Automatic Game Experience Identification in Educational Games
Resumo
One of the main challenges in the field of educational games is the automatic and implicit users' game experience identification. To face this challenge, we present an exploratory study by using a data-driven based approach for collecting and identifying this experience. We used two different data-mining techniques aiming to associate the user's data logs from an educational game with their game-like experience. Our main results indicate that it is possible to extract the automatic and implicit acquisition of the student's game experience in educational games and demonstrate how user's data logs drive their experiences. We also provided different associations between user data logs in educational games and the student's game experience.
Palavras-chave:
Educational Games, Game Experience, Data Mining
Referências
Bauckhage, C., Kersting, K., Sifa, R., Thurau, C., Drachen, A., and Canossa, A. (2012). How players lose interest in playing a game: An empirical study based on distributions of total playing times. In 2012 IEEE Conference on Computational Intelligence and Games (CIG), pages 139–146.
Bentler, P. M. and Chou, C.-P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1):78–117.
Borges, S. S., Durelli, V. H., Reis, H. M., Bittencourt, I. I., Mizoguchi, R., and Isotani, S. (2017). Brazilian Portuguese Cross-Cultural Adaptation and Validation of the Sus- ceptibility to Persuasion Scale (Br-STPS). In Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017, pages 72–73.
Charleer, S., Gerling, K., Gutiérrez, F., Cauwenbergh, H., Luycx, B., and Verbert, K. (2018). Real-time dashboards to support esports spectating. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play, pages 59–71. ACM.
Dziuban, C. D. and Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? some decision rules. Psychological bulletin, 81(6):358.
Eckardt, L. and Robra-Bissantz, S. (2018). Playtesting for a better gaming experience: Importance of an iterative design process for educational games research-in-progress. In Twenty-Second Pacific Asia Conference on Information Systems.
Hoaglin, D. C., Mosteller, F., and Tukey, J. W. (2000). Understanding robust and ex- ploratory data analysis. Number Sirsi) i9780471384915.
Hooshyar, D., Yousefi, M., and Lim, H. (2017). A systematic review of data-driven approaches in player modeling of educational games. Artificial Intelligence Review, pages 1–21.
Hooshyar, D., Yousefi, M., and Lim, H. (2018). Data-driven approaches to game player modeling: a systematic literature review. ACM Computing Surveys (CSUR), 50(6):90.
Huang, G.-B., Zhou, H., Ding, X., and Zhang, R. (2011). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2):513–529.
IJsselsteijn, W., De Kort, Y., and Poels, K. (2013). The game experience questionnaire. Eindhoven: Technische Universiteit Eindhoven.
Kaur, G. and Chhabra, A. (2014). Improved j48 classification algorithm for the prediction of diabetes. International Journal of Computer Applications, 98(22).
Kaur, H. (2018). A literature review from 2011 to 2014 on student’s academic perfor- mance prediction and analysis using decision tree algorithm. Journal of Global Re- search in Computer Science, 9(5):10–15.
Lara, C. A., Mitre-Hernandez, H., Flores, J., and Perez, H. (2018). Induction of emotional states in educational video games through a fuzzy control system. IEEE Transactions on Affective Computing, pages 1–1.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology.
Loehlin, J. C. (1998). Latent variable models: An introduction to factor, path, and struc- tural analysis. Lawrence Erlbaum Associates Publishers.
Mekler, E. D., Bopp, J. A., Tuch, A. N., and Opwis, K. (2014). A systematic review of quantitative studies on the enjoyment of digital entertainment games. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems, pages 927–936. ACM.
Oliveira, W., Toda, A., Palomino, P., Rodrigues, L., Shi, L., and Isotani, S. (2019). Towards automatic flow experience identification in educational systems: A theory- driven approach. In Proceedings of the 2019 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts. ACM.
Pedersen, C., Togelius, J., and Yannakakis, G. N. (2010). Modeling player experience for content creation. IEEE Transactions on Computational Intelligence and AI in Games, 2(1):54–67.
Petri, G. and von Wangenheim, C. G. (2016). How to evaluate educational games: a systematic. Journal of Universal Computer Science, 22(7):992–1021.
Rodrigues, L., Bonidia, R. P., and Brancher, J. D. (2017). A math educacional computer game using procedural content generation. In Brazilian Symposium on Computers in Education, volume 28, page 756.
Rodrigues, L. and Brancher, J. D. (2019). Playing an educational game featuring pro- cedural content generation: Which attributes impact players’ curiosity? RENOTE, 17(1).
