Enhancing Student Achievement through Active Learning and Predictive Modelling using Machine Learning Algorithms

Abstract

The integration of innovative pedagogical methods and data-driven technologies has the potential to significantly transform educational outcomes. This study investigates the impact of active learning strategies combined with predictive modelling techniques powered by machine learning algorithms to enhance student achievement. Active learning approaches—such as interactive discussions, collaborative exercises, and formative assessments—promote student engagement and deeper understanding. Simultaneously, predictive models built using algorithms like Random Forest, Support Vector Machines, and Neural Networks are used to analyze academic records, behavioral data, and participation metrics to forecast student performance and identify those at risk of underachievement. The findings reveal that this dual approach not only improves academic performance but also facilitates timely interventions and personalized learning pathways. This research underscores the importance of aligning teaching innovation with data analytics to create a more responsive, inclusive, and effective educational environment.