Learning analytics, education data mining, and personalization in health professions education

Janet Corral; Stathis Th Konstantinidis; Panagiotis D. Bamidis

doi:10.1016/B978-0-12-813144-2.00009-X

Learning analytics, education data mining, and personalization in health professions education

Janet Corral, Stathis Th Konstantinidis, Panagiotis D. Bamidis

Medicine

Research output: Chapter in Book/Report/Conference proceeding › Chapter

5 Scopus citations

Abstract

Personalization of student evaluation data and learning application usage has the potential to provide targeted feedback to support self-directed learning and expertise development among learners in health professions education. To provide personalization, health professions programs need to leverage both: (1) the technical infrastructure and analysis for existing student data, and (2) understand the interrelated contexts of learning, teaching, and expertise development within the clinical context. Personalization is more than feeding back results to learners; it also moves into intelligent tutors. Tips for successful adoption of personalization in health professions education, including security, legal, and administrative concerns, are discussed.

Original language	English (US)
Title of host publication	Digital Innovations in Healthcare Education and Training
Publisher	Elsevier
Pages	137-150
Number of pages	14
ISBN (Electronic)	9780128131442
ISBN (Print)	9780128131459
DOIs	https://doi.org/10.1016/B978-0-12-813144-2.00009-X
State	Published - Jan 1 2020

Keywords

education data mining
health professions education
learning analytics
personalization

ASJC Scopus subject areas

General Medicine

Access to Document

10.1016/B978-0-12-813144-2.00009-X

Cite this

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AB - Personalization of student evaluation data and learning application usage has the potential to provide targeted feedback to support self-directed learning and expertise development among learners in health professions education. To provide personalization, health professions programs need to leverage both: (1) the technical infrastructure and analysis for existing student data, and (2) understand the interrelated contexts of learning, teaching, and expertise development within the clinical context. Personalization is more than feeding back results to learners; it also moves into intelligent tutors. Tips for successful adoption of personalization in health professions education, including security, legal, and administrative concerns, are discussed.

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KW - learning analytics

KW - personalization

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ER -

Learning analytics, education data mining, and personalization in health professions education

Abstract

Keywords

ASJC Scopus subject areas

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