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Dynamic Cognitive Modeling for Adaptive Serious Games

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Adaptive Instructional Systems. Adaptation Strategies and Methods (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12793))

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Abstract

Cognitive modeling can be a viable tool to assess the cognitive state of the users and to determine their current learning needs. For instance, adaptive educational systems must match the learning needs by estimating the level of memorization or forgetting. The research question is, how to model latent cognitive variables such as memory degradation and how to make use of it for adaptivity scenarios in the e-learning context. Tools like cognitive architectures with established psychological underpinnings can help here. However, development of cognitive architecture models is often complex, domain- and application-specific and its transfer or general applicability is not evident. We present an innovative dynamic modeling approach which automatically creates declarative rules from interoperable activity stream observations to form models for the cognitive architecture ACT-R. The developed framework uses those models to analyze user actions according to their frequency, temporal occurrence and memory activation levels. An adaptive e-learning system can use the chunks’ activation levels to assess which concepts need repeated user attention. A prototype implementation for a serious game for process training demonstrates the feasibility of the approach.

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References

  1. ADL.net: Experience API (xAPI) specification 1.0.3. https://github.com/adlnet/xAPI-Spec/blob/xAPI-1.0.3/xAPI-Data.md. publisher: ADL

  2. Aleven, V., McLaren, B.M., Sewall, J., Koedinger, K.R.: The cognitive tutor authoring tools (CTAT): preliminary evaluation of efficiency gains. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 61–70. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_7

    Chapter  Google Scholar 

  3. Amant, R., Ritter, F.: Automated GOMS-to-ACT-r model generation. In: Proceedings of the 6. ICCM, Mahway, NJ:. p. 6 (2004)

    Google Scholar 

  4. Anderson, J., Christian, L., Taatgen, N., Sun, R.: Modeling Paradigms in ACT-r. In: Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, pp. 29–52. Cambridge University Press, Cambridge (2006)

    Google Scholar 

  5. Anderson, J.R., Gluck, K.: What Role do Cognitive Architectures Play in Intelligent Tutoring Systems. Cognition & Instruction: Twenty-five years of progress, pp. 227–262. Lawrence Erlbaum Associates, Inc., New Jersey (2001)

    Google Scholar 

  6. Asselman, A., Aammou, S., Nasseh, A.E.: Comparative study of cognitive architectures. Int. Res. J. Comput. Sci. (IRJCS) 9(2), 8–13 (2015)

    Google Scholar 

  7. Best, B.J., Lebiere, C., Scarpinatto, K.C.: Modeling synthetic opponents in MOUT training simulations using the ACT-R cognitive architecture. In: Proceedings of the 11th Computer Generated Forces Conference, pp. 2–56, 505–516 (2002)

    Google Scholar 

  8. Bowe, M.: Tin Can vs. Activity Streams (2013). https://tincanapi.com/tin-can-vs-activity-streams/

  9. Brusilovsky, P., Millán, E.: User models for adaptive hypermedia and adaptive educational systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 3–53. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_1

    Chapter  Google Scholar 

  10. Dörner, R., Göbel, S., Effelsberg, W., Wiemeyer, J. (eds.): Serious Games. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40612-1

    Book  Google Scholar 

  11. Gentile, M., Città, G., Lieto, A., Allegra, M.: Some notes on the possibile role of cognitive architectures in serious games. In: Liapis, A., Yannakakis, G.N., Gentile, M., Ninaus, M. (eds.) GALA 2019. LNCS, vol. 11899, pp. 231–241. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-34350-7_23

    Chapter  Google Scholar 

  12. Gudivada, V.N.: Cognitive computing: Concepts, architectures, systems, and applications. In: Gudivada, V.N., Raghavan, V.V., Govindaraju, V., Rao, C.R. (eds.) Handbook of Statistics, Cognitive Computing: Theory and Applications, vol. 35, pp. 3–38. Elsevier (2016). https://doi.org/10.1016/bs.host.2016.07.004

  13. Kiili, K., de Freitas, S., Arnab, S., Lainema, T.: The design principles for flow experience in educational games. Procedia Comput. Sci. 15, 78–91 (2012). https://doi.org/10.1016/j.procs.2012.10.060

    Article  Google Scholar 

  14. Kotseruba, I., Tsotsos, J.K.: 40 years of cognitive architectures: Core cognitive abilities and practical applications. Artif. Intell. Rev. (2018). https://doi.org/10.1007/s10462-018-9646-y

    Article  Google Scholar 

  15. Laird, J.E., et al.: SOAR: An architecture for general intelligence. Artif. Intell. 33, 1–64 (1987) https://doi.org/10.1016/0004-3702(87)90050-6

