Multiple Pathways to Learning Engagement in MOOCs: A Configurational Analysis Using FSQCA for Inclusive Digital Learning in Postgraduate Education

Abstract

The increasing complexity of digital learning environments challenges traditional linear approaches to understanding learner engagement in Massive Open Online Courses (MOOCs). This study adopts fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine how different configurations of environmental and cognitive factors contribute to high learning engagement among postgraduate students. Five key conditions are analyzed: Learning Environment Support (LES), Perceived Usefulness (PU), Satisfaction (SAT), Intrinsic Motivation (IM), and Self-Regulated Learning (SRL).

Using data collected from 432 postgraduate students enrolled in MOOCs-integrated courses, the study identifies multiple configurational pathways leading to learning engagement. The results indicate that LES functions as a quasi-necessary condition, while engagement emerges through three distinct pathways: system-driven, motivation-driven, and hybrid configurations. Among these, the hybrid pathway—combining strong environmental support with high intrinsic motivation and self-regulated learning—produces the highest level of engagement. The findings demonstrate key principles of complexity theory, including equifinality, causal asymmetry, and conjunctural causation.

Importantly, this study advances MOOC research by shifting from variable-centered explanations to a configurational perspective, emphasizing that learning engagement arises from multiple interacting conditions rather than isolated factors. In alignment with Sustainable Development Goal 4 (SDG 4), which promotes inclusive and equitable quality education, the identification of multiple learning pathways highlights the need for flexible and adaptive digital learning systems that accommodate diverse learner needs. The findings provide practical implications for designing inclusive and personalized MOOC environments in postgraduate education.