward and are maximal to cues predicting rewards with a probability of 0.5. Therefore, it has been suggested that sustained dopamine activity after cue presentation encodes the uncertainty of impending reward delivery. In the current study we examined the acquisition and maintenance of these neural correlates using fast-scan cyclic voltammetry in rats implanted with carbon fiber electrodes in the nucleus accumbens core during probabilistic Pavlovian conditioning. The advantage of this technique is that we can sample from the same animal and recording location throughout learning with single trial resolution. We report that dopamine release in the nucleus accumbens core contains correlates of both expected value and variance. A quantitative analysis of these signals throughout learning, and during the ongoing updating process after learning in probabilistic conditions, demonstrates that these correlates are dynamically encoded during these phases. Peak CS-evoked responses are correlated with expected value and predominate during early learning while a variance-correlated sustained CS signal develops during the post-asymptotic updating phase. Copyright © 2014 Elsevier Inc. All rights reserved.

Creative teaching method as a learning strategy for student midwives: A qualitative study.

PubMed

Rankin, Jean; Brown, Val

2016-03-01

Traditional ways of teaching in Higher Education are enhanced with adult-based approaches to learning within the curriculum. Adult-based learning enables students to take ownership of their own learning, working in independence using a holistic approach. Introducing creative activities promotes students to think in alternative ways to the traditional learning models. The study aimed to explore student midwives perceptions of a creative teaching method as a learning strategy. A qualitative design was used adopting a phenomenological approach to gain the lived experience of students within this learning culture. Purposive sampling was used to recruit student midwives (n=30). Individual interviews were conducted using semi-structured interviews with open-ended questions to gain subjective information. Data were transcribed and analyzed into useful and meaningful themes and emerging themes using Colaizzi's framework for analyzing qualitative data in a logical and systematic way. Over 500 meaningful statements were identified from the transcripts. Three key themes strongly emerged from the transcriptions. These included'meaningful learning','inspired to learn and achieve', and 'being connected'. A deep meaningful learning experience was found to be authentic in the context of theory and practice. Students were inspired to learn and achieve and positively highlighted the safe learning environment. The abilities of the facilitators were viewed positively in supporting student learning. This approach strengthened the relationships and social engagement with others in the peer group and the facilitators. On a less positive note, tensions and conflict were noted in group work and indirect negative comments about the approach from the teaching team. Incorporating creative teaching activities is a positive addition to the healthcare curriculum. Creativity is clearly an asset to the range of contemporary learning strategies. In doing so, higher education will continue to keep abreast of the needs of graduating students in a complex and rapidly changing professional environment. Copyright © 2015 Elsevier Ltd. All rights reserved.

Brain function during probabilistic learning in relation to IQ and level of education.

PubMed

van den Bos, Wouter; Crone, Eveline A; Güroğlu, Berna

2012-02-15

Knowing how to adapt your behavior based on feedback lies at the core of successful learning. We investigated the relation between brain function, grey matter volume, educational level and IQ in a Dutch adolescent sample. In total 45 healthy volunteers between ages 13 and 16 were recruited from schools for pre-vocational and pre-university education. For each individual, IQ was estimated using two subtests from the WISC-III-R (similarities and block design). While in the magnetic resonance imaging (MRI) scanner, participants performed a probabilistic learning task. Behavioral comparisons showed that participants with higher IQ used a more adaptive learning strategy after receiving positive feedback. Analysis of neural activation revealed that higher IQ was associated with increased activation in DLPFC and dACC when receiving positive feedback, specifically for rules with low reward probability (i.e., unexpected positive feedback). Furthermore, VBM analyses revealed that IQ correlated positively with grey matter volume within these regions. These results provide support for IQ-related individual differences in the developmental time courses of neural circuitry supporting feedback-based learning. Current findings are interpreted in terms of a prolonged window of flexibility and opportunity for adolescents with higher IQ scores. Copyright © 2011 Elsevier Ltd. All rights reserved.

Multi-agents and learning: Implications for Webusage mining.

PubMed

Lotfy, Hewayda M S; Khamis, Soheir M S; Aboghazalah, Maie M

2016-03-01

Characterization of user activities is an important issue in the design and maintenance of websites. Server weblog files have abundant information about the user's current interests. This information can be mined and analyzed therefore the administrators may be able to guide the users in their browsing activity so they may obtain relevant information in a shorter span of time to obtain user satisfaction. Web-based technology facilitates the creation of personally meaningful and socially useful knowledge through supportive interactions, communication and collaboration among educators, learners and information. This paper suggests a new methodology based on learning techniques for a Web-based Multiagent-based application to discover the hidden patterns in the user's visited links. It presents a new approach that involves unsupervised, reinforcement learning, and cooperation between agents. It is utilized to discover patterns that represent the user's profiles in a sample website into specific categories of materials using significance percentages. These profiles are used to make recommendations of interesting links and categories to the user. The experimental results of the approach showed successful user pattern recognition, and cooperative learning among agents to obtain user profiles. It indicates that combining different learning algorithms is capable of improving user satisfaction indicated by the percentage of precision, recall, the progressive category weight and F 1-measure.

Multi-agents and learning: Implications for Webusage mining

PubMed Central

Lotfy, Hewayda M.S.; Khamis, Soheir M.S.; Aboghazalah, Maie M.

2015-01-01

Characterization of user activities is an important issue in the design and maintenance of websites. Server weblog files have abundant information about the user’s current interests. This information can be mined and analyzed therefore the administrators may be able to guide the users in their browsing activity so they may obtain relevant information in a shorter span of time to obtain user satisfaction. Web-based technology facilitates the creation of personally meaningful and socially useful knowledge through supportive interactions, communication and collaboration among educators, learners and information. This paper suggests a new methodology based on learning techniques for a Web-based Multiagent-based application to discover the hidden patterns in the user’s visited links. It presents a new approach that involves unsupervised, reinforcement learning, and cooperation between agents. It is utilized to discover patterns that represent the user’s profiles in a sample website into specific categories of materials using significance percentages. These profiles are used to make recommendations of interesting links and categories to the user. The experimental results of the approach showed successful user pattern recognition, and cooperative learning among agents to obtain user profiles. It indicates that combining different learning algorithms is capable of improving user satisfaction indicated by the percentage of precision, recall, the progressive category weight and F1-measure. PMID:26966569

Assessing Students' Attitudes In A College Physics Course In Mexico

NASA Astrophysics Data System (ADS)

de la Garza, Jorge; Alarcon, Hugo

2010-10-01

Considering the benefits of modeling instruction in improving conceptual learning while students work more like scientists, an implementation was made in an introductory Physics course in a Mexican University. Recently Brewe, Kramer and O'Brien have observed positive attitudinal shifts using modeling instruction in a course with a reduced number of students. These results are opposite to previous observations with methodologies that promote active learning. Inspired in those results, the Colorado Learning Attitudes about Science Survey (CLASS) was applied as pre and post tests in two Mechanics courses with modeling. In comparison to the different categories of the CLASS, significant positive shifts have been determined in Overall, Sophistication in Problem Solving, and Applied Conceptual Understanding in a sample of 44 students.

A Preliminary Comparison of Motor Learning Across Different Non-invasive Brain Stimulation Paradigms Shows No Consistent Modulations.

PubMed

Lopez-Alonso, Virginia; Liew, Sook-Lei; Fernández Del Olmo, Miguel; Cheeran, Binith; Sandrini, Marco; Abe, Mitsunari; Cohen, Leonardo G

2018-01-01

Non-invasive brain stimulation (NIBS) has been widely explored as a way to safely modulate brain activity and alter human performance for nearly three decades. Research using NIBS has grown exponentially within the last decade with promising results across a variety of clinical and healthy populations. However, recent work has shown high inter-individual variability and a lack of reproducibility of previous results. Here, we conducted a small preliminary study to explore the effects of three of the most commonly used excitatory NIBS paradigms over the primary motor cortex (M1) on motor learning (Sequential Visuomotor Isometric Pinch Force Tracking Task) and secondarily relate changes in motor learning to changes in cortical excitability (MEP amplitude and SICI). We compared anodal transcranial direct current stimulation (tDCS), paired associative stimulation (PAS 25 ), and intermittent theta burst stimulation (iTBS), along with a sham tDCS control condition. Stimulation was applied prior to motor learning. Participants ( n = 28) were randomized into one of the four groups and were trained on a skilled motor task. Motor learning was measured immediately after training (online), 1 day after training (consolidation), and 1 week after training (retention). We did not find consistent differential effects on motor learning or cortical excitability across groups. Within the boundaries of our small sample sizes, we then assessed effect sizes across the NIBS groups that could help power future studies. These results, which require replication with larger samples, are consistent with previous reports of small and variable effect sizes of these interventions on motor learning.

A Preliminary Comparison of Motor Learning Across Different Non-invasive Brain Stimulation Paradigms Shows No Consistent Modulations

PubMed Central

Lopez-Alonso, Virginia; Liew, Sook-Lei; Fernández del Olmo, Miguel; Cheeran, Binith; Sandrini, Marco; Abe, Mitsunari; Cohen, Leonardo G.

2018-01-01

Non-invasive brain stimulation (NIBS) has been widely explored as a way to safely modulate brain activity and alter human performance for nearly three decades. Research using NIBS has grown exponentially within the last decade with promising results across a variety of clinical and healthy populations. However, recent work has shown high inter-individual variability and a lack of reproducibility of previous results. Here, we conducted a small preliminary study to explore the effects of three of the most commonly used excitatory NIBS paradigms over the primary motor cortex (M1) on motor learning (Sequential Visuomotor Isometric Pinch Force Tracking Task) and secondarily relate changes in motor learning to changes in cortical excitability (MEP amplitude and SICI). We compared anodal transcranial direct current stimulation (tDCS), paired associative stimulation (PAS25), and intermittent theta burst stimulation (iTBS), along with a sham tDCS control condition. Stimulation was applied prior to motor learning. Participants (n = 28) were randomized into one of the four groups and were trained on a skilled motor task. Motor learning was measured immediately after training (online), 1 day after training (consolidation), and 1 week after training (retention). We did not find consistent differential effects on motor learning or cortical excitability across groups. Within the boundaries of our small sample sizes, we then assessed effect sizes across the NIBS groups that could help power future studies. These results, which require replication with larger samples, are consistent with previous reports of small and variable effect sizes of these interventions on motor learning. PMID:29740271

Improving the interpersonal competences of head nurses through Peplau's theoretical active learning approach.

PubMed

Suhariyanto; Hariyati, Rr Tutik Sri; Ungsianik, Titin

2018-02-01

Effective interpersonal skills are essential for head nurses in governing and managing their work units. Therefore, an active learning strategy could be the key to enhance the interpersonal competences of head nurses. This study aimed to investigate the effects of Peplau's theoretical approach of active learning on the improvement of head nurses' interpersonal skills. This study used a pre-experimental design with one group having pretests and posttests, without control group. A total sample of 25 head nurses from inpatient units of a wellknown private hospital in Jakarta was involved in the study. Data were analyzed using the paired t-test. The results showed a significant increase in head nurses' knowledge following the training to strengthen their interpersonal roles (P=.003). The results also revealed significant increases in the head nurses' skills in playing the roles of leader (P=.006), guardian (P=.014), and teacher/speaker (P=.015). Nonetheless, the results showed no significant increases in the head nurses' skills in playing the roles of counselor (P=.092) and stranger (P=.182). Training in strengthening the interpersonal roles of head nurses significantly increased the head nurses' knowledge and skills. The results of the study suggested the continuation of active learning strategies to improve the interpersonal abilities of head nurses. Furthermore, these strategies could be used to build the abilities of head nurses in other managerial fields. Copyright © 2018 Elsevier España, S.L.U. All rights reserved.

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

Australasian emergency physicians: a learning and educational needs analysis. Part one: background and methodology. Profile of FACEM.

PubMed

Dent, Andrew W; Asadpour, Ali; Weiland, Tracey J; Paltridge, Debbie

2008-02-01

Fellows of the Australasian College for Emergency Medicine (FACEM) have opportunities to participate in a range of continuing professional development activities. To inform FACEM and assist those involved in planning continuing professional development interventions for FACEM, we undertook a learning needs analysis of emergency physicians. Exploratory study using survey methodology. Following questionnaire development by iterative feedback with emergency physicians and researchers, a mailed survey was distributed to all FACEM. The survey comprised eight items on work and demographic characteristics of FACEM, and 194 items on attitudes to existing learning opportunities, barriers to learning, and perceived learning needs and preferences. Fifty-eight percent (503/854) of all FACEM surveyed responded to the questionnaire, almost half of whom attained their FACEM after year 2000. The sample comprised mostly males (72.8%) with mean age of the sample 41.6 years, similar to ACEM database. Most respondents reported working in ACEM accredited hospitals (89%), major referral hospitals (54%), and practiced on both children and adults (78%). FACEM reported working on average 26.7 clinical hours per week with those at private hospitals working a greater proportion of clinical hours than other hospital types. As the first of six related reports, this paper documents the methodology used, including questionnaire development, and provides the demographics of responding FACEM, including the clinical and non-clinical hours worked and type of hospital of principal employment.

Tracking Active Learning in the Medical School Curriculum: A Learning-Centered Approach.

PubMed

McCoy, Lise; Pettit, Robin K; Kellar, Charlyn; Morgan, Christine

2018-01-01

Medical education is moving toward active learning during large group lecture sessions. This study investigated the saturation and breadth of active learning techniques implemented in first year medical school large group sessions. Data collection involved retrospective curriculum review and semistructured interviews with 20 faculty. The authors piloted a taxonomy of active learning techniques and mapped learning techniques to attributes of learning-centered instruction. Faculty implemented 25 different active learning techniques over the course of 9 first year courses. Of 646 hours of large group instruction, 476 (74%) involved at least 1 active learning component. The frequency and variety of active learning components integrated throughout the year 1 curriculum reflect faculty familiarity with active learning methods and their support of an active learning culture. This project has sparked reflection on teaching practices and facilitated an evolution from teacher-centered to learning-centered instruction.

Tracking Active Learning in the Medical School Curriculum: A Learning-Centered Approach

PubMed Central

McCoy, Lise; Pettit, Robin K; Kellar, Charlyn; Morgan, Christine

2018-01-01

Background: Medical education is moving toward active learning during large group lecture sessions. This study investigated the saturation and breadth of active learning techniques implemented in first year medical school large group sessions. Methods: Data collection involved retrospective curriculum review and semistructured interviews with 20 faculty. The authors piloted a taxonomy of active learning techniques and mapped learning techniques to attributes of learning-centered instruction. Results: Faculty implemented 25 different active learning techniques over the course of 9 first year courses. Of 646 hours of large group instruction, 476 (74%) involved at least 1 active learning component. Conclusions: The frequency and variety of active learning components integrated throughout the year 1 curriculum reflect faculty familiarity with active learning methods and their support of an active learning culture. This project has sparked reflection on teaching practices and facilitated an evolution from teacher-centered to learning-centered instruction. PMID:29707649

Quasi-experimental study on the effectiveness of a flipped classroom for teaching adult health nursing.

