Title: Leveraging learners' activity logs for course reading analytics using session-based indicators

Authors: Madjid Sadallah; Benoît Encelle; Azze-Eddine Maredj; Yannick Prié

Addresses: Computer Science Department, University of Bejaia, 06000 Bejaia, Algeria; CERIST Research Centre, Algiers 16030, Algeria ' University of Lyon 1, LIRIS - UMR 5205 CNRS, Lyon, France ' CERIST Research Centre, Algiers 16030, Algeria ' University of Nantes, LINA - UMR 6241 CNRS, Lyon, France

Abstract: A challenge that course authors face when reviewing their contents is to detect how to improve their courses in order to meet the expectations of their learners. In this paper, we propose an analytical approach that exploits learners' logs of reading to provide authors with insightful data about the consumption of their courses. We first model reading activity using the concept of reading-session and propose a new and efficient session identification. We then elaborate a list of indicators computed using learners' reading sessions that allow to represent their behaviour and to infer their needs. We evaluate our proposals with course authors and learners using logs from a major e-learning platform. Interesting results were found. This demonstrates the effectiveness of the approach in identifying aspects and parts of a course that may prevent it from being easily read and understood, and for guiding the authors through the analysis and review tasks.

Keywords: human computer interaction; web-based interaction; LMS; learning management systems; learning analytics; reading monitoring; reading indicators; revisions; web log mining; reading sessions; session identification.

DOI: 10.1504/IJTEL.2020.103815

International Journal of Technology Enhanced Learning, 2020 Vol.12 No.1, pp.53 - 78

Received: 13 Mar 2018
Accepted: 23 Aug 2018
Published online: 29 Nov 2019 *

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