Predicting Student Performance using Advanced Learning Analytics

TLDR

Educational Data Mining and Learning Analytics are emerging fields that extract knowledge from educational databases to predict student success, yet current models largely rely on academic performance and family income while overlooking family expenditures and personal information. This study aims to evaluate the predictive power of family expenditure and personal information features by analyzing data from scholarship‑holding students across Pakistani universities. We apply learning analytics techniques using discriminative and generative classification models to predict whether students will complete their degrees. The proposed approach outperforms existing methods by leveraging family expenditure and personal information features, and its results can inform higher‑education policy improvements.

Abstract

Educational Data Mining (EDM) and Learning Analytics (LA) research have emerged as interesting areas of research, which are unfolding useful knowledge from educational databases for many purposes such as predicting students' success. The ability to predict a student's performance can be beneficial for actions in modern educational systems. Existing methods have used features which are mostly related to academic performance, family income and family assets; while features belonging to family expenditures and students' personal information are usually ignored. In this paper, an effort is made to investigate aforementioned feature sets by collecting the scholarship holding students' data from different universities of Pakistan. Learning analytics, discriminative and generative classification models are applied to predict whether a student will be able to complete his degree or not. Experimental results show that proposed method significantly outperforms existing methods due to exploitation of family expenditures and students' personal information feature sets. Outcomes of this EDM/LA research can serve as policy improvement method in higher education.