bibliografia.txt

Artigos de referencia:
1. A. C. Graesser, P. Chipman, B. C. Haynes, and A. Olney, “AutoTutor: An intelligent tutoring system with mixed-initiative dialogue”, IEEE Transactions in Education, 2005, vol 48, pp. 612-618.
2. B. Kort, R. Reilly and R. W. Picard, “An Affective Model of Interplay Between Emotions and Learning: Reengineering Educational Pedagogy-Building a Learning Companion”, Proc. 2nd
IEEE Int’l Conf. Advanced
Learning Technologies (ICALT 2001), IEEE CS Press, 2001, pp. 0043.
3. C. Conati, “Probabilistic Assessment of User’s Emotions in Educational Games”, Journal of Applied Artificial Intelligence, 2002, vol 16, no. 7-8, pp. 555-575.
4. D. J. Litman and S. Silliman, “ITSPOKE: An intelligent tutoring spoken dialogue system”. Proc. 4th meeting of HLT/NAACL, 2004, pp. 52-54.
5. M. Pantic and L.J.M. Rothkrantz, “Towards an Affect-sensitive Multimodal Human-Computer Interaction”, IEEE Special Issue on Multimodal Human-Computer Interaction, 2003, vol. 91, no. 9, pp. 1370-1390.
6. P. Ekman and W. V. Friesen, The facial action coding system: A technique for the measurement of facial movement, Consulting Psychologists Press, 1978.
7. S. D. Craig, A. C. Graesser, J. Sullins, and B. Gholson. “Affect and learning: An exploratory look into the role of affect in learning”, Journal of Educational Media, 2004, vol. 29, pp. 241-250.
8. S. K. D’Mello, S. D. Craig, J. Sullins, and A. C. Graesser, “Predicting affective states through an emote-aloud procedure from AutoTutor’s mixed-initiative dialogue”, International Journal of Artificial Intelligence in Education, 2006, vol. 16, pp. 3-28.
9. A. C. Graesser, B. McDaniel, P. Chipman, A. Witherspoon, S. D’Mello, and B. Gholson, “Detection of Emotions During Learning with AutoTutor”, Proc. 28th
Ann. Conf. Cognitive Science Soc, (COGSCI 2006),
Cognitive Science Society, Inc., 2006, pp. 285-290.
10. R. el Kaliouby and P. Robinson, “Generalization of a vision-based computational model of mind-reading”, 1st Intl. Conf. on Affective Computing and Intelligent Interaction, LNCS 3784, Springer, 2005, pp 582-589.

Artigos que citam o artigo base:
It's Written on Your Face: Detecting Affective States from Facial Expressions while Learning Computer Programming
EMOTIONS DURING THE LEARNING OF DIFFICULT MATERIAL
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features
AUTOMATIC DETECTION OF LEARNER'S AFFECT FROM GROSS BODY LANGUAGE
Emotion Recognition for User Centred E-Learning
A hybrid intelligence-aided approach to affect-sensitive e-learning
Intelligent Tutoring System with Affective Learning for Mathematics
An Intelligent and Affective Tutoring System within a Social Network for Learning Mathematics
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
Incorporating Affect into Educational Design Patterns and Frameworks

Artigos que já baixei:
1. Calvo, R.A. & D’Mello, S., 2010. Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), pp.18–37.
2. Chen, J., 2007. Flow in games (and everything else). Communications of the ACM, 50(4), p.31. Available at: http://portal.acm.org/citation.cfm?doid=1232743.1232769.
3. Csikszentmihalyi, M., 1997. Flow and Education. NAMTA Journal, 22(2), pp.36–58. Available at: http://www.ppc.sas.upenn.edu/csikszentmihalyipowerpoint.pdf%5Cnhttp://www.eric.ed.gov/ERICWebPortal/detail?accno=EJ547968%5Cnhttp://www.eric.ed.gov/ERICWebPortal/detail?accno=EJ547966%5Cnhttp://www.eric.ed.gov/ERICWebPortal/detail?accno=EJ547967.
4. D’Mello, S., Graesser, A. & Picard, R.W., 2007. Toward an affect-sensitive autotutor. IEEE Intelligent Systems, 22(4), pp.53–61.
5. Faiola, A. et al., 2012. Correlating the effects of flow and telepresence in virtual worlds: Enhancing our understanding of user behavior in game-based learning. Computers in Human Behavior. Available at: http://dx.doi.org/10.1016/j.chb.2012.10.003.
6. Fu, F.L., Su, R.C. & Yu, S.C., 2009. EGameFlow: A scale to measure learners’ enjoyment of e-learning games. Computers and Education, 52(1), pp.101–112. Available at: http://dx.doi.org/10.1016/j.compedu.2008.07.004.
7. Hamari, J. & Koivisto, J., 2014. Measuring flow in gamification: Dispositional Flow Scale-2. Computers in Human Behavior, 40(February 2016), pp.133–143.
8. Heutte, J. & Martin-krumm, C., 2014. Echelle de flow en éducation (EduFlow). , (January 2016).
9. Jackson, S.A. & Marsh, H.W., 1996. Development and validation of a scale to measure optimal experience: The Flow State Scale. Journal of Sport & Exercise Psychology, 18, pp.17–35. Available at: http://search.ebscohost.com/login.aspx?direct=true&db=s3h&AN=9604091722&site=ehost-live.
10. Jackson, S. & Eklund, R., 2002. Assessing flow in physical activity: The Flow State Scale-2 and Dispositional Flow Scale-2. Journal of Sport & Exercise Psychology, 24(2), pp.133–150. Available at: http://psycnet.apa.org/psycinfo/2002-01878-003.
11. Jackson, S., Martin, A.J. & Eklund, R.C., 2008. Long and short measures of flow: the construct validity of the FSS-2, DFS-2, and new brief counterparts. Journal of sport & exercise psychology, 30(5), pp.561–587.
12. Lee, P.-M., Jheng, S.-Y. & Hsiao, T.-C., 2014. Towards Automatic Detecting Wether Student is in Flow. Proceedings of the 12th International Conference on Intelligent Tutoring Systems, pp.11–18.
13. Oliveira, W. et al., 2015. Flow Theory Applied to Computers and. , (July).
14. Sweetser, P. & Wyeth, P., 2005. GameFlow: A Model for Evaluating Player Enjoyment in Games. Comput. Entertain., 3(3), pp.3–3. Available at: http://doi.acm.org/10.1145/1077246.1077253.