The doctrine of normal tendency in active learning teaching methodology: investigations into probability distributions and averages
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ABSTRACT: Supplementary Information
The online version contains supplementary material available at 10.1007/s43545-021-00154-1.
SUBMITTER: Ridley D
PROVIDER: S-EPMC8164912 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundVarious evidence-based and student-centered strategies such as team-based learning (TBL), case-based learning (CBL), and flipped classroom have been recently applied to anatomy education and have shown to improve student engagement and interaction. These strategies shift the focus of teaching from knowledge transmission to knowledge construction by students and encourage the use of tasks. This study discusses the use of an active and engaging learning strategy to teach the musculoskeletal system to Year 1 MBBS students (Faculty of Medical Sciences, The University of the West Indies, Cave Hill, Barbados) and examines the correlation between assessment modalities and student performance.MethodsThe "Active and Engaging Learning Strategy" was used to assess student learning in the form of oral presentations. Students had presentations on muscle attachments, muscle actions, blood and nerve supply, and applied anatomy of the limb musculature. Questions on the limbs (Locomotor System) were included in pre and post-presentation spotters, in-course assessments, and final examinations. Percentages, paired t-test, independent sample t-test, and zero-order correlations were performed to confirm the results for the different objectives of the study.ResultsThe main modes of presentation chosen were poems (37.1%), followed by stories (21.2%), songs (11.4%), and skits (10.6%). The majority of students (84%) found the strategies beneficial and recommended such sessions for future cohorts (92%). Students achieved significantly better scores in post-presentation spotters (p < 0.01) and the marks of in-course and final examinations also showed significant improvement (p < 0.01).ConclusionOur study highlighted that the active and engaging learning strategy can be used as an effective learning tool in anatomy. Students were proactive in preparing the muscle presentations by utilizing their own creativity, curiosity, and intelligence. Further studies should be conducted using randomized controlled trials to assess the effectiveness of various learning strategies which could open a new door to medical education.
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Project description:How humans efficiently operate in a world with massive amounts of data that need to be processed, stored, and recalled has long been an unsettled question. Our physical and social environment needs to be represented in a structured way, which could be achieved by reducing input to latent variables in the form of probability distributions, as proposed by influential, probabilistic accounts of cognition and perception. However, few studies have investigated the neural processes underlying the brain's potential ability to represent a probability distribution's complex, global features. Here, we presented participants with a sequence of tones that formed a normal or a bimodal distribution. Using a novel, single-trial EEG analysis, we demonstrate a neural response that indexes the likelihood of an item, given previously presented items, and corresponds to the experienced tones' distribution. Our results indicate that the adult human brain can build a representation of the complex, global pattern of a probability distribution and offer a novel tool for an in-depth understanding of related neural mechanics.
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Project description:MotivationProbability distributions play a central role in mathematical and statistical modelling. The encoding, annotation and exchange of such models could be greatly simplified by a resource providing a common reference for the definition of probability distributions. Although some resources exist, no suitably detailed and complex ontology exists nor any database allowing programmatic access.ResultsProbOnto, is an ontology-based knowledge base of probability distributions, featuring more than 80 uni- and multivariate distributions with their defining functions, characteristics, relationships and re-parameterization formulas. It can be used for model annotation and facilitates the encoding of distribution-based models, related functions and quantities.Availability and implementationhttp://probonto.orgContactmjswat@ebi.ac.ukSupplementary informationSupplementary data are available at Bioinformatics online.
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