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.

翻译：学生成绩预测对于对高风险学生实施有效的预失败干预至关重要。本文分析了12种自然启发式算法在三个数据集上预测学生成绩的相对性能，这些数据集包括基于实例的点击流数据、课程内单次课程成绩以及同时学习多门课程时的成绩。研究发现，对于所有数据集，采用集成方法——使用自然启发式算法进行特征选择、传统机器学习算法进行分类——在将特征集规模缩减三分之二的同时，提高了预测准确性。