Predicting the Effect of Mobile Phone on Student Academic Performance Using Machine Learning

Shuaibu Yau¹ , Abdulsalam Abdulganiyu Ishola² , Abdu Ibrahim Adamu³

Section:Research Paper, Product Type: Journal-Paper
Vol.12 , Issue.5 , pp.10-17, Oct-2024

Online published on Oct 31, 2024

Copyright Š Shuaibu Yau, Abdulsalam Abdulganiyu Ishola, Abdu Ibrahim Adamu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The study aimed to predict the effect of mobile phones on students’ academic performance using Machine Learning, focusing on the Faculty of Science at the Federal University Birnin Kebbi (FUBK). With the increase in the prevalence of mobile phone usage among students, there is concern about its effect on academic performance. A total of 227 students partakes in the research, with a gender balance of 57.3% male and 42.7% female. Online Questionnaire was distributed for data collection among students from 200Level to 400Level, and responses were cleaned and preprocessed to ensure consistency and accuracy. Analysis was conducted using Python, employing two models: Random Forest (RF) and Decision Tree (DT). However, the results indicated that DT achieved an R²_score of 57%, whereas RF achieved an R²_score of 63% in predicting students` grade point averages based on their mobile phone usage. Other models, like Linear Regression, K-Nearest Neighbor (KNN), and Neural Network Regression, could also be explored for prediction purposes. This study recommends that universities consider implementing predictive models to inform students about their expected grade point averages, aiding them in focusing on their studies. Additionally, universities should encourage students to engage in meaningful tasks, providing additional data points for predictive models and improving their accuracy. Lastly, it is advised that universities be mindful of the ethical implications associated with the use of predictive models.

Key-Words / Index Term :
Academic performance, Semester grade point, Machine learning, Regression analysis, Mobile phone, FUBK

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