The Secret Sauce of Student Success: Cracking the Code by Navigating the Path to Personalized Learning with Educational Data Mining

The growing need for tailored learning experiences in post-secondary education has resulted in the adoption of educational data mining (EDM) methodologies to derive significant insights from educational data. The existing scholarly literatures suggest the utilisation of adaptive learning algorithms that integrate various data sources, such as student demographic information, academic performance, and physiological data, to offer individualised learning experiences for students. The algorithms have the capability to modulate the tempo of educational content in response to the cognitive burden experienced by students, which is gauged by their brainwave activity. This study explores the application of predictive models, such as classification, regression, and time-series analysis, in detecting patterns and trends in past data for the purpose of forecasting students’ forthcoming academic achievements. Predictive models have the potential to assist educators in making well-informed decisions aimed at enhancing course outcomes. This research introduces an approach to course improvement analytics that utilises diverse data sources, including student academic records, demographic data, and external platforms such as social media and online forums, to optimise educational results. Through the examination of this data, academic professionals can acquire valuable knowledge regarding student involvement, achievement, and conduct. The present study establishes that the utilisation of course improvement analytics yields valuable information regarding student engagement and behaviour, thereby enabling educators to make informed decisions aimed at enhancing students’ learning outcomes.