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Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques
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DOI：10.3390/computers14030083.png)
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Abstract
En 中文
This study investigates the use of educational data mining (EDM) techniques to predict student performance and enhance learning outcomes in higher education. Leveraging data from Moodle, a widely used learning management system (LMS), we analyzed 450 students' academic records spanning nine semesters. Five machine learning algorithms-k-nearest neighbors, random forest, logistic regression, decision trees, and neural networks-were applied to identify correlations between courses and predict grades. The results indicated that courses with strong correlations (+0.3 and above) significantly enhanced predictive accuracy, particularly in binary classification tasks. kNN and neural networks emerged as the most robust models, achieving F1 scores exceeding 0.8. These findings underscore the potential of EDM to optimize instructional strategies and support personalized learning pathways. This study offers insights into the effective application of data-driven approaches to improve educational outcomes and foster student success.
Keywords:
Moodle
learning management system (LMS)
educational data mining (EDM)
synchronous and asynchronous learning
student engagement
personalized learning
higher education
machine learning algorithms
online learning platforms
Journal
C
IF:
4.2
Papers:
1.5K
Citations:
3.3K
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