The mediating effect of deep learning in Communities of Practice for Decision Science Students

Communities of practice (CoPs) (Lave & Wenger, 1991) are groups of Decision Sciences undergraduate students preparing for supervisory roles after graduation. They collaborate to solve problems and share knowledge through gathering real-life cases from their professional communities. Biggs's (1993) learning theory distinguishes between surface learning, which involves rote memorization, and deep learning, characterised by critical thinking and a genuine understanding of core concepts. Deep learning is vital in connecting contextual factors such as teaching practices, learning environments, and assessment methods to students' learning outcomes. CoPs foster collective knowledge development through informal interactions and resource sharing (e.g. Lepistö & Hytti, 2021). They enhance innovativeness, knowledge management, and individual creativity (e.g. Cox, 2005). RQ: How does an individual learner's deep learning approach mediate the relationship between a shared sense of community of practice and the learner's course satisfaction? A two-stage structural equation modelling approach was applied with a sample of 201 undergraduate students in Hong Kong. The results support the hypothesis that deep strategy fully mediates the relationship between the shared sense of CoP and course satisfaction. This indicates that a collaborative learning environment enhances students’ intrinsic motivation and satisfaction, emphasising effective educational strategies to build strong professional communities. Limitations are impacted by a Hong Kong-only sample and a cross-sectional design preventing causal claims. Due to space limitations, literature in areas of the role of surface learning and elaboration of the CoP structure for replication are not covered in this paper.