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Curriculum Mapping with Academic Analytics in Medical and Healthcare Education

BACKGROUND: No universal solution, based on an approved pedagogical approach, exists to parametrically describe, effectively manage, and clearly visualize a higher education institution’s curriculum, including tools for unveiling relationships inside curricular datasets. OBJECTIVE: We aim to solve t...

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Autores principales: Komenda, Martin, Víta, Martin, Vaitsis, Christos, Schwarz, Daniel, Pokorná, Andrea, Zary, Nabil, Dušek, Ladislav
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666663/
https://www.ncbi.nlm.nih.gov/pubmed/26624281
http://dx.doi.org/10.1371/journal.pone.0143748
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author Komenda, Martin
Víta, Martin
Vaitsis, Christos
Schwarz, Daniel
Pokorná, Andrea
Zary, Nabil
Dušek, Ladislav
author_facet Komenda, Martin
Víta, Martin
Vaitsis, Christos
Schwarz, Daniel
Pokorná, Andrea
Zary, Nabil
Dušek, Ladislav
author_sort Komenda, Martin
collection PubMed
description BACKGROUND: No universal solution, based on an approved pedagogical approach, exists to parametrically describe, effectively manage, and clearly visualize a higher education institution’s curriculum, including tools for unveiling relationships inside curricular datasets. OBJECTIVE: We aim to solve the issue of medical curriculum mapping to improve understanding of the complex structure and content of medical education programs. Our effort is based on the long-term development and implementation of an original web-based platform, which supports an outcomes-based approach to medical and healthcare education and is suitable for repeated updates and adoption to curriculum innovations. METHODS: We adopted data exploration and visualization approaches in the context of medical curriculum innovations in higher education institutions domain. We have developed a robust platform, covering detailed formal metadata specifications down to the level of learning units, interconnections, and learning outcomes, in accordance with Bloom’s taxonomy and direct links to a particular biomedical nomenclature. Furthermore, we used selected modeling techniques and data mining methods to generate academic analytics reports from medical curriculum mapping datasets. RESULTS: We present a solution that allows users to effectively optimize a curriculum structure that is described with appropriate metadata, such as course attributes, learning units and outcomes, a standardized vocabulary nomenclature, and a tree structure of essential terms. We present a case study implementation that includes effective support for curriculum reengineering efforts of academics through a comprehensive overview of the General Medicine study program. Moreover, we introduce deep content analysis of a dataset that was captured with the use of the curriculum mapping platform; this may assist in detecting any potentially problematic areas, and hence it may help to construct a comprehensive overview for the subsequent global in-depth medical curriculum inspection. CONCLUSIONS: We have proposed, developed, and implemented an original framework for medical and healthcare curriculum innovations and harmonization, including: planning model, mapping model, and selected academic analytics extracted with the use of data mining.
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spelling pubmed-46666632015-12-10 Curriculum Mapping with Academic Analytics in Medical and Healthcare Education Komenda, Martin Víta, Martin Vaitsis, Christos Schwarz, Daniel Pokorná, Andrea Zary, Nabil Dušek, Ladislav PLoS One Research Article BACKGROUND: No universal solution, based on an approved pedagogical approach, exists to parametrically describe, effectively manage, and clearly visualize a higher education institution’s curriculum, including tools for unveiling relationships inside curricular datasets. OBJECTIVE: We aim to solve the issue of medical curriculum mapping to improve understanding of the complex structure and content of medical education programs. Our effort is based on the long-term development and implementation of an original web-based platform, which supports an outcomes-based approach to medical and healthcare education and is suitable for repeated updates and adoption to curriculum innovations. METHODS: We adopted data exploration and visualization approaches in the context of medical curriculum innovations in higher education institutions domain. We have developed a robust platform, covering detailed formal metadata specifications down to the level of learning units, interconnections, and learning outcomes, in accordance with Bloom’s taxonomy and direct links to a particular biomedical nomenclature. Furthermore, we used selected modeling techniques and data mining methods to generate academic analytics reports from medical curriculum mapping datasets. RESULTS: We present a solution that allows users to effectively optimize a curriculum structure that is described with appropriate metadata, such as course attributes, learning units and outcomes, a standardized vocabulary nomenclature, and a tree structure of essential terms. We present a case study implementation that includes effective support for curriculum reengineering efforts of academics through a comprehensive overview of the General Medicine study program. Moreover, we introduce deep content analysis of a dataset that was captured with the use of the curriculum mapping platform; this may assist in detecting any potentially problematic areas, and hence it may help to construct a comprehensive overview for the subsequent global in-depth medical curriculum inspection. CONCLUSIONS: We have proposed, developed, and implemented an original framework for medical and healthcare curriculum innovations and harmonization, including: planning model, mapping model, and selected academic analytics extracted with the use of data mining. Public Library of Science 2015-12-01 /pmc/articles/PMC4666663/ /pubmed/26624281 http://dx.doi.org/10.1371/journal.pone.0143748 Text en © 2015 Komenda et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Komenda, Martin
Víta, Martin
Vaitsis, Christos
Schwarz, Daniel
Pokorná, Andrea
Zary, Nabil
Dušek, Ladislav
Curriculum Mapping with Academic Analytics in Medical and Healthcare Education
title Curriculum Mapping with Academic Analytics in Medical and Healthcare Education
title_full Curriculum Mapping with Academic Analytics in Medical and Healthcare Education
title_fullStr Curriculum Mapping with Academic Analytics in Medical and Healthcare Education
title_full_unstemmed Curriculum Mapping with Academic Analytics in Medical and Healthcare Education
title_short Curriculum Mapping with Academic Analytics in Medical and Healthcare Education
title_sort curriculum mapping with academic analytics in medical and healthcare education
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4666663/
https://www.ncbi.nlm.nih.gov/pubmed/26624281
http://dx.doi.org/10.1371/journal.pone.0143748
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