Analyzing the Capabilities of Nature-inspired Feature Selection Algorithms in Predicting Student Performance

TL;DR

This study evaluates the effectiveness of nature-inspired feature selection algorithms within ensemble models to improve student performance prediction accuracy and reduce feature set size across multiple educational datasets.

Contribution

It introduces a Swarm Intelligence ML engine (SIMLe) and demonstrates that combining nature-inspired feature selection with traditional classifiers enhances prediction accuracy and reduces feature complexity.

Findings

Ensemble models with nature-inspired feature selection outperform previous algorithms.

Predictive accuracy increased significantly across all datasets.

Feature set size was reduced by up to 65%.

Abstract

Predicting student performance is key in leveraging effective pre-failure interventions for at-risk students. As educational data grows larger, more effective means of analyzing student data in a timely manner are needed in order to provide useful predictions and interventions. In this paper, an analysis was conducted to determine the relative performance of a suite of nature-inspired algorithms in the feature-selection portion of ensemble algorithms used to predict student performance. A Swarm Intelligence ML engine (SIMLe) was developed to run this suite in tandem with a series of traditional ML classification algorithms to analyze three student datasets: instance-based clickstream data, hybrid single-course performance, and student meta-performance when taking multiple courses simultaneously. These results were then compared to previous predictive algorithms and, for all datasets analyzed, it was found that leveraging an ensemble approach using nature-inspired algorithms for feature selection and traditional ML algorithms for classification significantly increased predictive accuracy while also reducing feature set size by up to 65 percent.