A Cognitive Skill Classification Based on Multi Objective Optimization Using Learning Vector Quantization for Serious Games

Moh. Aries Syufagi, Mochamad Hariadi, Mauridhi Hery Purnomo


Nowadays, serious games and game technology are poised to transform the way of educating and training students at all levels. However, pedagogical value in games do not help novice students learn, too many memorizing and reduce learning process due to no information of player’s ability. To asses the cognitive level of player ability, we propose a Cognitive Skill Game (CSG). CSG improves this cognitive concept to monitor how players interact with the game. This game employs Learning Vector Quantization (LVQ) for optimizing the cognitive skill input classification of the player. CSG is using teacher’s data to obtain the neuron vector of cognitive skill pattern supervise. Three clusters multi objective target will be classified as; trial and error, carefully and, expert cognitive skill. In the game play experiments employ 33 respondent players demonstrates that 61% of players have high trial and error, 21% have high carefully, and 18% have high expert cognitive skill. CSG may provide information to game engine when a player needs help or when wanting a formidable challenge. The game engine will provide the appropriate tasks according to players’ ability. CSG will help balance the emotions of players, so players do not get bored and frustrated. 

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Chen, S. & Michael, D., Proof of Learning: Assessment in Serious Games, Gamasutra, http://www.cedma-europe.org/newsletterarticles/ misc/Proof of Learning - Assessment in Serious games (Oct%2005).pdf (23 Augustus 2010).

Marsh, T., Wong, W.L., Carriazo, E., Nocera, L., Yang, K.,Varma, A., Yoon, H., Huang, Y-L, Kyriakakis, C. & Shahabi, C., User Experiences and Lessons Learned from Developing and Implementing an Immersive Game for the Science Classroom, Information laboratory (InfoLAB), University of Southern California, http://infolab.usc.edu/DocsDemos/hci05.pdf, (17 June 2009).

Clark, D., Game and E-Learning, Sunderland: Caspian Learning, www.caspianlearning.co.uk, (22 April 2009).

Ndahi H., The Use of Innovative Methods to Deliver Technology Education Laboratory Courses via Distance Learning: A Strategy to Increase Enrollment, Journal of Technology Education, 2006. http://scholar.lib.vt.edu/ejournals/JTE/ v17n2/ndahi.html, (1 June 2009).

Hayashi, A., Chen, C.C. & Terase, H., Aligning IT Skills Training With Online Asynchronous Learning Multimedia Technologies, Information Systems Education Journal (ISEDJ), 3(26), http://isedj.org /3/26/ISEDJ.3(26).Hayashi.pdf (3 Juni 2009).

Ololube, N.P., Appraising The Relationship Between ICT Usage and Integration and The Standard of Teacher Education Programs in A Developing Economy, International Journal of Education and Development using Information and Communication Technology (IJEDICT), 2(3), pp. 70-85, 2006.

Arnseth, H.C., Learning to Play or Playing to Learn - A Critical Account of the Models of Communication Informing Educational Research on Computer Gameplay, The International Journal of Computer Game Research, 6(1), http://gamestudies.org/0601/articles/arnseth, (26 April 2009).

Smith, J.H., The Games Economists Play - Implications of Economic Game Theory for the Study of Computer Games, The International Journal of Computer Game Research, 6(1), http://gamestudies.org/ 0601/articles/heide_smith, (16 April 2009)

Clark, R.E., Evaluating the Learning and Motivation Effects of Serious Games, Rosier school of Education Center for Creative Technologies, http://projects.ict.usc.edu/itgs/talks/Clark_Serious Games Evaluation.ppt, (28 Augustus 2010).

Mayer, R.E., Should There Be A Three-Strikes Rule Against Pure Discovery Learning, American Psychologist, 59(1), pp. 14-19, 2004.

Kirschner, P., Sweller, J., & Clark, R.E., Why Minimally Guided Learning Does Not Work: an Analysis of The Failure of Discovery Learning, Problem-Based Learning, Experiential Learning and Inquiry-Based Learning, Educational Psychologist, 41(2), pp. 75-86, 2006.

Clark, R.E. & Choi, S., Five Design Principles for Experiments on The Effects of Animated Pedagogical Agents, Journal of Educational Computing Research, 32(3), pp. 209-223, 2005.

Inal, Y. & Cagiltay, K., Flow Experiences of Children in An Interactive Social Game Environment, British Journal of Educational Technology, 38 (3), pp. 455-464, 2007.

Shute, V.J., Ventura, M., Bauer, M., & Rivera, D.Z., Melding the Power of Serious Games and Embedded Assessment to Monitor and Foster Learning: Flow and Grow, Parson, http://a.parsons.edu/~loretta/ Fassessment_archive/GAMES_Shute_FINAL.pdf (5 October 2011)

Bosch, P., Dalinghaus, K., Hammer, B., Reuter, J-P., Schrader, B., Steffens, T. & Umbach, C., Cognitive Architecture: The Integration of Rules and Patterns, Institute of Cognitive Science University of Osnabrück, 2003.

Conde, T. & Thalmann, D., Autonomous Virtual Agents Learning a Cognitive Model and Evolving, EPFL Virtual Reality Lab, IVA 2005, LNCS 3661, pp. 88-98, 2005.

Conati C. & Klawe M., Socially Intelligent Agents in Educational Games, University of British Columbia, citeseerx.ist.psu.edu/viewdoc/ download?doi= (7 June 2011).

Isnaini, H., Classification Efficiency Problem Solving Integer Arithmetic Operation Junior High School Class 7 With Game Using LVQ Method, Master Theses of Electrical Engineering, Department of Electrical Engineering FTI, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, 2009.

Harini, S.M., Classification of Comprehensive Learning Achievement Effectivity in Senior High School Students Based on Mathematical Logic Game Using LVQ Method, Master Theses of Electrical Engineering, Department of Electrical Engineering FTI, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, 2009.

Syufagi, M.A., Hariadi, M. & Purnomo, M.H., Model of Mental Effort Assessment in Pedagogic Games Based On LVQ Method, SESINDO2008 Conference, Department of Information System FTI ITS ISBN: 978-979-18985-0-8, pp. 556-564, 2008

Abramson, M. & Wechsler, H., A Distributed Reinforcement Learning Approach to Pattern Inference in Go, CiteSeerx, http://citeseerx.ist.psu. edu/viewdoc/download?doi=, (7 June 2011).

Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J. & Torkkola K., LVQ_PAK The Learning Vector Quantization Program Package Version 3.1 (April 7, 1995), The LVQ Programming Team of the Helsinki University of Technology Laboratory of Computer and Information Science, Rakentajanaukio 2 C, SF-02150 Espoo FINLAND, 1995.

Chen, C-R., Tsai, L-T. &Yang, C-C., A Neural Network Approach for Random Samples to Stratified Psychometrical Population, Proceedings of the WSEAS International Conference on SOCIOLOGY, PSYCHOLOGY, PHILOSOPHY, pp. 51-54, Penang, 2010.

Song, H-H. & Lee, S-W., LVQ Combined with Simulated Annealing for Optimal Design of Large-set Reference Models, Neural Networks, 9(2), pp. 329-336, Elsevier Science Ltd, 1996

Kim, D.K., Lee, S.H., Kim, B-S. & Moon G., Generalized LVQ for Optimal Reference Vectors using a Differentiable MIN Module, Proceedings of International Conference on Neural Information Processing, pp. 1937-1942, Seoul, 1994.

DOI: http://dx.doi.org/10.5614%2Fitbj.ict.2011.5.3.3


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