Toward optimized attendance management in education: A machine learning and cloud computing approach

Abstract

The main objective of the research is to optimize and streamline the attendance recording and monitoring process for learning sessions by applying advanced technologies such as Machine Learning and Cloud Computing. The methodology employed is based on the XP (Extreme Programming) 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 leveraged to validate 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 research 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 research has successfully implemented a mobile application that revolutionizes attendance recording and monitoring in educational institutions, harnessing the power of Machine Learning and Cloud Computing to enhance efficiency and the user experience.

Keywords: Attendance records; cloud computing; education; machine learning; mobile application; process optimization