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research-article

A Minimalistic Approach to Predict and Understand the Relation of App Usage with Students' Academic Performance

Authors:

Md Sabbir Ahmed,

Rahat Jahangir Rony,

Mohammad Abdul Hadi,

Nova AhmedAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 7, Issue MHCI

Article No.: 193, Pages 1 - 28

https://doi.org/10.1145/3604240

Published: 13 September 2023 Publication History

Abstract

Due to usage of self-reported data which may contain biasness, the existing studies may not unveil the exact relation between academic grades and app categories such as Video. Additionally, the existing systems' requirement for data of prolonged period to predict grades may not facilitate early intervention to improve it. Thus, we presented an app that retrieves past 7 days' actual app usage data within a second (Mean=0.31s, SD=1.1s). Our analysis on 124 Bangladeshi students' real-time data demonstrates app usage sessions have a significant (p<0.05) negative association with CGPA. However, the Productivity and Books categories have a significant positive association whereas Video has a significant negative association. Moreover, the high and low CGPA holders have significantly different app usage behavior. Leveraging only the instantly accessed data, our machine learning model predicts CGPA within ±0.36 of the actual CGPA. We discuss the design implications that can be potential for students to improve grades.

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Cited By

Ahmed MHasan TIslam SAhmed N(2024)Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide StudyJMIR Research Protocols10.2196/5154013(e51540)Online publication date: 24-Apr-2024
https://doi.org/10.2196/51540

Index Terms

A Minimalistic Approach to Predict and Understand the Relation of App Usage with Students' Academic Performance
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 7, Issue MHCI

MHCI

September 2023

1017 pages

EISSN:2573-0142

DOI:10.1145/3624512

Editor:
Jeff Nichols
Apple Inc., United States

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Copyright © 2023 ACM.

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Published: 13 September 2023

Published in PACMHCI Volume 7, Issue MHCI

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Ahmed MHasan TIslam SAhmed N(2024)Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide StudyJMIR Research Protocols10.2196/5154013(e51540)Online publication date: 24-Apr-2024
https://doi.org/10.2196/51540

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