An Ontology-based Recommender System for Identifying Learners’ Confusion in MOOCs
- Resource Type
- Conference
- Authors
- Tazi, Kaoutar; Azzouzi, Salma; Charaf, My El Hassan
- Source
- 2023 7th IEEE Congress on Information Science and Technology (CiSt) Information Science and Technology (CiSt), 2023 7th IEEE Congress on. :492-496 Dec, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Adaptive learning
Adaptation models
Information science
Electronic learning
Ontologies
Classification algorithms
Recommender systems
Dropout
MOOC
ontology
- Language
- ISSN
- 2327-1884
The rapid spread of MOOCs (massive open online courses) allows learners to benefit from these courses by having access to quality education. However, MOOCs experience high dropout rates due largely to learners’ confusion of some basic concepts. This article explores the use of an ontology-based recommender system to identify the degree of confusion related to a specific concept. For each concept cited in a post extracted from MOOC discussion forums, the degree of confusion is analyzed using our ontology and classification algorithm. Therefore, our primary objective is to extract and analyze all the prerequisite information needed to understand a given concept in order to develop an adaptive learning model and engage learners through the understanding of the course material.