  16. Ma, W., Adesope, O.O., Nesbit, J.C., Liu, Q.: Intelligent tutoring systems and learning outcomes: a meta-analysis. J. Educ. Psychol. 106(4), 901–918 (2014). https://doi.org/10.1037/a0037123

    Article  Google Scholar 

  17. Mills, C., Dalgarno, B.: A conceptual model for game based intelligent tutoring systems. In: Proceedings of ASCILITE - Australian Society for Computers in Learning in Tertiary Education Annual Conference 2007, pp. 692–702. Australasian Society for Computers in Learning in Tertiary Education (2007)

    Google Scholar 

  18. Prensky, M.: Digital game-based learning. Comput. Entertainment (CIE) 1(1), p. 21 (2003). https://doi.org/10.1145/950566.950596

  19. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach (3rd Edition). Prentice Hall (2009)

    Google Scholar 

  20. Salvucci, D.D., Lee, F.J.: Simple cognitive modeling in a complex cognitive architecture. In: Proceedings of the conference on Human factors in computing systems, CHI 2003, p. 265. ACM Press (2003). https://doi.org/10.1145/642611.642658

  21. Serrano-Laguna, A., Martínez-Ortiz, I., Haag, J., Regan, D., Johnson, A., Fernández-Manjón, B.: Applying standards to systematize learning analytics in serious games. Comput. Stand. Interfaces 50, 116–123 (2017)

    Article  Google Scholar 

  22. Shute, V., Zapata-Rivera, D.: Adaptive educational systems. Adapt. Technol. Training Educ. 7(1), 1–35 (2012). https://doi.org/10.1017/CBO9781139049580.004

    Article  Google Scholar 

  23. Snell, J., Prodromou, E.: Activity streams 2.0 (2017). https://www.w3.org/TR/2017/REC-activitystreams-core-20170523/

  24. Streicher, A., Roller, W.: Interoperable adaptivity and learning analytics for serious games in image interpretation. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 598–601. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66610-5_71

    Chapter  Google Scholar 

  25. Streicher, A., Szentes, D., Gundermann, A.: Game-based training for complex multi-institutional exercises of joint forces. In: Verbert, K., Sharples, M., Klobučar, T. (eds.) EC-TEL 2016. LNCS, vol. 9891, pp. 497–502. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45153-4_49

    Chapter  Google Scholar 

  26. Taatgen, N., Anderson, J.R.: The past, present, and future of cognitive architectures. Topics Cogn. Sci. 2(4), 693–704 (2010). https://doi.org/10.1111/j.1756-8765.2009.01063.x

    Article  Google Scholar 

  27. Thórisson, K., Helgasson, H.: Cognitive architectures and autonomy: a comparative review. J. Artif. Gen. Intell. 3(2), 1–30 (2012)

    Article  Google Scholar 

  28. Van Eck, R.: Building artificially intelligent learning games. In: Gibson, D., Aldrich, C., Prensky, M. (eds.) Games and Simulations in Online Learning: Research and Development Frameworks, pp. 271–307. IGI Global (2007). https://doi.org/10.4018/978-1-59904-304-3.ch014

  29. Woolf, B.P.: Building Intelligent Interactive Tutors. Morgan Kaufmann, Burlington (2009)

    Google Scholar 

  30. Wray, R.E., Woods, A.: A cognitive systems approach to tailoring learner practice. In: Proceedings of the Second Annual Conference on Advances in Cognitive Systems ACS, vol. 21, p. 18 (2013)

    Google Scholar 

  31. Yannakakis, G.N.: Game AI revisited. In: Proceedings of the 9th Conference on Computing Frontiers, CF 2012, p. 285. ACM Press, Cagliari, Italy (2012). https://doi.org/10.1145/2212908.2212954

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Acknowledgments

The underlying project to this article is funded by the Federal Office of Bundeswehr Equipment, Information Technology and In-Service Support under promotional references.

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Authors and Affiliations

  1. Fraunhofer IOSB, Karlsruhe, Germany

    Alexander Streicher & Wolfgang Roller

  2. KIT, Karlsruhe, Germany

    Julius Busch

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  1. Alexander Streicher
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  2. Julius Busch
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Correspondence to Alexander Streicher .

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Editors and Affiliations

  1. Soar Technology, Inc., Orlando, FL, USA

    Robert A. Sottilare

  2. Fraunhofer FKIE, Wachtberg, Germany

    Jessica Schwarz

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Streicher, A., Busch, J., Roller, W. (2021). Dynamic Cognitive Modeling for Adaptive Serious Games. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. Adaptation Strategies and Methods. HCII 2021. Lecture Notes in Computer Science(), vol 12793. Springer, Cham. https://doi.org/10.1007/978-3-030-77873-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-77873-6_12

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  • Online ISBN: 978-3-030-77873-6

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