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      What Are We Looking for in Computer-Based Learning Interventions in Medical Education? A Systematic Review

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          Abstract

          Background

          Computer-based learning (CBL) has been widely used in medical education, and reports regarding its usage and effectiveness have ranged broadly. Most work has been done on the effectiveness of CBL approaches versus traditional methods, and little has been done on the comparative effects of CBL versus CBL methodologies. These findings urged other authors to recommend such studies in hopes of improving knowledge about which CBL methods work best in which settings.

          Objective

          In this systematic review, we aimed to characterize recent studies of the development of software platforms and interventions in medical education, search for common points among studies, and assess whether recommendations for CBL research are being taken into consideration.

          Methods

          We conducted a systematic review of the literature published from 2003 through 2013. We included studies written in English, specifically in medical education, regarding either the development of instructional software or interventions using instructional software, during training or practice, that reported learner attitudes, satisfaction, knowledge, skills, or software usage. We conducted 2 latent class analyses to group articles according to platform features and intervention characteristics. In addition, we analyzed references and citations for abstracted articles.

          Results

          We analyzed 251 articles. The number of publications rose over time, and they encompassed most medical disciplines, learning settings, and training levels, totaling 25 different platforms specifically for medical education. We uncovered 4 latent classes for educational software, characteristically making use of multimedia (115/251, 45.8%), text (64/251, 25.5%), Web conferencing (54/251, 21.5%), and instructional design principles (18/251, 7.2%). We found 3 classes for intervention outcomes: knowledge and attitudes (175/212, 82.6%), knowledge, attitudes, and skills (11.8%), and online activity (12/212, 5.7%). About a quarter of the articles (58/227, 25.6%) did not hold references or citations in common with other articles. The number of common references and citations increased in articles reporting instructional design principles ( P=.03), articles measuring online activities ( P=.01), and articles citing a review by Cook and colleagues on CBL ( P=.04). There was an association between number of citations and studies comparing CBL versus CBL, independent of publication date ( P=.02).

          Conclusions

          Studies in this field vary highly, and a high number of software systems are being developed. It seems that past recommendations regarding CBL interventions are being taken into consideration. A move into a more student-centered model, a focus on implementing reusable software platforms for specific learning contexts, and the analysis of online activity to track and predict outcomes are relevant areas for future research in this field.

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          Most cited references 139

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          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

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            The NumPy array: a structure for efficient numerical computation

            In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.
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              “Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics,” in

               T. WHITE,  T Bruns,  S Lee (1990)
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                Author and article information

                Affiliations
                1Department of Medical Education and Simulation Faculty of Medicine University of Porto PortoPortugal
                2Department of Clinical Neurosciences and Mental Health, Medical Psychology Unit Faculty of Medicine University of Porto PortoPortugal
                3Department of Clinical Epidemiology, Predictive Medicine and Public Health Faculty of Medicine University of Porto PortoPortugal
                Author notes
                Corresponding Author: Tiago Taveira-Gomes tiago.taveira@ 123456me.com
                Contributors
                , ORCID: http://orcid.org/0000-0002-0998-6000
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                August 2016
                01 August 2016
                : 18
                : 8
                27480053 4985611 v18i8e204 10.2196/jmir.5461
                (Reviewer), (Reviewer), (Reviewer),
                ©Tiago Taveira-Gomes, Patrícia Ferreira, Isabel Taveira-Gomes, Milton Severo, Maria Amélia Ferreira. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 01.08.2016.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

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