Emotions play a significant role in many human mental activities, including decision making, motivation, and cognition. Various intelligent and expert systems can be empowered with emotionally intelligent capabilities, especially systems that interact with humans and mimic human behavior. In this thesis we design and Implement an emotional intelligent tutor system based on example tracing that can learn cognitive skills. When students try to solve an example they can be controlled by emotional intelligent tutor system.
It verifies the correctness of each step that students make. When the student couldn’t make a step correct, the tutor system may also provide more informative feedback for them. Feedbacks are divided into two types: general feedback and emotional feedback. Emotional feedbacks are showed based on the current emotional state of the student and general feedbacks that can showed regardless of the emotional state. Emotion detection module of this system can detect five emotions that have been shown to be related to the learning process: delight, neutral, confusion, boredom, and frustration through their typing behavior an answer features in an educational context. Not only the implemented system be able to detect important emotions, but also detect the type of mistakes that has been committed by student and it will show suitable feedback according to its detections.
Finally, 30 students from high school (middle) and 14 students of the primary school were selected for testing software. The results show students that solve the examples by emotional intelligent tutor system can improve their level of learning.
- Irani, R., and Kazemifard, M. (2015). “An Emotional Intelligent Tutor System based on Example Tracing”. In Proceeding of the 6th International Conference of Cognitive Science (ICCS), Institute for Cognitive Science studies, Tehran, Iran, p.11.