Affective Recognition and Gamification Applied to Learning Algorithmic Logic and Programming
DOI:
https://doi.org/10.24320/redie.2018.20.3.1636Keywords:
Educational software, computer assisted, gamification, educational environment.Supporting Agencies:
Tecnológico Nacional de México, Instituto Tecnológico de CuliacánAbstract
This paper presents a learning environment that uses affective recognition techniques and gamification to teach algorithmic logic and programming. This environment was evaluated and contrasted with engineering students. The method consisted in assessing student learning of algorithmic logic using traditional techniques compared to learning with automatic recognition of emotions and motivational management using gamification. Tests and surveys were conducted with 42 students, who were divided into two groups, and two different system configurations were used to assess the gamification techniques implemented. The results showed that learning is statistically better if students’ affective state is taken into account, and if students are motivated through gamification.Downloads
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Sharma, S., Ghorpade, S., Sahni, A. y Saluja, N. (2014). Survey of Intelligent Tutoring Systems : a review on the development of expert/intelligent tutoring systems, various teaching strategies and expert tutoring system design suggestions. International Journal of Engineering Research & Technology, 3(11), 37-42. Recuperado de https://www.ijert.org/browse/volume-3-2014/november-2014-edition
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Charsky, D. (2010). From edutainment to serious games: a change in the use of game characteristics. Games and Culture, 5(2), 177-198. doi:10.1177/1555412009354727
Clark, R. C. y Mayer, R. E. (2016). E-learning and the science of instruction: proven guidelines for consumers and designers of multimedia learning. San Francisco, CA: John Wiley & Sons.
Crawford, S. y Domínguez, J. J. (October, 2013). MIT app inventor: enabling personal mobile computing. Conferencia presentada en el Programming for Mobile and Touch PRoMoTo 2013, Indianapolis, IN. Recuperado de arXiv:1310.2830v2
Deterding, S., Sicart, M., Nacke, L., O’Hara, K. y Dixon, D. (2011). Gamification: using game design elements in non-game contexts. Actas 2011 Annual Conference Extended Abstracts on Human Factors in Computing Systems, 2425-2428. Vancouver, Canadá. doi:10.1145/1979742.1979575
Fraser, N. (2013). Google blockly [a visual programming editor]. Recuperado de https://neil.fraser.name/news/2013/12/31/
Immordino‐Yang, M. H. y Damasio, A. (2007). We feel, therefore we learn: the relevance of affective and social neuroscience to education. Mind, Brain and Education, 1(1), 3-10.
Kazimoglu, C., Kiernan, M., Bacon, L. y MacKinnon, L. (2012). A serious game for developing computational thinking and learning introductory computer programming. Procedia-Social and Behavioral Sciences, 47, 1991-1999. Recuperado de doi:10.1016/j.sbspro.2012.06.938
Landowska, A. (October, 2014). Affective learning manifesto –10 Years Later. In R. Ørngreen y K. T. Levinsen (Eds.), Proceedings of the 13th European Conference on e‐Learning ECEL‐2014 (pp. 281-288).
Loannidou, A., Bennett, V., Repenning, A., Koh, K. H. y Basawapatna, A. (Abril, 2011). Computational thinking patterns. Documento presentado en 2011 Annual Meeting of the American Educational Research Association (AERA), Nueva Orláans, LA. Recuperado de https://files.eric.ed.gov/fulltext/ED520742.pdf
Pekrun, R. (2014). Emotions and learning. International Academy of Education's Educational Practices Series. Belley, Francia: Gonnet Imprimeur. Recuperado de http://unesdoc.unesco.org/images/0022/002276/227679e.pdf
Pekrun, R., Elliot, A. J. y Maier, M. A. (2009). Achievement goals and achievement emotions: testing a model of their joint relations with academic performance. Journal of Educational Psychology, 101(1), 115-135. Recuperado de doi:10.1037/a0013383
Picard, R. W. y Klein, J. (2002). Computers that recognise and respond to user emotion: theoretical and practical implications. Interacting with Computers, 14(2), 141-169. doi:10.1016/S0953-5438(01)00055-8
Sharma, S., Ghorpade, S., Sahni, A. y Saluja, N. (2014). Survey of Intelligent Tutoring Systems : a review on the development of expert/intelligent tutoring systems, various teaching strategies and expert tutoring system design suggestions. International Journal of Engineering Research & Technology, 3(11), 37-42. Recuperado de https://www.ijert.org/browse/volume-3-2014/november-2014-edition
Tessler, J., Beth, B. y Lin, C. (2013). Using cargo-bot to provide contextualized learning of recursion. En actas Ninth Annual International ACM Conference on International Computing Education Research, ICER ‘13 (pp. 161-168), NuevaYork. doi:10.1145/2493394.2493411
Utting, I., Cooper, S., Kölling, M., Maloney, J. y Resnick, M. (2010). Alice, greenfoot, and scratch-a discussion. Journal ACM Transactions on Computing Education, 10(4), 1-11. doi:10.1145/1868358.1868364
Weragama, D. S. (2013). Intelligent tutoring system for learning PHP, (Tesis doctoral), Queensland University of Technology. Recuperada de https://eprints.qut.edu.au/63202/
Zatarain, R., Barrón-Estrada, M. L. y Muñoz, G. (2016). PREMOC: Plataforma de Reconocimiento Multimodal de Emociones. Trabajo presentado en el 8o. Congreso Mexicano de Inteligencia Artificial (COMIA), Tonanzintla, Puebla, México.
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Article abstract page views: 2418
Published
2018-09-07
