Student demands extracted from MOOC review data.

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.

高等职业教育是我国国民教育体系的核心组成部分，肩负着培养高技术技能应用型人才的重要使命。大规模开放在线课程（Massive Open Online Courses，MOOCs）的广泛应用，有效完善了我国高等职业教育的课程体系。与此同时，部分MOOCs存在课程质量欠佳的问题。因此，从课程质量可持续提升的视角出发，我们提出了一种面向我国高等职业教育MOOCs学生评论挖掘与分析的数据驱动框架。在该框架中，我们首先结合网络爬虫与文本挖掘技术，挖掘隐藏在MOOC评论中的多层次学生需求；随后采用人工神经网络与KANO模型对提取的学生需求进行分类，据此设计出兼具有效性与可持续性的MOOC质量提升策略。基于我国高等职业教育MOOCs的真实数据集，我们验证了所提出的数据驱动框架的有效性。