IMPROVING ONLINE LEARNING USING DEEP LEARNING AND STUDENT’S INTELLIGENCES

Year 2025, Volume: 26 Issue: 2, 39 - 52, 01.04.2025

Jamal Eddine Rafiq , Abdelali Zakrani Mohammed Amraouy Said Nouh Abdellah Bennane

Abstract

The emergence of online learning has sparked increased interest in predicting learners’ academic performance to enhance teaching effectiveness and personalized learning. In this context, we propose a complex model APPMLT-CBT which aimes to predict learners’ performance in online learning settings. This systemic model integrates cognitive, social, emotional, contextual, and normative aspects to predict the learners’ performance in online learning environment. This model, based on Competency-Based Learning Traces, takes a holistic approach by integrating various data reflecting knowledge acquisition and skills development. By Taking into account the exchanges among the learners, as well as the interactions with their teachers and the complexity of their online learning environment, the model aims to provide accurate and informed predictions of academic performance. This study provides a detailed overview of the APPMLT-CBT model, its data collection methodology, and discusses its potential implications for online teaching. Results suggest that the model can serve as a robust framework for improving online teaching and learning while offering a deep understanding of the underlying mechanisms of online learning.

Keywords

Learner’s intelligences, predicting academic performance, competency-based learning, deep learning, online learning

References

• Adejo, O. W., & Connolly, T. (2018). Predicting student academic performance using multi-model heterogeneous ensemble approach. Journal of Applied Research in Higher Education, 10(1), 61–75. https://doi.org/10.1108/JARHE-09-2017-0113
• Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student’ performance prediction using machine learning techniques. Education Sciences, 11(9). https://doi.org/10.3390/ educsci11090552
• Amraouy, M., Bellafkih, M., Bennane, A., & Himmi, M. M. (2022). Online Competency-Based Assessment (OCBA): From Conceptual Model to Operational Authoring System. International Journal of Interactive Mobile Technologies, 16(4), 46–57. https://doi.org/10.3991/ijim.v16i04.28373
• Amraouy, M., Bellafkih, M., Bennane, A., & Talaghzi, J. (2023). Sentiment Analysis for Competence-Based e-Assessment Using Machine Learning and Lexicon Approach. The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), March 5–7, 2023. AICV 2023. https://doi.org/10.1007/978-3-031-27762-7_31
• Amraouy, M., Bennane, A., Himmi, M. M., Bellafkih, M., & Benomar, A. (2020). Detecting the Learner’s Motivational State in Online Learning Situation towards Adaptive Learning Environments. ACM International Conference Proceeding Series, 127–132. https://doi.org/10.1145/3419604.3419760