Exploring Motivation and Learning Factors in Digital Media Through the Lens of Activity Theory and Behavioural Influences

Abstract

Digital media has transformed learning by means of its interactive platforms. We must understand what drives students if we are to maximise online learning environments. Using present approaches, it is challenging to assess the complex relationships among digital media engagement, forms of motivation, and learning outcomes. Conventional approaches' inability to synthesise theoretical and behavioural information leads to scattered assessments of learning effectiveness. Structural Equation Modelling-assisted Digital Media Engagement (SEM-DME) is a technique proposed in this work to evaluate motivation and learning components in Massive Open Online Courses (MOOCs). It seeks to release these limitations. Sem-DME can replicate the interaction of students, tools, and social elements by merging Activity Theory with behavioural data analysis. The proposed method evaluates learning outcomes with respect to platform design, peer interactions, feedback systems, intrinsic and extrinsic motivation, and so on. According to the findings, gamification, peer collaboration, and tailored feedback dramatically increase knowledge retention and engagement. Coordinating digital media strategies with student needs helps the SEM-DME framework provide a good analytical model for improving massive open online courses (MOOCs). This study advances digital learning by providing a data-driven approach for increasing engagement and motivation.