A Frugal Model for Accurate Early Student Failure Prediction

TL;DR

The paper introduces the Frugal Early Prediction (FEP) model that efficiently predicts student failure early with reduced data usage and improved accuracy, benefiting educational institutions with resource constraints.

Contribution

It presents a novel hybrid model that selectively uses additional data to balance prediction accuracy and data frugality in early student failure prediction.

Findings

27% reduction in data consumption

7.3% average accuracy gain

Effective resource utilization in educational settings

Abstract

Predicting student success or failure is vital for timely interventions and personalized support. Early failure prediction is particularly crucial, yet limited data availability in the early stages poses challenges, one of the possible solutions is to make use of additional data from other contexts, however, this might lead to overconsumption with no guarantee of better results. To address this, we propose the Frugal Early Prediction (FEP) model, a new hybrid model that selectively incorporates additional data, promoting data frugality and efficient resource utilization. Experiments conducted on a public dataset from a VLE demonstrate FEP's effectiveness in reducing data usage, a primary goal of this research.Experiments showcase a remarkable 27% reduction in data consumption, compared to a systematic use of additional data, aligning with our commitment to data frugality and offering substantial benefits to educational institutions seeking efficient data consumption. Additionally, FEP also excels in enhancing prediction accuracy. Compared to traditional approaches, FEP achieves an average accuracy gain of 7.3%. This not only highlights the practicality and efficiency of FEP but also its superiority in performance, while respecting resource constraints, providing beneficial findings for educational institutions seeking data frugality.