Machine Learning Applications for Predicting Student Academic Performance in Digital Learning Environments

Antoine Laurent

Machine Learning Applications for Predicting Student Academic Performance in Digital Learning Environments

Author(s)	Antoine Laurent
Country	France
Abstract	Digital learning environments generate substantial quantities of educational data describing student participation, assessment behavior, resource access, assignment submission, and interaction with online course materials. These data provide opportunities for developing predictive models that can identify patterns associated with academic performance. Machine learning techniques can process multiple behavioral and academic indicators simultaneously and may help educational institutions recognize students who require additional academic support. However, prediction quality depends on data relevance, model selection, class distribution, and transparent evaluation procedures. This hypothetical methodological study examines machine learning applications for predicting student academic performance in digital learning environments. The proposed framework integrates academic history, learning-management-system activity, assessment engagement, assignment behavior, and participation indicators. A hypothetical dataset containing 1,200 student records is divided into training, validation, and testing subsets. Three conceptual prediction configurations are compared: logistic regression, decision-tree classification, and an ensemble machine learning model. The illustrative analysis evaluates classification performance through accuracy, precision, recall, and F1 score. A second analytical comparison examines model performance across different levels of digital engagement. Under the assigned hypothetical values, the ensemble configuration records the strongest overall classification performance, while prediction accuracy differs across engagement groups. These numerical results are illustrative and do not represent an implemented educational analytics experiment. The study concludes that machine learning can provide a useful methodological foundation for academic-performance prediction when educational data are handled carefully and predictions are interpreted as decision-support information rather than definitive judgments about students. Future empirical research should examine model fairness, institutional transferability, data quality, feature stability, and the educational consequences of predictive interventions.
Keywords	Machine Learning, Student Performance Prediction, Digital Learning Environments, Learning Analytics, Educational Data Mining, Academic Performance, Online Learning, Predictive Analytics, Student Engagement, Educational Technology.
Field	Engineering
Published In	Volume 1, Issue 3, July-August 2025
Published On	2025-08-07

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Machine Learning Applications for Predicting Student Academic Performance in Digital Learning Environments

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