A Comparative Analysis of the Capabilities of Nature-inspired Feature Selection Algorithms in Predicting Student Performance
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Predicting student performance is key in leveraging effective pre-failure interventions for at-risk students. In this paper, I have analyzed the relative performance of a suite of 12 nature-inspired algorithms when used to predict student performance across 3 datasets consisting of instance-based clickstream data, intra-course single-course performance, and performance when taking multiple courses simultaneously. I found that, for all datasets, leveraging an ensemble approach using NIAs for feature selection and traditional ML algorithms for classification increased predictive accuracy while also reducing feature set size by 2/3.
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