Rodrigues, L. A. L. and Brancher, J. D. (2018). Improving players’ profiles clustering from game data through feature extraction. In 2018 17th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pages 177–17709.
Santos, W. O., Gomes, T., and Silva, C. (2017). Towards to flow state identification in educational games: An empirical study. In Brazilian Symposium on Computers in Education, volume 28, page 927.
Santos, W. O. and Isotani, S. (2018). Desenvolvimento de jogos educativos? desafios, oportunidades e direcionamentos de pesquisa. RENOTE, 16(2).
Shaker, N., Yannakakis, G., and Togelius, J. (2010). Towards automatic personalized content generation for platform games. In Sixth Artificial Intelligence and Interactive Digital Entertainment Conference.
Spector, P. E. (1994). Using self-report questionnaires in ob research: A comment on the use of a controversial method. Journal of organizational behavior, 15(5):385–392.
Viera, A. J., Garrett, J. M., et al. (2005). Understanding interobserver agreement: the kappa statistic. Fam med, 37(5):360–363.
Wohlin, C., Runeson, P., Hst, M., Ohlsson, M. C., Regnell, B., and Wessln, A. (2012). Ex- perimentation in Software Engineering. Springer Publishing Company, Incorporated.
Wood, R. D. T., Griffiths, M. K. D., and Eatough, V. A. (2004). Online data collection from video game players: Methodological issues. CYBERPSYCHOLOGY & BEHAV- IOR, 7(5):511–518.
Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Philip, S. Y., et al. (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14(1):1–37.
Xu, H., Wu, Z., Lin, T., Tang, N., and Hua, L. (2016). Automatic content generation in tetris game based on emotion modeling. In 2016 Nicograph International (NicoInt), pages 139–139.
Yannakakis, G. N., Spronck, P., Loiacono, D., and André, E. (2013). Player Modeling. In Lucas, S. M., Mateas, M., Preuss, M., Spronck, P., and Togelius, J., editors, Artificial and Computational Intelligence in Games, volume 6 of Dagstuhl Follow-Ups, pages 45–59. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany.
Yannakakis, G. N. and Togelius, J. (2011). Experience-driven procedural content genera- tion. IEEE Transactions on Affective Computing, 2(3):147–161.
Zammitto, V. (2009). Game research, measuring gaming preferences. In Proceedings of the 2009 conference on future play on@ GDC Canada, pages 15–16. ACM.
Bentler, P. M. and Chou, C.-P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1):78–117.
Borges, S. S., Durelli, V. H., Reis, H. M., Bittencourt, I. I., Mizoguchi, R., and Isotani, S. (2017). Brazilian Portuguese Cross-Cultural Adaptation and Validation of the Sus- ceptibility to Persuasion Scale (Br-STPS). In Proceedings - IEEE 17th International Conference on Advanced Learning Technologies, ICALT 2017, pages 72–73.
Charleer, S., Gerling, K., Gutiérrez, F., Cauwenbergh, H., Luycx, B., and Verbert, K. (2018). Real-time dashboards to support esports spectating. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play, pages 59–71. ACM.
Dziuban, C. D. and Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? some decision rules. Psychological bulletin, 81(6):358.
Eckardt, L. and Robra-Bissantz, S. (2018). Playtesting for a better gaming experience: Importance of an iterative design process for educational games research-in-progress. In Twenty-Second Pacific Asia Conference on Information Systems.
Hoaglin, D. C., Mosteller, F., and Tukey, J. W. (2000). Understanding robust and ex- ploratory data analysis. Number Sirsi) i9780471384915.
Hooshyar, D., Yousefi, M., and Lim, H. (2017). A systematic review of data-driven approaches in player modeling of educational games. Artificial Intelligence Review, pages 1–21.
Hooshyar, D., Yousefi, M., and Lim, H. (2018). Data-driven approaches to game player modeling: a systematic literature review. ACM Computing Surveys (CSUR), 50(6):90.
Huang, G.-B., Zhou, H., Ding, X., and Zhang, R. (2011). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2):513–529.
IJsselsteijn, W., De Kort, Y., and Poels, K. (2013). The game experience questionnaire. Eindhoven: Technische Universiteit Eindhoven.
Kaur, G. and Chhabra, A. (2014). Improved j48 classification algorithm for the prediction of diabetes. International Journal of Computer Applications, 98(22).
