研究基于机器学习的学习成就分类的研究
Jianwei Dong1,2, Ruishuang Sun3, Zhipeng Yan4
1College of Educational Science, Xinjiang Normal University, Urumqi, China.
PloS one
|June 18, 2025
概括
预测学生的学业成绩对于教育至关重要. 这项研究使用高斯分布数据增强 (GDO) 和机器学习模型提高了分类准确性,达到94.12%的准确率.
科学领域:
- 教育数据挖掘教育数据挖掘
- 机器学习在教育中的应用
- 教育中的人工智能
背景情况:
- 学业成绩是教育质量和学生学习成果的关键指标.
- 传统的学术绩效分类方法的准确性很低,并与非线性关系和数据稀疏性作斗争.
- 准确预测学术成绩可以为教育战略和政策制定提供信息.
研究的目的:
- 分析影响学业成绩的各种学生特征.
- 使用先进的计算技术,提高学生绩效分类的准确性和稳定性.
- 探索各种机器学习和深度学习模型的有效性,并与数据增强相结合,用于分级分类.
主要方法:
- 分析学生数据,包括个人信息,学术记录,出席率,家庭背景和课外活动.
- 应用基于高斯分布的数据增量 (GDO) 来提高数据质量和模型稳定性.
- 评估多种机器学习 (ML) 和深度学习 (DL) 模型,包括辐射基函数网络 (RBFN),用于具有多种特征组合和增强策略的分类任务.
主要成果:
- 使用教育习惯特征和GDO数据增强的RBFN模型实现了最高的性能.
- 获得了94.12%的分类准确率和94.46%的F1得分,具有最佳的模型和功能集.
- 通过差异同质性和P值分析验证合成数据的有效性,并评估过量采样率的影响.
结论:
- 拟议的GDO技术与ML/DL模型相结合,显著提高了学生年级分类的准确性和稳定性.
- 教育习惯的特征是高度预测学术表现,当增强与GDO.
- 这项研究为教育数据分析,学生干预策略和智能教育系统的进步提供了宝贵的见解.
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