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.

预测学生表现是为处于风险学生提供有效的预防措施至关重要。本文通过分析 12 种自然启发算法在预测学生表现上的相对性能，研究了应用于包含基于实例的点击流数据、课内单课程表现以及同时学习多个课程的 3 个数据集。发现在所有数据集中，利用基于自然启发算法的特征选择和传统机器学习算法进行集成，能够提高预测准确性的同时将特征集大小减少了 2/3。