School of management, Shanghai University, Shanghai, China.
School of Cultural Heritage and Information Management, Shanghai University, Shanghai, China.
PLoS One. 2024 Mar 13;19(3):e0298675. doi: 10.1371/journal.pone.0298675. eCollection 2024.
Higher vocational education is the core component of China's national education system and shoulders the mission of cultivating high-skilled and applied talents. The wide application of Massive Open Online Courses (MOOCs) has effectively improved the curriculum system of China's higher vocational education. In the meantime, some MOOCs suffer from poor course quality. Therefore, from the perspective of sustainable course quality improvement, we propose a data-driven framework for mining and analyzing student reviews in China's higher vocational education MOOCs. In our framework, we first mine multi-level student demands hidden in MOOC reviews by combining web crawlers and text mining. Then we use an artificial neural network and the KANO model to classify the extracted student demands, thereby designing effective and sustainable MOOC quality improvement strategies. Based on the real data from China's higher vocational education MOOCs, we validate the effectiveness of the proposed data-driven framework.
高等职业教育是中国国民教育体系的核心组成部分，肩负着培养高技能应用型人才的使命。大规模开放在线课程（MOOCs）的广泛应用，有效完善了中国高等职业教育的课程体系。与此同时，一些 MOOCs 课程质量较差。因此，从可持续课程质量提升的角度出发，我们提出了一个针对中国高等职业教育 MOOC 中学生评论挖掘和分析的数据驱动框架。在我们的框架中，我们首先通过结合网络爬虫和文本挖掘，挖掘 MOOC 评论中隐藏的多层次学生需求。然后，我们使用人工神经网络和 KANO 模型对提取的学生需求进行分类，从而设计有效的、可持续的 MOOC 质量提升策略。基于来自中国高等职业教育 MOOC 的真实数据，我们验证了所提出的数据驱动框架的有效性。
