Improving Massive Open Online Course Quality in Higher Education by Addressing Student Needs Using Quality Function Deployment

Massive Open Online Courses (MOOCs) are playing an increasingly important role in higher education. However, some MOOCs still suffer from low quality, which hinders the sustainable development of higher education. Course characteristics reflect students’ needs for online learning and have a significant impact on the quality of MOOCs. In the course improvement process, existing research has neither improved the MOOC quality from the perspective of student needs nor has it considered resource constraints. Therefore, to deal with this situation, we propose a student-needs-driven MOOC quality improvement framework. In this framework, we first map students’ differentiated needs for MOOCs into quality characteristics based on quality function deployment (QFD). Then, we formulate a mixed-integer linear programming model to produce MOOC quality improvement policies. The effectiveness of the proposed framework is verified by real-world data from China’s higher education MOOCs. We also investigate the impacts of budget, cost, and student needs on student satisfaction. Our results revealed that to significantly improve student satisfaction, the course budget needs to be increased by a small amount or the course cost needs to be greatly reduced. Our research provides an effective decision-making reference for MOOC educators to improve course quality

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.</div

Student demand classification method based on the KANO model.

Student demand classification method based on the KANO model.</p

The sentiment polarity of student demands in MOOC reviews.

The sentiment polarity of student demands in MOOC reviews.</p

The accuracy as the training of the neural network.

The accuracy as the training of the neural network.</p

The loss as the training of the neural network.

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.</div

The KANO model for MOOCs in higher vocational education.

The KANO model for MOOCs in higher vocational education.</p

Example of the <i>n</i>-th demand for MOOCs.

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.</div

Relationship map of student demands.

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.</div

Classification results for the student demands.

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.</div