PubMed

Park, Esther O; Park, Ji Hyun

2018-04-01

The effectiveness of flipped learning as one of the teaching methods of active learning has been left unexamined in nursing majors, compared to the frequent attempts to uncover the effectiveness of it in other disciplines. The purpose of this study was to reveal the effectiveness of flipped learning pedagogy in an adult health nursing course, controlling for other variables. The study applied a quasi-experimental approach, comparing pre- and post-test results in learning outcomes. Included in this analysis were the records of 81 junior nursing major students. The convenience sampling method was used to select the participants. Those in the experimental group were exposed to a flipped classroom experience that was given after the completion of their traditional class. The students' learning outcomes and the level of critical thinking skills were evaluated before and after the intervention of the flipped classroom. After the flipped classroom experience, the scores of the students' achievement in subject topics and critical thinking skills, specifically intellectual integrity and creativity, showed a greater level of increase than those of their controlled counterparts. This remained true even after controlling for previous academic performance and the level of creativity. This study confirmed the effectiveness of the flipped classroom as a measure of active learning by applying a quantitative approach. But, regarding the significance of the initial contribution of flipped learning in the discipline of nursing science, carrying out a more authentic experimental study could justify the impact of flipped learning pedagogy. © 2017 Japan Academy of Nursing Science.

Piaget and Organic Chemistry: Teaching Introductory Organic Chemistry through Learning Cycles

NASA Astrophysics Data System (ADS)

Libby, R. Daniel

1995-07-01

This paper describes the first application of the Piaget-based learning cycle technique (Atkin & Karplus, Sci. Teach. 1962, 29, 45-51) to an introductory organic chemistry course. It also presents the step-by-step process used to convert a lecture course into a discussion-based active learning course. The course is taught in a series of learning cycles. A learning cycle is a three phase process that provides opportunities for students to explore new material and work with an instructor to recognize logical patterns in data, and devise and test hypotheses. In this application, the first phase, exploration, involves out-of-class student evaluation of data in attempts to identify significant trends and develop hypotheses that might explain the trends in terms of fundamental scientific principles. In the second phase, concept invention, the students and instructor work together in-class to evaluate student hypotheses and find concepts that work best in explaining the data. The third phase, application, is an out-of-class application of the concept to new situations. The development of learning cycles from lecture notes is presented as an 8 step procedure. The process involves revaluation and restructuring of the course material to maintain a continuity of concept development according to the instructor's logic, dividing topics into individual concepts or techniques, and refocusing the presentation in terms of large numbers of examples that can serve as data for students in their exploration and application activities. A sample learning cycle and suggestions for ways of limited implementation of learning cycles into existing courses are also provided.

Perceptions of Teaching Methods for Preclinical Oral Surgery: A Comparison with Learning Styles

PubMed Central

Omar, Esam

2017-01-01

Purpose: Dental extraction is a routine part of clinical dental practice. For this reason, understanding the way how students’ extraction knowledge and skills development are important. Problem Statement and Objectives: To date, there is no accredited statement about the most effective method for the teaching of exodontia to dental students. Students have different abilities and preferences regarding how they learn and process information. This is defined as learning style. In this study, the effectiveness of active learning in the teaching of preclinical oral surgery was examined. The personality type of the groups involved in this study was determined, and the possible effect of personality type on learning style was investigated. Method: This study was undertaken over five years from 2011 to 2015. The sample consisted of 115 students and eight staff members. Questionnaires were submitted by 68 students and all eight staff members involved. Three measures were used in the study: The Index of Learning Styles (Felder and Soloman, 1991), the Myers-Briggs Type Indicator (MBTI), and the styles of learning typology (Grasha and Hruska-Riechmann). Results and Discussion: Findings indicated that demonstration and minimal clinical exposure give students personal validation. Frequent feedback on their work is strongly indicated to build the cognitive, psychomotor, and interpersonal skills needed from preclinical oral surgery courses. Conclusion: Small group cooperative active learning in the form of demonstration and minimal clinical exposure that gives frequent feedback and students’ personal validation on their work is strongly indicated to build the skills needed for preclinical oral surgery courses. PMID:28357004

Examining the Efficacy of the Modified Story Memory Technique (mSMT) in Persons With TBI Using Functional Magnetic Resonance Imaging (fMRI): The TBI-MEM Trial.

PubMed

Chiaravalloti, Nancy D; Dobryakova, Ekaterina; Wylie, Glenn R; DeLuca, John

2015-01-01

New learning and memory deficits are common following traumatic brain injury (TBI). Yet few studies have examined the efficacy of memory retraining in TBI through the most methodologically vigorous randomized clinical trial. Our previous research has demonstrated that the modified Story Memory Technique (mSMT) significantly improves new learning and memory in multiple sclerosis. The present double-blind, placebo-controlled, randomized clinical trial examined changes in cerebral activation on functional magnetic resonance imaging following mSMT treatment in persons with TBI. Eighteen individuals with TBI were randomly assigned to treatment (n = 9) or placebo (n = 9) groups. Baseline and follow-up functional magnetic resonance imaging was collected during a list-learning task. Significant differences in cerebral activation from before to after treatment were noted in regions belonging to the default mode network and executive control network in the treatment group only. Results are interpreted in light of these networks. Activation differences between the groups likely reflect increased use of strategies taught during treatment. This study demonstrates a significant change in cerebral activation resulting from the mSMT in a TBI sample. Findings are consistent with previous work in multiple sclerosis. Behavioral interventions can show significant changes in the brain, validating clinical utility.

Registered nurses' perception of self-efficacy and competence in smoking cessation after participating in a web-based learning activity.

PubMed

Rosvall, Annica; Carlson, Elisabeth

2017-12-01

To describe how registered nurses having undergone a web-based learning activity perceive their self-efficacy and competence to support patients with smoking cessation in connection with surgery. Smoking cessation in connection with surgery reduces postoperative complications, and the support patients get from registered nurses may be important in helping them become smoke-free in connection with their surgery. Therefore, registered nurses are in need of enhanced understanding about which kind of counselling is the most effective for smoking cessation. Educating large groups of registered nurses in a digital environment appears to be a flexible and cost-effective way. A convergent mixed-method design with data collection was done using questionnaires (n = 47) and semistructured interviews (n = 11). Inclusion criteria were registered nurses in surgical wards. The samples were nonprobability and modified nested. Descriptive statistics and content analysis were used for data analysis. After completing the web-based learning activity, the registered nurses perception was that of good self-efficacy and increased competence in supporting patients with smoking cessation in connection with surgery. They improved their understanding of how to talk about smoking cessation with patients in dialogue using open-ended questions. Nevertheless, the registered nurses requested opportunities for dialogue and interaction with colleagues or topic experts. The results indicate that registered nurses can enhance their competence in supporting patients to embrace smoking cessation by learning in a digital environment. Self-efficacy and understanding of the topic seems to motivate registered nurses to counsel patients about smoking cessation. Findings from this study will be of particular interest to educators in healthcare settings who can devise further development of web-based learning activities. © 2017 John Wiley & Sons Ltd.

Learning Strategies in Matching to Sample: If-then and Configural Learning by Pigeons

PubMed Central

Katz, Jeffrey S.; Bodily, Kent D.; Wright, Anthony A.

2008-01-01

Pigeons learned a matching-to-sample task with a split training-set design in which half of the stimulus displays were untrained and tested following acquisition. Transfer to the untrained displays along with no novel-stimulus transfer indicated that these pigeons learned the task (partially) via if-then rules. Comparisons to other performance measures indicated that they also partially learned the task via configural learning (learning the gestalt of the whole stimulus display). Differences in the FR-sample requirement (1 vs. 20) had no systematic effect on the type of learning or level of learning obtained. Differences from a previous study (Wright, 1997) are discussed, including the effect of displaying the stimuli vertically (traditional display orientation) or horizontally from the floor. PMID:18079071

An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

2015-01-01

Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

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 MNO problem: 1) hierarchical top-down clustering in an input space in order to remove redundancy when data are clustered, and 2) a general method (independent on classifier) which gives posterior probabilities that can be used to define the classifier confidence and corresponding proposals for new measurement points. The basic ideas and procedures are explained by applying simulated data sets. The real case study deals with the analysis and mapping of soil types, which is a multi-class classification problem. Maps of soil types are important for the analysis and 3D modeling of heavy metals migration in soil and prediction risk mapping. The results obtained demonstrate the high quality of SVM mapping and efficiency of monitoring network optimization by using active learning approaches. The research was partly supported by SNSF projects No. 200021-126505 and 200020-121835.

Biomimetic Sniffing Improves the Detection Performance of a 3D Printed Nose of a Dog and a Commercial Trace Vapor Detector

NASA Astrophysics Data System (ADS)

Staymates, Matthew E.; Maccrehan, William A.; Staymates, Jessica L.; Kunz, Roderick R.; Mendum, Thomas; Ong, Ta-Hsuan; Geurtsen, Geoffrey; Gillen, Greg J.; Craven, Brent A.

2016-12-01

Unlike current chemical trace detection technology, dogs actively sniff to acquire an odor sample. Flow visualization experiments with an anatomically-similar 3D printed dog’s nose revealed the external aerodynamics during canine sniffing, where ventral-laterally expired air jets entrain odorant-laden air toward the nose, thereby extending the “aerodynamic reach” for inspiration of otherwise inaccessible odors. Chemical sampling and detection experiments quantified two modes of operation with the artificial nose-active sniffing and continuous inspiration-and demonstrated an increase in odorant detection by a factor of up to 18 for active sniffing. A 16-fold improvement in detection was demonstrated with a commercially-available explosives detector by applying this bio-inspired design principle and making the device “sniff” like a dog. These lessons learned from the dog may benefit the next-generation of vapor samplers for explosives, narcotics, pathogens, or even cancer, and could inform future bio-inspired designs for optimized sampling of odor plumes.

Biomimetic Sniffing Improves the Detection Performance of a 3D Printed Nose of a Dog and a Commercial Trace Vapor Detector

PubMed Central

Staymates, Matthew E.; MacCrehan, William A.; Staymates, Jessica L.; Kunz, Roderick R.; Mendum, Thomas; Ong, Ta-Hsuan; Geurtsen, Geoffrey; Gillen, Greg J.; Craven, Brent A.

2016-01-01

Unlike current chemical trace detection technology, dogs actively sniff to acquire an odor sample. Flow visualization experiments with an anatomically-similar 3D printed dog’s nose revealed the external aerodynamics during canine sniffing, where ventral-laterally expired air jets entrain odorant-laden air toward the nose, thereby extending the “aerodynamic reach” for inspiration of otherwise inaccessible odors. Chemical sampling and detection experiments quantified two modes of operation with the artificial nose-active sniffing and continuous inspiration-and demonstrated an increase in odorant detection by a factor of up to 18 for active sniffing. A 16-fold improvement in detection was demonstrated with a commercially-available explosives detector by applying this bio-inspired design principle and making the device “sniff” like a dog. These lessons learned from the dog may benefit the next-generation of vapor samplers for explosives, narcotics, pathogens, or even cancer, and could inform future bio-inspired designs for optimized sampling of odor plumes. PMID:27906156

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.

Prefrontal electroencephalographic activity during the working memory processes involved in a sexually motivated task in male rats.

PubMed

Hernández-González, Marisela; Almanza-Sepúlveda, Mayra Linné; Olvera-Cortés, María Esther; Gutiérrez-Guzmán, Blanca Erika; Guevara, Miguel Angel

2012-08-01

The prefrontal cortex is involved in working memory functions, and several studies using food or drink as rewards have demonstrated that the rat is capable of performing tasks that involve working memory. Sexual activity is another highly-rewarding, motivated behaviour that has proven to be an efficient incentive in classical operant tasks. The objective of this study was to determine whether the functional activity of the medial prefrontal cortex (mPFC) changes in relation to the working memory processes involved in a sexually motivated task performed in male rats. Thus, male Wistar rats implanted in the mPFC were subjected to a nonmatching-to-sample task in a T-maze using sexual interaction as a reinforcer during a 4-day training period. On the basis of their performance during training, the rats were classified as 'good-learners' or 'bad-learners'. Only the good-learner rats showed an increase in the absolute power of the 8-13 Hz band during both the sample and test runs; a finding that could be related to learning of the working memory elements entailed in the task. During the maintenance phase only (i.e., once the rule had been learned well), the good-learner rats also showed an increased correlation of the 8-13 Hz band during the sample run, indicating that a high degree of coupling between the prefrontal cortices is necessary for the processing required to allow the rats to make correct decisions in the maintenance phase. Taken together, these data show that mPFC activity changes in relation to the working memory processes involved in a sexually motivated task in male rats.

[Multifamily therapy in children with learning disabilities].

PubMed

Retzlaff, Rüdiger; Brazil, Susanne; Goll-Kopka, Andrea

2008-01-01

Multifamily therapy is an evidence-based method used in the treatment and prevention of severe psychiatric disorders, behavioral problems and physical illnesses in children, adolescents and adults. For preventive family-oriented work with children with learning disorders there is a lack of therapeutic models. This article presents results from an innovative pilot project--multiple family groups for families with a learning disabled child of primary school age (six to eleven years old). Based on a systemic approach, this resource-oriented program integrates creative, activity-based interventions and group therapy techniques and conveys a comprehensive understanding of the challenges associated with learning disorders. Because of the pilot character of the study and the small sample size, the results have to be interpreted with care. The results do however clearly support the wider implementation and evaluation of the program in child guidance clinics, social-pediatric centers, as well as child and adolescent clinics and schools.

Reflexive photography: an alternative method for documenting the learning process of cultural competence.

PubMed

Amerson, Roxanne; Livingston, Wade G

2014-04-01

This qualitative descriptive study used reflexive photography to evaluate the learning process of cultural competence during an international service-learning project in Guatemala. Reflexive photography is an innovative qualitative research technique that examines participants' interactions with their environment through their personal reflections on images that they captured during their experience. A purposive sample of 10 baccalaureate nursing students traveled to Guatemala, where they conducted family and community assessments, engaged in home visits, and provided health education. Data collection involved over 100 photographs and a personal interview with each student. The themes developed from the photographs and interviews provided insight into the activities of an international experience that influence the cognitive, practical, and affective learning of cultural competence. Making home visits and teaching others from a different culture increased students' transcultural self-efficacy. Reflexive photography is a more robust method of self-reflection, especially for visual learners.

Millennial generation student nurses' perceptions of the impact of multiple technologies on learning.

PubMed

Montenery, Susan M; Walker, Marjorie; Sorensen, Elizabeth; Thompson, Rhonda; Kirklin, Dena; White, Robin; Ross, Carl

2013-01-01

To determine how millennial nursing students perceive the effects of instructional technology on their attentiveness, knowledge, critical thinking, and satisfaction. BACKGROUND Millennial learners develop critical thinking through experimentation, active participation, and multitasking with rapid shifts between technological devices. They desire immediate feedback. METHOD; A descriptive, longitudinal, anonymous survey design was used with a convenience sample of 108 sophomore, junior, and senior baccalaureate nursing students (participation rates 95 percent, winter, 85 percent, spring). Audience response, virtual learning, simulation, and computerized testing technologies were used. An investigator-designed instrument measured attentiveness, knowledge, critical thinking, and satisfaction (Cronbach's alphas 0.73, winter; 0.84, spring). Participants positively rated the audience response, virtual learning, and simulation instructional technologies on their class participation, learning, attention, and satisfaction. They strongly preferred computerized testing. Consistent with other studies, these students engaged positively with new teaching strategies using contemporary instructional technology. Faculty should consider using instructional technologies.

