多维学生绩效预测模型 (MSPP):用于准确的学术分类和分析的先进框架
1Department of Networking & Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
MethodsX
|January 27, 2025
概括
本研究引入了一种新的多维学生绩效预测模型 (MSPP),以准确预测学生的成绩. MSPP模型增强了教育数据分析,以改善学习干预和可靠的分类.
科学领域:
- 教育数据挖掘教育数据挖掘
- 教育中的人工智能
- 机器学习 机器学习
背景情况:
- 传统的学生绩效预测模型与多维,不平衡和时间教育数据作斗争,导致次优分类.
- 现有的方法往往提供了通用的见解,而不是量身定制的预测,限制了干预措施的有效性.
研究的目的:
- 提出一个新的多维学生绩效预测模型 (MSPP) 来准确预测学生的学业成绩.
- 解决现有模型在处理复杂教育数据集方面的局限性,并提高学生分类的准确性.
主要方法:
- 开发了MSPP模型,集成先进的数据预处理,特征工程和深度学习技术,包括图形神经网络层.
- 采用自适应式超参数调整和可解释的AI (XAI) 功能来处理不平衡和时间数据.
- 利用特定领域的预处理来构建稀疏,异质的学术数据以进行多类分类.
主要成果:
- MSPP模型实现了高精度 (76%),精度 (0.79) 和宏观F1得分 (0.73),优于现有模型.
- 显著降低了假阳性率 (FPR) 到0.15,提高了预测可靠性.
- 在多个学生表现类别 (区别,通过,失败,撤回) 中展示了改进的精确回忆.
结论:
- 该MSPP模型提供了一个强大的框架,通过结合上下文信息和多层次分析来准确预测学生的成绩.
- 该模型处理复杂数据并提供可靠的分类的能力支持开发有效的个性化教育干预措施.
- 这种方法为从稀疏,异质的学术数据中概括见解提供了基础,改善了教育成果.
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