使用机器学习对学习管理系统问题的数据集进行数学测试问题的分类
Gun Il Kim1, Sungtae Kim2, Beakcheol Jang1
1Graduate School of Information, Yonsei University, Seoul, South Korea.
PloS one
|October 18, 2023
概括
这项研究使用机器学习根据难度对数学问题进行分类,改进了个性化的学习建议. XGBoost模型实现了85.7%的准确性,提高了学生的学习效率.
科学领域:
- 教育技术的教育技术
- 机器学习在教育中的应用
- 人工智能在学习管理系统中的应用
背景情况:
- 学生表现出各种各样的数学能力,需要量身定制的教育内容.
- 学习管理系统 (LMS) 利用人工智能和数据库来个性化在线学习体验.
- 对于LMS来说,对问题的难度进行分类至关重要,以推适当的内容并提高学习效率.
研究的目的:
- 通过使用机器学习技术,根据难度级别分类大规模的数学测试项目.
- 确定与问题难度相关的重要变量.
- 为了评估不同机器学习模型对问题的难度分类的性能.
主要方法:
- 测试分析以确定变量 (答案率,问题类型,解决时间) 和难度水平之间的显著相关性.
- 使用机器学习模型对数学项目的分类:逻辑回归 (LR),随机森林 (RF) 和XGBoost.
- 使用准确度,精度,回忆,F1得分,AUC-ROC,科恩的卡帕和MCC等指标对模型的评估.
主要成果:
- 答案率,问题类型和解决时间与问题难度有显著的相关性.
- XGBoost模型表现出卓越的性能,达到85.7%的精度和85.8%的F1分数.
- 机器学习模型有效地根据难度对数学测试项目进行了分类.
结论:
- 机器学习,特别是XGBoost,为分类数学问题难度提供了有效的方法.
- 这些分类可以作为LMS的辅助工具,为学习者推合适的问题.
- 基于难度的个性化问题建议可以提高整体学习效率.
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