Wang Dongxuan, Lian Dapeng, Xing Yazhou, Dong Shiying, Sun Xinyu, Yu Jia
Department of Science and Technology, Hebei Agricultural University, Huanghua, China.
College of Humanities and Management, Hebei Agricultural University, Huanghua, China.
Front Psychol. 2022 Apr 22;13:881859. doi: 10.3389/fpsyg.2022.881859. eCollection 2022.
To effectively improve students' performance and help educators monitor students' learning situations, many colleges are committed to establishing systems that explore the influencing factors and predict student academic performance. However, because different colleges have different situations, the previous research results may not be applicable to ordinary Chinese colleges. This paper has two main objectives: to analyze the fluctuation of Chinese ordinary college student academic performance and to establish systems to predict performance. First, according to previous research results and the current situation of Chinese college students, a questionnaire was designed to collect data. Second, the chi-square test was used to analyze the contents of the questionnaire and identify the main features. Third, taking the main features as input, four classification prediction models are established by machine learning. Some traits of the students who did not pass all the examinations were also discovered. It might help student counselors and educators to take targeted measures. The experiment shows that the support vector machine classifier (SVC) model has the best and most stable effect. The average recall rate, precision rate, and accuracy rate reached 82.83%, 86.18%, and 80.96%, respectively.
为有效提高学生成绩并帮助教育工作者监测学生的学习情况，许多高校致力于建立探索影响因素并预测学生学业成绩的系统。然而，由于不同高校情况各异，以往的研究结果可能不适用于中国普通高校。本文有两个主要目标：分析中国普通高校学生学业成绩的波动情况并建立成绩预测系统。首先，根据以往研究结果和中国大学生的现状，设计了一份问卷来收集数据。其次，使用卡方检验分析问卷内容并确定主要特征。第三，以主要特征为输入，通过机器学习建立四个分类预测模型。还发现了所有考试都未通过的学生的一些特点。这可能有助于学生辅导员和教育工作者采取针对性措施。实验表明，支持向量机分类器（SVC）模型效果最佳且最稳定。平均召回率、精确率和准确率分别达到82.83%、86.18%和80.96%。
