Student Performance Prediction with Optimum Multilabel Ensemble Model

TL;DR

This paper presents a multi-label ensemble model using SVM, RF, KNN, and MLP to predict high school students' performance in five courses, employing advanced partitioning and label transformation techniques for improved accuracy.

Contribution

The paper introduces a novel multi-label ensemble approach with optimized partitioning and label powerset transformation for student performance prediction.

Findings

The proposed model outperforms traditional multi-label methods in accuracy.

Partitioning schemes significantly improve prediction performance.

Multi-label ensemble approach effectively predicts student performance across multiple courses.

Abstract

One of the important measures of quality of education is the performance of students in the academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and how to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Mult-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using state-of-the-art partitioning schemes to divide the label space into smaller spaces and use Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.