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All Journal International Journal of Electrical and Computer Engineering International Journal of Evaluation and Research in Education (IJERE)
Abdelali, Zakrani
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Computer Science & IT Education Electrical & Electronics Engineering

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Predicting academic performance: toward a model based on machine learning and learner’s intelligences Rafiq, Jamal Eddine; Abdelali, Zakrani; Amraouy, Mohammed; Nouh, Said; Bennane, Abdellah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp645-653

Abstract

With the rapid evolution of online learning environments, the ability to predict students' academic performance has become crucial for personalizing and enhancing the educational experience. In this article, we present a predictive model based on machine learning techniques, designed to be integrated into online learning platforms using the competency-based approach. This model leverages features from four key dimensions: demographic, social, emotional, and cognitive, to accurately predict learners' academic performance. We detail the methodology for collecting and processing learning traces, distinguishing between explicit traces, such as demographic data, and implicit traces, which capture learners' interactions and behaviors during their learning process. The analysis of these data not only improves the accuracy of performance predictions but also provides valuable insights into skill acquisition and learners' personal development. The results of this study demonstrate the potential of this model to transform online education by making it more adaptive and focused on individual learners' needs.
Enhancing academic performance prediction in online learning through hybrid machine learning models Eddine Rafiq, Jamal; Abdelali, Zakrani; Amraouy, Mohammed; Nouh, Said
International Journal of Evaluation and Research in Education (IJERE) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v15i1.33590

Abstract

Faced with the rise of online learning platforms, predicting learners’ academic performance has become a major concern to personalize and enhance educational journeys. However, traditional predictive models struggle to effectively integrate emotional and social factors. This article introduces a hybrid predictive model that combines random forests (RF) for selecting the most relevant features and multiple regression (MR) to forecast academic performance. The data is sourced from three online learning platforms and encompasses both implicit traces (learner interactions and behaviors) and explicit traces (demographic characteristics). Following a selection and merging process, the final dataset comprises 1,003,392 records and 42 features, categorized into six types of indicators: cognitive, emotional, social, normative, contextual, and demographic. The results demonstrate that this hybrid model outperforms traditional approaches and other machine learning (ML) techniques in terms of predictive accuracy, achieving an R² of 0.9372 and a root mean square error (RMSE) of 0.1022. The incorporation of explicit and implicit traces helps better capture the intricate interactions among the different data dimensions, significantly enhancing prediction quality. This work represents a notable advancement in the field of academic performance prediction. It also sheds light on challenges associated with the increasing complexity of models, paving the way for future research to develop more generalizable approaches.
Co-Authors Amraouy, Mohammed Bennane, Abdellah Eddine Rafiq, Jamal Nouh, Said Rafiq, Jamal Eddine