The Home Language Environment of Monolingual and Bilingual Children and Their Language Proficiency

ERIC Educational Resources Information Center

Scheele, Anna F.; Leseman, Paul P. M.; Mayo, Aziza Y.

2010-01-01

This study investigated the relationships between home language learning activities and vocabulary in a sample of monolingual native Dutch (n = 58) and bilingual immigrant Moroccan-Dutch (n = 46) and Turkish-Dutch (n = 55) 3-year-olds, speaking Tarifit-Berber, a nonscripted language, and Turkish as their first language (L1), respectively. Despite…

Teacher Opinions on Ministry of National Education 2002, 2006, and 2013 Preschool Education Curricula in Turkey

ERIC Educational Resources Information Center

Durmusoglu, Mine Canan

2017-01-01

This study was aimed to make a historical review by collecting and comparing teachers' opinions on target-behaviors/learning-objectives outcomes, content, plans, activities, practices and assessment of the Ministry of National Education (MoNE), Turkey 2002, 2006, and 2013 preschool education curricula (PEC) in six categories. The sample group of…

Barriers to Application of E-Learning in Training Activities of SMEs

ERIC Educational Resources Information Center

Anderson, Randy J.; Wielicki, Tomasz; Anderson, Lydia E.

2010-01-01

This paper reports on the on-going study of Small and Mid-Size Enterprises (SMEs) in the Central California concerning their use of Information and Communication Technology (ICT). This research project analyzed data from a sample of 161 SMEs. Specifically, this part of the study is investigating the major barriers to applications of e-learning…

For the Love of the Game: Game- Versus Lecture-Based Learning With Generation Z Patients.

PubMed

Adamson, Mary A; Chen, Hengyi; Kackley, Russell; Micheal, Alicia

2018-02-01

The current study evaluated adolescent patients' enjoyment of and knowledge gained from game-based learning compared with an interactive lecture format on the topic of mood disorders. It was hypothesized that game-based learning would be statistically more effective than a lecture in knowledge acquisition and satisfaction scores. A pre-post design was implemented in which a convenience sample of 160 adolescent patients were randomized to either a lecture (n = 80) or game-based (n = 80) group. Both groups completed a pretest/posttest and satisfaction survey. Results showed that both groups had significant improvement in knowledge from pretest compared to posttest. Game-based learning was statistically more effective than the interactive lecture in knowledge achievement and satisfaction scores. This finding supports the contention that game-based learning is an active technique that may be used with patient education. [Journal of Psychosocial Nursing and Mental Health Services, 56(2), 29-36.]. Copyright 2018, SLACK Incorporated.

The development of a questionnaire to measure students' motivation towards science learning

NASA Astrophysics Data System (ADS)

Tuan, Hsiao-Lin; Chin, Chi-Chin; Shieh, Shyang-Horng

2005-06-01

The purpose of this study was to develop a questionnaire that measures students' motivation toward science learning (SMTSL). Six scales were developed: self-efficacy, active learning strategies, science learning value, performance goal, achievement goal, and learning environment stimulation. In total, 1407 junior high school students from central Taiwan, varying in grades, sex, and achievements, were selected by stratified random sampling to respond to the questionnaire. The Cronbach alpha for the entire questionnaire was 0.89; for each scale, alpha ranged from 0.70 to 0.89. There were significant correlations (p?<?0.01) of the SMTSL questionnaire with students' science attitudes (r?=?0.41), and with the science achievement test in previous and current semesters (rp?=?0.40 and rc?=?0.41). High motivators and low motivators showed a significant difference (p?<?0.01) on their SMTSL scores. Findings of the study confirmed the validity and reliability of the SMTSL questionnaire. Implications for using the SMTSL questionnaire in research and in class are discussed in the paper.

Enhancing Earth Science And IT Literacy Through Environmental Science Information Technology Activities

NASA Astrophysics Data System (ADS)

Cuff, K. E.; Molinaro, M.

2004-12-01

The Environmental Science Information Technology Activities (ESITA) program provides grades 9 and 10 students with under-represented minority backgrounds in the East San Francisco Bay Area with real-world opportunities to learn about and apply information technologies through a series of project-based activities related to environmental science. Supported by the NSF Information Technology Experiences for Students and Teachers (ITEST) program, ESITA activities engage students in the use of newly acquired information technology (IT) skills and understandings while performing air and water quality research investigations. One project that ESITA students have become involved in relates to the currently relevant issue of elevated levels of lead found in drinking waters in Washington, D.C. Students based in the Bay Area have initiated and maintained E-mail correspondence with children who attend elementary schools in the D.C. area. After receiving a thorough explanation of required sampling procedures devised by the Bay Area students, the elementary school children have sent 500 ml water samples from their homes and schools to Berkeley along with information about the locations from which the water samples were collected. These samples were then prepared for lead analysis at Lawrence Hall of Science by ESITA students, who used resulting data to perform a preliminary assessment of the geospatial distribution of lead trouble spots throughout Washington, DC. Later, ESITA student scientists will work with students from the UC Berkeley School of Public Health to develop surveys and questionnaires that generate high quality information useful with regard to assessing the impact of the current lead crisis on younger children in the Washington, D.C. area. Through the application of new understandings to current, real-world environmental problems and issues such as that related to lead, positive changes in students' attitudes towards IT and science have occurred, which accompany increases in their content learning and skills acquisition abilities.

Home Care Learning Model for Medical Students in Chile: A Mixed Methods Study

PubMed Central

Gonzalez, Carolina

2014-01-01

Introduction. The relevance of home care training is not questioned. However, there are no reported learning models to teach in this setting. Aims. To develop and evaluate a learning model to teach home care to medical students. Methods. Stage 1: Learning Model Design. Tutors teaching home care and a sample of medical students were invited to focus groups analyzed according to the grounded theory. Later, the researchers designed the learning model, which was approved by all participants. Stage 2: Learning Assessment. All students in their family medicine internship at Pontificia Universidad Catolica de Chile were invited to participate in a nonrandomized before-and-after pilot trial, assessing changes in their perception towards home care and satisfaction with the learning model. Results. Stage 1: Six tutors and eight students participated in the focus groups. The learning model includes activities before, during, and after the visits. Stage 2: 105 students (88.2%) participated. We observed improvement in all home care training domains (P ≤ 0.001) and a high satisfaction with the model. Students with previous home visit experiences and who participated with nurses and social workers reported more learning. Conclusions. We report an effective learning model to train medical students in home care. Limitations and recommendations for future studies are discussed. PMID:24967327

Impact of a learning circle intervention across academic and service contexts on developing a learning culture.

PubMed

Walker, Rachel; Henderson, Amanda; Cooke, Marie; Creedy, Debra

2011-05-01

Partnerships between university schools of nursing and health services lead to successful learning experiences for students and staff. A purposive sample of academics and students from a university school of nursing and clinicians from three health institutions involved in clinical learning (n=73) actively participated in a learning circles intervention conducted over 5 months in south east Queensland. Learning circle discussions resulted in enhanced communication and shared understanding regarding: (1) staff attitudes towards students, expectations and student assessment; (2) strategies enhancing preparation of students, mechanisms for greater support of and recognition of clinicians; (3) challenges faced by staff in the complex processes of leadership in clinical nursing education; (4) construction of learning, ideas for improving communication, networking and sharing; and (5) questioning routine practices that may not enhance student learning. Pre-post surveys of hospital staff (n=310) revealed significant differences across three sub-scales of 'accomplishment' (t=-3.98, p<.001), 'recognition' (t=-2.22, p<.027) and 'influence' (t=-11.82, p<.001) but not 'affiliation'. Learning circles can positively enhance organisational learning culture. The intervention enabled participants to recognise mutual goals. Further investigation around staff perception of their influence on their workplace is required. Copyright © 2010 Elsevier Ltd. All rights reserved.

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

C-MORE Science Kits: Putting Technology in the Hands of K-12 Teachers and Students

NASA Astrophysics Data System (ADS)

Achilles, K.; Weersing, K.; Daniels, C.; Puniwai, N.; Matsuzaki, J.; Bruno, B. C.

2008-12-01

The Center for Microbial Oceanography: Research and Education (C-MORE) is a NSF Science and Technology Center based at the University of Hawaii. The C-MORE education and outreach program offers a variety of resources and professional development opportunities for science educators, including online resources, participation in oceanography research cruises, teacher-training workshops, mini-grants to incorporate microbial oceanography-related content and activities into their classroom and, most recently, C- MORE science kits. C-MORE science kits provide hands-on classroom, field, and laboratory activities related to microbial oceanography for K-12 students. Each kit comes with complete materials and instructions, and is available free of charge to Hawaii's public school teachers. Several kits are available nationwide. C-MORE science kits cover a range of topics and technologies and are targeted at various grade levels. Here is a sampling of some available kits: 1) Marine Murder Mystery: The Case of the Missing Zooxanthellae. Students learn about the effect of climate change and other environmental threats on coral reef destruction through a murder-mystery experience. Participants also learn how to use DNA to identify a suspect. Grades levels: 3-8. 2) Statistical sampling. Students learn basic statistics through an exercise in random sampling, with applications to microbial oceanography. The laptops provided with this kit enable students to enter, analyze, and graph their data using EXCEL. Grades levels: 6-12. 3) Chlorophyll Lab. A research-quality fluorometer is used to measure the chlorophyll content in marine and freshwater systems. This enables students to compare biomass concentrations in samples collected from various locations. Grades levels: 9-12. 4) Conductivity-Temperature-Depth (CTD). Students predict how certain variables (e.g., temperature, pressure, chlorophyll, oxygen) vary with depth. A CTD, attached to a laptop computer, is deployed into deep water off a dock or a ship to collect real-time data and test their hypotheses. Grades levels: 9-12.

Generalization bounds of ERM-based learning processes for continuous-time Markov chains.

PubMed

Zhang, Chao; Tao, Dacheng

2012-12-01

Many existing results on statistical learning theory are based on the assumption that samples are independently and identically distributed (i.i.d.). However, the assumption of i.i.d. samples is not suitable for practical application to problems in which samples are time dependent. In this paper, we are mainly concerned with the empirical risk minimization (ERM) based learning process for time-dependent samples drawn from a continuous-time Markov chain. This learning process covers many kinds of practical applications, e.g., the prediction for a time series and the estimation of channel state information. Thus, it is significant to study its theoretical properties including the generalization bound, the asymptotic convergence, and the rate of convergence. It is noteworthy that, since samples are time dependent in this learning process, the concerns of this paper cannot (at least straightforwardly) be addressed by existing methods developed under the sample i.i.d. assumption. We first develop a deviation inequality for a sequence of time-dependent samples drawn from a continuous-time Markov chain and present a symmetrization inequality for such a sequence. By using the resultant deviation inequality and symmetrization inequality, we then obtain the generalization bounds of the ERM-based learning process for time-dependent samples drawn from a continuous-time Markov chain. Finally, based on the resultant generalization bounds, we analyze the asymptotic convergence and the rate of convergence of the learning process.

Low-Rank Tensor Subspace Learning for RGB-D Action Recognition.

PubMed

Jia, Chengcheng; Fu, Yun

2016-07-09

Since RGB-D action data inherently equip with extra depth information compared with RGB data, recently many works employ RGB-D data in a third-order tensor representation containing spatio-temporal structure to find a subspace for action recognition. However, there are two main challenges of these methods. First, the dimension of subspace is usually fixed manually. Second, preserving local information by finding intraclass and inter-class neighbors from a manifold is highly timeconsuming. In this paper, we learn a tensor subspace, whose dimension is learned automatically by low-rank learning, for RGB-D action recognition. Particularly, the tensor samples are factorized to obtain three Projection Matrices (PMs) by Tucker Decomposition, where all the PMs are performed by nuclear norm in a close-form to obtain the tensor ranks which are used as tensor subspace dimension. Additionally, we extract the discriminant and local information from a manifold using a graph constraint. This graph preserves the local knowledge inherently, which is faster than the previous way by calculating both the intra-class and inter-class neighbors of each sample. We evaluate the proposed method on four widely used RGB-D action datasets including MSRDailyActivity3D, MSRActionPairs, MSRActionPairs skeleton and UTKinect-Action3D datasets, and the experimental results show higher accuracy and efficiency of the proposed method.

Analysis of student attitudes towards e-learning using Fishbein Multiattribute approach

NASA Astrophysics Data System (ADS)

Jasuli

2018-01-01

This research aimed to know students’ attitudes toward e-learning and to determine what attributes were considered to be dominant by students toward the use of e-learning. The research population was all postgraduate students in the 2016 academic year at Universitas Negeri Surabaya. The sampling technique is using nonprobability sampling and purposive sampling with the sample totaled 100 respondents. The research instrument is using questionnaire with semantic differential scale. The models used to analyze is multi-attribute Fishbein model. The findings indicated that student attitudes toward e-learning are positive and easy accessibility which is considered as the most important attribute by students toward the use of e-learning.

Learning to actively cope with stress in female mice.

PubMed

Lyons, David M; Buckmaster, Christine L; Schatzberg, Alan F

2018-06-09

Repeated exposure to a same-sex resident stranger enhances subsequent indications of active coping that generalize across multiple contexts in intruder male mice. Here we investigate female mice for comparable learning to cope training effects. Stress coping research focused on females is important because stress related mood and anxiety disorders are more prevalent in women than men. Female mice were monitored for coping behavior in open-field, object-exploration, and tail-suspension tests conducted after repeated exposure to a same-sex resident stranger. During repeated exposure sessions of training staged in the resident's home cage, behavioral measures of aggression and risk assessment were collected and plasma measures of the stress hormone corticosterone were obtained from separate samples of mice. Repeated exposure to a same-sex resident stranger subsequently enhanced active coping behavior exemplified by diminished freezing and increased center entries in the open-field, shorter object-exploration latencies, and a tendency toward decreased immobility on tail-suspension tests. Open-field locomotion considered as an index of non-specific activity was not increased by repeated sessions of exposure and did not correlate significantly with any measure of active coping. During repeated sessions of exposure to a same-sex resident stranger, risk assessment behavior and consistent but limited aggression occurred and corticosterone responses increased over repeated sessions. Exposure to a same-sex resident stranger is mildly stressful and promotes learning to actively cope in mice assessed in three different contexts. Copyright © 2018. Published by Elsevier Ltd.

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

PubMed

Young, Jonathan D; Cai, Chunhui; Lu, Xinghua

2017-10-03

One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data. Deep learning is a group of machine learning algorithms that use multiple layers of hidden units to capture hierarchically related, alternative representations of the input data. We hypothesize that this hierarchical structure learned by deep learning will be related to the cellular signaling system. Robust deep learning model selection identified a network architecture that is biologically plausible. Our model selection results indicated that the 1st hidden layer of our deep learning model should contain about 1300 hidden units to most effectively capture the covariance structure of the input data. This agrees with the estimated number of human transcription factors, which is approximately 1400. This result lends support to our hypothesis that the 1st hidden layer of a deep learning model trained on gene expression data may represent signals related to transcription factor activation. Using the 3rd hidden layer representation of each tumor as learned by our unsupervised deep learning model, we performed consensus clustering on all tumor samples-leading to the discovery of clusters of glioblastoma multiforme with differential survival. One of these clusters contained all of the glioblastoma samples with G-CIMP, a known methylation phenotype driven by the IDH1 mutation and associated with favorable prognosis, suggesting that the hidden units in the 3rd hidden layer representations captured a methylation signal without explicitly using methylation data as input. We also found differentially expressed genes and well-known mutations (NF1, IDH1, EGFR) that were uniquely correlated with each of these clusters. Exploring these unique genes and mutations will allow us to further investigate the disease mechanisms underlying each of these clusters. In summary, we show that a deep learning model can be trained to represent biologically and clinically meaningful abstractions of cancer gene expression data. Understanding what additional relationships these hidden layer abstractions have with the cancer cellular signaling system could have a significant impact on the understanding and treatment of cancer.

