MLRec: A Machine Learning-Based Recommendation System for High School Students Context of Bangladesh

Abstract

Social media and mobile devices, commonly referred to as socimedevices, have become integral to students’ daily lives, influencing both their academic performance and overall well-being. Depending on usage patterns, these technologies can positively or negatively impact students’ education. In recent years, many researchers have introduced several models, including neural networks (NNs), machine learning (ML), and deep learning (DL), to identify the impact on student academic performance using a socimedevice. Here, we propose a comparative model named the MLRec model, where we assess how well different machine learning methods predict the dynamics of student life and provide a recommendation to society, parents, and academic advisors. Here, we have preprocessed our real dataset by various methods, which is collected from 10 schools and has 25 features totaling 275 instances from different districts of Bangladesh. After that, we applied 15 ML algorithms for training and testing. Then, we compared the algorithms using criteria such as accuracy, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R2), Explained Variance (EV), and Tweedie Deviance Score (D2). Subsequently, we selected the Extra Tree Classifier (ETC) algorithm based on its superior performance, achieving an accuracy of 86%, an MSE of 25%, and an EV of 40%. We also used Explainable AI (LIME and SHAP) techniques to visualize the root causes of social networks’ effects on students’ school performance. Our results show that using social media excessively adversely affects academic pursuits.