利用混合深度学习方法在电子学习中进行个性化检索.
Sidra Tahir1, Yaser Hafeez1, Mamoona Humayun2
1University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, Pakistan.
PloS one
|November 13, 2024
概括
本研究介绍了基于深度学习的课程推系统 (DLCRS),以应对在线学习的挑战. DLCRS提供了准确的,以学习者为中心的课程建议,达到89.62%的AUC得分.
科学领域:
- 人工智能的人工智能
- 教育技术的教育技术.
- 计算机科学 计算机科学
背景情况:
- 随着COVID-19的流行,在线学习平台的普及加速了.
- 多样化的用户知识框架在选择适当的在线课程材料方面带来了挑战.
- 现有的推系统很难提供个性化和准确的课程建议.
研究的目的:
- 提出一个新的基于深度学习的课程推系统 (DLCRS).
- 提高在线课程推的准确性和以学习者为中心.
- 解决当前系统在处理用户知识变化的局限性.
主要方法:
- 开发了一种混合顺序GRU+adam优化器,与协作过相结合.
- 集成的学习者和课程功能提取模块使用语言模型 (ELMO) 嵌入的词嵌入.
- 对来自公共组织的真实数据集进行了广泛的实验.
主要成果:
- 在ROC曲线下的面积 (AUC) 获得了89.62%的显著得分.
- 证明了0.88的命中率,表明了课程建议的高准确性.
- 在实验性评估中表现优于现有的高级课程推系统.
结论:
- 拟议的DLCRS有效地提供了准确和以学习者为中心的课程建议.
- 该系统的可行性得到了强大的实验结果和高精度指标的支持.
- DLCRS为导航在线教育资源的复杂性提供了一个有希望的解决方案.
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