PLoS One. 2015 Dec 01;10(12):e0143748. doi: 10.1371/journal.pone.0143748. eCollection 2015.

Curriculum Mapping with Academic Analytics in Medical and Healthcare Education.

PloS one

Martin Komenda, Martin Víta, Christos Vaitsis, Daniel Schwarz, Andrea Pokorná, Nabil Zary, Ladislav Dušek

PMID: 26624281 PMCID: PMC4666663 DOI: 10.1371/journal.pone.0143748

Abstract

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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MeSH terms

• Curriculum*
• Education, Medical*
• Humans
• Models, Statistical*

Publication Types

• Journal Article
• Research Support, Non-U.S. Gov't