Enhancing Educational Quality Through AI and Data Science: A Study on Motivational Factors and Interventional Impact

Abstract

Student motivation is critical in academic success, driven by a combination of intrinsic and extrinsic factors. This study investigates the motivators influencing students in an academic program. Using a mixed-methods approach, the research combines qualitative interviews and quantitative data analysis to find the effects of various factors on student motivation. A Management Change Programme (MCP) was implemented as an intervention. A pre and post-test design is used, evaluating motivational shifts before and after the intervention. Data analysis techniques, including dimensionality reduction and reliability analysis, were used to construct a data-driven model. Results show that prior to the MCP, 59% of students were motivated, which increased to 66% post-intervention. Discriminant validity was confirmed, and the model demonstrated good fit(SRMR: 0.0783). Path coefficient analysis revealed that Placements had the strongest positive impact on students' Pursuit for Excellence (0.3429), followed by Program Content (0.3039), and College/Department Activities (0.1892).