Yash Chaudhari, Ashutosh Patil, Kalash Laddha, Om Ahir, Gauresh Gadhe, Prof. Amarnath Chadchankar
Abstract
Keeping track of student attendance is one of those tasks that every college deals with daily, yet most institutions still rely on the same old manual methods that eat into lecture time and leave room for errors. We ran into this problem ourselves and started thinking — why should a teacher have to call out 60 names when only 4 or 5 students are actually absent? That simple question led us to build SAMS, the Smart Attendance Management System. Instead of marking who is present, SAMS marks everyone as present by default and only requires the faculty to mark those who are absent. Beyond that, we noticed another issue: a student who missed half the semester early on but has been attending every class recently gets treated the same as one with the opposite pattern. That does not seem fair. So we developed the Time-Weighted Attendance Scoring (TWAS) Algorithm, which gives more importance to recent lectures than older ones. The result is an attendance score that actually reflects how engaged a student is right now, not just historically. The system also generates downloadable reports for each student, all through a web interface that needs no special hardware to run.
We built SAMS because we saw a genuine problem that existing solutions were not solving. Manual attendance wastes teaching time, and flat percentage scoring does not capture whether a student has improved or declined over the course of a semester. The default-present mechanism addresses the first problem by reducing faculty actions per session to just the number of absentees. The TWAS Algorithm addresses the second by giving recent lectures more influence over the final score than older ones. Table I shows clearly that two students with the same total absences can have very different TWAS scores depending on when those absences occurred — which is exactly what a fair attendance metric should reflect. SAMS does all of this without requiring any hardware beyond what a college already has.
References
[1] Gupta, R., Sharma, A., Verma, S., Pandey, A., and Agarwal, P. (2024). Design and Implementation of a Web-Based Attendance Management System. International Journal of Creative Research Thoughts (IJCRT), 12(4), ISSN: 2320-2882. Available: https://www.ijcrt.org/papers/IJCRT24A4698.pdf
[2] Mazlan, M. A., et al. (2025). I-Attend: RFID-Based Attendance Management System for Secondary Schools. Journal of Educational Technology Systems. doi:10.1177/00472395251326811
[3] El Mrabet, H., and Ait Moussa, A. (2020). Smart Classroom Environment via IoT in Basic and Secondary Education. Transactions on Machine Learning and Artificial Intelligence, 8(2). doi:10.14738/tmlai.82.7857
[4] Zerach, J. R. A. (2024). Case Study of Attendance System Design Based on Web and Mobile. Journal La Multiapp, 5(5), 759-785. doi:10.37899/journallamultiapp.v5i5.1663
[5] Oluyemi, T. T., Oyediji, F. T., and Oyebiyi, A. J. (2024). Development of an Attendance Management System Using Facial Recognition Technology. Journal of Engineering Research and Reports, 26(10), 297-307. doi:10.9734/jerr/2024/v26i101307
[6] Nuhi, A., and Memeti, A. (2021). Smart Attendance System Using QR Code. In Proceedings of the IEEE International Conference on Smart Systems and Technologies (SST). doi:10.1109/SST52890.2021.9578073
[7] Tripathi, A., Choudhary, A., Srivastava, A. K., Kharbas, V. K., and Shukla, V. (2024). A Real-Time IoT-Enabled Biometric Attendance System. In Lecture Notes in Networks and Systems (Vol. 918), Springer, Singapore, pp. 619-630. doi:10.1007/978-981-97-0641-9_42
[8] Shoewu, O. O., et al. (2024). Development of an IoT-Based Biometric Attendance Management System. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 12(9). doi:10.22214/ijraset.2024.61370
[9] Mohan, A., and Singh, A. (2024). Implementing E-Attendance Using Blockchain Technology. International Journal of Progressive Research in Engineering Management and Science (IJPREMS), 4(5), 629-631.
[10] Balasubramaniam, S., Rao, P., and Mishra, N. (2024). Machine Learning and Blockchain Based Real-Time Attendance Monitoring System. IOSR Journal of Engineering, 14(4), 61-68.
[11] Ahmed, M. M., et al. (2024). Student Performance Prediction Using Machine Learning Algorithms. Applied Computational Intelligence and Soft Computing, 2024, 4067721. doi:10.1155/2024/4067721
[12] Xiao, P. W. (2024). Design and Implementation of a Student Attendance Management System based on Springboot and Vue Technology. Frontiers in Computing and Intelligent Systems, 7(2). doi:10.54097/fcis.v7i2.21096
[13] Zhao, M., Zhao, G., and Qu, M. (2022). College Smart Classroom Attendance Management System Based on Internet of Things. Scientific Programming, 2022, 4953721. doi:10.1155/2022/4953721
[14] Kuvshinov, K., Matulevicius, R., and Taveter, K. (2024). STAMP: Blockchain and DAO-based Student Attendance Management Platform. In Proceedings of the 2024 7th International Balkan Conference on Communications and Networking (BalkanCom). doi:10.1109/BalkanCom61808.2024.10557210
[15] Psyridou, M., et al. (2024). Machine learning predicts upper secondary education dropout as early as the end of primary school. Scientific Reports, 14, 12889. doi:10.1038/s41598-024-63629-0
How to Cite This Paper
Yash Chaudhari, Ashutosh Patil, Kalash Laddha, Om Ahir, Gauresh Gadhe, Prof. Amarnath Chadchankar (2026). SMART ATTENDANCE MANAGEMENT SYSTEM WITH TIME-WEIGHTED ATTENDANCE SCORING (TWAS) ALGORITHM. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
