IoT and AI Based Student’s Attendance Monitoring System to Mitigate the Dropout in Non-boarding Secondary Schools of Rwanda: A Case Study of Wisdom School Musanze
DOI:
https://doi.org/10.47672/ejt.1383Keywords:
Biometric, internet of things, sensor, fingerprint, regression and decision-tree.Abstract
Purpose: This project aimed to test an IoT and AI based system that monitor students from home to schools, during class hours and from school to home and notify parents and school administrators about the irregularity observed to their respective children.
Methodology: In this project, secondary data was used and was retrieved from the school’s record of Wisdom School Musanze located in Musanze District. The main data to consider were sex whether male or female. Another important data was orphanage,whether pupil is orphan or not orphan, and school fees payment by checking whether student paid school fees or had not paid. These mentioned data were taken randomly from senior one (S1) to senior six (S6) in academic year 2020-2021.
Findings: The system is equipped of a finger print sensor to register and verify students and staff attendance, a Passive Infrared (PIR) sensor to detect the presence of human to wake-up the device, a real time clock to synchronize each generated report with the local time. A web application is developed to allow students real-time monitoring for parents and school administrators and the system is be able to generate a daily, monthly and annually report.
Unique contribution to theory, practice and policy: Classification machine learning with decision-tree algorithm is used to analyze data and generate a model to evaluate the impact of monitoring attendance on preventing students to dropout. The generated model with accuracy of 91.4% shows that keeping students’ attendance at high percentage would reduce significantly the dropout rate in secondary schools of Rwanda.
Downloads
References
Chaniago, M. B., & Junaidi, A. (2016,). SMS Gateway and barcode technology for presence of students in SMK Unggulan Terpadu PGII Bandung: A case study. In 2016 4th International Conference on Cyber and IT Service Management (pp. 1-4). IEEE.
Dimitri et al., (2018). Assessment of Repetition and Dropout in Basic Education in Rwanda Full Report From data to policy Acknowledgements.Rwanda,kigali
Dinca, L. M., & Hancke, G. (2017, June). Behavioural sensor data as randomness source for IoT devices. In 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE) (pp. 2038-2043). IEEE.
Freebrowsinglink. (2021). https://www.freebrowsinglink.com/chinese-schools-using-smart-uniforms/.
German, R., & Barber, K. S. (2017). Current biometric adoption and trends. The University of Texas at Austin. Retrieved from identity. utexas. edu/assets/uploads/publications/Current-Biometric-Adoption-and-Trends. pdf.
Jain, A., Hong, L., & Pankanti, S. (2000). Biometric identification. Communications of the ACM, 43(2), 90-98.
Jomon Joseph1, K. P. Zacharia. (2013). Automatic Attendance Management System Using Face Recognition. International Journal of Science and Research (IJSR).
Kanyoni, M., Gishoma, D., & Ndahindwa, V. (2015). Prevalence of psychoactive substance use among youth in Rwanda. BMC research notes, 8(1), 1-8.
Luminositylab. (2021). https://theluminositylab.com/projects/guardian-drones/.
MINEDUC. (2020). Rwanda education statistics 2018. kigali. Retrieved 10 15, 2021, from http://197.243.16.104/~mineduc/newweb/fileadmin/user_upload/pdf_files/2018_Rwanda_Education_Statistics.pdf
Narzullaev, A., Muminov, Z., & Narzullaev, M. (2021, July). Wi-Fi based student attendance recording system using logistic regression classification algorithm. In AIP Conference Proceedings (Vol. 2365, No. 1, p. 030004). AIP Publishing LLC.
Ngamije, J., & Yadufashije, C. (2016). Drugs use among the youth In Rwanda: A public health concern and family’challenge. International Journal of emergencies computer and technology, 3(9), 4591-4603.
Seifedine, K., & Mohamad, S. (2010). Wireless attendance management system based on iris recognition. Scientific Research and essays, 5(12), 1428-1435.
Shehu, V., & Dika, A. (2010, June). Using real time computer vision algorithms in automatic attendance management systems. In Proceedings of the ITI 2010, 32nd international conference on information technology interfaces (pp. 397-402). IEEE.
Shoewu, O., Olaniyi, O. M., & Lawson, A. (2011). Embedded Computer Computer-Based Lecture Attendance Based Management System.
Shoewu, O., & Idowu, O. A. (2012). Development of attendance management system using biometrics. The Pacific Journal of Science and Technology, 13(1), 300-307.
Srinidhi, M. B., & Roy, R. (2015, January). A web enabled secured system for attendance monitoring and real time location tracking using Biometric and Radio Frequency Identification (RFID) technology. In 2015 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE.
Tiwari, T., Tiwari, T., & Tiwari, S. BIOMETRICS BASED USER AUTHENTICATION.
Uwizeye, D., Muhayiteto, R., Kantarama, E., Wiehler, S., & Murangwa, Y. (2020). Prevalence of teenage pregnancy and the associated contextual correlates in Rwanda. Heliyon, 6(10), e05037
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Munyaneza Midas Adolphe, Gasana James Madson, Uwimana Josephine, Shumbusho Jean Pierre, Nzayisenga Joselyne, Gafeza Gaspard, Niyonzima Martin
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