Analysis of dermatology resident self-reported successful learning styles and implications for core competency curriculum development.

PubMed

Stratman, Erik J; Vogel, Curt A; Reck, Samuel J; Mukesh, Bickol N

2008-01-01

There are different teaching styles for delivering competency-based curricula. The education literature suggests that learning is maximized when teaching is delivered in a style preferred by learners. To determine if dermatology residents report learning style preferences aligned with adult learning. Dermatology residents attending an introductory cutaneous biology course completed a learning styles inventory assessing self-reported success in 35 active and passive learning activities. The 35 learning activities were ranked in order of preference by learners. Mean overall ratings for active learning activities were significantly higher than for passive learning activities (P = 0.002). Trends in dermatology resident learning style preferences should be considered during program curriculum development. Programs should integrate a variety of curriculum delivery methods to accommodate various learning styles, with an emphasis on the active learning styles preferred by residents.

The contributions of digital technologies in the teaching of nursing skills: an integrative review.

PubMed

Silveira, Maurício de Souza; Cogo, Ana Luísa Petersen

2017-07-13

To analyze the contributions of digital educational technologies used in teaching nursing skills. Integrative literature review, search in five databases, from 2006 to 2015 combining the descriptors 'education, nursing', 'educational technology', 'computer-assisted instruction' or related terms in English. Sample of 30 articles grouped in the thematic categories 'technology in the simulation with manikin', 'incentive to learning' and 'teaching of nursing skills'. It was identified different formats of digital educational technologies used in teaching Nursing skills such as videos, learning management system, applications, hypertext, games, virtual reality simulators. These digital materials collaborated in the acquisition of theoretical references that subsidize the practices, enhancing the teaching and enable the use of active learning methods, breaking with the traditional teaching of demonstrating and repeating procedures.

New insights into olivo-cerebellar circuits for learning from a small training sample.

PubMed

Tokuda, Isao T; Hoang, Huu; Kawato, Mitsuo

2017-10-01

Artificial intelligence such as deep neural networks exhibited remarkable performance in simulated video games and 'Go'. In contrast, most humanoid robots in the DARPA Robotics Challenge fell down to ground. The dramatic contrast in performance is mainly due to differences in the amount of training data, which is huge and small, respectively. Animals are not allowed with millions of the failed trials, which lead to injury and death. Humans fall only several thousand times before they balance and walk. We hypothesize that a unique closed-loop neural circuit formed by the Purkinje cells, the cerebellar deep nucleus and the inferior olive in and around the cerebellum and the highest density of gap junctions, which regulate synchronous activities of the inferior olive nucleus, are computational machinery for learning from a small sample. We discuss recent experimental and computational advances associated with this hypothesis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Performance of Activity Classification Algorithms in Free-living Older Adults

PubMed Central

Sasaki, Jeffer Eidi; Hickey, Amanda; Staudenmayer, John; John, Dinesh; Kent, Jane A.; Freedson, Patty S.

2015-01-01

Purpose To compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults. Methods Thirty-five older adults (21F and 14M ; 70.8 ± 4.9 y) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors (dominant hip, wrist, and ankle). Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz. Fifteen of the participants also wore the GT3X+ in free-living settings and were directly observed for 2-3 hours. Time- and frequency- domain features from acceleration signals of each monitor were used to train Random Forest (RF) and Support Vector Machine (SVM) models to classify five activity types: sedentary, standing, household, locomotion, and recreational activities. All algorithms were trained on lab data (RFLab and SVMLab) and free-living data (RFFL and SVMFL) using 20 s signal sampling windows. Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique. Results Overall classification accuracy rates for the algorithms developed from lab data were between 49% (wrist) to 55% (ankle) for the SVMLab algorithms, and 49% (wrist) to 54% (ankle) for RFLab algorithms. The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58% (wrist) to 69% (ankle) and from 61% (wrist) to 67% (ankle), respectively. Conclusion Our algorithms developed on free-living accelerometer data were more accurate in classifying activity type in free-living older adults than our algorithms developed on laboratory accelerometer data. Future studies should consider using free-living accelerometer data to train machine-learning algorithms in older adults. PMID:26673129

Performance of Activity Classification Algorithms in Free-Living Older Adults.

PubMed

Sasaki, Jeffer Eidi; Hickey, Amanda M; Staudenmayer, John W; John, Dinesh; Kent, Jane A; Freedson, Patty S

2016-05-01

The objective of this study is to compare activity type classification rates of machine learning algorithms trained on laboratory versus free-living accelerometer data in older adults. Thirty-five older adults (21 females and 14 males, 70.8 ± 4.9 yr) performed selected activities in the laboratory while wearing three ActiGraph GT3X+ activity monitors (in the dominant hip, wrist, and ankle; ActiGraph, LLC, Pensacola, FL). Monitors were initialized to collect raw acceleration data at a sampling rate of 80 Hz. Fifteen of the participants also wore GT3X+ in free-living settings and were directly observed for 2-3 h. Time- and frequency-domain features from acceleration signals of each monitor were used to train random forest (RF) and support vector machine (SVM) models to classify five activity types: sedentary, standing, household, locomotion, and recreational activities. All algorithms were trained on laboratory data (RFLab and SVMLab) and free-living data (RFFL and SVMFL) using 20-s signal sampling windows. Classification accuracy rates of both types of algorithms were tested on free-living data using a leave-one-out technique. Overall classification accuracy rates for the algorithms developed from laboratory data were between 49% (wrist) and 55% (ankle) for the SVMLab algorithms and 49% (wrist) to 54% (ankle) for the RFLab algorithms. The classification accuracy rates for SVMFL and RFFL algorithms ranged from 58% (wrist) to 69% (ankle) and from 61% (wrist) to 67% (ankle), respectively. Our algorithms developed on free-living accelerometer data were more accurate in classifying the activity type in free-living older adults than those on our algorithms developed on laboratory accelerometer data. Future studies should consider using free-living accelerometer data to train machine learning algorithms in older adults.

Uranium series dating of Allan Hills ice

NASA Technical Reports Server (NTRS)

Fireman, E. L.

1986-01-01

Uranium-238 decay series nuclides dissolved in Antarctic ice samples were measured in areas of both high and low concentrations of volcanic glass shards. Ice from the Allan Hills site (high shard content) had high Ra-226, Th-230 and U-234 activities but similarly low U-238 activities in comparison with Antarctic ice samples without shards. The Ra-226, Th-230 and U-234 excesses were found to be proportional to the shard content, while the U-238 decay series results were consistent with the assumption that alpha decay products recoiled into the ice from the shards. Through this method of uranium series dating, it was learned that the Allen Hills Cul de Sac ice is approximately 325,000 years old.

More is less: Learning but not relaxing buffers deviance under job stressors.

PubMed

Zhang, Chen; Mayer, David M; Hwang, Eunbit

2018-02-01

Workplace deviance harms the well-being of an organization and its members. Unfortunately, theory and prior research suggest that deviance is associated with job stressors, which are endemic to work organizations and often cannot be easily eliminated. To address this conundrum, we explore actions individuals can take at work that serve as buffering conditions for the positive relationship between job stressors and deviant behavior. Drawing upon conservation of resources theory, we examine a resource-building activity (i.e., learning something new at work) and a demand-shielding activity (i.e., taking time for relaxation at work) as potential boundary conditions. In 2 studies with employee samples using complementary designs, we find support for the buffering role of learning but not for relaxation. When employees learn new things at work, the relationship between hindrance stressors and deviance is weaker; as is the indirect relationship mediated by negative emotions. Taking time for relaxation at work did not show a moderating role in either study. Therefore, although relaxation is a response that individuals might be inclined to turn to for counteracting work stress, our findings suggest that, when it comes to addressing negative emotions and deviance in stressful work environments, building positive resources by learning something new at work could be more useful. In that way, doing more (i.e., learning, and not relaxing) is associated with less (deviance) in the face of job stressors. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Relationships among constructivist learning environment perceptions, motivational beliefs, self-regulation and science achievement

NASA Astrophysics Data System (ADS)

Kingir, Sevgi; Tas, Yasemin; Gok, Gulsum; Sungur Vural, Semra

2013-11-01

Background. There are attempts to integrate learning environment research with motivation and self-regulation research that considers social context influences an individual's motivation, self-regulation and, in turn, academic performance. Purpose. This study explored the relationships among constructivist learning environment perception variables (personal relevance, uncertainty, shared control, critical voice, student negotiation), motivational beliefs (self-efficacy, intrinsic interest, goal orientation), self-regulation, and science achievement. Sample. The sample for this study comprised 802 Grade 8 students from 14 public middle schools in a district of Ankara in Turkey. Design and methods. Students were administered 4 instruments: Constructivist Learning Environment Survey, Goal Achievement Questionnaire, Motivated Strategies for Learning Questionnaire, and Science Achievement Test. LISREL 8.7 program with SIMPLIS programming language was used to test the conceptual model. Providing appropriate fit indices for the proposed model, the standardized path coefficients for direct effects were examined. Results. At least one dimension of the constructivist learning environment was associated with students' intrinsic interest, goal orientation, self-efficacy, self-regulation, and science achievement. Self-efficacy emerged as the strongest predictor of both mastery and performance avoidance goals rather than the approach goals. Intrinsic value was found to be significantly linked to science achievement through its effect on self-regulation. The relationships between self-efficacy and self-regulation and between goal orientation and science achievement were not significant. Conclusion. In a classroom environment supporting student autonomy and control, students tend to develop higher interest in tasks, use more self-regulatory strategies, and demonstrate higher academic performance. Science teachers are highly recommended to consider these findings when designing their lessons. For the creation of such a learning environment, teachers can design open-ended inquiry activities in which students have opportunities to take responsibility, reflect on their views, and accomplish challenging tasks.

Linear combinations come alive in crossover designs.

PubMed

Shuster, Jonathan J

2017-10-30

Before learning anything about statistical inference in beginning service courses in biostatistics, students learn how to calculate the mean and variance of linear combinations of random variables. Practical precalculus examples of the importance of these exercises can be helpful for instructors, the target audience of this paper. We shall present applications to the "1-sample" and "2-sample" methods for randomized short-term 2-treatment crossover studies, where patients experience both treatments in random order with a "washout" between the active treatment periods. First, we show that the 2-sample method is preferred as it eliminates "conditional bias" when sample sizes by order differ and produces a smaller variance. We also demonstrate that it is usually advisable to use the differences in posttests (ignoring baseline and post washout values) rather than the differences between the changes in treatment from the start of the period to the end of the period ("delta of delta"). Although the intent is not to provide a definitive discussion of crossover designs, we provide a section and references to excellent alternative methods, where instructors can provide motivation to students to explore the topic in greater detail in future readings or courses. Copyright © 2017 John Wiley & Sons, Ltd.

Imbalanced Learning for Functional State Assessment

NASA Technical Reports Server (NTRS)

Li, Feng; McKenzie, Frederick; Li, Jiang; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom

2011-01-01

This paper presents results of several imbalanced learning techniques applied to operator functional state assessment where the data is highly imbalanced, i.e., some function states (majority classes) have much more training samples than other states (minority classes). Conventional machine learning techniques usually tend to classify all data samples into majority classes and perform poorly for minority classes. In this study, we implemented five imbalanced learning techniques, including random undersampling, random over-sampling, synthetic minority over-sampling technique (SMOTE), borderline-SMOTE and adaptive synthetic sampling (ADASYN) to solve this problem. Experimental results on a benchmark driving lest dataset show thai accuracies for minority classes could be improved dramatically with a cost of slight performance degradations for majority classes,

Correlates and perceived outcomes of four types of employee development activity.

PubMed

Birdi, K; Allan, C; Warr, P

1997-12-01

Participation in 4 different types of development activity was studied in a sample of manufacturing employees (N = 1,798). It was found that similar sets of variables were linked to greater participation in 3 activities: required training courses in work time, work-based development activity in work time, and career planning activity in work time or an individual's own time. Three kinds of reported benefits were studied, and the occurrence of these benefits was found to vary between different types of development activity. Overall job satisfaction and organizational commitment were significantly associated with prior participation in required training courses and work-based development activity. However, voluntary learning in one's own time was completely unrelated to these work attitudes.

Improving the Laboratory Experience for Introductory Geology Students Using Active Learning and Evidence-Based Reform

NASA Astrophysics Data System (ADS)

Oien, R. P.; Anders, A. M.; Long, A.

2014-12-01

We present the initial results of transitioning laboratory activities in an introductory physical geology course from passive to active learning. Educational research demonstrates that student-driven investigations promote increased engagement and better retention of material. Surveys of students in introductory physical geology helped us identify lab activities which do not engage students. We designed new lab activities to be more collaborative, open-ended and "hands-on". Student feedback was most negative for lab activities which are computer-based. In response, we have removed computers from the lab space and increased the length and number of activities involving physical manipulation of samples and models. These changes required investment in lab equipment and supplies. New lab activities also include student-driven exploration of data with open-ended responses. Student-evaluations of the new lab activities will be compiled during Fall 2014 and Spring 2015 to allow us to measure the impact of the changes on student satisfaction and we will report on our findings to date. Modification of this course has been sponsored by NSF's Widening Implementation & Demonstration of Evidence Based Reforms (WIDER) program through grant #1347722 to the University of Illinois. The overall goal of the grant is to increase retention and satisfaction of STEM students in introductory courses.

Classroom learning and achievement: how the complexity of classroom interaction impacts students' learning

NASA Astrophysics Data System (ADS)

Podschuweit, Sören; Bernholt, Sascha; Brückmann, Maja

2016-05-01

Background: Complexity models have provided a suitable framework in various domains to assess students' educational achievement. Complexity is often used as the analytical focus when regarding learning outcomes, i.e. when analyzing written tests or problem-centered interviews. Numerous studies reveal negative correlations between the complexity of a task and the probability of a student solving it. Purpose: Thus far, few detailed investigations explore the importance of complexity in actual classroom lessons. Moreover, the few efforts made so far revealed inconsistencies. Hence, the present study sheds light on the influence the complexity of students' and teachers' class contributions have on students' learning outcomes. Sample: Videos of 10 German 8th grade physics courses covering three consecutive lessons on two topics each (electricity, mechanics) have been analyzed. The sample includes 10 teachers and 290 students. Design and methods: Students' and teachers' verbal contributions were coded manual-based according to the level of complexity. Additionally, pre-post testing of knowledge in electricity and mechanics was applied to assess the students' learning gain. ANOVA analysis was used to characterize the influence of the complexity on the learning gain. Results: Results indicate that the mean level of complexity in classroom contributions explains a large portion of variance in post-test results on class level. Despite this overarching trend, taking classroom activities into account as well reveals even more fine-grained patterns, leading to more specific relations between the complexity in the classroom and students' achievement. Conclusions: In conclusion, we argue for more reflected teaching approaches intended to gradually increase class complexity to foster students' level of competency.

