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

Abstract

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.