Time complexity of the ant colony algorithm.

Students’ performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students’ performance can guide students’ learning in time. Aiming at the low accuracy problem of single model in students’ performance prediction, a combination prediction method is put forward based on ant colony algorithm. First, considering the characteristics of students’ learning behavior and the characteristics of the models, decision tree (DT), support vector regression (SVR) and BP neural network (BP) are selected to establish three prediction models. Then, an ant colony algorithm (ACO) is proposed to calculate the weight of each model of the combination prediction model. The combination prediction method was compared with the single Machine learning (ML) models and other methods in terms of accuracy and running time. The combination prediction model with mean square error (MSE) of 0.0089 has higher performance than DT with MSE of 0.0326, SVR with MSE of 0.0229 and BP with MSE of 0.0148. To investigate the efficacy of the combination prediction model, other prediction models are used for a comparative study. The combination prediction model with MSE of 0.0089 has higher performance than GS-XGBoost with MSE of 0.0131, PSO-SVR with MSE of 0.0117 and IDA-SVR with MSE of 0.0092. Meanwhile, the running speed of the combination prediction model is also faster than the above three methods.

学生成绩是高校教学质量评价的重要考量因素。对学生成绩开展预测与分析，能够及时为学生的学习提供指导。针对单一模型在学生成绩预测中准确率偏低的问题，本文提出一种基于蚁群算法（ACO）的组合预测方法。首先，结合学生学习行为特征与模型自身特性，选取决策树（DT）、支持向量回归（SVR）与BP神经网络（BP）构建三个基础预测模型；随后，提出通过蚁群算法计算组合预测模型各单模型的权重。将该组合预测方法与单一机器学习（ML）模型及其他同类方法，从准确率与运行时长两个维度开展对比实验。结果显示，均方误差（MSE）为0.0089的组合预测模型，其性能优于MSE为0.0326的决策树（DT）、MSE为0.0229的支持向量回归（SVR）以及MSE为0.0148的BP神经网络（BP）。为进一步验证该组合预测模型的有效性，本文还选取其他预测模型开展对比研究。结果表明，MSE为0.0089的组合预测模型，其性能同样优于MSE为0.0131的GS-XGBoost、MSE为0.0117的PSO-SVR以及MSE为0.0092的IDA-SVR；同时，该组合预测模型的运行速度也快于上述三种方法。