ABSTRACT

This study aims to address the various challenges existing in classroom management within higher education institutions by proposing and developing a comprehensive classroom management system based on computer vision and data mining technologies. Firstly, we introduce a facial recognition-based attendance management system to overcome the shortcomings of traditional attendance methods. This system automates attendance tracking by accurately identifying students’ presence, thus eliminating time wastage and inaccuracies associated with manual methods such as roll call. Secondly, we present a gradebook management system based on data management principles, enabling real-time monitoring and statistical analysis of student performance. This system effectively manages and analyzes student grade data, providing valuable insights for instructional decision-making. Additionally, the researcher explored a homework assignment and submission system integrated within classroom management, aiming to enhance efficiency and convenience in handling assignments. This system facilitates seamless distribution and collection of student assignments, along with prompt feedback, fostering interactive teaching and learning. Lastly, we investigate a decision support system utilizing data mining techniques to identify underlying patterns and trends in student data. This system assists educational administrators in making informed decisions by analyzing vast amounts of student information. The outcomes of this research are expected to offer new insights and approaches for advancing classroom management practices, thereby improving teaching quality and student learning outcomes.

Keywords: Classroom management system, facial recognition, data management, homework assignment, data mining, decision support