Affective Computing: A Closer View of Self-Reported Instruments in Education
The trends of affective computing have rapidly become an issue in educational settings. Even self-reported instruments have been the most popular class of instruments for emotional experience assessment over the past years, there are deficiencies in the in-depth literature review and classification of research based on these affective recognition instruments. For that reason, this study focused on the self-reported instruments and reviewed 18 related studies from IEEE Xplore, ScienceDirect, and Springer link databases published from 2010 to 2015, and categorized them based on affective recognition instrument, affective classification, and learning domain. Finally, this study provides insight and future direction on self-reported affective computing instruments for both researchers and practitioners.
Alberdi, A., Aztiria, A., & Basarab, A. (2016). Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review. Journal of biomedical informatics, 59, 49-75.
Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2015). Predicting students’ emotions using machine learning techniques. Paper presented at the Artificial Intelligence in Education.
Ammar, M. B., & Neji, M. (2006). A multi-agent based system for affective peer-e-learning. Paper presented at the Proceedings of the 2nd international conference on Mobile multimedia communications.
Barreto, A., Zhai, J., & Adjouadi, M. (2007). Non-intrusive physiological monitoring for automated stress detection in human-computer interaction Human–Computer Interaction (pp. 29-38): Springer.
Burić, I., Sorić, I., & Penezić, Z. (2016). Emotion regulation in academic domain: Development and validation of the academic emotion regulation questionnaire (AERQ). Personality and Individual Differences, 96, 138-147.
Caballé, S. (2015). Towards a Multi-modal Emotion-awareness e-Learning System. Paper presented at the Intelligent Networking and Collaborative Systems (INCOS), 2015 International Conference on.
Calvo, R. A., & D'Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. Affective Computing, IEEE Transactions on, 1(1), 18-37.
Chao, C.-J., Lin, H.-C. K., Lin, J.-W., & Tseng, Y.-C. (2012). An Affective Learning Interface with an Interactive Animated Agent. Paper presented at the Digital Game and Intelligent Toy Enhanced Learning (DIGITEL), 2012 IEEE Fourth International Conference on.
Duo, S., & Song, L. X. (2010). Research on E-learning system based on affective computing. Paper presented at the Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on.
Eyharabide, V., Amandi, A., Courgeon, M., Clavel, C., Zakaria, C., & Martin, J.-C. (2011). An ontology for predicting students' emotions during a quiz. Comparison with self-reported emotions. Paper presented at the Affective Computational Intelligence (WACI), 2011 IEEE Workshop on.
Feidakis, M., Caballé, S., Daradoumis, T., Jiménez, D. G., & Conesa, J. (2014). Providing emotion awareness and affective feedback to virtualised collaborative learning scenarios. International Journal of Continuing Engineering Education and Life Long Learning 6, 24(2), 141-167.
Gu, X., Li, Q., & Diao, R. (2011). Research of e-learning intelligent affective model based on BDI agent with learning materials Advances in Computer Science, Intelligent System and Environment (pp. 99-104): Springer.
Handayani, D., Yaacob, H., Rahman, A., Wahab, A., Sediono, W., & Shah, A. (2014). Systematic review of computational modeling of mood and emotion. Paper presented at the Information and Communication Technology for The Muslim World (ICT4M), 2014 The 5th International Conference on.
Harley, J. M., Bouchet, F., & Azevedo, R. (2013). Aligning and comparing data on emotions experienced during learning with MetaTutor. Paper presented at the Artificial Intelligence in Education.
Harley, J. M., Bouchet, F., Hussain, M. S., Azevedo, R., & Calvo, R. (2015). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48, 615-625.
Harley, J. M., Carter, C. C., Papaionnou, N., Bouchet, F., Landis, R. S., Azevedo, R., & Karabachian, L. (2015). Examining the Predictive Relationship Between Personality and Emotion Traits and Learners’ Agent-Direct Emotions. Paper presented at the Artificial Intelligence in Education.
Hussain, M. S., AlZoubi, O., Calvo, R. A., & D’Mello, S. K. (2011). Affect detection from multichannel physiology during learning sessions with AutoTutor. Paper presented at the Artificial Intelligence in Education.
Ismaail, M., & Mohd Zahid Syed Zainal Ariffin, S. (2014). Adapting to leamer's emotions through Animated Pedagogical Agent. Paper presented at the User Science and Engineering (i-USEr), 2014 3rd International Conference on.
Jaques, P. A., Vicari, R., Pesty, S., & Martin, J.-C. (2011). Evaluating a cognitive-based affective student model Affective Computing and Intelligent Interaction (pp. 599-608): Springer.
Kaklauskas, A., Kuzminske, A., Zavadskas, E. K., Daniunas, A., Kaklauskas, G., Seniut, M., . . . Juozapaitis, A. (2015). Affective tutoring system for built environment management. Computers & Education, 82, 202-216.
Lin, H.-C. K., Wu, C.-H., & Hsueh, Y.-P. (2014). The influence of using affective tutoring system in accounting remedial instruction on learning performance and usability. Computers in Human Behavior, 41, 514-522.
Linnenbrink, E. A. (2006). Emotion research in education: Theoretical and methodological perspectives on the integration of affect, motivation, and cognition. Educational Psychology Review, 18(4), 307-314.
Malekzadeh, M., Mustafa, M. B., & Lahsasna, A. (2015). A review of emotion regulation in intelligent tutoring systems. Educational Technology & Society, 18(4), 435-445.
Moridis, C., & Economides, A. (2008). Toward computer-aided affective learning systems: a literature review. Journal of Educational Computing Research, 39(4), 313-337.
Muñoz, K., Mc Kevitt, P., Lunney, T., Noguez, J., & Neri, L. (2010). PlayPhysics: an emotional games learning environment for teaching physics Knowledge Science, Engineering and Management (pp. 400-411): Springer.
Noteborn, G., Carbonell, K. B., Dailey-Hebert, A., & Gijselaers, W. (2012). The role of emotions and task significance in Virtual Education. The Internet and Higher Education, 15(3), 176-183.
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36-48.
Salmeron-Majadas, S., Santos, O. C., & Boticario, J. G. (2014). An evaluation of mouse and keyboard interaction indicators towards non-intrusive and low cost affective modeling in an educational context. Procedia Computer Science, 35, 691-700.
Sandanayake, T., & Madurapperuma, A. (2013). Affective e-learning model for recognising learner emotions in online learning environment. Paper presented at the Advances in ICT for Emerging Regions (ICTer), 2013 International Conference on.
Sandanayake, T., Madurapperuma, A., & Dias, D. (2011). Affective E Learning Model for Recognising Learner Emotions. International Journal of Information and Education Technology, 1(4), 315.
Subramainan, L., Yusoff, M. Z. M., & Mahmoud, M. A. (2015). A classification of emotions study in software agent and robotics applications research. Paper presented at the Agents, Multi-Agent Systems and Robotics (ISAMSR), 2015 International Symposium on.
Vogel-Walcutt, J. J., Fiorella, L., Carper, T., & Schatz, S. (2012). The definition, assessment, and mitigation of state boredom within educational settings: A comprehensive review. Educational Psychology Review, 24(1), 89-111.
Wu, C. H., Huang, Y. M., & Hwang, J. P. (2015). Review of affective computing in education/learning: Trends and challenges. British Journal of Educational Technology.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.