在中国的高等职业教育中,学生评价的数据驱动分析MOOC:质量改进的前景
PloS one
|March 13, 2024
概括
本研究引入了一个数据驱动的框架,以改善高等职业教育中的大规模开放在线课程 (MOOC). 它分析学生的评论,以可持续地提高课程质量.
科学领域:
- 教育技术的教育技术
- 教育中的数据科学教育中的数据科学
背景情况:
- 大规模开放在线课程 (MOOC) 对中国的高等职业教育至关重要,旨在培养熟练的人才.
- 尽管MOOC有好处,但课程质量不一致,对可持续发展构成挑战.
研究的目的:
- 提出一个数据驱动的框架来分析学生评论,以提高MOOC在高等职业教育的质量.
- 识别和解决多层次的学生对可持续课程增强的需求.
主要方法:
- 网络爬虫和文本挖掘被用来从MOOC评论中提取学生的需求.
- 使用人工神经网络和KANO模型对需求进行分类,并为质量改进策略提供信息.
主要成果:
- 该框架成功地从现实世界MOOC数据中挖掘了多层次的学生需求.
- 该分析为设计有效和可持续的MOOC质量改善策略提供了见解.
结论:
- 拟议的数据驱动框架有效地提高了MOOC在高等职业教育中的质量.
- 通过系统地分析学生的反,可以实现MOOC的可持续改进.
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