Comparative study on emotions analysis from facial expressions in children with and without learning disabilities in virtual learning environment
- 303 Downloads
Children with Learning Disabilities (LDs) show some emotional difficulties and behavioral problems in classroom compared with their peers without LDs. Emotions constitute an important part of the learning process. Recent evidence suggests that the use of Information and Communication Technology (ICT) in special education permits to remove barriers in learning for the target children. Besides, it offers a learning environment for a diversity of emotional experiences. In this present study, we explored the benefits of ICT use to identify the ways in which emotions are involved during the learning process in Virtual Learning Environments (VLE). We conducted a user study with 42 children divided into two groups; experimental group (n = 14) and age matched control group (n = 28) to compare their emotional experiences in VLE. We used advances in Artificial Intelligence (AI) to detect children’s emotions through their facial expressions by analyzing seven basic facial emotion expressions (angry, disgust, fear, happy, sad, surprise and neutral) while playing an educational game. The initial results indicate that emotions are present in VLE and they appear to suggest that children with LDs experience the same emotions as their peers without LDs in VLE. Besides, they show that children with LDs experience less negative emotions compared to literature evidence about the presence of a higher level of negative emotions in classroom.
KeywordsLearning disabilities (LDs) Information and communication technology (ICT) Assistive technology (AT) Artificial intelligence (AI) Virtual learning environment (VLE) Emotion recognition
This work was financially supported by an Excellence Grant accorded to Nihal Ouherrou (3UCD2018) and Oussama Elhammoumi (11UAE2017) by the National Center of Scientific and Technical Research (CNRST)-Minister of National Education, Higher Education, Staff Training and Scientific Research, Morocco.
The authors would like to acknowledge the president and staff at Speech-Language Pathology Service-Health center, El Jadida Morocco and also children who have participated in this study. The authors would like also to thank the speech therapist Ilham Elhousni for her valuable suggestions and recommendations and the stuff at the primary school l’Ange Bleu El Jadida for their cooperation.
- Adam, T., & Tatnall, A. (2010). Use of ICT to assist students with learning difficulties: An Actor-Network Analysis. In Key competencies in the knowledge society (pp. 1–11). Springer.Google Scholar
- Adebisi, R. O., Liman, N. A., & Longpoe, P. K. (2015). Using assistive Technology in Teaching Children with learning disabilities in the 21st century. Journal of Education and Practice, 6(24), 14–20.Google Scholar
- Ahmad, W. F. W., Akhir, E. A. P., & Azmee, S. (2010). Game-based learning courseware for children with learning disabilities (pp. 1–4). IEEE. https://doi.org/10.1109/ITSIM.2010.5561303.
- AL Zyoudi, M. (2010). Differences in self-concept among student with and without learning disabilities in Al Karak District in Jordan. International Journal of Special Education, 25(2), 72–77.Google Scholar
- Alexander, S., Sarrafzadeh, A., & Hill, S. (2006). Easy with eve: A functional affective tutoring system. In Workshop on Motivational and Affective Issues in ITS. 8th International Conference on ITS (pp. 5–12). Citeseer.Google Scholar
- Benmarrakchi, F. E., El Kafi, J., & Elhore, A. (2016). Supporting dyslexic’s learning style preferences in adaptive virtual learning environment (pp. 1–6). IEEE. https://doi.org/10.1109/ICEMIS.2016.7745294.
- Benmarrakchi, F., El Kafi, J., Elhore, A., & Haie, S. (2017c). Exploring the use of the ICT in supporting dyslexic students’ preferred learning styles: A preliminary evaluation. Education and Information Technologies, 22(6), 2939–2957. https://doi.org/10.1007/s10639-016-9551-4.CrossRefGoogle Scholar
- Bowlby, J. (1969). Attachment, Vol. 1 of attachment and loss. New York: Basic Books.Google Scholar
- Calleja, G. (2011). In-game: From immersion to incorporation. MIT Press.Google Scholar
- Carver, C. S., & Scheier, M. F. (2012). Perspectives on personality. Pearson education.Google Scholar
- Cortiella, C., & Horowitz, S. H. (2014). The state of learning disabilities: Facts, trends and emerging issues (pp. 2–45). New York: National Center for Learning Disabilities.Google Scholar
- Forgas, J. P. (2012). Feeling and speaking: Affective influences on communication strategies and language use. Social Cognition and Communication, 63–81.Google Scholar
- Forgas, J. P. (2017). Mood effects on cognition: Affective influences on the content and process of information processing and behavior. In Emotions and affect in human factors and human-computer interaction (pp. 89–122). Elsevier. https://doi.org/10.1016/B978-0-12-801851-4.00003-3.
