Face Recognition based Artificial Intelligence With AttendX Technology for Student Attendance

AttendX is a platform that uses a facial recognition system to perform attendance. AttendX is a solution for attendance systems that exist in the scope of work or agency. AttendX can make attendance activities more efficient because of its features, making it easier for users to attend. To register AttendX, only use the face (left, right, front side, wearing a mask) and a brief bio of the person who registers. AttendX can verify the face of the user who has registered and is stored in the database. This application is designed with high-speed facial recognition technology. It can be used to solve the current problem, preventing the spread of the covid-19 virus, because AttendX can also recognize the user's face when wearing a mask. This study provides a real-world view of technological developments, significantly facilitating the use of facial recognition technology to complement those presented on the Internet, the latest face recognition and verification, and propose possible use cases for various applications. AttendXN et facial recognition function, the method used is ResNet and a hierarchical feedforward network to achieve the planned results. © 2022 IEEE.

AI Present: Random Interval Query and Face Recognition Attendance System for Classroom using Machine Learning

COVID-19 has triggered a global crisis never been seen before. In numerous sectors of society, the epidemic has produced a huge demand for new problem-solving solutions. Important difficulties have arisen in the field of education and related learning technologies because of the shift from eye to eye to online virtual learning. To battle the spread of COVID-19, several countries have temporarily closed educational institutions. Teachers all across the world have been forced to use internet conferencing as a result of educational institution closures. Online forums' virtual classes are widely considered as the only option to meet face to face in physical classes. In this aspect, teachers face a significant issue in managing student attendance in virtual classes. Student participation is a proportion of their involvement in the study, and it is linked to their active learning. Keeping track of student presence during virtual learning, on the other hand, is a major difficulty. Naming students who appear to be present in classrooms is a simple but time-consuming operation. Due to COVID-19 outbreak and widespread usage of virtual aids, disaster risk management must build a proper tracking system for monitoring student involvement and participation during virtual learning as soon as possible. This research work introduces a novel strategy to solve the significant demand created by the epidemic. Further, the proposed research study introduces the random interval query and face recognition based attendance management system to build a highly efficient and robust learning participation management system (from now on, AI Present). As far as known, no mechanized framework has been developed for following the students' participation and guarantee their involvement during the learning process. © 2022 IEEE.

Relationships Between Undergraduate Student Performance, Engagement, and Attendance in an Online Environment

Monitoring student attendance and engagement is common practice during undergraduate courses at university. Attendance data typically show a strong positive relationship with student performance and regular monitoring is an important tool to identify students who may require additional academic provisions, wellbeing support and pastoral care, for example. However, most of the previous studies and our framework for monitoring attendance and engagement is based on traditional on-campus, in-person delivery. Accelerated by the COVID-19 pandemic, our transition to online teaching delivery requires us to re-evaluate what constitutes attendance and engagement in a purely online setting and what are the most accurate ways of monitoring. Here, I show how statistics derived from student interaction with a virtual learning environment, Canvas, can be used as a monitoring tool. I show how basic statistics such as the number and frequency of page views are not adequate and do not correlate with student performance. A more in-depth analysis of video viewing duration, rather than simple page clicks/views is required, and weakly correlates with student performance. Lastly, I provide a discussion of the potential pitfalls and advantages of collecting such data and provide a perspective on some of the associated challenges.

Impact of Blended Learning on Engineering Student Attendance Post COVID-19

This paper aims at exploring the impact of applying a blended learning approach on the classroom attendance of engineering students post COVID-19. In this study, an interactive e-learning teaching approach, including synchronous online teaching delivery, was used as an alternative option in addition to regular face-to-face teaching. This approach was offered to all students attending two engineering modules, whether being still stranded off-campus or couldn't participate face-to-face for any reason. The major research question of this study is: given that engineering modules can be delivered effectively and interactively online, how will this affect student face-to-face attendance in the classroom? All students who attended the modules were asked to complete a quantitative survey where they could give their views on the quality of the tuition they had received and their opinions on the effectiveness of the online delivery. Evaluative data was obtained from a variety of means to provide a coherent case study. The paper presents comments and feedback from the students and some proposed implications for engineering student attendance when alternative online module delivery is an option. The outcomes of this study could help to inform pedagogies for future engineering education. © 2021 IEEE.