机器学习
计算机科学
人工智能
一般化
教育数据挖掘
性能预测
过程(计算)
质量(理念)
学业成绩
数学教育
心理学
模拟
数学
数学分析
哲学
认识论
操作系统
作者
Lihong Zhao,Jiaolong Ren,Zhang Li,Zhao Hong
出处
期刊:Sustainability
[MDPI AG]
日期:2023-08-18
卷期号:15 (16): 12531-12531
被引量:3
摘要
Academic performance evaluation is essential to enhance educational affection and improve educational quality and level. However, evaluating academic performance is difficult due to the complexity and nonlinear education process and learning behavior. Recently, machine learning technology has been adopted in Educational Data Mining (EDM) to predict and evaluate students’ academic performance. This study developed a quantitative prediction model of academic performance and investigated the performance of various machine learning algorithms and the influencing factors based on the collected educational data. The results conclude that machine learning provided an excellent tool to characterize educational behavior and represent the nonlinear relationship between academic performance and its influencing factors. Although the performance of various methods has some differences, all could be used to capture the complex and implicit educational law and behavior. Furthermore, machine learning methods that fully consider various factors have better prediction and generalization performance. In order to characterize the educational law well and evaluate accurately the academic performance, it is necessary to consider as many influencing factors as possible in the machine learning model.
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