A Bayesian Classification Network-based Learning Status Management System in an Intelligent Classroom

Awareness of students' learning status, and maintaining students' focus and attention during class are important issues in classroom management. Several observation instruments have been designed for human observers to document students' engagement in class, but the processes are time-consuming and laborious. Recently, with the development of artificial intelligent technologies, artificial intelligence in education (AIED) has become an important research topic. Several studies have applied image recognition technologies to determine students' learning status. However, little research has employed both sensor technology and image recognition technology in learning status analysis. Moreover, it remains unknown if learning status analysis is accurate enough to substitute for human observers. Furthermore, no feedback has been provided individually to students to manage their learning status by maintaining their attention in class. In this paper, a learning status management system in an intelligent classroom is proposed. Several types of information about students were detected and collected by both sensor technology and image recognition technology, and a Bayesian classification network was employed to inference the students' learning status. Moreover, the system includes a feedback mechanism, which not only provides the results of the just-in-time learning status analysis to teachers, but also notifies students who are detected as being unfocused in class. Two experiments were conducted to verify the accuracy and effectiveness of the proposed system. Results showed that the learning status analysis highly corresponded to the observation of human beings, and the students were more attentive in class.