A Regression Model and a Combination of Academic and Non-Academic Features to Predict Student Academic Performance

Abstract

Predicting academic performance provides an effective way for students and faculties to monitor their academic progress. The identification of the most significant features was a key outcome of this research, and the college/university databases from online learning platforms are the main academic data sets used to ascertain performance. However, previous research emphasized the addition of other significant features in the prediction of academic performance. Universities’ organizational features include non-academic essential data used in determining student success, but no research has utilized this data to predict student academic performance. Generally, to evaluate binary classification, the relationship between the predicted classifications and the true classifications is analyzed, this approach can lead to the loss of important information from the data. Furthermore, to avoid such loss, this research proposes a regression model by comparing six regression algorithms, and combining academic and non-academic features for prediction student academic performance. Among the various models examined, the gradient-boosted trees regression model demonstrated the lowest error rate. The proposed features have been observed to have a significant impact on academic performance, making them suitable for use in predictions.

Students Final Academic Score Prediction Using Boosting Regression Algorithms

“…Regression analysis algorithms are fundamental components of ensemble learning frameworks [20]. These are highly effective in capturing intricate relationships between input features and target variables, making them suitable for various prediction tasks [21]. Among the plethora of regression algorithms available, XGBoost, LGBM, and Catboost [22]- [24] are short form for extreme boost gradient regression.…”