使用随机森林算法预测学生课程成绩:预测因子的重要性分析

IF 3.4 Q2 NEUROSCIENCES Trends in Neuroscience and Education Pub Date : 2023-09-17 DOI:10.1016/j.tine.2023.100214
Mirna Nachouki, Elfadil A. Mohamed, Riyadh Mehdi, Mahmoud Abou Naaj
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引用次数: 0

摘要

背景大学需要找到提高学生保留率的策略。预测学生的学习成绩使各机构能够识别成绩不佳的学生,并采取适当行动提高学生完成学业和降低辍学率。方法在这项工作中,我们提出了一个基于随机森林方法的模型,使用七个输入预测因子来预测学生的课程成绩,并发现它们在确定课程成绩中的相对重要性。七个预测因子来自650名计算机专业本科生的成绩单和记录数据。结果我们的研究结果表明,平均绩点和高中成绩是课程成绩的两个最重要的预测因素。课程类别和上课率具有同等的重要性。课程交付模式没有显著影响。结论我们的研究结果表明,有风险的学生可以确定具有挑战性的课程,并采取适当的行动、程序和政策。
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Student course grade prediction using the random forest algorithm: Analysis of predictors' importance

Background

Universities need to find strategies for improving student retention rates. Predicting student academic performance enables institutions to identify underachievers and take appropriate actions to increase student completion and lower dropout rates.

Method

In this work, we proposed a model based on random forest methodology to predict students' course performance using seven input predictors and find their relative importance in determining the course grade. Seven predictors were derived from transcripts and recorded data from 650 undergraduate computing students.

Results

Our findings indicate that grade point average and high school score were the two most significant predictors of a course grade. The course category and class attendance percentage have equal importance. Course delivery mode does not have a significant effect.

Conclusion

Our findings show that courses students at risk find challenging can be identified, and appropriate actions, procedures, and policies can be taken.

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来源期刊
CiteScore
6.30
自引率
6.10%
发文量
22
审稿时长
65 days
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