Modern concepts of education are increasingly focused on e-learning and distance learning. Expectations from them are at least the same efficiency, but also results higher than those obtained by the traditional education system. In distance learning systems the modules of assistants (tutors, helpers) are very important. They provide immediate feedback both to the student and the distance learning system. Tracking and recognizing emotions in distance learning systems is of great importance, especially in the adaptive capacity of automated education systems towards the student, but also in a corrective role in the distance learning process itself. Here we present a model for evaluating students based on automatic recognition of emotions during task solving.
- Anohina A. (2007). Advances in Intelligent Tutoring Systems: Problem-solving Modes and Model of Hints. International Journal of Computers, Communications & Control Vol. II No. 1, (pp. 48-55).
- Bojanić M. & Delić V. (2009). Automatic emotion recognition in speech: possibilities and significance, Conference INFOTEH-JAHORINA, Vol. 8 Ref. B-III-8, (pp. 223-227).
- Bosch L. (2003). Emotions, speech and the ASR framework, Speech Communication 40, (pp. 213-225).
- Brophy J. (2013). Motivating students to learn. New Jersey: Routledge.
- Busso C., Deng Z., Yildirim S., Bulut M., Lee M.C., Kazemzadeh A., Lee S., Neumann U., Narayanan S. (2004). Analysis of Emotion Recognition using Facial Expressions, Speech and Multimodal Information. The 6th international conference on Multimodal interfaces, ICMI ‘04. (pp. 205-211).
- Cerri S., Clancey W., Papadourakis G., Panourgia K.K. (2012). Intelligent Tutoring Systems, Intelligent Tutoring Systems: Proceedings of the 11th International Conference, Vol. 7135. (pp. 1-10). Crete, Greece: Springer-Verlag Berlin Heidelberg.
- Delić V. & Sečujski M. (2008). Transakcioni model verbalne interakcije čovek-mašina (Transactional Model of Human-Machine Speech Interaction), Conference DOGS, Kelebija, (pp. 8-15).
- Goleman D. (2005). Emotional Intelligence, The 10th Anniversary Edition, New York: Bantam Books.
- Guin N. & Lefevre M. (2013). Artificial Intelligence in Education, Artificial Intelligence in Education: Proceedings of AIED 2013, Vol. 7926. (Eds.) Lane H., Yacef K., Mostow J., Pavlik P., Memphis TN, USA: Springer Berlin Heidelberg.
- Jacobs D.C. (2005). The Application of Informal Feedback Intervention as a Communication Management Tool in Learning Organisations, Doctoral Thesis, University of Pretoria.
- Jovičić S.T., Kašić Z., Đorđević M., Vojnović M., Rajković M., & Savković J. (2003). Formiranje korpusa govorne ekspresije emocija i stavova u srpskom jeziku-GEES (Creation of a Corpus of Speech Expression of Emotions and Attitudes in the Serbian Language), XI Telekomunikacioni forum TELFOR, Beograd.
- Nipkow T. (2012). Teaching Semantics with a Proof Assistant: No More LSD Trip Proofs. Viktor Kuncak & Andrey Rybalchenko, (Eds.) VMCAI Verification, Model Checking, and Abstract Interpretation, Philadelphia, USA, Springer (pp. 24-38).
- Ochs M. & Frasson C. (2004). Optimal Emotional Conditions for Learning with an Intel- ligent Tutoring System, J.C. Lester et al. (Eds.), ITS 2004, LNCS 3220 Springer-Verlag Berlin Heidelberg, (pp. 845-847).
- Picard R.W. and Cosier G. (1997). Affective intelligence — the missing link?, BT Technol- ogy Journal, Vol. 15 No 4. (pp. 151-162).
- Przybylska I. (2016). Emotional Intelligence and Burnout in the Teaching Profession, The New Educational Review, Vol. 43 No 1, Wydawnictwo Adam Marszałek, Toruń (pp. 41-53).
- Sutton R.E. (2004). Emotion regulation goals and strategies, Social Psychology of Education 7, (pp. 379-398).
- Wenger E. (1987). Artificial intelligence and tutoring systems: Computational and Cognitive Approaches to the Communication of Knowledge, San Francisco, CA, USA : Morgan Kaufmann Publishers Inc.
- Yilmaz-Soylu M. & Akkoyunlu B. (2009). The effect of learning styles on achievement in different learning environments. The Turkish Online Journal of Educational Technology 8(6).