Real time Learning State Evaluation System for Elearning

Abstract

In the information age, the popularity of the Internet is profoundly changing the way people live. In this context, the online classrooms, which are spawned as a supplement to traditional classroom teaching, have increasingly provided people with new ways to learn knowledge and skills. However, under the online classroom scene, it is difficult for the learner's learning state to be effectively monitored, so it is more difficult for the learner to get feedback during the learning process than the traditional classroom. The traditional method of student status assessment is based on questionnaires and case studies, which requires a lot of manpower and material resources. Therefore, it is of great practical significance to study the intelligent detection of learning state in online classroom scenarios. At present, there is relatively little research on the intelligent monitoring of online classroom learning status, and various physiological and psychological dimensions have often not been considered comprehensively. Without the latter, it is difficult to systematically reflect the learning state, and there is also a lack of solutions capable of real-time intelligent monitoring of learning state in real-life environment. Therefore, this thesis takes the online classroom as the research environment, combined with the research in the fields of education and psychology, proposes a three-dimensional learning state evaluation model based on emotional state, state of concentration and fatigue state, and realizes a set of user identity. A real-time intelligent system named SmartTutor for learning state monitoring is identified, thereby assisting online classroom participants in establishing an effective closed loop of teaching and learning.