Optimizing Attendance Management in Educational Institutions Through Mobile Technologies - A Machine Learning and Cloud Computing Approach

Aug 3, 2026

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The study optimizes and streamlines attendance recording and monitoring for learning sessions by applying machine learning and cloud computing. Using the extreme programming (XP) project management approach, it documents the end-to-end implementation from concept to deployment. Firebase is adopted for efficient and secure management of student information and attendance records, while its machine learning kit verifies attendance through QR codes. Testing with fifth-year high school students shows reduced attendance-recording time and high teacher and student acceptance of the ASYS mobile application.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Introduction

Literature review

Research background

Methodology

Extreme programming (XP) approach

Firebase database and security

QR-code verification with Firebase ML kit

System Design and Implementation

User interface for teachers and students

Evaluation and Results

Test with fifth-year high school students

Time reduction and user satisfaction

Conclusion

JIM International Journal of Interactive Mobile Technologies [Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org) iJIM | eISSN: 1865-7923 | Vol. 18 No. 12 (2024) | [https://doi.org/10.3991/ijim.v18i12.46917](https://doi.org/10.3991/ijim.v18i12.46917) PAPER Optimizing Attendance Management in Educational Institutions Through Mobile Technologies: A Machine Learning and Cloud Computing Approach Nicolas Esleyder CaytuiroSilva1, Benjamin MarazaQuispe2(􀀍), Eveling Gloria Castro-Gutierrez2, Karina Rosas-Paredes1, Jose Alfredo SullaTorres1, Manuel Alfredo Alcázar-Holguin2, Walter Choquehuanca-Quispe2 1Universidad Católica de Santa María, Arequipa, Perú 2Universidad Nacional de San Agustín, Arequipa, Perú [bmaraza@unsa.edu.pe](bmaraza@unsa.edu.pe) ABSTRACT The primary goal of the study is to optimize and streamline the attendance recording and monitoring process for learning sessions by leveraging advanced technologies such as machine learning and cloud computing. The methodology employed is based on the extreme programming (XP) project management approach. Throughout its phases, the entire implementation process of the application, from conception to launch, is described in detail. Firebase is used as the database manager to ensure the efficiency and security of student information and attendance records. Additionally, the Firebase machine learning kit is used to verify attendance registration through QR codes. The application was tested with fifth-year high school students from an educational institution. The user interface has been designed to be attractive, intuitive, and easy to use for both teachers and students. The study results demonstrate that the use of this application significantly reduces the time spent on attendance recording compared to traditional methods. There has been a high level of satisfaction and acceptance of the “ASYS” application among teachers and students. In conclusion, this study has successfully implemented a mobile application that revolutionizes attendance recording and monitoring in educational institutions. It harnesses the power of machine learning and cloud computing to enhance efficiency and the user experience. KEYWORDS attendance records, mobile application, machine learning, cloud computing, education, process optimization 1 INTRODUCTION 1.1 Literature review The process of attendance registration and control in educational institutions is essential, but it is often complex and time-consuming. Traditional methods of Caytuiro-Silva, N. E., Maraza-Quispe, B., Castro-Gutierrez, E.G., Rosas-Paredes, K., Sulla-Torres, J.A., Alcázar-Holguin, M.A., Choquehuanca-Quispe, W.(2024) . Optimizing Attendance Management in Educational Institutions Through Mobile Technologies: A Machine Learning and Cloud Computing Approach. International Journal of Interactive Mobile Technologies (iJIM), 18(12), pp. 112–128. [https://doi.org/10.3991/ijim.v18i12.46917](https://doi.org/10.3991/ijim.v18i12.46917)[ ](https://doi.org/10.3991/ijim.v18i12.46917)[Article submitted 2023-11-21. Revision uploaded 2024-03-11. Final acceptance 2024-03-12.](Article submitted 2023-11-21. Revision uploaded 2024-03-11. Final acceptance 2024-03-12.) © 2024 by the authors of this article. Published under CC-BY. 112 International Journal of Interactive Mobile Technologies (iJIM) iJIM | Vol. 18 No. 12 (2024) Optimizing Attendance Management in Educational Institutions Through Mobile Technologies registration, which often involve paper attendance sheets or rudimentary electronic systems, can be error-prone, inefficient, and require a significant amount of manual administrative work by teachers and institution staff. Additionally, these methods may not be effective enough to ensure accurate tracking of student attendance [1] . During the first quarter of 2021, 88.5% of the total internet user population accessedit through mobile phones or smartph

FAQs

What technologies does the study use to improve attendance management?

It leverages machine learning and cloud computing, implemented through a mobile application with Firebase for data management and verification via QR codes using the Firebase machine learning kit.

How is the application implemented and managed in the study?

Implementation follows the extreme programming (XP) project management approach, with the paper describing each phase from conception through launch.

What evidence is provided to show the benefits of the proposed approach?

Tests with fifth-year high school students indicate the application significantly reduces the time spent on attendance recording compared with traditional methods, along with high satisfaction and acceptance from teachers and students.

Aug 3, 2026

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