A Learning Analytic Model for Smart Classroom

Qunbo Wang, Wenjun Wu, Yuxing Qi

Abstract: With the popularity of Smart Classroom, it is necessary to study corresponding learning analytic methods to assist instructors. However, little research has investigated analyzing hidden state in class, which is an important analysis work. Therefore, focusing on the interactive learning through individual Pad devices, we propose a Learning Analytic Model to analyze hidden state with students’ sequential behaviors that automatically recorded by devices. The model segments the class’ process into multiple phases and construct a Hidden Markov Model (HMM) to infer students’ state. In addition, a web page is developed to show students’ behaviors and related analysis results intuitively. The experiment shows our model can fine-grained analyze and feedback the learning state of students in the smart classroom, which effectively help instructors improve teaching methods.