SAPPNet:使用神经网络在COVID-19期间预测学生的学业成绩
Naveed Ur Rehman Junejo1,2,3, Qingsheng Huang4, Xiaoqing Dong1
1School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, 521041, China.
Scientific reports
|October 19, 2024
概括
一个新的深度学习模型,SAPPNet,通过分析空间和时间数据,准确地预测学生的学业成绩. 这种先进的模型超越了传统的方法,提供了更好的教育管理见解.
科学领域:
- 教育技术的教育技术
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 由于各种因素,预测学生的学业表现具有挑战性.
- 现有的预测模型往往无法满足教育管理需求.
- 由于COVID-19的流行,引入了影响学生参与和结果的新变量.
研究的目的:
- 提出一个新的深度学习 (DL) 模型,学生学业绩预测网络 (SAPPNet),用于准确的成绩预测.
- 评估SAPPNet的性能与经典机器学习 (ML) 和其他DL模型相比.
- 利用包括人口统计,数字工具使用和心理因素在内的全面数据集.
主要方法:
- 开发了SAPPNet,这是一个DL模型,包含静态特征的空间卷积模块和动态变化的时间模块.
- 利用约旦大学的数据集,包括COVID-19前后关于学生属性和行为的信息.
- 将SAPPNet与ML模型 (SVM,KNN,决策树,随机森林) 和其他DL模型 (ANN,CNN,LSTM) 进行比较.
主要成果:
- 与所有基准测试方法相比,SAPPNet表现优越.
- 该模型实现了高准确性,精度,回忆和F1得分.
- 空间和时间模块的集成显著提高了预测能力.
结论:
- 在预测学生学业成绩方面,SAPPNet提供了显著的进步.
- 该模型捕捉空间和时间依赖的能力为教育管理提供了宝贵的见解.
- 这项研究为分析教育数据集和改进学生支持系统开辟了新的途径.
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