New Measures for Offline Evaluation of Learning Path Recommenders - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year :

New Measures for Offline Evaluation of Learning Path Recommenders

Abstract

Recommending students useful and effective learning paths is highly valuable to improve their learning experience. The evaluation of the effectiveness of this recommendation is a challenging task that can be performed online or offline. Online evaluation is highly popular but it relies on actual path recommendations to students, which may have dramatic implications. Offline evaluation relies on static datasets of students' learning activities and simulates paths recommendations. Although easier to run, it is difficult to accurately evaluate offline the effectiveness of a learning path recommendation. To tackle this issue, this work proposes simple offline evaluation measures. We show that they actually allow to characterise and differentiate the algorithms.
Fichier principal
Vignette du fichier
ectel2020.pdf (220.13 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02974676 , version 1 (22-10-2020)

Identifiers

Cite

Zhao Zhang, Armelle Brun, Anne Boyer. New Measures for Offline Evaluation of Learning Path Recommenders. 15th European Conference on Technology Enhanced Learning, EC-TEL 2020, Sep 2020, Heidelberg, Germany. ⟨10.1007/978-3-030-57717-9_19⟩. ⟨hal-02974676⟩
117 View
307 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More