Personalized Student Attribute Inference

TL;DR

This paper introduces a personalized approach to predict student performance by creating individualized attributes, aiming to improve early detection of students at risk of failure compared to traditional methods.

Contribution

The work proposes the Personalized Student Attribute Inference (PSAI) model, which captures individual student backgrounds for better performance prediction.

Findings

Personalized attributes improve prediction accuracy.

PSAI outperforms naive attribute-based models.

Neural networks yield the best results among tested algorithms.

Abstract

Accurately predicting their future performance can ensure students successful graduation, and help them save both time and money. However, achieving such predictions faces two challenges, mainly due to the diversity of students' background and the necessity of continuously tracking their evolving progress. The goal of this work is to create a system able to automatically detect students in difficulty, for instance predicting if they are likely to fail a course. We compare a naive approach widely used in the literature, which uses attributes available in the data set (like the grades), with a personalized approach we called Personalized Student Attribute Inference (PSAI). With our model, we create personalized attributes to capture the specific background of each student. Both approaches are compared using machine learning algorithms like decision trees, support vector machine or neural networks.