Sampling over Nonuniform Distributions: A Neural Efficiency Account of the Primacy Effect in Statistical Learning.

PubMed

Karuza, Elisabeth A; Li, Ping; Weiss, Daniel J; Bulgarelli, Federica; Zinszer, Benjamin D; Aslin, Richard N

2016-10-01

Successful knowledge acquisition requires a cognitive system that is both sensitive to statistical information and able to distinguish among multiple structures (i.e., to detect pattern shifts and form distinct representations). Extensive behavioral evidence has highlighted the importance of cues to structural change, demonstrating how, without them, learners fail to detect pattern shifts and are biased in favor of early experience. Here, we seek a neural account of the mechanism underpinning this primacy effect in learning. During fMRI scanning, adult participants were presented with two artificial languages: a familiar language (L1) on which they had been pretrained followed by a novel language (L2). The languages were composed of the same syllable inventory organized according to unique statistical structures. In the absence of cues to the transition between languages, posttest familiarity judgments revealed that learners on average more accurately segmented words from the familiar language compared with the novel one. Univariate activation and functional connectivity analyses showed that participants with the strongest learning of L1 had decreased recruitment of fronto-subcortical and posterior parietal regions, in addition to a dissociation between downstream regions and early auditory cortex. Participants with a strong new language learning capacity (i.e., higher L2 scores) showed the opposite trend. Thus, we suggest that a bias toward neural efficiency, particularly as manifested by decreased sampling from the environment, accounts for the primacy effect in learning. Potential implications of this hypothesis are discussed, including the possibility that "inefficient" learning systems may be more sensitive to structural changes in a dynamic environment.

Sampling in the Snow: High School Winter Field Experiences Provide Relevant, Real World Connections Between Scientific Practices and Disciplinary Core Ideas

NASA Astrophysics Data System (ADS)

Hanson, E. W.; Burakowski, E. A.

2014-12-01

For much of the northern United States, the months surrounding the winter solstice are times of increased darkness, low temperatures, and frozen landscapes. It's a time when many high school science educators, who otherwise would venture outside with their classes, hunker down and are wary of the outdoors. However, a plethora of learning opportunities lies just beyond the classroom. Working collaboratively, a high school science teacher and a snow scientist have developed multiple activities to engage students in the scientific process of collecting, analyzing and interpreting the winter world using snow data to (1) learn about the insulative properties of snow, and (2) to learn about the role of snow cover on winter climate through its reflective properties while participating in a volunteer network that collects snow depth, albedo (reflectivity), and density data. These outdoor field-based snow investigations incorporate Next Generation Science Standards (NGSS) and disciplinary core ideas, including ESS2.C: The roles of water in Earth's surface processes and ESS2.D: Weather and Climate. Additionally, the lesson plans presented address Common Core State Standards (CCSS) in Mathematics, including the creation and analysis of bar graphs and time series plots (CCSS.Math.HSS-ID.A.1) and xy scatter plots (CCSS.Math.HSS-ID.B.6). High school students participating in the 2013/2014 snow sampling season described their outdoor learning experience as "authentic" and "hands-on" as compared to traditional class indoors. They emphasized that learning outdoors was essential to their understanding of underlying content and concepts because they "learn through actual experience."

Embedding spiritual value through science learning

NASA Astrophysics Data System (ADS)

Johan, H.; Suhandi, A.; Wulan, A. R.; Widiasih; Ruyani, A.; Karyadi, B.; Sipriyadi

2018-05-01

The purpose of this study was to embed spiritual value through science learning program especially earth planet. Various phenomena in earth planet describe a divinity of super power. This study used quasi experimental method with one group pre-test-post-test design. Convenience sampling was conducted in this study. 23 pre-service physics teacher was involved. Pre-test and post-test used a questionnaire had been conducted to collected data of spiritual attitude. Open ended question had been utilized at post-test to collected data. A fourth indicators of spiritual value related to divinity of God was used to embed spiritual value. The results show a shifted of students’ awareness to divinity of God. Before implementing the earth planet learning, 85.8% of total students strongly agree that learning activity embed spiritual value while after learning process, it increased be 93.4%. After learning earth planet, it known that students’ spiritual value was influenced by character of earth planet concept which unobservable and media visual which display each incredible phenomena process in our earth planet. It can be concluded that spiritual value can be embedded through unobservable phenomena of during learning earth planet process.

Relationships among Teachers' Self-Efficacy and Students' Motivation, Atmosphere, and Satisfaction in Physical Education

ERIC Educational Resources Information Center

Pan, Yi-Hsiang

2014-01-01

The purpose of this study was to confirm the relationships among teachers' self-efficacy, and students' learning motivation, learning atmosphere, and learning satisfaction in senior high school physical education (PE). A sample of 462 PE teachers and 2681 students was drawn using stratified random sampling and cluster sampling from high schools in…

Earth, Air, Fire, & Water: Resource Guide 6. The Arts and Learning, Interdisciplinary Resources for Education.

ERIC Educational Resources Information Center

Lee, Ronald T., Ed.

This resource guide is intended to aid practitioners in the design of new curriculum units or the enrichment of existing units by suggesting activities and resources in the topic areas of earth, air, fire, and water. Special projects and trips relating to these topic areas are proposed. A sample arts networking system used to integrate various…

Exploring the Effects of Active Learning on Retaining Essential Concepts in Secondary and Junior High Classrooms

ERIC Educational Resources Information Center

Bachelor, Robin L.; Vaughan, Patrick M.; Wall, Connie M.

2012-01-01

This report describes a program for improving retention of essential concepts exhibited by junior high and high school students. The purpose of the study was to increase cognitive retention in order to increase student success. The target sample consisted of junior high students in the seventh grade and high school students in grades nine through…

Chemistry and the Periodic Table: Teacher's Guide Levels A, B, and C. Preliminary Limited Edition.

ERIC Educational Resources Information Center

Cambridge Physics Outlet, Woburn, MA. Education Programs Dept.

This is a two-part curriculum package for the teaching of chemistry and the periodic table. The first part, the Teacher's Guide, contains information necessary for using the equipment in a typical classroom including learning goals, vocabulary, math skills, and sample data for each activity. The second part of the package consists of photocopy…

The Structure of the Atom: Teacher's Guide Levels A, B, and C. Preliminary Limited Edition.

ERIC Educational Resources Information Center

Cambridge Physics Outlet, Woburn, MA. Education Programs Dept.

This is a two-part curriculum package for teaching the structure of atoms. The first part--the Teacher's Guide--contains information necessary for using the equipment in a typical classroom including learning goals, vocabulary, math skills, and sample data for each activity. The second part of the package consists of photocopy masters for a set of…

Evidence of Two Theoretical Models Observed in Young Children with Disabilities Who Are Beginning to Learn to Write

ERIC Educational Resources Information Center

Staples, Amy; Edmister, Evette

2012-01-01

This study examined the composing process and communication of students aged 5-8 identified with intellectual disabilities. An open-ended writing activity called Big Paper was implemented at least once every 2 weeks for a 6-month period. Qualitative methods were utilized to analyze writing samples, videotapes of writing sessions, and transcripts…

Why Girl Students Achieve English Presentation Learning Significantly Better in Shanghai University of Engineering Science (SUES)

ERIC Educational Resources Information Center

Zhu, Wen; Liu, Zhixin

2017-01-01

In non literature major dominated university, it is obviously noted that girl students' English (as the second language) presentation scores often higher than boy students in the same teaching environment and evaluation system. A 397 samples' survey has been studied from the aspects of after school activities and sleep schedule to discuss if any…

Computers for Cognitive Development in Early Childhood--The Teacher's Role in the Computer Learning Environment

ERIC Educational Resources Information Center

Nir-Gal, Ofra; Klein, Pnina S.

2004-01-01

This study was designed to examine the effect of different kinds of adult mediation on the cognitive performance of young children who used computers. The study sample included 150 kindergarten children aged 5-6. The findings indicate that children who engaged in adult-mediated computer activity improved the level of their cognitive performance on…

An Investigation of Eighth Grade Students' Problem Posing Skills (Turkey Sample)

ERIC Educational Resources Information Center

Arikan, Elif Esra; Ünal, Hasan

2015-01-01

To pose a problem refers to the creative activity for mathematics education. The purpose of the study was to explore the eighth grade students' problem posing ability. Three learning domains such as requiring four operations, fractions and geometry were chosen for this reason. There were two classes which were coded as class A and class B. Class A…

The Beautiful Brain: A Unit for Grades 5-9 with Further Explorations for Gifted and Talented.

ERIC Educational Resources Information Center

Struve, Nancy

The unit provides information on the study of the human brain for students in grades 5-9 with suggestions for extending the lessons for gifted and talented students. Learning activities are offered for ten lessons (sample subtopics in parentheses); introduction to the unit (student pretest and posttest); brain growth; medulla-oblongata-reptilian…

Learning from Non-Reported Data: Interpreting Missing Body Mass Index Values in Young Children

ERIC Educational Resources Information Center

Arbour-Nicitopoulos, Kelly P.; Faulkner, Guy E.; Leatherdale, Scott T.

2010-01-01

The objective of this study was to examine the pattern of relations between missing weight and height (BMI) data and a range of demographic, physical activity, sedentary behavior, and academic measures in a young sample of elementary school children. A secondary analysis of a large cross-sectional study, PLAY-On, was conducted using self-reported…

Emotional maturity of medical students impacting their adult learning skills in a newly established public medical school at the east coast of Malaysian Peninsula.

PubMed

Bhagat, Vidya; Haque, Mainul; Bin Abu Bakar, Yasrul Izad; Husain, Rohayah; Khairi, Che Mat

2016-01-01

Emotional maturity (EM) is defined as the ability of an individual to respond to situations, control emotions, and behave in an adult manner when dealing with others. EM is associated with adult learning skill, which is an important aspect of professional development as stated in the principles of andragogy. These principles are basically a characteristic feature of adult learning, which is defined as "the entire range of formal, non-formal, and informal learning activities that are undertaken by adults after an initial education and training, which result in the acquisition of new knowledge and skills". The purpose of this study is to find out the influence of EM on adult learning among Years I and II medical students of Universiti Sultan Zainal Abidin (UniSZA). The study population included preclinical medical students of UniSZA from Years I and II of the academic session 2015/2016. The convenient sampling technique was used to select the sample. Data were collected using "EM scale" to evaluate emotional level and adult learning scale to assess the adult learning scores. Out of 120 questionnaires, only six response sheets were not complete and the remaining 114 (95%) were complete. Among the study participants, 23.7% (27) and 76.3% (87) were males and females, respectively. The data were then compiled and analyzed using SPSS Version 22. The Pearson's correlation method was used to find the significance of their association. The results revealed a significant correlation between EM and adult learning scores ( r =0.40, p <0.001). Thus, the study result supports the prediction, and based on the current findings, it can be concluded that there is a significant correlation between EM and adult learning and it has an effect on the students. Medical faculty members should give more emphasis on these aspects to produce health professionals. Henceforward, researchers can expect with optimism that the country will create more rational medical doctors.

Social Sciences, Grades 3, 6, 8, 10, 12. State Goals for Learning and Sample Learning Objectives.

ERIC Educational Resources Information Center

Illinois State Board of Education, Springfield. Dept. of School Improvement Services.

This document, developed by the Illinois State Board of Education, identifies five goals for learning in the social sciences, and provides sample learning objectives for grades 3, 6, 8, 10, 12, which are consistent with these goals. The state goals for learning are broadly stated expressions of what the Illinois State Board of Education wants and…

Fine Arts, Grades 3, 6, 8, 10, 12. State Goals for Learning and Sample Learning Objectives.

ERIC Educational Resources Information Center

Illinois State Board of Education, Springfield. Dept. of School Improvement Services.

This document, developed by the Illinois State Boaord of Education, identifies five state goals for learning in the fine arts, and provides sample learning objectives for grades, 3, 6, 8, 10, 12, which are consistent with the goals. The state goals for learning are broadly stated expressions of what the Illinois State Board of Education expects…

Collaboration for Education with the Apple Learning Interchange

NASA Astrophysics Data System (ADS)

Young, Patrick A.; Zimmerman, T.; Knierman, K. A.

2006-12-01

We present a progressive effort to deliver online education and outreach resources in collaboration with the Apple Learning Interchange, a free community for educators. We have created a resource site with astronomy activities, video training for the activities, and the possibility of interactive training through video chat services. Also in development is an online textbook for graduate and advanced undergraduate courses in stellar evolution, featuring an updatable and annotated text with multimedia content, online lectures, podcasts, and a framework for interactive simulation activities. Both sites will be highly interactive, combining online discussions, the opportunity for live video interaction, and a growing library of student work samples. This effort promises to provide a compelling model for collaboration between science educators and corporations. As scientists, we provide content knowledge and a compelling reason to communicate, while Apple provides technical expertise, a deep knowledge of online education, and a way for us to reach a wide audience of higher education, community outreach, and K-12 educators.

Constructing cardiovascular fitness knowledge in physical education

PubMed Central

Zhang, Tan; Chen, Ang; Chen, Senlin; Hong, Deockki; Loflin, Jerry; Ennis, Catherine

2015-01-01

In physical education, it has become necessary for children to learn kinesiological knowledge for understanding the benefits of physical activity and developing a physically active lifestyle. This study was conducted to determine the extent to which cognitive assignments about healthful living and fitness contributed to knowledge growth on cardiorespiratory fitness and health. Fourth grade students (N = 616) from 15 randomly sampled urban elementary schools completed 34 cognitive assignments related to the cardiorespiratory physical activities they were engaged in across 10 lessons. Performance on the assignments were analyzed in relation to their knowledge gain measured using a standardized knowledge test. A multivariate discriminant analysis revealed that the cognitive assignments contributed to knowledge gain but the contribution varied assignment by assignment. A multiple regression analysis indicated that students’ assignment performance by lesson contributed positively to their knowledge growth scores. A content analysis based on the constructivist learning framework showed that observing–reasoning assignments contributed the most to knowledge growth. Analytical and analytical–application assignments contributed less than the constructivist theories would predict. PMID:25995702

Lessons learned and insights from the implementation of a food and physical activity policy to prevent obesity in Mexican schools: An analysis of nationally representative survey results.

PubMed

Théodore, Florence L; Moreno-Saracho, Jessica E; Bonvecchio, Anabelle; Morales-Ruán, María Del Carmen; Tolentino-Mayo, Lizbeth; López-Olmedo, Nancy; Shamah-Levy, Teresa; Rivera, Juan A

2018-01-01

Obesity is a serious problem among children in Mexico. In 2010, the government implemented a national food and physical activity policy in elementary schools, to prevent obesity. The goal of this study is to assess the implementation of this policy, using the logic model from a descriptive survey with national representativeness at the elementary school level and based on a stratified cluster design. We used a systematic random sampling of schools (n = 122), stratified into public and private. We administered questionnaires to 116 principals, 165 members of the Food and Physical Activity Committees, 132 food school food vendors, 119 teachers, 348 parents. This study evidences a significant deviation in implementation from what had been planned. Our lessons learned are the importance to: base the design/implementation of the policy on a theoretical framework, make programs appealing to stakeholders, select concrete and measurable objective or goals, and support stakeholders during the implementation process.

