Impact of dimensionality reduction techniques on student performance prediction using machine learning
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Abstract
This study addresses the crucial issue of predicting student performance in educational data mining (EDM) by proposing an Adaptive Dimensionality Reduction Algorithm (ADRA). ADRA efficiently reduces the dimensionality of student data, encompassing various academic, demographic, behavioral, social, and health-related features. It achieves this by iteratively selecting the most relevant features based on a combined normalized mean rank of five feature ranking methods. This reduction in dimensionality enhances the performance of predictive models and provides valuable insights into the key factors influencing student performance. The study evaluates ADRA using four different student performance datasets and six machine learning algorithms, comparing it to three existing dimensionality reduction methods. The results show that ADRA achieves an average dimensionality reduction factor of 6.2 while maintaing comprable accuracy with other mehtods.
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