一个适应性和个性化的框架,用于在线学习中的top-N课程建议
Samina Amin1, M Irfan Uddin2, Ala Abdulsalam Alarood3
1Institute of Computing, Kohat University of Science and Technology (KUST), Kohat, 26000, Pakistan.
Scientific reports
|May 6, 2024
概括
使用深度强化学习 (DRL) 的个性化推系统 (RS) 增强了大规模开放在线课程 (MOOC). DRR模型提供量身定制的学习路径和课程建议,改善在线学习体验.
科学领域:
- 教育技术的教育技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 大规模开放在线课程 (MOOCs) 提供了巨大的学习机会,但由于信息过载,可以让学习者感到压倒.
- 个性化推系统 (RSs) 对于有效导航教育内容至关重要.
- 现有的机器学习和强化学习 (RL) 方法对电子学习中的多任务建议有局限性.
研究的目的:
- 为MOOCs开发一个适应性推系统,解决传统方法的局限性.
- 通过考虑学习者特定的因素来个性化学习体验.
- 改善资源获取和学习者参与在线学习环境.
主要方法:
- 将深度强化学习 (DRL) 框架与多代理方法集成.
- 基于DRL的Actor-Critic模型 (DRR) 的开发,用于推中的顺序决策.
- 将学习者的情绪,学习风格,偏好,能力和适应性难度等级纳入个性化过程中.
主要成果:
- 拟议的DRR模型在MOOC数据集的广泛实验中表现出比基线模型更好的性能.
- 该系统有效地提供顶级的课程建议和个性化的学习路径.
- 对100K Coursera课程审查数据集的验证证实了该模型对长期建议的有效性.
结论:
- DRR模型为MOOC中的个性化建议提供了一个强大的解决方案.
- 这项研究通过指导有效的课程推系统的设计,有助于推进电子学习技术.
- 这些发现促进了更相关和个性化的建议,增强了学生的整体在线学习体验.
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