An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning

Abstract

Student performance prediction has become a hot research topic. Most of the existing prediction models are built by a machine learning method. They are interested in prediction accuracy but pay less attention to interpretability. We propose a stacking ensemble model to predict and analyze student performance in academic competition. In this model, student performance is classified into two symmetrical categorical classes. To improve accuracy, three machine learning algorithms, including support vector machine (SVM), random forest, and AdaBoost are established in the first level and then integrated by logistic regression via stacking. A feature importance analysis was applied to identify important variables. The experimental data were collected from four academic years in Hankou University. According to comparative studies on five evaluation metrics (precision, recall, F1, error, and area under the receiver operating characteristic curve ( AUC ) in this analysis, the proposed model generally performs better than compared models. The important variables identified from the analysis are interpretable, they can be used as guidance to select potential students.

revisión sobre la predicción del rendimiento académico mediante métodos de ensamble

“…Stacking is an algorithm that has the particularity of combining the predictions from different learning algorithms, and the final model results from the combination of the predictions of the simple models, called base-learners [22], [33], [37]. To reduce the risk of overfitting, simple models such as meta-learners [38] are usually chosen.…”

An Analysis of Students’ failing in University Based on Least Square Method and a New arctan − exp Logistic Regression Function

“…In the quantitative model, the main methods used are machine-learning algorithms, such as deep neural network [10], logistic regression model [11][12][13][14][15][16], and some other combined models. Yousafzai et al [10] have used the attention-based bidirectional long short-term memory (BiLSTM) network combined with an attention mechanism model, which is based on advanced feature classification and prediction.…”

“…Wang et al [13] have conducted a student survey and analyzed the results of professional English, which provided strong support for teaching reform. Yan and Liu, Sugilar et al and Okewu et al [14][15][16] have also made progress in quantitative analysis of student data and research on student behavior and achievement.…”

A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment

“…This study aimed to provide a machine learning framework to predict students' performance using a hybrid of classification and ensemble methods. The ensemble method uses multiple classification algorithms strategically generated and integrated to get a better prediction performance than the performance obtained from a single algorithm [13][14]. It combines the best-selected techniques as the final prediction model [15].…”