On the Technologies and Systems for Student Attendance Tracking

Abstract

Manual student attendance tracking, by calling student names from a check list or taking students' signs on a paper, has gone into history. Nowadays, modern technologies have already enabled the development of various automatized attendance tracking systems. These technologies include: Radio-Frequency Identification (RFID), Biometric (fingerprint, face or voice recognition), Barcode identification and Bluetooth communication technologies, that are implemented over an IP infrastructure as a platform. But, all these technologies perform in a different manner and exhibit certain functional limitations considering the given implementation. The main motivation for this research was to explore the possibilities for overcoming the issues of current systems for attendance tracking, considering the limitations of the technologies employed. Hence, the core contribution of this research can be considered as a fourfold, i.e. i) it presents the most frequently used technologies in the development of attendance tracking systems, ii) it reviews a range of existing student attendance tracking systems, iii) it defines criteria for performance evaluation of the technologies employed in student attendance tracking, from a perspective of educational institutions, and iv) it evaluates the mostly used technologies according to the predefined functional criteria. As a summary of the evaluation it provides directions for future development of a student attendance tracking system that would address the explored issues and functional limitations.

Game theme based instructional module to teach loops and choice statements in computer science courses

“…As a further work, GTI modules evaluation using questionnaire to find the motivation of students and the effectiveness of the approach is planned. Modern technologies like automatized attendance tracking systems for tracking students' attendance and obtaining their feedback can be used [11]. We hope that the results of the evaluation of GTI modules will show that instructional modules can efficiently promote learning by encouraging the students' participation.…”

Smart-Hadir – Mobile Based Attendance Management System

“…Face recognition, iris-based, and voice recognition systems are seldom found in the market due to its high latency rate during the recognition process. The biometric-based systems have a long execution time of 4.29 seconds on average [23]. These systems are inefficient in areas with a large number of participants, such as colleges and universities.…”

A Lightweight Face Recognition Model Using Convolutional Neural Network for Monitoring Students in E-Learning

“…If there is any fraud or cheating in learning that is not acceptable, it will greatly affect learning outcomes of learners and quality of educational systems. Therefore, educational systems and a specific of e-learning systems need to provide ability of identifying and monitoring learners' activities [24].…”