学业导师制提升课程成绩的机器学习评价方法

学业导师制是实施和完善学分制的一种辅助制度,可以有效地提高学生的综合素质、创新精神和实践能力。为了量化地分析引入学业导师制度对学生课程成绩的影响程度,基于传统的机器学习模型,提出一种基于多元线性回归模型的机器学习评价方法。该方法主要分为数据的预处理、数据的特征筛选、模型的训练、交叉验证以及成绩预测等5个阶段。最后,根据学生的成绩数据进行实验分析,对比学业导师制实施前后学生成绩的变化情况,验证了学业导师制能够有效提升专业课程的及格率和优良率。

An Evaluation Method of Machine Learning for Academic Performance and Tutorial System

The tutorial system is an auxiliary system for implementing and perfecting the credit system, which can effectively improve the comprehensive quality, innovative spirit and practical ability of students. In order to analyze the influence of the tutorial system on student course performance, this paper proposes an evaluation method of machine learning based on multiple linear regression models. The method is mainly divided into five stages: data preprocessing, data feature selection, model training, cross-validation, and score prediction. Finally, an experimental analysis was conducted on the course score. The comparison results proved that the tutorial system can effectively improve the pass rate and excellent rate of the course score.