Combining active learning and semi-supervised learning techniques to extract protein interaction sentences.

PubMed

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2011-11-24

Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task. We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly. By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure. Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.

Signs of neurobehavioral dysfunction in a sample of learning disabled children: stability and concurrent validity.

PubMed

Morrison, D C; Hinshaw, S P; Carte, E T

1985-12-01

Of 270 learning disabled children with average intelligence and significant delays in reading comprehension a sample of 37 were evaluated for signs of neurobehavioral dysfunction. All such signs--primitive reflexes, equilibrium reactions, and postrotary nystagmus--were reliably assessed. A subsample of 19 children was compared with developmentally normal and mentally retarded samples for the occurrence of tonic neck reflexes and equilibrium reactions. The learning disabled children consistently showed deviancies like those of the retarded children; both of these groups differed from the normal children on most measures. These deviant responses persisted over a 9-mo. period for the learning disabled group. Compared with norms, the total learning disabled sample displayed hyponystagmus, and this depressed nystagmus persisted for 11 mo. Results are discussed in relation to the lack of correlation among the various signs of neurobehavioral dysfunction in the learning disabled children.

The Impact of Brain Breaks Classroom-Based Physical Activities on Attitudes toward Physical Activity in Polish School Children in Third to Fifth Grade.

PubMed

Glapa, Agata; Grzesiak, Joanna; Laudanska-Krzeminska, Ida; Chin, Ming-Kai; Edginton, Christopher R; Mok, Magdalena Mo Ching; Bronikowski, Michal

2018-02-21

The purpose of this study was to examine the effectiveness of the Brain Breaks® Physical Activity Solutions in changing attitudes toward physical activity of school children in a community in Poland. In 2015, a sample of 326 pupils aged 9-11 years old from 19 classes at three selected primary schools were randomly assigned to control and experimental groups within the study. During the classes, children in the experimental group performed physical activities two times per day in three to five minutes using Brain Breaks® videos for four months, while the control group did not use the videos during the test period. Students' attitudes toward physical activities were assessed before and after the intervention using the "Attitudes toward Physical Activity Scale". Repeated measures of ANOVA were used to examine the change from pre- to post-intervention. Overall, a repeated measures ANOVA indicated time-by-group interaction effects in 'Self-efficacy on learning with video exercises', F(1.32) = 75.28, p = 0.00, η2 = 0.19. Although the changes are minor, there were benefits of the intervention. It may be concluded that HOPSports Brain Breaks® Physical Activity Program contributes to better self-efficacy on learning while using video exercise of primary school children.

Contextual Approach with Guided Discovery Learning and Brain Based Learning in Geometry Learning

NASA Astrophysics Data System (ADS)

Kartikaningtyas, V.; Kusmayadi, T. A.; Riyadi

2017-09-01

The aim of this study was to combine the contextual approach with Guided Discovery Learning (GDL) and Brain Based Learning (BBL) in geometry learning of junior high school. Furthermore, this study analysed the effect of contextual approach with GDL and BBL in geometry learning. GDL-contextual and BBL-contextual was built from the steps of GDL and BBL that combined with the principles of contextual approach. To validate the models, it uses quasi experiment which used two experiment groups. The sample had been chosen by stratified cluster random sampling. The sample was 150 students of grade 8th in junior high school. The data were collected through the student’s mathematics achievement test that given after the treatment of each group. The data analysed by using one way ANOVA with different cell. The result shows that GDL-contextual has not different effect than BBL-contextual on mathematics achievement in geometry learning. It means both the two models could be used in mathematics learning as the innovative way in geometry learning.

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…

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…

"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…

Exploring the Effects of Active Learning on High School Students' Outcomes and Teachers' Perceptions of Biotechnology and Genetics Instruction

ERIC Educational Resources Information Center

Mueller, Ashley L.; Knobloch, Neil A.; Orvis, Kathryn S.

2015-01-01

Active learning can engage high school students to learn science, yet there is limited understanding if active learning can help students learn challenging science concepts such as genetics and biotechnology. This quasi-experimental study explored the effects of active learning compared to passive learning regarding high school students'…

Using Fluid Inclusions to Bring Phase Diagrams to Life in a Guided Inquiry Instructional Setting

NASA Astrophysics Data System (ADS)

Farver, J. R.; Onasch, C.

2011-12-01

A fundamental concept in mineralogy, petrology, and geochemistry is the generation and interpretation of phase diagrams for various systems. We have developed an exercise to strengthen student's familiarity with and confidence in employing phase diagrams by using fluid inclusions. The activity follows the 5Es (Engagement, Exploration, Explanation, Extension, Evaluation) guided inquiry instructional model in order to best facilitate student learning. The exercise follows an activity adapted from Brady (1992) wherein students collect data to generate the phase diagram for the Ice-Water-NaCl system. The engagement activity involves using a USGS-type fluid inclusion heating-cooling stage with a camera and projection system. We typically employ either a doubly-polished quartz sample or a cleaved section of fluorite and select a typical two phase (L + V) aqueous inclusion. Students first observe the inclusion at room temperature and pressure and are asked to predict what would happen if the sample is heated. Students then watch as the sample is heated to its homogenization temperature (Th) and are asked to explain what they see. The sample is then cooled until completely frozen and then slowly warmed until the first ice melting (at the eutectic, Te) and then until all ice melts (Tm). Again, students are asked to explain what they see and, if necessary, they are guided to remember the earlier phase diagram activity. The process is then repeated while students follow along the appropriate phase diagrams. In this fashion, students literally see the changes in phases present and their relative abundances as they move through the phase diagram. The engagement activity generates student interest in the exercise to insure minds-on as well as hands-on exploration. The exploration activities involve students observing and describing a wide range of fluid inclusion types (e.g., CO2, daughter crystals, multiple inclusion trails, etc) and hands-on collection of Th and Tm data for a selected sample. Using a fluorite sample (Denton Mine) yields excellent results and a meaningful extension activity. Each student collects Th and Tm data that are then combined and class histograms are generated and interpreted. At this point, a general explanation of fluid inclusions is provided to bring together the student's observations and to assess their understanding. The extension activity involves using the Th, Te, and Tm data obtained for primary inclusions to constrain the true trapping temperature (Tt). The isochore is calculated and plotted on a P-T plot. Using the geothermal gradient for the sample locale, students calculate the hydrostatic and lithostatic gradients for the region and plot these on the P-T diagram in order to constrain the possible range in Tt. Finally, based upon the salinity and Tt range, students determine what ore fluid type is represented (MVT). The evaluation includes observation of participation, answers to questions posed during the engagement activity, and a written report that includes answers to refining and open-ended questions as well as a reflection on their learning. This activity strengthens student's understanding of phase diagrams while introducing them to the importance of fluids in the crust.

The Use of Student Consulting Projects as an Active Learning Pedagogy: A Case Study in a Production/Operations Management Course

ERIC Educational Resources Information Center

Heriot, Kirk C.; Cook, Ron; Jones, Rita C.; Simpson, Leo

2008-01-01

Active learning has attracted considerable attention in higher education in response to concerns about how and what students are learning. There are many different forms of active learning, yet most of them are classroom based. We propose an alternative to active learning in the classroom through active learning outside of the classroom in the…

SOLAR FLARE PREDICTION USING SDO/HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM

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

Bobra, M. G.; Couvidat, S., E-mail: couvidat@stanford.edu

2015-01-10

We attempt to forecast M- and X-class solar flares using a machine-learning algorithm, called support vector machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument to continuously map the full-disk photospheric vector magnetic field from space. Most flare forecasting efforts described in the literature use either line-of-sight magnetograms or a relatively small number of ground-based vector magnetograms. This is the first time a large data set of vector magnetograms has been used to forecast solar flares. We build a catalog of flaring and non-flaring active regions sampled from a databasemore » of 2071 active regions, comprised of 1.5 million active region patches of vector magnetic field data, and characterize each active region by 25 parameters. We then train and test the machine-learning algorithm and we estimate its performances using forecast verification metrics with an emphasis on the true skill statistic (TSS). We obtain relatively high TSS scores and overall predictive abilities. We surmise that this is partly due to fine-tuning the SVM for this purpose and also to an advantageous set of features that can only be calculated from vector magnetic field data. We also apply a feature selection algorithm to determine which of our 25 features are useful for discriminating between flaring and non-flaring active regions and conclude that only a handful are needed for good predictive abilities.« less

Motor-response learning at a process control panel by an autonomous robot

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

Spelt, P.F.; de Saussure, G.; Lyness, E.

1988-01-01

The Center for Engineering Systems Advanced Research (CESAR) was founded at Oak Ridge National Laboratory (ORNL) by the Department of Energy's Office of Energy Research/Division of Engineering and Geoscience (DOE-OER/DEG) to conduct basic research in the area of intelligent machines. Therefore, researchers at the CESAR Laboratory are engaged in a variety of research activities in the field of machine learning. In this paper, we describe our approach to a class of machine learning which involves motor response acquisition using feedback from trial-and-error learning. Our formulation is being experimentally validated using an autonomous robot, learning tasks of control panel monitoring andmore » manipulation for effect process control. The CLIPS Expert System and the associated knowledge base used by the robot in the learning process, which reside in a hypercube computer aboard the robot, are described in detail. Benchmark testing of the learning process on a robot/control panel simulation system consisting of two intercommunicating computers is presented, along with results of sample problems used to train and test the expert system. These data illustrate machine learning and the resulting performance improvement in the robot for problems similar to, but not identical with, those on which the robot was trained. Conclusions are drawn concerning the learning problems, and implications for future work on machine learning for autonomous robots are discussed. 16 refs., 4 figs., 1 tab.« less

Dissociation between active and observational learning from positive and negative feedback in Parkinsonism.

PubMed

Kobza, Stefan; Ferrea, Stefano; Schnitzler, Alfons; Pollok, Bettina; Südmeyer, Martin; Bellebaum, Christian

2012-01-01

Feedback to both actively performed and observed behaviour allows adaptation of future actions. Positive feedback leads to increased activity of dopamine neurons in the substantia nigra, whereas dopamine neuron activity is decreased following negative feedback. Dopamine level reduction in unmedicated Parkinson's Disease patients has been shown to lead to a negative learning bias, i.e. enhanced learning from negative feedback. Recent findings suggest that the neural mechanisms of active and observational learning from feedback might differ, with the striatum playing a less prominent role in observational learning. Therefore, it was hypothesized that unmedicated Parkinson's Disease patients would show a negative learning bias only in active but not in observational learning. In a between-group design, 19 Parkinson's Disease patients and 40 healthy controls engaged in either an active or an observational probabilistic feedback-learning task. For both tasks, transfer phases aimed to assess the bias to learn better from positive or negative feedback. As expected, actively learning patients showed a negative learning bias, whereas controls learned better from positive feedback. In contrast, no difference between patients and controls emerged for observational learning, with both groups showing better learning from positive feedback. These findings add to neural models of reinforcement-learning by suggesting that dopamine-modulated input to the striatum plays a minor role in observational learning from feedback. Future research will have to elucidate the specific neural underpinnings of observational learning.

Learning Needs of Hospitalized Patients With Heart Failure in Singapore: A Descriptive Correlational Study.

PubMed

Ong, Shu-Fen; Foong, Pamela Pei-Mei; Seah, Juanna Shen-Hwei; Elangovan, Lavanya; Wang, Wenru

2017-11-28

Understanding the learning needs of patients with heart failure (HF) is important in reducing the incidence of HF-related hospital readmissions. Sociocultural differences are known to influence patient learning needs. However, most HF learning needs studies have been conducted on Western populations. The aim of this study was to investigate the learning needs of hospitalized patients with HF in Singapore. A cross-sectional, descriptive correlational design was adopted using a questionnaire survey that included the Heart Failure Learning Needs Inventory and sociodemographic and clinical datasheets. A convenience sample of 97 patients with HF was recruited from an acute hospital in Singapore. Findings revealed that education topics relating to signs and symptoms, risk factors, general HF information, and medications were perceived by participants as the most important. Contrastingly, education topics relating to diet, activity, and psychological factors were poorly valued. The only significant demographic factor that was correlated to the patients' learning needs was monthly household income, which correlated to education on HF risk factors and general HF information. This study supports the necessity of carefully prioritizing patient education topics in line with patient learning needs. Furthermore, education should be culturally sensitive and take into account the unique values, needs, and situations of patients.

The drift diffusion model as the choice rule in reinforcement learning

PubMed Central

Frank, Michael J.

2017-01-01

Current reinforcement-learning models often assume simplified decision processes that do not fully reflect the dynamic complexities of choice processes. Conversely, sequential-sampling models of decision making account for both choice accuracy and response time, but assume that decisions are based on static decision values. To combine these two computational models of decision making and learning, we implemented reinforcement-learning models in which the drift diffusion model describes the choice process, thereby capturing both within- and across-trial dynamics. To exemplify the utility of this approach, we quantitatively fit data from a common reinforcement-learning paradigm using hierarchical Bayesian parameter estimation, and compared model variants to determine whether they could capture the effects of stimulant medication in adult patients with attention-deficit hyper-activity disorder (ADHD). The model with the best relative fit provided a good description of the learning process, choices, and response times. A parameter recovery experiment showed that the hierarchical Bayesian modeling approach enabled accurate estimation of the model parameters. The model approach described here, using simultaneous estimation of reinforcement-learning and drift diffusion model parameters, shows promise for revealing new insights into the cognitive and neural mechanisms of learning and decision making, as well as the alteration of such processes in clinical groups. PMID:27966103

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…

Confidence Preserving Machine for Facial Action Unit Detection

PubMed Central

Zeng, Jiabei; Chu, Wen-Sheng; De la Torre, Fernando; Cohn, Jeffrey F.; Xiong, Zhang

2016-01-01

Facial action unit (AU) detection from video has been a long-standing problem in automated facial expression analysis. While progress has been made, accurate detection of facial AUs remains challenging due to ubiquitous sources of errors, such as inter-personal variability, pose, and low-intensity AUs. In this paper, we refer to samples causing such errors as hard samples, and the remaining as easy samples. To address learning with the hard samples, we propose the Confidence Preserving Machine (CPM), a novel two-stage learning framework that combines multiple classifiers following an “easy-to-hard” strategy. During the training stage, CPM learns two confident classifiers. Each classifier focuses on separating easy samples of one class from all else, and thus preserves confidence on predicting each class. During the testing stage, the confident classifiers provide “virtual labels” for easy test samples. Given the virtual labels, we propose a quasi-semi-supervised (QSS) learning strategy to learn a person-specific (PS) classifier. The QSS strategy employs a spatio-temporal smoothness that encourages similar predictions for samples within a spatio-temporal neighborhood. In addition, to further improve detection performance, we introduce two CPM extensions: iCPM that iteratively augments training samples to train the confident classifiers, and kCPM that kernelizes the original CPM model to promote nonlinearity. Experiments on four spontaneous datasets GFT [15], BP4D [56], DISFA [42], and RU-FACS [3] illustrate the benefits of the proposed CPM models over baseline methods and state-of-the-art semisupervised learning and transfer learning methods. PMID:27479964

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.

