Abstract
Gamification of learning material has received much interest from researchers in the past years. This paper aims to further improve such learning experience by applying socio-cognitive gamification to educational games. Dynamic difficulty adjustment (DDA) is a well-known tool in optimizing gaming experience. It is a process to control the parameters in a video game automatically based on user experience in real-time. This method can be extended by using a biofeedback-approach, where certain aspects of the player’s ability is estimated based on physiological measurement (e.g. eye tracking, ECG, EEG). Here, we outline the design of a biofeedback-based framework that supports dynamic difficulty adjustment in educational games. It has a universal architecture, so the concept can be employed to engage users in non-game contexts as well. The framework accepts input from the games, from the physiological sensors and from the so-called supervisor unit. This special unit empowers a new social aspect by enabling another user to observe or intervene during the interaction. To explain the game-user interaction itself in educational games we propose a hybrid model.
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References
Baranyi P, Csapo A (2012) Definition and synergies of cognitive infocommunications. Acta Polytechnica Hungarica 9:67–83
Browne K, Anand C, Gosse E (2014) Gamification and serious game approaches for adult literacy tablet software. Entertain Comput 5(3):135–146
Chen J (2007) Flow in games (and everything else). Commun ACM 50(4):31–34
Danzi G, Santana AHP, Furtado AWB, Gouveia AR, Leitao A, Ramalho GL (2003) Online adaptation of computer games agents: a reinforcement learning approach. In: II Workshop de Jogos e Entretenimento Digital, pp 105–112
Deterding S, Dixon D, Khaled R, Nacke L (2011) From game design elements to gamefulness: defining “Gamification”. In: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, MindTrek ’11. ACM, New York, pp 9–15
van Erp JBF, Lotte F, Tangermann M (2012) Brain-computer interfaces: beyond medical applications. IEEE Comput 45(4):26–34
Fritz T, Begel A, Müller SC, Yigit-Elliott S, Züger M (2014) Using psycho-physiological measures to assess task difficulty in software development. In: Proceedings of the 36th International Conference on Software Engineering (ICSE) 2014. ACM Press, New York, pp 402–413. doi:10.1145/2568225.2568266. http://dl.acm.org/citation.cfm?id=2568225.2568266
Grimes D, Tan DS, Hudson SE, Shenoy P, Rao RP (2008) Feasibility and pragmatics of classifying working memory load with an electroencephalograph. In: Proceeding of the twenty-sixth annual CHI conference on Human factors in computing systems, CHI ’08. ACM Press, New York, p 835. doi:10.1145/1357054.1357187
Hamari J, Koivisto J, Sarsa H (2014) Does gamification work? A literature review of empirical studies on gamification. In: 47th Hawaii International Conference on System Sciences (HICSS). IEEE, pp 3025–3034
Hercegfi K (2011) Improved temporal resolution heart rate variability monitoring-pilot results of non-laboratory experiments targeting future assessment of human-computer interaction. Int J Occup Saf Ergon 17(2):105–17
Hunicke R (2005) The case for dynamic difficulty adjustment in games. In: Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology. ACM, pp 429–433
Hunicke R, Chapman V (2004) Ai for dynamic difficulty adjustment in games. In: Challenges in Game Artificial Intelligence AAAI Workshop, pp 91–96
Järvinen A (2009) Game design for social networks: interaction design for playful dispositions. In: Proceedings of the 2009 ACM SIGGRAPH Symposium on Video Games, Sandbox ’09. ACM, New York, pp 95–102
Jennings-Teats M, Smith G, Wardrip-Fruin N (2010) Polymorph: dynamic difficulty adjustment through level generation. In: Proceedings of the 2010 Workshop on Procedural Content Generation in Games. ACM, p 11
Liu C, Agrawal P, Sarkar N, Chen S (2009) Dynamic difficulty adjustment in computer games through real-time anxiety-based affective feedback. Int J Hum Comput Interact 25(6):506–529
Nijholt A (2009) Bci for games: A ‘state of the art’ survey. In: Proceedings of the 7th International Conference on Entertainment Computing, ICEC ’08. Springer, Berlin, Heidelberg, pp 225–228
Parnandi A, Son Y, Gutierrez-Osuna R (2013) A control-theoretic approach to adaptive physiological games. In: Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), pp 7–12
Petersen MK, Stahlhut C, Stopczynski A, Larsen JE, Hansen LK (2011) Smartphones get emotional: mind reading images and reconstructing the neural sources. In: Proceedings of the 4th international conference on Affective computing and intelligent interaction—volume Part II, ACII’11. Springer, Berlin, Heidelberg, pp 578–587
Piquado T, Isaacowitz D, Wingfield A (2010) Pupillometry as a measure of cognitive effort in younger and older adults. Psychophysiology 47(3):560–9
Poole A, Ball LJ (2005) Eye tracking in human-computer interaction and usability research: Current status and future. In: Ghaoui, C. (Ed) Prospects, Chapter in Encyclopedia of Human–Computer Interaction. Idea Group Inc, Pennsylvania
Ray William J, Cole HW (1985) Eeg alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes. Science 228:750–752
Reiner M, Gelfeld TM (2013) Estimating mental workload through event-related fluctuations of pupil area during a task in a virtual world. Int J Psychophysiol 93(1):38–44. doi:10.1016/j.ijpsycho.2013.11.002. http://www.sciencedirect.com/science/article/pii/S0167876013003267
Sallai G (2012) The cradle of cognitive infocommunications. Acta Polytechnica Hungarica 9(1):171–181
Simões J, Redondo RD, Vilas AF (2013) A social gamification framework for a K-6 learning platform. Comput Hum Behav 29(2):345–353
Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. The MIT Press, Cambridge
Szegletes L, Forstner B (2013) Reusable framework for the development of adaptive games. In: IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom), pp 601–606
Vapnik V, Lerner A (1963) Pattern recognition using generalized portrait method. Autom Remote Control 24:774–780
Acknowledgments
This work was partially supported by the European Union and the European Social Fund through project FuturICT.hu (Grant no. TAMOP-4.2.2.C-11/1/KONV-2012-0013) organized by VIKING Zrt. Balatonfüred. This work was partially supported by the Hungarian Government, managed by the National Development Agency, and financed by the Research and Technology Innovation Fund (Grant no. KMR 12-1-2012-0441).
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Szegletes, L., Koles, M. & Forstner, B. Socio-cognitive gamification: general framework for educational games. J Multimodal User Interfaces 9, 395–401 (2015). https://doi.org/10.1007/s12193-015-0183-6
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DOI: https://doi.org/10.1007/s12193-015-0183-6