Modeling the Interrelationships Among Environmental and Cognitive Factors in MOOCs: Toward an Innovative Relational Learning Frameworkin Postgraduate Education

Abstract

This study aims to model the interrelationships among environmental and cognitive factors in Massive Open Online Courses (MOOCs) and to propose an innovative relational learning framework for postgraduate education. While prior research has predominantly focused on isolated predictors of learning outcomes, this study reconceptualizes learning as a dynamic and interconnected process shaped by multiple interacting mechanisms. Grounded in the Expectation–Confirmation Model (ECM) and Social Cognitive Theory (SCT), the proposed framework integrates key constructs, including learning environment support, perceived usefulness, satisfaction, intrinsic motivation, and self-regulated learning, to explain how learning engagement emerges within a relational system.

A quantitative research design was employed, with data collected from 432 postgraduate students enrolled in MOOCs-integrated courses. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to examine both direct and indirect relationships among variables. The findings indicate that learning environment support plays a foundational role in shaping cognitive evaluations, particularly through perceived usefulness as a critical mediating mechanism. Intrinsic motivation significantly influences self-regulated learning, which in turn enhances learning engagement alongside satisfaction.

Importantly, the results support the proposed relational learning framework, demonstrating that learning outcomes emerge from structured interrelationships among environmental, cognitive, and motivational factors rather than from isolated variables. In alignment with Sustainable Development Goal 9 (SDG 9), which emphasizes innovation and the development of resilient systems, this study contributes to advancing theoretical innovation in digital learning by introducing a mechanism-based relational perspective. The findings also provide practical implications for designing more adaptive and sustainable online learning environments in postgraduate education