Quantitative analysis reveals that student satisfaction and engagement influence continuance intention in MOOCs, suggesting effective course design is crucial.
Background and Aim: The integration of Massive Open Online Courses (MOOCs) in higher education has transformed traditional learning methods, particularly in ideological and political education (IPE). This study examines students' perceptions of using MOOCs for IPE courses at a vocational university, focusing on factors influencing satisfaction and continuance intention. Drawing on the Expectation Confirmation Model (ECM) and the Task-Technology Fit (TTF) model, the research investigates how perceived usefulness, confirmation, task-technology fit, and learning engagement affect students’ satisfaction and their intention to continue using MOOCs. Moreover, the study advances theory by integrating the ECM and TTF frameworks, highlighting the mediating role of satisfaction and providing a nuanced explanation of continuance intention in the context of IPE. Results: A quantitative approach was employed, surveying 248 students to analyze their experiences. Structural Equation Modeling (SEM) was used to test the hypothesized relationships between variables. Results: The results indicate that perceived usefulness significantly influences satisfaction (β = 0.42, p < 0.001), and confirmation has a significant positive effect on satisfaction (β = 0.38, p < 0.001). Task-technology fit and learning engagement also positively impact satisfaction (β = 0.35 and 0.31, respectively, p < 0.001). Additionally, satisfaction significantly affects students’ continuance intention (β = 0.47, p < 0.001), with perceived usefulness, task-technology fit, and learning engagement playing crucial roles in shaping students’ willingness to continue using MOOCs. Conclusion: The findings underscore the importance of course design, interactive content, and platform usability in enhancing learning outcomes. The study provides empirical support for the theoretical integration of ECM and TTF in explaining MOOCs continuance, offering valuable insights for optimizing MOOCs in IPE.
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X.L. Zhang (2025) studied this question.
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