使用神经网络在在线教育的早期阶段准确的多类别学生绩效预测
Naveed Ur Rehman Junejo1,2, Muhammad Wasim Nawaz3, Qingsheng Huang4
1School of Physics and Electronic Engineering, Hanshan Normal University, Chaozhou, 521041, China.
Scientific reports
|May 9, 2025
概括
本研究引入了一种新的神经网络模型,用于预测多个类别 (杰出,失败,通过,撤回) 的在线课程中的学生表现. 该模型显著提高了早期预测的准确性,更早地识别了有风险的学生.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 学习分析学习分析
背景情况:
- 准确预测在线学习中的学生表现对于及时干预至关重要.
- 现有的研究往往侧重于二元结果 (通过/失败),忽视了多类别的性能预测.
- 在线课程中早期识别有风险的学生仍然是一个重大挑战.
研究的目的:
- 开发和验证一种基于神经网络的新方法,用于在线课程中预测学生在多个类别 (杰出,失败,通过,退出) 的表现.
- 能够在网上课程的开始和整个过程中早期识别弱势学生.
- 在预测准确性和早期检测能力方面改进现有方法.
主要方法:
- 利用开放大学学习分析 (OULA) 数据集,预处理人口统计,评估和虚拟学习环境 (VLE) 交互数据.
- 开发了一种新的神经网络模型,将学生的VLE点击流数据的工程特征纳入其中,汇总以表示日常参与.
- 将拟议的模型与包括ANN-LSTM,随机森林 (RF) 和深度前神经网络 (DFFNN) 在内的基线方法进行了比较.
主要成果:
- 拟议的神经网络模型在准确性,精度,回忆和F1分数方面显著超过了基线模型.
- 与现有的最先进的方法相比,实现了大约[公式:参见文本]更高的预测准确性.
- 在时间过程进展中表现出卓越的预测能力,即使在初始[公式:参见文本]阶段也保持高准确度.
结论:
- 新型神经网络方法为在线教育中的多类别学生绩效预测提供了强大的解决方案.
- 早期预测学生的成绩是可行的,并通过分析VLE交互模式显著增强.
- 开发的模型为教育工作者和机构提供了一个有价值的工具,以支持学生在在线学习环境中的成功.
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