Does a student's attendance and a classroom task significantly enhance learning outcomes? Implications of policy in gamely and management education

Abstract

Technological elements have always influenced learning outcomes in classroom settings. The influence of gamification as a technological tool has been widely researched at primary and secondary levels of education. However, no previous model focused on constructing it to measure the learning outcomes at the postgraduate level of management education, specifically by considering elements of the National Education Policy 2023. The current study outlines this gap and signifies its unique contribution by answering three research questions formulated using the FINER approach to get policy insights. First, the study explores relevance and significance among various constructs (latent variables), such as student attendance, task influence, academic performance, and knowledge retention, affecting expected learning performance directly or indirectly via gamification, directly affecting expected learning outcomes. Secondly, select constructs are assessed based on the serial mediation effect and positive association via gamification with expected learning outcomes. Thirdly, the study validated student attendance (at the beginning and end stages), the moderating effect of task completion by students (orientation, motivation, teacher feedback, and time management), and their learning intent and expected outcomes via gamification construct. Using structural equation modeling, the model hypotheses were validated on primary data-based instruments and based on a sample collected from student respondents (executives) in a typical management classroom of a postgraduate-level management course. The reliability and validity of the research model were assessed. In addition, assessing the effect size and predictive relevance, the performance of select constructs, such as academic learning and knowledge retention, was highly significant. In contrast, student attendance and tasks showed less importance. The study concludes and shows theoretical and policy implications for management education research and artificial intelligence, with a few particular data biases and heterogeneity in data sets the future research directions for management education.