Adaptation of the Multi-Concept Multivariate Elo Rating System to Medical Students' Training Data-Reference-Cited by-同舟云学术

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Adaptation of the Multi-Concept Multivariate Elo Rating System to Medical Students' Training Data

Published:2024-03-18 Issue: Volume:58 Page:123-133
ISSN:
Container-title:Proceedings of the 14th Learning Analytics and Knowledge Conference
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Author:

Kandemir Erva Nihan¹^ORCID,Vie Jill-Jênn²^ORCID,Sanchez-Ayte Adam³^ORCID,Palombi Olivier⁴^ORCID,Ramus Franck⁵^ORCID

Affiliation:

1. Ecole Normale Supérieure, PSL University, CNRS, France

2. Soda team, Inria Saclay, France

3. Université Numeriqué en Santé et Sport (UNESS), France

4. Université Grenoble Alpes, Grenoble INP, CNRS, Inria, LIG, France

5. Ecole Normale Supérieure, PSL University, EHESS, CNRS, France

Funder

Agence Nationale de la Recherche

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3636555.3636858

Reference35 articles.

1. Knowledge Tracing: A Survey

2. Solmaz Abdi, Hassan Khosravi, and Shazia Sadiq. 2021. Modelling Learners in Adaptive Educational Systems: A Multivariate Glicko-Based Approach. In LAK21: 11th International Learning Analytics and Knowledge Conference. 497–503.

3. Solmaz Abdi, Hassan Khosravi, Shazia Sadiq, and Dragan Gasevic. 2019. A multivariate ELO-based learner model for adaptive educational systems. In EDM 2019-Proceedings of the 12th International Conference on Educational Data Mining. International Educational Data Mining Society, 228–233.

4. Using personalized learning as an instructional approach to motivate learners in online higher education: Learner self-determination and intrinsic motivation

5. Data science for analyzing and improving educational processes

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