Predicting and Interpreting Student Performance Using Machine Learning in Blended Learning Environments in a Jordanian School Context
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Date
0024-06
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The British University in Dubai (BUiD)
Abstract
The rise of the Internet has led to the widespread adoption of digital learning platforms, revolutionising the creation, access, and delivery of digital educational resources. These platforms enhance academic performance by fostering collaborative learning environments and generating extensive data from every user interaction. Machine learning algorithms can process large and complex datasets to identify patterns and trends that may not be immediately apparent. By analysing the data generated from these learning platforms with ML techniques, we can uncover detailed insights into student performance. Accurately predicting student performance can help educators tailor teaching methods and interventions to individual needs. This study focuses on predicting and interpreting student performance in a blended learning environment using ML in a Jordanian school context. The primary aim of this research is to employ machine learning models and SHAP (SHapley Additive exPlanations) to predict and understand student performance. A dataset generated by a digital learning platform used by a private school in Jordan is utilised. Various ML algorithms, such as Support Vector Machines, Logistic Regression, K-Nearest Neighbors, Naïve Bayes, Decision Trees, Random Forest, AdaBoost, Bagging, and Artificial Neural Networks are applied to predict student performance. SHAP values are used to interpret these predictions, offering insights into the factors most impacting student outcomes. Key findings indicate that ensemble methods like Random Forest and Bagging outperform other models in predicting student performance, achieving higher accuracy at 95.90% and 95.48%, respectively, as well as balanced precision and recall, which are crucial for accurately identifying both high- and low-performing students. The findings suggest that using these ensemble methods allows for more reliable predictions and better-informed educational strategies. The analysis reveals that individual features, such as engagement with learning materials and worksheets, significantly influence student performance. By understanding these specific factors and their impacts, educators can tailor interventions more effectively to individual needs, thereby enhancing the educational outcomes and supporting personalised learning. The findings underscore the potential of data-driven strategies to enhance educational outcomes and support personalised learning.