一种混合DEMATEL和社交网络分析模型,以确定影响学习者对MOOC满意度的因素
Sadra Ahmadi1,2, Zahra Nourmohamadzadeh1,2, Babak Amiri1,2
1Cyberspace Research Institute, Shahid Beheshti University, Iran, Tehran.
Heliyon
|July 31, 2023
概括
分析用户对大规模开放在线课程 (MOOC) 的反,揭示了影响学习者满意度的关键因素. 优先考虑这些元素可以显著提高课程完成率和平台的有效性.
科学领域:
- 教育技术的教育技术
- 数据科学数据科学数据科学
- 学习分析学习分析
背景情况:
- 大规模开放在线课程 (MOOCs) 面临着一个重大的挑战,因为学习者完成率很低.
- 关于MOOC完成的现有研究通常依赖于短期的用户行为数据,限制了长期的改进策略.
- 分析用户生成的反为了解和增强MOOC体验提供了一个有价值的,尽管未被充分利用的资源.
研究的目的:
- 通过分析学习者评论来识别和优先考虑影响MOOC用户满意度的因素.
- 开发一种基于用户反和经验的MOOC平台优化方法.
- 通过全面的反分析来弥补MOOCs长期改进策略的差距.
主要方法:
- 应用情绪分析和主题建模到受欢迎的Coursera课程 (如数据科学,数据驱动决策) 的用户评论中.
- 利用决策试验和评估实验室 (DEMATEL) 的分析来根据相互关系对因素进行排名.
- 采用网络分析来优先考虑改进的因素,以最大限度地提高用户满意度.
主要成果:
- 提出的方法成功地确定了MOOC中的关键优点和弱点.
- 对用户反的分析确定了影响学习者满意度的具体因素.
- 实现了改进领域的优先级,指导着努力加强MOOC整体平台.
结论:
- 情绪分析,主题建模和DEMATEL分析的综合方法有效地从用户反中提取可操作的见解.
- 这种方法为MOOC平台提供了一个框架,以系统地改进课程设计和用户体验.
- 确定因素的优先考虑可以提高学习者满意度,并可能提高课程完成率.
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