Hybrid AI approaches for classifying student social media addiction and its academic impact

Abstract

The excessive use of social media by students has become a concern because it may hinder their academic progress, mental health, and overall well-being. It examines how various machine learning and deep learning models can predict and classify a student’s social media addiction using demographic details, social media activities, and the impact on sleep and grades. Data from many countries and different education levels were collected. This data was further processed to handle missing values and convert categorical data into numerical form. The addiction score was divided into three groups: low, medium, and high. The data was scaled for features and split into 80% for training and 20% for testing to prepare for model training. The analysis involved models such as SVM, XGBoost+CNN, CNN+SVM, MLP, and vision transformer. CNN+SVM and MLP achieved the highest accuracy (0.9220) with training times of 24.23 and 11.58 seconds. Notably, the highest training accuracy was observed with MLP (0.9263), surpassing other models. Combining multiple layers in these hybrid approaches helped process the data more effectively and extract key features from the tabular information. Hyperparameter optimisation improved the performance of ML, DL, and hybrid models. The results show that ensemble or hybrid models perform better than standalone DL models. The study highlights how ML and DL can help identify students who may struggle academically and suggest ways to support them. For future research, larger datasets or real-time data could be used to improve the models and address some current limitations.