Exploring Effectiveness and Moderators of Language Learning Strategy Instruction on Second Language and Self-Regulated Learning Outcomes

ERIC Educational Resources Information Center

Ardasheva, Yuliya; Wang, Zhe; Adesope, Olusola O.; Valentine, Jeffrey C.

2017-01-01

This meta-analysis synthesized recent research on strategy instruction (SI) effectiveness to estimate SI effects and their moderators for two domains: second/foreign language and self-regulated learning. A total of 37 studies (47 independent samples) for language domain and 16 studies (17 independent samples) for self-regulated learning domain…

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.

The influence of learning styles, enrollment status and gender on academic performance of optometry undergraduates.

PubMed

Prajapati, Bhavna; Dunne, Mark; Bartlett, Hannah; Cubbidge, Robert

2011-01-01

This cross-sectional study was designed to determine whether the academic performance of optometry undergraduates is influenced by enrollment status, learning style or gender. Three hundred and sixty undergraduates in all 3 years of the optometry degree course at Aston University during 2008-2009 were asked for their informed consent to participate in this study. Enrollment status was known from admissions records. An Index of Learning Styles (http://www4.nscu.edu/unity/lockers/users/f/felder/public/Learning-Styles.html) determined learning style preference with respect to four different learning style axes; active-reflective, sensing-intuitive, visual-verbal and sequential-global. The influence of these factors on academic performance was investigated. Two hundred and seventy students agreed to take part (75% of the cohort). 63% of the sample was female. There were 213 home non-graduates (entrants from the UK or European Union without a bachelor's degree or higher), 14 home graduates (entrants from the UK or European Union with a bachelor's degree or higher), 28 international non-graduates (entrants from outside the UK or European Union without a bachelor's degree or higher) and 15 international graduates (entrants from outside the UK or European Union with a bachelor's degree or higher). The majority of students were balanced learners (between 48% and 64% across four learning style axes). Any preferences were towards active, sensing, visual and sequential learning styles. Of the factors investigated in this study, learning styles were influenced by gender; females expressed a disproportionate preference for the reflective and visual learning styles. Academic performance was influenced by enrollment status; international graduates (95% confidence limits: 64-72%) outperformed all other student groups (home non graduates, 60-62%; international non graduates, 55-63%) apart from home graduates (57-69%). Our research has shown that the majority of optometry students have balanced learning styles and, from the factors studied, academic performance is only influenced by enrollment status. Although learning style questionnaires offer suggestions on how to improve learning efficacy, our findings indicate that current teaching methods do not need to be altered to suit varying learning style preferences as balanced learning styles can easily adapt to any teaching style (Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review. London, UK: Learning and Skills Research Centre, 2004). © 2010 The College of Optometrists.

Research and Teaching: Instructor Use of Group Active Learning in an Introductory Biology Sequence

ERIC Educational Resources Information Center

Auerbach, Anna Jo; Schussler, Elisabeth E.

2016-01-01

Active learning (or learner-centered) pedagogies have been shown to enhance student learning in introductory biology courses. Student collaboration has also been shown to enhance student learning and may be a critical part of effective active learning practices. This study focused on documenting the use of individual active learning and group…

Mobile learning in medicine

NASA Astrophysics Data System (ADS)

Serkan Güllüoüǧlu, Sabri

2013-03-01

This paper outlines the main infrastructure for implicating mobile learning in medicine and present a sample mobile learning application for medical learning within the framework of mobile learning systems. Mobile technology is developing nowadays. In this case it will be useful to develop different learning environments using these innovations in internet based distance education. M-learning makes the most of being on location, providing immediate access, being connected, and acknowledges learning that occurs beyond formal learning settings, in places such as the workplace, home, and outdoors. Central to m-learning is the principle that it is the learner who is mobile rather than the device used to deliver m learning. The integration of mobile technologies into training has made learning more accessible and portable. Mobile technologies make it possible for a learner to have access to a computer and subsequently learning material and activities; at any time and in any place. Mobile devices can include: mobile phone, personal digital assistants (PDAs), personal digital media players (eg iPods, MP3 players), portable digital media players, portable digital multimedia players. Mobile learning (m-learning) is particularly important in medical education, and the major users of mobile devices are in the field of medicine. The contexts and environment in which learning occurs necessitates m-learning. Medical students are placed in hospital/clinical settings very early in training and require access to course information and to record and reflect on their experiences while on the move. As a result of this paper, this paper strives to compare and contrast mobile learning with normal learning in medicine from various perspectives and give insights and advises into the essential characteristics of both for sustaining medical education.

Family functioning and early learning practices in immigrant homes.

PubMed

Jung, Sunyoung; Fuller, Bruce; Galindo, Claudia

2012-01-01

Poverty-related developmental-risk theories dominate accounts of uneven levels of household functioning and effects on children. But immigrant parents may sustain norms and practices-stemming from heritage culture, selective migration, and social support-that buffer economic exigencies. Comparable levels of social-emotional functioning in homes of foreign-born Latino mothers were observed relative to native-born Whites, despite sharp social-class disparities, but learning activities were much weaker, drawing on a national sample of mothers with children aging from 9 to 48months (n=5,300). Asian-heritage mothers reported weaker social functioning-greater martial conflict and depression-yet stronger learning practices. Mothers' migration history, ethnicity, and social support helped to explain levels of functioning, after taking into account multiple indicators of class and poverty. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

Transformation of Cortex-wide Emergent Properties during Motor Learning.

PubMed

Makino, Hiroshi; Ren, Chi; Liu, Haixin; Kim, An Na; Kondapaneni, Neehar; Liu, Xin; Kuzum, Duygu; Komiyama, Takaki

2017-05-17

Learning involves a transformation of brain-wide operation dynamics. However, our understanding of learning-related changes in macroscopic dynamics is limited. Here, we monitored cortex-wide activity of the mouse brain using wide-field calcium imaging while the mouse learned a motor task over weeks. Over learning, the sequential activity across cortical modules became temporally more compressed, and its trial-by-trial variability decreased. Moreover, a new flow of activity emerged during learning, originating from premotor cortex (M2), and M2 became predictive of the activity of many other modules. Inactivation experiments showed that M2 is critical for the post-learning dynamics in the cortex-wide activity. Furthermore, two-photon calcium imaging revealed that M2 ensemble activity also showed earlier activity onset and reduced variability with learning, which was accompanied by changes in the activity-movement relationship. These results reveal newly emergent properties of macroscopic cortical dynamics during motor learning and highlight the importance of M2 in controlling learned movements. Copyright © 2017 Elsevier Inc. All rights reserved.

An Investigation of English Learning of a Sample of Students Who Are Deaf or Hard of Hearing and English Learners and a Sample of Students Who Are English Learners

ERIC Educational Resources Information Center

Becker, Sharon J.

2017-01-01

The purpose of this study was to investigate the English learning of a sample of students who are deaf or hard of hearing and English learners (DHH EL) and a sample of students who are English learners (EL). The English language learning of four students who were DHH EL and four students who were EL was explored through a multiple-case study using…

Nursing students' satisfaction of the clinical learning environment: a research study.

PubMed

Papastavrou, Evridiki; Dimitriadou, Maria; Tsangari, Haritini; Andreou, Christos

2016-01-01

The acquisition of quality clinical experience within a supportive and pedagogically adjusted clinical learning environment is a significant concern for educational institutions. The quality of clinical learning usually reflects the quality of the curriculum structure. The assessment of the clinical settings as learning environment is a significant concern within the contemporary nursing education. The nursing students' satisfaction is considered as an important factor of such assessment, contributing to any potential reforms in order to optimize the learning activities and achievements within clinical settings. The aim of the study was to investigate nursing students' satisfaction of the clinical settings as learning environments. A quantitative descriptive, correlational design was used. A sample of 463 undergraduate nursing students from the three universities in Cyprus were participated. Data were collected using the Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T). Nursing students were highly satisfied with the clinical learning environment and their satisfaction has been positively related to all clinical learning environment constructs namely the pedagogical atmosphere, the Ward Manager's leadership style, the premises of Nursing in the ward, the supervisory relationship (mentor) and the role of the Nurse Teacher (p < 0.001). Students who had a named mentor reported more satisfied with the supervisory relationship. The frequency of meetings among the students and the mentors increased the students' satisfaction with the clinical learning environment. It was also revealed that 1st year students were found to be more satisfied than the students in other years. The supervisory relationship was evaluated by the students as the most influential factor in their satisfaction with the clinical learning environment. Student's acceptance within the nursing team and a well-documented individual nursing care is also related with students' satisfaction. The pedagogical atmosphere is considered pivotal, with reference to students' learning activities and competent development within the clinical setting. Therefore, satisfaction could be used as an important contributing factor towards the development of clinical learning environments in order to satisfy the needs and expectations of students. The value of the development of an organized mentorship system is illustrated in the study.

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

ASPECT: A Survey to Assess Student Perspective of Engagement in an Active-Learning Classroom

ERIC Educational Resources Information Center

Wiggins, Benjamin L.; Eddy, Sarah L.; Wener-Fligner, Leah; Freisem, Karen; Grunspan, Daniel Z.; Theobald, Elli J.; Timbrook, Jerry; Crowe, Alison J.

2017-01-01

The primary measure used to determine relative effectiveness of in-class activities has been student performance on pre/posttests. However, in today's active-learning classrooms, learning is a social activity, requiring students to interact and learn from their peers. To develop effective active-learning exercises that engage students, it is…

Student Motivation from and Resistance to Active Learning Rooted in Essential Science Practices

NASA Astrophysics Data System (ADS)

Owens, David C.; Sadler, Troy D.; Barlow, Angela T.; Smith-Walters, Cindi

2017-12-01

Several studies have found active learning to enhance students' motivation and attitudes. Yet, faculty indicate that students resist active learning and censure them on evaluations after incorporating active learning into their instruction, resulting in an apparent paradox. We argue that the disparity in findings across previous studies is the result of variation in the active learning instruction that was implemented. The purpose of this study was to illuminate sources of motivation from and resistance to active learning that resulted from a novel, exemplary active-learning approach rooted in essential science practices and supported by science education literature. This approach was enacted over the course of 4 weeks in eight sections of an introductory undergraduate biology laboratory course. A plant concept inventory, administered to students as a pre-, post-, and delayed-posttest indicated significant proximal and distal learning gains. Qualitative analysis of open-response questionnaires and interviews elucidated sources of motivation and resistance that resulted from this active-learning approach. Several participants indicated this approach enhanced interest, creativity, and motivation to prepare, and resulted in a challenging learning environment that facilitated the sharing of diverse perspectives and the development of a community of learners. Sources of resistance to active learning included participants' unfamiliarity with essential science practices, having to struggle with uncertainty in the absence of authoritative information, and the extra effort required to actively construct knowledge as compared to learning via traditional, teacher-centered instruction. Implications for implementation, including tips for reducing student resistance to active learning, are discussed.

Broca's area and the language instinct.

PubMed

Musso, Mariacristina; Moro, Andrea; Glauche, Volkmar; Rijntjes, Michel; Reichenbach, Jürgen; Büchel, Christian; Weiller, Cornelius

2003-07-01

Language acquisition in humans relies on abilities like abstraction and use of syntactic rules, which are absent in other animals. The neural correlate of acquiring new linguistic competence was investigated with two functional magnetic resonance imaging (fMRI) studies. German native speakers learned a sample of 'real' grammatical rules of different languages (Italian or Japanese), which, although parametrically different, follow the universal principles of grammar (UG). Activity during this task was compared with that during a task that involved learning 'unreal' rules of language. 'Unreal' rules were obtained manipulating the original two languages; they used the same lexicon as Italian or Japanese, but were linguistically illegal, as they violated the principles of UG. Increase of activation over time in Broca's area was specific for 'real' language acquisition only, independent of the kind of language. Thus, in Broca's area, biological constraints and language experience interact to enable linguistic competence for a new language.

Sleep and the Price of Plasticity: From Synaptic and Cellular Homeostasis to Memory Consolidation and Integration

PubMed Central

Tononi, Giulio; Cirelli, Chiara

2014-01-01

Summary Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the off-line, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This review considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity. PMID:24411729

Enhancement of problem solving ability of high school students through learning with real engagement in active problem solving (REAPS) model on the concept of heat transfer

NASA Astrophysics Data System (ADS)

Yulindar, A.; Setiawan, A.; Liliawati, W.

2018-05-01

This study aims to influence the enhancement of problem solving ability before and after learning using Real Engagement in Active Problem Solving (REAPS) model on the concept of heat transfer. The research method used is quantitative method with 35 high school students in Pontianak as sample. The result of problem solving ability of students is obtained through the test in the form of 3 description questions. The instrument has tested the validity by the expert judgment and field testing that obtained the validity value of 0.84. Based on data analysis, the value of N-Gain is 0.43 and the enhancement of students’ problem solving ability is in medium category. This was caused of students who are less accurate in calculating the results of answers and they also have limited time in doing the questions given.

Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration.

PubMed

Tononi, Giulio; Cirelli, Chiara

2014-01-08

Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the offline, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This Perspective considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity. Copyright © 2014 Elsevier Inc. All rights reserved.

Dissociation between Active and Observational Learning from Positive and Negative Feedback in Parkinsonism

PubMed Central

Kobza, Stefan; Ferrea, Stefano; Schnitzler, Alfons; Pollok, Bettina

2012-01-01

Feedback to both actively performed and observed behaviour allows adaptation of future actions. Positive feedback leads to increased activity of dopamine neurons in the substantia nigra, whereas dopamine neuron activity is decreased following negative feedback. Dopamine level reduction in unmedicated Parkinson’s Disease patients has been shown to lead to a negative learning bias, i.e. enhanced learning from negative feedback. Recent findings suggest that the neural mechanisms of active and observational learning from feedback might differ, with the striatum playing a less prominent role in observational learning. Therefore, it was hypothesized that unmedicated Parkinson’s Disease patients would show a negative learning bias only in active but not in observational learning. In a between-group design, 19 Parkinson’s Disease patients and 40 healthy controls engaged in either an active or an observational probabilistic feedback-learning task. For both tasks, transfer phases aimed to assess the bias to learn better from positive or negative feedback. As expected, actively learning patients showed a negative learning bias, whereas controls learned better from positive feedback. In contrast, no difference between patients and controls emerged for observational learning, with both groups showing better learning from positive feedback. These findings add to neural models of reinforcement-learning by suggesting that dopamine-modulated input to the striatum plays a minor role in observational learning from feedback. Future research will have to elucidate the specific neural underpinnings of observational learning. PMID:23185586

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…

The Effect of Outdoor Learning Activities on the Development of Preschool Children

ERIC Educational Resources Information Center

Yildirim, Günseli; Özyilmaz Akamca, Güzin

2017-01-01

Learning ought to be supported by both in class activities and outdoor activities contributing to structuring knowledge. Outdoor activities allow children to actively participate and to learn by doing. Learning requires a lot of work and activities. These activities, which provide primary experiences, help children to change theoretical knowledge…

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.

PubMed

Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

2016-07-05

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data

PubMed Central

Aliper, Alexander; Plis, Sergey; Artemov, Artem; Ulloa, Alvaro; Mamoshina, Polina; Zhavoronkov, Alex

2016-01-01

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF‐7 and PC‐3 cell lines from the LINCS project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled dataset of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both gene and pathway level classification, DNN convincingly outperformed support vector machine (SVM) model on every multiclass classification problem, however, models based on a pathway level classification perform better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development. PMID:27200455

A Panel Analysis of Student Mathematics Achievement in the US in the 1990s: Does Increasing the Amount of Time in Learning Activities Affect Math Achievement?