- Graetz, K. A. (2006). The psychology of learning environments. Learning Spaces, 6.Google Scholar
- Hammoumi, O. E., Benmarrakchi, F.E., Ouherrou, N., El Kafi, J., & ElHore, A. (2018). Emotion Recognition in E-learning Systems. In 2018 6th International Conference on Multimedia Computing and Systems (ICMCS) (pp. 1–6). https://doi.org/10.1109/ICMCS.2018.8525872.
- Hassan, A. E. H. (2015). Emotional and behavioral problems of children with learning disabilities. Journal of Educational Policy and Entrepreneurial Research (JEPER), 2(10), 66–74.Google Scholar
- Horvat, M., Kukolja, D., & Ivanec, D. (2015). Comparing affective responses to standardized pictures and videos: A study report. ArXiv:1505.07398 [Cs]. Retrieved from https://arxiv.org/ftp/arxiv/papers/1505/1505.07398.pdf.
- Jacko, J. A. (2012). Human computer interaction handbook: Fundamentals, evolving technologies, and emerging applications, third edition. CRC Press.Google Scholar
- Kanade, T., Cohn, J. F., & Tian, Y. (2000). Comprehensive database for facial expression analysis. In Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) (pp. 46–53). https://doi.org/10.1109/AFGR.2000.840611.
- Kort, B., Reilly, R., & Picard, R. W. (2001). An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion. In Proceedings IEEE International Conference on Advanced Learning Technologies (pp. 43–46). https://doi.org/10.1109/ICALT.2001.943850.
- Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010). The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops (pp. 94–101). https://doi.org/10.1109/CVPRW.2010.5543262.
- Lundqvist, D., Flykt, A., & Öhman, A. (1998). The Karolinska directed emotional faces (KDEF). CD ROM from Department of Clinical Neuroscience, Psychology Section, Karolinska Institutet, (1998).Google Scholar
- Pekrun, R. (1992). The impact of emotions on learning and achievement: Towards a theory of cognitive/motivational mediators. Applied Psychology, 41(4), 359–376. https://doi.org/10.1111/j.1464-0597.1992.tb00712.x.CrossRefGoogle Scholar
- Sarrafzadeh, A., Hosseini, H. G., Fan, C., & Overmyer, S. P. (2003). Facial expression analysis for estimating learner’s emotional state in intelligent tutoring systems. In Proceedings 3rd IEEE International Conference on Advanced Technologies (pp. 336–337). https://doi.org/10.1109/ICALT.2003.1215111.
- Shen, L., Leon, E., Callaghan, V., & Shen, R. (2007). Exploratory research on an affective elearning model. In International Workshop on Blended Learning (pp. 15–17).Google Scholar
- Shen, L., Wang, M., & Shen, R. (2009). Affective e-learning: Using" emotional" data to improve learning in pervasive learning environment. Journal of Educational Technology & Society, 12(2).Google Scholar
- Sorour, A. S., Mohamed, N. A., & El-Maksoud, M. M. A. (2014). Emotional and behavioral problems of primary school children with and without learning disabilities: A comparative study. Journal of Education and Practice, 5(8), 1–11.Google Scholar
- Tam, H. E., & Hawkins, R. (2012). Self-concept and depression levels of students with dyslexia in Singapore.Google Scholar
- Toro, P. A., Weissberg, R. P., Guare, J., & Liebenstein, N. L. (1990). A comparison of children with and without learning disabilities on social problem-solving skill, school behavior, and family background. Journal of Learning Disabilities, 23(2), 115–120. https://doi.org/10.1177/002221949002300207.CrossRefGoogle Scholar
- Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 (Vol. 1, pp. I-511-I–518 vol.1). https://doi.org/10.1109/CVPR.2001.990517.
- Yannakakis, G. N., & Paiva, A. (2014). Emotion in games. Handbook on Affective Computing, 459–471.Google Scholar