PREDICT LEARNERS’ PERFORMANCE USING AN ONTOLOGICAL-BASED MODEL ON AN E-LEARNING PLATFORM: PREDICT LEARNERS’ PERFORMANCE

Safa Ridha Albo Abdullah

doi:10.30572/2018/KJE/160316

Authors

Safa Ridha Albo Abdullah Department of Cyber Security, College of Information Technology, University of Babylon, Babil, Iraq

DOI:

https://doi.org/10.30572/2018/KJE/160316

Keywords:

Student Performance , Transfer Learning , Model Portability , Ontology, SWRL, Machine Learning, Decision Tree

Abstract

In learning analytics and educational data mining, a prominent challenge is posed by the lack of portability and transferability of predictive models across different courses. A novel ontology-based decision tree model is introduced in this study, which significantly enhances portability by incorporating semantic features. Unlike conventional decision tree models that are static and course-specific, the approach allows for the dynamic generation and adaptation of decision tree rules using ontologies, thus enabling seamless application across multiple courses without compromising accuracy. The core innovation of the method lies in the use of the Semantic Web Rule Language (SWRL) to integrate decision tree rules into an ontological framework, allowing reasoning and adaptation based on domain-specific semantics. By embedding decision tree rules that differentiate between pass and fail outcomes within an ontology, superior portability, and adaptability are achieved across courses with similar usage patterns. Applied to the Open University Learning Design (OULD) science courses dataset, advanced machine learning techniques were utilized to derive predictive rules, achieving an exceptional prediction accuracy of 97%. This demonstrates not only the effectiveness of the model but also its potential for robust transferability across various educational contexts, marking a significant advancement over existing methods

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