Abstract
It is argued that reflecting on the in-game performance in a serious game is important for facilitating learning transfer. A way to facilitate such a reflection is by means of a so-called debriefing phase. However, a human facilitated debriefing is expensive, time consuming and not always possible. Therefore, an automatic self-debriefing facility for serious games would be desirable. However, a general approach for creating such an automatic self-debriefing system for serious games doesn’t exist. As a first step towards the development of such a framework, we targeted a specific type of serious games, i.e., games displaying realistic behavior and having multiple possible paths to a solution. In addition, we decided to start with the development of a debriefing system for a concrete case, a serious game about cyber bullying in social networks. In particular, in this paper, we focus on different visualizations that could be used for such an automatic debriefing. We combined a textual feedback with three different types of visualizations. A prototype was implemented and evaluated with the goal of comparing the three visualizations and gathering first feedback on the usability and effectiveness. The results indicate that the visualizations did help the participants in having a better understanding of the outcome of the game and that there was a clear preference for one of the three visualizations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Peters, V.A.M., Vissers, G.A.N.: A simple classification model for debriefing simulation games. Simul. Gaming 35(1), 70–84 (2004)
Fanning, R.M., Gaba, D.M.: The role of debriefing in simulation-based learning. Simul. Healthc. 2(2), 115–125 (2007)
Crookall, D.: Serious games, debriefing, and simulation/gaming as a discipline. Simul. Gaming 41(6), 898–920 (2010)
Verpoorten, D., Westera, W., Specht, M.: Reflection amplifiers in online courses: a classification framework. J. Interact. Learn. Res. 22, 167–190 (2011)
Winne, P.H.: Experimenting to bootstrap self-regulated learning. J. Educ. Psychol. 89(3), 397–410 (1997)
Cebolledo, E., De Troyer, O.: Modelling social network interactions in games. In: Intelligent Narrative Technologies and Social Believability in Games: Papers from the AIIDE 2015 Joint Workshop, pp. 82–88 (2015)
Vig, J., Sen, S., Riedl, J.: Tagsplanations: explaining recommendations using tags. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, pp. 47–56 (2009)
Medler, B., Magerko, B.: Analytics of play: using information visualization and gameplay practices for visualizing video game data. Parsons J. Inf. Mapp. 3(1), 1–12 (2011)
Wallner, G., Kriglstein, S.: Visualization-based analysis of gameplay data – a review of literature. Entertain. Comput. 4(3), 143–155 (2013)
Medler, B., John, M., Lane, J.: Data cracker: developing a visual game analytic tool for analyzing online gameplay. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2365–2374 (2011)
Hoobler, N., Humphreys, G., Agrawala, M.: Visualizing competitive behaviors in multi-user virtual environments. In: Proceedings of the Conference on Visualization 2004, pp. 163–170 (2004)
Andersen, E., Liu, Y.-E., Apter, E., Boucher-Genesse, F., Popovic, Z.: Gameplay analysis through state projection. In: Proceedings of the 5th International Conference on the Foundations of Digital Games, pp. 1–8 (2010)
Harpstead, E., Myers, B., Aleven, V.: In search of learning: facilitating data analysis in educational games. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 79–88 (2013)
Nicholson, S.: Completing the experience: debriefing in experiential educational games. Syst. Cybern. Inf. 11(6), 27–31 (2013)
Cleophas, C.: Designing serious games for revenue management training and strategy development. In: Proceedings of the 2012 Winter Simulation Conference, pp. 140:1–140:12 (2012)
Pavlov, O.V., Saeed, K., Robinson, L.W.: Improving instructional simulation with structural debriefing. Simul. Gaming 46(3–4), 383–403 (2015)
Cebolledo, E., Troyer, O.: iATTAC: a system for autonomous agents and dynamic social interactions – the architecture. In: Göbel, S., Ma, M., Baalsrud Hauge, J., Oliveira, M.F., Wiemeyer, J., Wendel, V. (eds.) JCSG 2015. LNCS, vol. 9090, pp. 135–146. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19126-3_12
Reiss, S.: The Normal Personality: A New Way of Thinking About People. Cambridge University Press, Cambridge (2008)
Berne, E.: Transactional Analysis in Psychotherapy: A Systematic Individual and Social Psychiatry. Ravenio Books, Helsinki (2016)
Lazar, J., Feng, J., Hochheiser, H.: Research Methods in Human Computer Interaction. Wiley, Hoboken (2010)
Brooke, J.: SUS - a quick and dirty usability scale. Usability Eval. Ind. 189(194), 4–7 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
De Troyer, O., Helalouch, A., Debruyne, C. (2016). Towards Computer-Supported Self-debriefing of a Serious Game Against Cyber Bullying. In: Bottino, R., Jeuring, J., Veltkamp, R. (eds) Games and Learning Alliance. GALA 2016. Lecture Notes in Computer Science(), vol 10056. Springer, Cham. https://doi.org/10.1007/978-3-319-50182-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-50182-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50181-9
Online ISBN: 978-3-319-50182-6
eBook Packages: Computer ScienceComputer Science (R0)