Emorec: a new approach for detecting and improving the emotional state of learners in an e-learning environment

In e-learning environments, several activities are offered to learners, including learning and assessment activities. During these activities, the learner may encounter difficulties, such as blocking situations or lack of motivation. This paper presents a new approach to detect these difficulties based on the learner’s emotional states and recommends solutions to motivate him/her. Our first contribution is recognizing the learner’s emotional state using two modules: facial expression recognition using a Gabor filter bank and vocal emotion recognition using MFCC features. Concerning the recommendations in these cases of difficulties, a psychological and/or pedagogical recommendation generator is proposed in this study; it is our second contribution. Three experiments to validate our approach were conducted on two groups of students: test and control. The results indicate that the proposed method improves the learner’s emotional state, motivation, and engagement time in the system.