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Updated: May 22, 2025

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一个适应性反系统,以提高学习者的学习能力
Hafiz Muhammad Qadir1, Rafaqat Alam Khan1, Mudassar Rasool2
1Department of Software Engineering, Lahore Garrison University, Lahore, 54810, Pakistan.
Scientific reports
|May 18, 2025
概括
本研究介绍了学习管理系统的自适应反系统,以帮助教师识别学习挑战. 组合学习技术,如堆叠,提高了自动评估的准确性,以获得更好的学生成果.
科学领域:
- 教育技术的教育技术
- 机器学习在教育中的应用
背景情况:
- 有效的教学需要了解学生的优缺点.
- 早期识别学习挑战可以提高学业成绩.
- 量身定制的反增强了学生的动力和学术成功.
研究的目的:
- 为学习管理系统 (LMS) 开发一个强大的自适应反系统.
- 利用实例级探索来识别特定的学习成果决定因素.
- 提高自动化评估,并为高等学生提供精确的反.
主要方法:
- 在LMS中的实例级探索.
- 实施集体学习技术,包括堆叠,囊网络,支持矢量机 (SVM),随机森林,决策树和K-最近邻居 (KNN).
- 开发一个用户友好的网络服务器来提供反.
主要成果:
- 堆叠实现了最高准确度的76.70%.
- 支持矢量机 (SVM) 证明了0.78.8的最高精度.
- 这项研究验证了集体学习对教育反的有效性.
结论:
- 适应性反系统有效地支持教师识别和解决学习挑战.
- 该系统增强了自动评估,为高等学生提供有意义的反.
- 技术和教学的整合改善了教育经验,特别是在以结果为基础的教育系统.
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