Kaur, H. (2018). A literature review from 2011 to 2014 on student’s academic perfor- mance prediction and analysis using decision tree algorithm. Journal of Global Re- search in Computer Science, 9(5):10–15.
Lara, C. A., Mitre-Hernandez, H., Flores, J., and Perez, H. (2018). Induction of emotional states in educational video games through a fuzzy control system. IEEE Transactions on Affective Computing, pages 1–1.
Likert, R. (1932). A technique for the measurement of attitudes. Archives of psychology.
Loehlin, J. C. (1998). Latent variable models: An introduction to factor, path, and struc- tural analysis. Lawrence Erlbaum Associates Publishers.
Mekler, E. D., Bopp, J. A., Tuch, A. N., and Opwis, K. (2014). A systematic review of quantitative studies on the enjoyment of digital entertainment games. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems, pages 927–936. ACM.
Oliveira, W., Toda, A., Palomino, P., Rodrigues, L., Shi, L., and Isotani, S. (2019). Towards automatic flow experience identification in educational systems: A theory- driven approach. In Proceedings of the 2019 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts. ACM.
Pedersen, C., Togelius, J., and Yannakakis, G. N. (2010). Modeling player experience for content creation. IEEE Transactions on Computational Intelligence and AI in Games, 2(1):54–67.
Petri, G. and von Wangenheim, C. G. (2016). How to evaluate educational games: a systematic. Journal of Universal Computer Science, 22(7):992–1021.
Rodrigues, L., Bonidia, R. P., and Brancher, J. D. (2017). A math educacional computer game using procedural content generation. In Brazilian Symposium on Computers in Education, volume 28, page 756.
Rodrigues, L. and Brancher, J. D. (2019). Playing an educational game featuring pro- cedural content generation: Which attributes impact players’ curiosity? RENOTE, 17(1).
Rodrigues, L. A. L. and Brancher, J. D. (2018). Improving players’ profiles clustering from game data through feature extraction. In 2018 17th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pages 177–17709.
Santos, W. O., Gomes, T., and Silva, C. (2017). Towards to flow state identification in educational games: An empirical study. In Brazilian Symposium on Computers in Education, volume 28, page 927.
Santos, W. O. and Isotani, S. (2018). Desenvolvimento de jogos educativos? desafios, oportunidades e direcionamentos de pesquisa. RENOTE, 16(2).
Shaker, N., Yannakakis, G., and Togelius, J. (2010). Towards automatic personalized content generation for platform games. In Sixth Artificial Intelligence and Interactive Digital Entertainment Conference.
Spector, P. E. (1994). Using self-report questionnaires in ob research: A comment on the use of a controversial method. Journal of organizational behavior, 15(5):385–392.
Viera, A. J., Garrett, J. M., et al. (2005). Understanding interobserver agreement: the kappa statistic. Fam med, 37(5):360–363.
Wohlin, C., Runeson, P., Hst, M., Ohlsson, M. C., Regnell, B., and Wessln, A. (2012). Ex- perimentation in Software Engineering. Springer Publishing Company, Incorporated.
Wood, R. D. T., Griffiths, M. K. D., and Eatough, V. A. (2004). Online data collection from video game players: Methodological issues. CYBERPSYCHOLOGY & BEHAV- IOR, 7(5):511–518.
Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Philip, S. Y., et al. (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14(1):1–37.
Xu, H., Wu, Z., Lin, T., Tang, N., and Hua, L. (2016). Automatic content generation in tetris game based on emotion modeling. In 2016 Nicograph International (NicoInt), pages 139–139.
Yannakakis, G. N., Spronck, P., Loiacono, D., and André, E. (2013). Player Modeling. In Lucas, S. M., Mateas, M., Preuss, M., Spronck, P., and Togelius, J., editors, Artificial and Computational Intelligence in Games, volume 6 of Dagstuhl Follow-Ups, pages 45–59. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany.
Yannakakis, G. N. and Togelius, J. (2011). Experience-driven procedural content genera- tion. IEEE Transactions on Affective Computing, 2(3):147–161.
Zammitto, V. (2009). Game research, measuring gaming preferences. In Proceedings of the 2009 conference on future play on@ GDC Canada, pages 15–16. ACM.
Publicado
11/11/2019
Como Citar
OLIVEIRA, Wilk; RODRIGUES, Luiz; TODA, Armando M.; PALOMINO, Paula T.; ISOTANI, Seiji.
Automatic Game Experience Identification in Educational Games. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 952-961.
DOI: https://doi.org/10.5753/cbie.sbie.2019.952.
