Skip to Main content Skip to Navigation
Journal articles

Learners’ navigation behavior identification based on trace analysis

Nabila Bousbia 1 Issam Rebaï 2, 3 Jean-Marc Labat 4 Amar Balla 1
2 Lab-STICC_TB_CID_IHSEV
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192)
Abstract : Identifying learners’ behaviors and learning preferences or styles in a Web-based learning environment is crucial for organizing the tracking and specifying how and when assistance is needed. Moreover, it helps online course designers to adapt the learning material in a way that guarantees individualized learning, and helps learners to acquire meta-cognitive knowledge. The goal of this research is to identify learners’ behaviors and learning styles automatically during training sessions, based on trace analysis. In this paper, we focus on the identification of learners’ behaviors through our system: Indicators for the Deduction of Learning Styles. We shall first present our trace analysis approach. Then, we shall propose a ‘navigation type’ indicator to analyze learners’ behaviors and we shall define a method for calculating it. To this end, we shall build a decision tree based on semantic assumptions and tests. To validate our approach, and improve the proposed calculation method, we shall present and discuss the results of two experiments that we conducted.
Document type :
Journal articles
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-02549078
Contributor : Bibliothèque Télécom Bretagne <>
Submitted on : Tuesday, April 21, 2020 - 10:58:19 AM
Last modification on : Wednesday, June 24, 2020 - 4:19:35 PM

Links full text

Identifiers

Citation

Nabila Bousbia, Issam Rebaï, Jean-Marc Labat, Amar Balla. Learners’ navigation behavior identification based on trace analysis. User Modeling and User-Adapted Interaction, Springer Verlag, 2010, 20 (5), pp.455-494. ⟨10.1007/s11257-010-9081-5⟩. ⟨hal-02549078⟩

Share

Metrics

Record views

39