Enhancing Knowledge Transfer in Nano-Learning Through User Motivation and Satisfaction

Abstract

This study investigates the effectiveness of nano-learning environments specifically short-form educational videos within higher education, focusing on the cultural, technological, and pedagogical dynamics that shape learner engagement and outcomes. Grounded in both quantitative and qualitative methodologies, the research explores how factors such as video length, content design, user motivation, engagement, and satisfaction influence learning effectiveness. Using Partial Least Squares Structural Equation Modelling (PLS-SEM) and thematic analysis of interviews with university educators in Chengdu, China, the study identifies that well-structured content and optimal video duration significantly enhance learner engagement, which in turn boosts short-video watch times and overall learning effectiveness. Additionally, the study reveals that while learners value personalization and flexibility, they also require structured support and cognitive depth. Platform algorithms, though enhancing motivation, may compromise autonomy and critical engagement. The study recommends the integration of reflective, culturally sensitive design features to balance personalization with deeper learning. The novelty of this research lies in its multidimensional analysis of nano-learning merging behavioural data with cultural and semantic insights to inform the development of inclusive, pedagogically sound micro-learning systems in global educational contexts.