From Social Media Reactions to Grades: A Machine Learning-Based SocialNet Analysis for Academic Performance Prediction

Muhammad Ramzan; Naeem Ahmed

doi:10.4108/airo.8171

Authors

Muhammad Ramzan University of Haripur https://orcid.org/0009-0001-3689-7433
Naeem Ahmed Nanjing University of Information Science and Technology

DOI:

https://doi.org/10.4108/airo.8171

Keywords:

SocialNet Analysis, Machine Learning, Student Performance, Education, Learning Technologies

Abstract

The impact of social media on student academic performance has garnered significant research interest in recent years. The pervasive use of social networking sites (SNS) among college and university students, both in and outside classrooms, has raised concerns about its potential effects on academic achievement. This study investigates the relationship between social media usage and academic performance through a dataset of 550 participants. Machine learning models, including Random Forest, Decision Trees, and Long Short-Term Memory (LSTM), were employed to analyze and predict the impact of social media on students' academic outcomes. The models were trained using clean and well-engineered data. The results indicate a moderate influence of social media usage on academic performance, with the LSTM model outperforming traditional approaches in predictive accuracy. These findings highlight the importance of considering sequential usage patterns in understanding the academic implications of social media.

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