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@article{1320646,
author = {Komenda, Martin and Víta, Martin and Vaitsis, Christos and Schwarz, Daniel and Pokorná, Andrea and Zary, Nabil and Dušek, Ladislav},
article_location = {San Francisco},
article_number = {12},
doi = {http://dx.doi.org/10.1371/journal.pone.0143748},
keywords = {COMPETENCE; CENTRALITY},
language = {eng},
issn = {1932-6203},
journal = {Plos One},
title = {Curriculum Mapping with Academic Analytics in Medical and Healthcare Education},
volume = {10},
year = {2015}
}
TY - JOUR
ID - 1320646
AU - Komenda, Martin - Víta, Martin - Vaitsis, Christos - Schwarz, Daniel - Pokorná, Andrea - Zary, Nabil - Dušek, Ladislav
PY - 2015
TI - Curriculum Mapping with Academic Analytics in Medical and Healthcare Education
JF - Plos One
VL - 10
IS - 12
SP - 1-18
EP - 1-18
PB - Public Library Science
SN - 19326203
KW - COMPETENCE
KW - CENTRALITY
N2 - 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.
ER -
KOMENDA, Martin, Martin VÍTA, Christos VAITSIS, Daniel SCHWARZ, Andrea POKORNÁ, Nabil ZARY and Ladislav DUŠEK. Curriculum Mapping with Academic Analytics in Medical and Healthcare Education. \textit{Plos One}. San Francisco: Public Library Science, 2015, vol.~10, No~12, p.~1-18. ISSN~1932-6203. Available from: https://dx.doi.org/10.1371/journal.pone.0143748.
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