Discover and improve learners' feelings in e-learning using fuzzy inference system

Subject Areas : Virtual Learning

Leily Ghomashchi ¹ , Mohammad Reza Motadel ² , Abbas Toloie Ashlaghi ³

1 - PhD student of IT Management , Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Assistant Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Professor Departman of , Faculty of Reality Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

Received: 2020-09-24 Accepted : 2021-09-05 Published : 2021-09-23

Keywords: academic emotions, Fuzzy Inference System, Internet of Thinghs,

Abstract :

Due to the separation of teachers and students in the online teaching system and not receiving the moods of learners and applying appropriate feedback, this study seeks to design an intelligent system that can first detect learners' emotions remotely and then suggest educational scenarios to the teacher. Increases positive emotions and reduces negative emotions in learners. This research was conducted in 1998. The study population is the tenth grade mathematics students of Farzanegan 7 High School in Tehran. The students were divided into 5 groups of 15 people, each of whom was exposed to one of the situations of happiness, anger, fear, despair and sadness, and their facial information was received and recorded through a webcam. Data analysis in this study was performed by data mining method by Clementine software. By comparing the change in the range of emotions recorded before the implementation of the training scenario and then by data mining method with the help of Cummins algorithm that first clustering and then classification The results show that after the implementation of educational scenarios, changes were made in the ranges and increased the mean of positive emotions and decreased the mean of negative emotions.

References:

Al-Awni, A. (2016). Mood extraction using facial features to improvelearning curves of students in e-learning systems.

International Journal of Advanced Computer Science and Applications, 7(11), 444-453

. ‏(in Persian). Ashwin, T.S., Jose, J., Raghu, G.,& Reddy, G.R. (2015). An e-learning system with multifacial emotion recognition using supervised machine learning. In2015 IEEE seventh international conference on technology for education (T4E), 23-26.

Charoenpit, S.,& Ohkura, M. (2015). Exploring emotion in an e-learning system using eye tracking. International Journal of Affective Engineering. (IJAE-D),14-29.

Chu, H.C., Tsai, W.W., Liao, M.J.,& Chen, Y.M. (2018). Facial emotion recognition with transition detection for students with high-functioning autism in adaptive e-learning. Soft Computing, 22(9), 73-99.

Ismail, N.M. (2015). EFL. Saudi students’ class emotions and their contributions to their English achievement at Taif University. International Journal of Psychological Studies, 7(4), 19-42

. ‏(in Persian). Kadivar, P., Farzad, W., Kavousian, J., & Nikdel, F. (2017). Validation of Pakran Academic Emotions Questionnaire. Educational Innovations Quarterly, 8(32), 7-38 (in Persian).

Kobayashi, H., & Tatsukami, Y. (2013). A prototype design for wireless EMG capturing system with stimulation feedback. International Conference on Human System Interactions (HSI),501-506.

Kohoulat, N., Hayat, A.A., Dehghani, Mr., Kojuri, J., Amini, M. (2017). Medical students’ academic emotions: the role of perceived learning environment. Journal of advances in medical education & professionalism, 5(2),78-83

. ‏(in Persian). Krithika, L.B., & GG, L.P. (2016). Student emotion recognition system (SERS) for e-learning improvement based on learner concentration metric. Procedia Computer Science,67-76.

Lian, Z., Li, Y., Tao, J.H., Huang, J., & Niu, M.Y. (2020). Expression Analysis Based on Face Regions in Read-world Conditions. International Journal of Automation and Computing. 17(1), 96-107. Magdin, M., Turcani, M.,& Hudec, L. (2016). Evaluating the Emotional State of a User Using a Webcam. International Journal of Interactive Multimedia & Artificial Intelligence,4(1). Ramos, A.L., Dadiz, B.G.,& Santos, A.B. (2020). Classifying Emotion based on Facial Expression Analysis using Gabor Filter: A Basis for Adaptive Effective Teaching Strategy. InComputational Science and Technology, 469-479.

Samara, A., Galway, L., Bond, R., &Wang, H. (2019). Affective state detection via facial expression analysis within a human–computer interaction context. Journal of Ambient Intelligence and Humanized Computing , 10,2175–2184. Wilhelm, T. (2019). Towards Facial Expression Analysis in a Driver Assistance System, 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), 1-4.

Xu, R., Chen, J., Han, J., Tan, L.,& Xu, L. (2019). Towards emotion-sensitive learning cognitive state analysis of big data in education: deep learning-based facial expression analysis using ordinal information Computing, 1-6. Zhou, F., Kong, S.,& Charless, C. (2020). Facial Expression Analysis Using Dimensional Emotion Model,Neurocomputing, 392 ,38-49.