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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.
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Contributor : Armelle Brun Connect in order to contact the contributor
Submitted on : Thursday, October 22, 2020 - 3:19:30 PM
Last modification on : Wednesday, November 3, 2021 - 7:09:18 AM
Long-term archiving on: : Saturday, January 23, 2021 - 6:25:48 PM


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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⟩



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