New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

Dublin Core

Title

New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS

Subject

LMS
Learning styles
FSLSM
Dropout
Decision Tree
Rules-based

Description

Learning style is a significant learner-difference factor. Each learner has a preferred learning style and a different way of processing and understanding the novelty. In this paper, a new approach that automatically identify learners learning styles based on their interaction with the Learning Management System (LMS) is introduced. To implement this approach, the traces of 920 enrolled learners in three agronomy courses were exploited using an unsupervised clustering method to group learners according to their degree of engagement. The decision tree classification algorithm relies on the decision rules construction, which is widely adopted to identify the accurate learning style. As missing good decision rules would lead to learning style misclassification, the Felder-Silverman Learning Style Model (FSLSM) is used as it is among the most adopted models in the technology of quality improvement process. The results of this research highlight that most learners prefer the global learning style.

Creator

Benabbes, Khalid
Hmedna, Brahim
Housni, Khalid
Zellou, Ahmed
El Mezouary, Ali

Source

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 19 (2022); pp. 61-80
1865-7923

Publisher

International Association of Online Engineering (IAOE), Vienna, Austria

Date

2022-10-19

Rights

Copyright (c) 2022 khalid Benabbes, Brahim Hmedna, Khalid Housni, Ahmed Zellou, Ali El Mezouary
https://creativecommons.org/licenses/by/4.0

Relation

Format

application/pdf

Language

eng

Type

info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article

Identifier

Citation

Khalid Benabbes et al., New Automatic Hybrid Approach for Tracking Learner Comprehension Progress in the LMS, International Association of Online Engineering (IAOE), Vienna, Austria, 2022, accessed November 23, 2024, https://igi.indrastra.com/items/show/2376

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