Unveiling Patterns Enhancing Predictive Models and Empowering Educators with Explainable AI in Predicting Students Academic Performance

Abstract

Educational Data Mining (EDM) is an emerging field that focuses on extracting valuable insights and patterns from educational data to enhance teaching and learning processes. With a thorough study of educational data, the creation of predictive models, and the incorporation of explainable AI, this research proposal intends to advance the field of EDM by enabling teachers to foresee exceptional student achievement. The proposal highlights the significance of EDM in uncovering correlations between student engagement, performance, and learning outcomes, identifying at-risk students, and enhancing instructional design. The objectives include conducting exploratory data analysis, preprocessing the data, developing predictive models, and integrating explainable AI. The proposed methodology involves data collection, preprocessing, exploratory data analysis, data mining techniques, and predictive modeling. The expected outcomes comprise valuable insights into educational data patterns, a pre-processed dataset, predictive models for identifying at-risk students, and explainable AI models. The research aims to contribute to the domain of EDM by improving teaching and learning processes and enhancing student outcomes in the educational system. newline