Abstract
Despite the success of Learning Analytics (LA), there are two obstacles to its application in educational games, including transparency in assessing educational outcomes in real-time gameplay, and clarity in representing those results to players. Open learner model (OLM) is a valuable instrument with capability to improve learning that meets such challenges. However, OLMs usually suffer issues concerning interactivity and transparency, which mostly regard the assessment mechanism that is used to evaluate learners’ knowledge. Tackling down transparency issues would offer context for interpreting and comparing learner model information, as well as promoting interactivity. As there is lack of studies investigating the potential of OLMs in educational games, we argue that this work can provide a valuable starting point for applying OLMs or adaptive visualizations of players’ learner models within gameplay sessions, which, in turn, can help to address both issues of application of LA to game research and OLMs. As a case study, we introduce the proposed approach into our adaptive computational thinking game.
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References
Steiner, C.M., Kickmeier-Rus, M.D., Albert, D.: Making sense of game-based user data: learning analytics in applied games. In: International Association for Development of the Information Society, pp. 21–24 (2015)
El Mawas, N., Hooshyar, D., Yang, Y.: Investigating the learning impact of autothinking educational game on adults: a case study of France. In: CSEDU (2), pp. 188–196 (2020)
Partovi, T., Razavi, M.R.: The effect of game-based learning on academic achievement motivation of elementary school students. Learn. Motiv. 68, 101592 (2019)
Zhonggen, Yu.: A meta-analysis of use of serious games in education over a decade. Int. J. Comput. Games Technol. 2019, 1–8 (2019)
Hooshyar, D., Yousefi, M., Lim, H.: Data-driven approaches to game player modeling: a systematic literature review. ACM Comput. Surv. (CSUR) 50(6), 1–19 (2018)
Van Eck, R.: Digital game-based learning: It’s not just the digital natives who are restless. EDUCAUSE Rev. 41(2), 16 (2006)
Westera, W., Nadolski, R.J., Hummel, H.G.K., Wopereis, I.G.J.H.: Serious games for higher education: a framework for reducing design complexity. J. Comput. Assist. Learn. 24(5), 420–432 (2008)
Hauge, J.B., et al.: Implications of learning analytics for serious game design. In: 14th International Conference on Advanced Learning Technologies, pp. 230–232. IEEE (2014)
Daniel, B.K.: Big data and Learning Analytics in Higher Education. Springer, New York (2016)
Hooshyar, D., Pedaste, M., Saks, K., Leijen, Ä., Bardone, E., Wang, M.: Open learner models in supporting self-regulated learning in higher education: a systematic literature review. Comput. Educ. 154, 103878 (2020)
Hooshyar, D., Kori, K., Pedaste, M., Bardone, E.: The potential of open learner models to promote active thinking by enhancing self-regulated learning in online higher education learning environments. Br. J. Edu. Technol. 50(5), 2365–2386 (2019)
Westera, W., Nadolski, R., Hummel, H.: Serious gaming analytics: What students log files tell us about gaming and learning (2014)
Serrano-Laguna, Á., Torrente, J., Moreno-Ger, P., Fernández-Manjón, B.: Tracing a little for big improvements: application of learning analytics and videogames for student assessment. Procedia Comput. Sci. 15, 203–209 (2012)
Serrano-Laguna, Á., Torrente, J., Moreno-Ger, P., Fernández-Manjón, B.: Application of learning analytics in educational videogames. Entertainment Comput. 5(4), 313–322 (2014)
Shute, V.J., Ventura, M., Bauer, M., Zapata-Rivera, D.: Melding the power of serious games and embedded assessment to monitor and foster learning. Serious Games: Mech. Effects 2, 295–321 (2009)
Kickmeier-Rust, M.D., Albert, D.: Micro-adaptivity: protecting immersion in didactically adaptive digital educational games. J. Comput. Assist. Learn. 26(2), 95–105 (2010)
Vieira, C., Parsons, P., Byrd, V.: Visual learning analytics of educational data: a systematic literature review and research agenda. Comput. Educ. 122, 119–135 (2018)
Kay, J., Bull, S.: New opportunities with open learner models and visual learning analytics. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M.F. (eds.) AIED 2015. LNCS (LNAI), vol. 9112, pp. 666–669. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19773-9_87
Self, J.A.: Bypassing the intractable problem of student modelling. Intelligent tutoring systems: at the crossroads of artificial intelligence and education, vol. 41, pp. 1–26 (1990)
Van Labeke, N., Brna, P., Morales, R.: Opening up the interpretation process in an open learner model. Int. J. Artif. Intell. Educ. 17(3), 305–338 (2007)
Ginon, B., Boscolo, C., Johnson, M.D., Bull, S.: Persuading an Open learner model in the context of a university course: an exploratory study. In: Micarelli, A., Stamper, J., Panourgia, K. (eds.) ITS 2016. LNCS, vol. 9684, pp. 307–313. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39583-8_34
Johan, R., Bull, S.: Consultation of misconceptions representations by students in education-related courses. In: AIED, pp. 565–572 (2009)
Bull, S., McKay, M.: An open learner model for children and teachers: inspecting knowledge level of individuals and peers. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 646–655. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30139-4_61
Suleman, R.M., Mizoguchi, R., Ikeda, M.: A new perspective of negotiation-based dialog to enhance metacognitive skills in the context of open learner models. Int. J. Artif. Intell. Educ. 26(4), 1069–1115 (2016)
Minović, M., Milovanović, M., Šošević, U., González, M.Á.C.: Visualisation of student learning model in serious games. Comput. Hum. Behav. 47, 98–107 (2015)
Hooshyar, D., Lim, H., Pedaste, M., Yang, K., Fathi, M., Yang, Y.: AutoThinking: an adaptive computational thinking game. In: Rønningsbakk, L., Wu, T.-T., Sandnes, F.E., Huang, Y.-M. (eds.) ICITL 2019. LNCS, vol. 11937, pp. 381–391. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35343-8_41
Chen, Z.-H., Chou, C.-Y., Deng, Y.-C., Chan, T.-W.: Active open learner models as animal companions: motivating children to learn through interacting with My-Pet and Our-Pet. Int. J. Artif. Intell. Educ. 17(2), 145–167 (2007)
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This research was supported by the University of Tartu ASTRA Project PER ASPERA, financed by the European Regional Development Fund.
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Hooshyar, D., Bardone, E., Mawas, N.E., Yang, Y. (2020). Transparent Player Model: Adaptive Visualization of Learner Model in Educational Games. In: Huang, TC., Wu, TT., Barroso, J., Sandnes, F.E., Martins, P., Huang, YM. (eds) Innovative Technologies and Learning. ICITL 2020. Lecture Notes in Computer Science(), vol 12555. Springer, Cham. https://doi.org/10.1007/978-3-030-63885-6_39
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