A Review: An Approach for Secondary School Students Performance using Machine Learning and Data Mining
Keywords:
Attribute selection, Ensemble learning, Educational data mining, Hybrid model, Learning analytics, Machine learning, Online learningAbstract
The measurement of Students' Academic Performance (SAP) stands as a crucial gauge for assessing the standing of students within an academic institution. This metric enables instructors and education administrators to obtain a precise assessment of students across various courses in a specific semester. Additionally, it serves as an invaluable indicator for students to reflect on their strategies, encouraging improvements for enhanced performance in subsequent semesters. Each institution establishes its own set of criteria for evaluating student performance. This variation arises from a deficiency in researching existing prediction techniques, leading to the quest for the most effective methodology in predicting students' academic progress and performance. Another significant factor is the insufficient exploration of relevant factors influencing student achievement in specific courses. To comprehensively comprehend this issue, a thorough literature survey on predicting student performance through the application of data mining techniques is suggested. This paper objective is to enhance student academic performance by employing advanced machine learning techniques. The utilization of these techniques not only aims to elevate student outcomes but also holds the potential to yield benefits for faculty members, students, educators, and the overall institutional management.
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