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

Corral, Janet; Konstantinidis, Stathis; Bamidis, Panagiotis

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.

Citation

Corral, J., Konstantinidis, S., & Bamidis, P. (2020). Learning analytics, education data mining and personalisation in health professions education. In S. Konstantinidis, P. Bamidis, & N. Zary (Eds.), Digital Innovations in Healthcare Education and Training. Elsevier

• Online Publication Date: Sep 8, 2020
• Publication Date: Sep 8, 2020
• Deposit Date: Mar 21, 2019
• Publisher: Elsevier
• Book Title: Digital Innovations in Healthcare Education and Training
• Chapter Number: 8
• ISBN: 9780128131442
• Public URL: https://nottingham-repository.worktribe.com/output/1673210
• Related Public URLs: https://www.elsevier.com/books/digital-innovations-in-healthcare-education-and-training/konstantinidis/978-0-12-813144-2
• Contract Date: Jan 15, 2019