Sensor Based Affective Learning Information Process System for Intelligent Tutoring System

Abstract

Abstract As smartphones like televisions or cars are used every day and everybody, smartphones are used to access to digital content and various ICT services. Especially, smartphone is equipped with many types of sensors so that various multimedia content can use smartphone sensors. If users’ information through sensors would be analysed, users’ movement, emotion, and states can be inferred. Especially, on e-learning environment, students’ learning states can be decided by students’ information that are collected by smartphone sensors, since more than 90% of students of Korea National Open University have access to learning contents through their smartphones in mobile learning environment. However, previous researches focused on interactions between students and learning contents. And there are few methods, technology, and decision models for students to track their learning activity, learning interests, concentration and emotions. In this paper, we propose a learning reaction analysis state model and Student Activity Analysis System. In this research, sensed learning activity information is collected by sensors of smartphone. Students’ learning activity information can be classified into mandatory and optional information. In Student Activity Analysis System, students’ learning emotional state is decided by learning reaction analysis state model. The students’ learning emotional state could be used for intelligent tutoring system (ITS) that construct personal learning strategy. Since students’ learning activity information is collected and analyzed from the information technology viewpoint, it is possible to measure the concentration and interest of learning contents of students regardless of pedagogical model.