Impact of Smartphone Usage on Students’ Academic Performance Using Contemporary Deep Learning Models

Abstract

The rapid growth of smartphone usage among students has created both opportunities and challenges in the academic environment. The goal of this study is to examine how different smartphone usage behaviors—such as screen time, application preferences, and late-night activity—affect student academic performance. The methods combine advanced deep learning approaches, including Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM), with carefully preprocessed smartphone interaction data collected from university students between 2023 and 2025. The models were trained to capture both spatial and sequential usage patterns, with hybrid architectures applied to maximize predictive capability. The findings show that deep learning models consistently outperform traditional machine learning techniques, with the CNN-BiLSTM hybrid achieving the highest accuracy of 92.4%. This confirms that smartphone usage patterns are strong predictors of academic outcomes, particularly when late-night use and excessive social media activity are present. The novelty of this research lies in its integration of diverse behavioral features—ranging from app-specific time allocation to multitasking frequency—into deep learning pipelines, offering actionable insights for educators and policymakers. This work advances the field of educational data science by providing a reliable framework for predicting performance and guiding balanced digital practices among students.