ERIC Educational Resources Information Center

Aksoy, Tevfik; Link, Charles R.

2000-01-01

Uses panel estimation techniques to estimate econometric models of mathematics achievement determinants for a nationally representative sample of high-school students. Extra time spent on math homework increases test scores; an extra hour of TV viewing negatively affects scores. Longer math periods also help. (Contains 56 references.) (MLH)

Learning About Wisconsin: Activities, Historical Documents, and Resources Linked to Wisconsin's Model Academic Standards for Social Studies in Grades 4-12. Bulletin No. 99238.

ERIC Educational Resources Information Center

Fortier, John D.; Grady, Susan M.; Prickette, Karen R.

Wisconsin's Model Academic Standards for Social Studies provide direction for curriculum, instruction, assessment, and professional development. The standards identify eras and themes in Wisconsin history. Many of these standards can be taught using content related to the study of Wisconsin. The sample lessons included in this document identify…

"DEAR ROCK, WHAT'S YOUR DESTINY? Ancient and modern uses of rocks in industry, building and art."

NASA Astrophysics Data System (ADS)

Pennesi, Daniela

2015-04-01

The project is for students of first grade of secondary school. The activity is a game, virtual or real of associations between rock and soil samples with their uses in industry, building and art. The students, alone or in a team, have to form pairs having available various samples of rocks, soils and building materials as bags of cement, tiles.. They have images of colonnades, staircases of famous churches, cave paintings and colors. The project is multidisciplinary. During the activity, the teachers of art and technical education are involved with and the teacher of sciences. The game can be used as an introduction for the rocks' classification. The inquiry in team, is a good way to learn the several uses of mineral resources.

Supporting Faculty Learning About Teaching: The On the Cutting Edge Website

NASA Astrophysics Data System (ADS)

Fox, S.; Iverson, E. A.; Manduca, C. A.; Kirk, K. B.; McDaris, J. R.; Ormand, C. J.; Bruckner, M. Z.

2011-12-01

The On the Cutting Edge website captures information about teaching geoscience from workshop participants and leaders. Designed to both support workshop participants in making use of ideas developed at the workshop and to allow a broader audience to access these ideas, the site includes more than 4900 pages of content in 39 topical collections with more than 1400 community-contributed teaching activities. The site is well used: in 2010, 850,000 visitors made more than one million visits to the site viewing more than 2.1 million pages. To obtain a more detailed understanding of site use within our target population, we interviewed a sample of 30 geoscience faculty. Five primary uses were described repeatedly and in depth: finding ideas for teaching, understanding what colleagues are doing in specific teaching situations, learning about methods, tools, or topic in education or geoscience, finding visualizations, and networking or career planning. Interviewees could describe particular instances where they made use of teaching materials and could cite reasons why they believed this improved student learning. To understand how these uses are manifest in the weblogs, a sample of 73 sessions that lasted at least 10 minutes, and viewed 10 or more pages were selected from March 2009 logs. Sessions were selected to sample heavy use of one or more topical collections, and to sample the diversity of log characteristics. The sessions were described qualitatively and the resulting descriptions categorized. Four recognizable use patterns emerged: activity browsing in some cases combined with study of a pedagogic method, browsing visualizations and associated topical content, digging deep within a particular topical collection, and cross-site browsing. These patterns seem consistent with the uses reported in the interviews. An analysis of characteristics of all sessions in 2008 viewing 10 or more pages indicate that the major uses described in the interview study by 30 faculty are in fact widespread among the 16,000 users seeing 10 or more pages. The most widespread identifiable use is finding teaching activities or finding out what colleagues are doing in a particular teaching situation (20-40% of use). Roughly 30% of use appears to be related to seeking visualizations for class. Another 20% of use includes learning about pedagogic methods, though that may not be the users' intention when they enter the site. As in the interview study, use associated with finding career information is significant though less common (10% of use). The relative distribution of page views across modules is well aligned with the reported uses, and offers further confirmation that these uses are widely represented in the deep sessions.

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.

Enhanced multisensory integration and motor reactivation after active motor learning of audiovisual associations.

PubMed

Butler, Andrew J; James, Thomas W; James, Karin Harman

2011-11-01

Everyday experience affords us many opportunities to learn about objects through multiple senses using physical interaction. Previous work has shown that active motor learning of unisensory items enhances memory and leads to the involvement of motor systems during subsequent perception. However, the impact of active motor learning on subsequent perception and recognition of associations among multiple senses has not been investigated. Twenty participants were included in an fMRI study that explored the impact of active motor learning on subsequent processing of unisensory and multisensory stimuli. Participants were exposed to visuo-motor associations between novel objects and novel sounds either through self-generated actions on the objects or by observing an experimenter produce the actions. Immediately after exposure, accuracy, RT, and BOLD fMRI measures were collected with unisensory and multisensory stimuli in associative perception and recognition tasks. Response times during audiovisual associative and unisensory recognition were enhanced by active learning, as was accuracy during audiovisual associative recognition. The difference in motor cortex activation between old and new associations was greater for the active than the passive group. Furthermore, functional connectivity between visual and motor cortices was stronger after active learning than passive learning. Active learning also led to greater activation of the fusiform gyrus during subsequent unisensory visual perception. Finally, brain regions implicated in audiovisual integration (e.g., STS) showed greater multisensory gain after active learning than after passive learning. Overall, the results show that active motor learning modulates the processing of multisensory associations.

Active Learning: Qualitative Inquiries into Vocabulary Instruction in Chinese L2 Classrooms

ERIC Educational Resources Information Center

Shen, Helen H.; Xu, Wenjing

2015-01-01

Active learning emerged as a new approach to learning in the 1980s. The core concept of active learning involves engaging students not only in actively exploring knowledge but also in reflecting on their own learning process in order to become more effective learners. Because the nonalphabetic nature of the Chinese writing system makes learning to…

Positivity effect in healthy aging in observational but not active feedback-learning.

PubMed

Bellebaum, Christian; Rustemeier, Martina; Daum, Irene

2012-01-01

The present study investigated the impact of healthy aging on the bias to learn from positive or negative performance feedback in observational and active feedback learning. In active learning, a previous study had already shown a negative learning bias in healthy seniors older than 75 years, while no bias was found for younger seniors. However, healthy aging is accompanied by a 'positivity effect', a tendency to primarily attend to stimuli with positive valence. Based on recent findings of dissociable neural mechanisms in active and observational feedback learning, the positivity effect was hypothesized to influence older participants' observational feedback learning in particular. In two separate experiments, groups of young (mean age 27) and older participants (mean age 60 years) completed an observational or active learning task designed to differentially assess positive and negative learning. Older but not younger observational learners showed a significant bias to learn better from positive than negative feedback. In accordance with previous findings, no bias was found for active learning. This pattern of results is discussed in terms of differences in the neural underpinnings of active and observational learning from performance feedback.

K-4 Keepers Collection: A Service Learning Teacher Professional Development Program

NASA Astrophysics Data System (ADS)

Schwerin, T. G.; Blaney, L.; Myers, R. J.

2011-12-01

This poster focuses on the K-4 Keepers Collection, a service-learning program developed for the Earth System Science Education Alliance (ESSEA). ESSEA is a NOAA-, NASA- and NSF-supported program of teacher professional development that increases teachers' pedagogical content knowledge of climate-related Earth system science. The ESSEA program -- whether used in formal higher education courses or frequented by individual teachers who look for classroom activities in the environmental sciences -- provides a full suite of activities, lessons and units for teachers' use. The ESSEA network consists of 45 universities and education centers addressing climate and environment issues. K-4 Keepers Collection - ESSEA K-4 module collections focus on five specific themes of content development: spheres, Polar Regions, oceans, climate and service learning. The K-4 Keepers collection provides the opportunity for teachers to explore topics and learning projects promoting stewardship of the Earth's land, water, air and living things. Examination of the impacts of usage and pollution on water, air, land and living things through service-learning projects allows students to become informed stewards. All of the modules include short-term sample projects that either educate or initiate action involving caring for the environment. The K-4 Keepers course requires teachers to develop similar short or long-term projects for implementation in their classrooms. Objectives include: 1. Increase elementary teachers' environmental literacy addressing ocean, coastal, Great Lakes, stewardship, weather and climate science standards and using NOAA and NASA resources. 2. Develop elementary teachers' efficacy in employing service learning projects focused on conserving and preserving Earth's land, air, water and living things. 3. Prepare college faculty to incorporate service learning and environmental literacy into their courses through professional development and modules on the ESSEA website.

Educational and Community Outreach Efforts by the United States Polar Rock Repository during the International Polar Year

NASA Astrophysics Data System (ADS)

Grunow, A.; Codispoti, J. E.

2010-12-01

The US Polar Rock Repository (USPRR) houses more than 19,000 rock samples from polar regions and these samples are made available to the scientific, educational and museum community. The USPRR has been active in promoting polar earth science to educational and community groups. During the past year, outreach efforts reached over 12,000 people. The USPRR outreach involve tours of the facility, school presentations, online laboratory exercises, working with the Columbus Metro Parks, teaching at summer camps, teaching special geology field assignments at the middle school level, as well as offering an ‘Antarctic Rock Box’ that contains representative samples of the three types of rocks, minerals, fossils, and books and activities about geology and Antarctica. The rock box activities have been designed and reviewed by educators and scientists to use as an educational supplement to the Earth Science course of study. The activities have been designed around the Academic Content Standards: k-12 Science manual published by the Ohio Department of Education to ensure that the activities and topics are focused on those mandated by the state of Ohio. The USPRR website has a Virtual Web Antarctic Expedition with many activities for Middle to High School age students. The students learn about how to plan a field season, safety techniques, how to make a remote field camp, identify what equipment is needed, learn about the different transportation choices, weather issues, understanding GPS, etc. Educational and community networks have been built in part, by directly contacting individuals at an institution and partnering with them on educational outreach. The institutions have been very interested in doing this because it brings scientists to the classroom and to the public. This type of outreach has also served as an opening for children to consider possible career choices in science that they may not have considered before. In many of the presentations, a female geologist has been the presenter, and this shows children that anyone can be a scientist/geologist. The most effective way to reach future scientists is to have fun, hands-on, energetic activities. Some children do well with classroom discussion and learning, but many do not. Following discussion of a polar earth science topic, we do hands-on activities to help students understand what has just been discussed. Projects in which the students make their own observations and conclusions have been very successful. One of the most useful things that the USPRR has done is to provide educators with activities to help drive the polar earth science ideas home. It is also helpful to give teachers concise background information to each activity and then provide reliable online resources if they should want more detailed information. In order to continue to improve the USPRR educational resources, questionnaires and/or evaluation forms are given to the participants. The evaluation replies have been especially helpful in reformatting our educational Antarctic rock boxes.

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 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;…

Positive Attitude Toward Math Supports Early Academic Success: Behavioral Evidence and Neurocognitive Mechanisms.

PubMed

Chen, Lang; Bae, Se Ri; Battista, Christian; Qin, Shaozheng; Chen, Tianwen; Evans, Tanya M; Menon, Vinod

2018-03-01

Positive attitude is thought to impact academic achievement and learning in children, but little is known about its underlying neurocognitive mechanisms. Using a large behavioral sample of 240 children, we found that positive attitude toward math uniquely predicted math achievement, even after we accounted for multiple other cognitive-affective factors. We then investigated the neural mechanisms underlying the link between positive attitude and academic achievement in two independent cohorts of children (discovery cohort: n = 47; replication cohort: n = 28) and tested competing hypotheses regarding the differential roles of affective-motivational and learning-memory systems. In both cohorts, we found that positive attitude was associated with increased engagement of the hippocampal learning-memory system. Structural equation modeling further revealed that, in both cohorts, increased hippocampal activity and more frequent use of efficient memory-based strategies mediated the relation between positive attitude and higher math achievement. Our study is the first to elucidate the neurocognitive mechanisms by which positive attitude influences learning and academic achievement.

The effect of remediation on reducing misconception: a metaanalysis of student thesis on physics education

NASA Astrophysics Data System (ADS)

Oktavianty, E.; Haratua, T. M. S.; Anuru, M.

2018-05-01

The purpose of this study is to compare the effects of various remediation practices in reducing the number of student misconceptions on physics concepts. This research synthesizes 68 thesis undergraduate students of physics education which are published in Tanjungpura University library 2009-2016 period. In this study, the guidance in the form of checklist in conducting the study arranged to facilitate the understanding and assessment of the scientific work. Based on the analysis result, the average of effect size of all the synthesized thesis is 1.13. There are six forms of remedial misconceptions performed by physics education students, such as re-learning, feedback, integration of remediation in learning, physical activity, utilization of other learning resources and interviews. In addition, sampling techniques and test reliability were have contributed to the effect size of the study. Therefore, it is expected that the results of this study can be considered in preparing the remediation of misconceptions on physics learning in the future.

Experiential learning in practice: An ethnographic study among nursing students and preceptors.

PubMed

Rodríguez-García, Marta; Medina-Moya, José Luis; González-Pascual, Juan Luis; Cardenete-Reyes, César

2018-03-01

This study aimed to explore the reflective dialogues and processes that take place between preceptors and their nursing students and to examine how preceptors make use of their expert knowledge in order to enhance students' experiential learning during clinical placements. Two 30-h courses on reflective teaching were conducted. The study sample included 15 preceptors and 27 undergraduate nursing students. Data were collected during the course and during clinical placements at two X hospitals. Data collection included non-participatory observation and informal conversations with preceptors, in-depth interviews and focus groups. Preceptors used a series of strategies to promote experiential learning; these included creating links with practice, the use of examples, allowing students to adopt professional roles and enhancing autonomy. The value of preceptors is their wealth of professional experience, which is key during the learning process of nursing students. Preceptors must learn to master the art of questioning and stimulating reflective dialogues, in order to stimulate students' critical thinking and encourage them to resolve common problems that arise during practice. Students demand a more active role in their own learning processes. Copyright © 2017. Published by Elsevier Ltd.

Learning concepts of cinenurducation: an integrative review.

PubMed

Oh, Jina; Kang, Jeongae; De Gagne, Jennie C

2012-11-01

Cinenurducation is the use of films in both didactic and clinical nursing education. Although films are already used as instructional aids in nursing education, few studies have been made that demonstrate the learning concepts that can be attributed to this particular teaching strategy. The purpose of this paper is to describe the learning concepts of cinenurducation and its conceptual metaphor based on a review of literature. The databases CINAHL, MEDLINE, PsychINFO, ERIC, EBSCO, ProQuest Library Journal, and Scopus databases were searched for articles. Fifteen peer-reviewed articles were selected through title and abstract screening from "films in nursing" related articles found in internationally published articles in English from the past 20 years. Four common concepts emerged that relate to cinenurducation: (a) student-centered, (b) experiential, (c) reflective, and (d) problem-solving learning. Current literature corroborates cinenurducation as an effective teaching strategy with its learning activities in nursing education. Future studies may include instructional guides of sample films that could be practically used in various domains to teach nursing competencies, as well as in the development of evaluation criteria and standards to assess students' learning outcomes. Copyright © 2012 Elsevier Ltd. All rights reserved.