Abstract
Student reflection has been shown to be important for learning in educational domains. In this study, we embedded a student reflection task into a video game to diagnose how players were constructing new knowledge. The game took place in a space station in which odd things had been happening. In order to secure a position on the space station, players had to improve their decision making and solve the mystery. As part of the game narrative, players reflected on each learning opportunity or mini-game by providing hints for future players at the end of each round. A corpus of 674 hints from 41 players, playing a 60-min version of the game were coded independently by two coders. Coding covered four levels of understanding in the hints and ranged from a simple restatement of information to a deeper reflection that integrated ideas and created new knowledge. Analyzing hints provided an in-game learning measure that may complement other measures and a way to understand game play experience that did not interrupt game flow. This study provides some recommendations for the design of embedding user hints into video games.
Keywords
- Self-explanation
- Adaptive learning environment
- Video games
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptions

References
Aleven, V., Koedinger, K.: An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor. Cogn. Sci. 26(2), 147–179 (2002)
Atkinson, R.K., Renkl, A., Merrill, M.M.: Transitioning from studying examples to solving problems: effects of self-explanation prompts and fading worked-out steps. J. Educ. Psychol. 95(4), 774 (2003)
Anzai, Y., Simon, H.A.: The theory of learning by doing. Psychol. Rev. 86, 124–140 (1979)
Bell, B.S., Kozlowski, S.W.J.: Active learning: effects of core training design elements on self-regulatory processes, learning, and adaptability. J. Appl. Psychol. 93, 296–316 (2008)
Chi, M.: Constructing self-explanations and scaffolded explanations in tutoring. Appl. Cogn. Psychol. 10, 33–49 (1996)
Cooke, N., Shope, S.: Designing a synthetic task environment. In: Schiflett, S.G., Elliott, L.R., Salas, E., Coovert, M.D. (eds.) Scaled Worlds: Development, Validation, and Application, pp. 263–278. Ashgate, Surry (2004)
Gee, J.P.: What Video Games Have to Teach Us About Learning and Literacy. Palgrave Macmillan, New York (2003)
Graesser, A., Hu, X., Sottilare, R.: Intelligent tutoring systems. In: The International Handbook of the Learning Sciences, pp. 246–255. Routledge, New York (2018)
Holmes, D.: Dimensions of projection. Psychol. Bull. 69, 248–268 (1968)
Kozlowski, S.W.J., DeShon, R.P.: A psychological fidelity approach to simulation-based training: theory, research, and principles. In: Schiflett, S.G., Elliott, L.R., Salas, E., Coovert, M.D. (eds.) Scaled Worlds: Development, Validation, and Application, pp. 263–278. Ashgate, Surry (2004)
Mayer, R.E., Griffity, E., Naftaly, I., Rothman, D.: Increased interestingness of extraneous details leads to decreased learning. J. Exp. Psychol. Appl. 14, 329–339 (2008)
Mayer, R.E.: Computer Games for Learning: An Evidence-Based Approach. MIT Press, Cambridge (2014)
Mullinix, G., et al.: Heuristica: designing a serious game for improving decision making. Paper Presented at the IEEE Games Innovation Conference (IGIC), Vancouver, BC, pp. 250–255 (2013)
O’Neil, H.F., Wainess, R., Baker, E.L.: Classification of learning outcomes: evidence from the computer games literature. Curric. J. 16, 455–474 (2005)
Payne, J.W., Bettman, J.R., Johnson, E.J.: Adaptive strategy selection in decision making. J. Exp. Psychol. Learn. Mem. Cogn. 14(3), 534–545 (1988)
Renkl, A.: Learning from worked-out examples: a study on individual differences. Cogn. Sci. 21, 1–29 (1997)
Renkl, A., Stark, R., Gruber, H., Mandl, H.: Learning from worked-out examples: the effects of example variability and elicited self-explanations. Contemp. Educ. Psychol. 23, 90–108 (1998)
Roll, I., Aleven, V., Koedinger, K.R.: The invention lab: using a hybrid of model tracing and constraint-based modeling to offer intelligent support in inquiry environments. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6094, pp. 115–124. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13388-6_16
Roose, K., Veinott, E.: Roller coaster park manager by day problem solver by night: effect of video game play on problem solving. In: Extended Abstracts Publication of the Annual Symposium on Computer-Human Interaction in Play, Amsterdam, Netherlands, pp. 277–282. ACM (2017)
Salas, E., Wildman, J.L., Piccolo, R.F.: Using simulation-based training to enhance management education. Acad. Manag. Learn. Educ. 9, 559–573 (2009)
Steinkuelhler, C., Squire, K., Barab, S.: Games, Learning, and Society, pp. 271–442. Cambridge University Press, Cambridge (2012)
Tversky, A., Kahneman, D.: Judgment under uncertainty: heuristics and biases. Science 185, 1124–1131 (1974)
VanLehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46, 197–221 (2011)
Veinott, E., et al.: The effect of camera perspective and session duration on training decision making in a serious video game. In: 2013 IEEE International Games Innovation Conference (IGIC), pp. 256–262. IEEE (2013)
Veinott, E, et al.: The effect of cognitive and visual fidelity on decision making: is more information better? In: 2014 IEEE International GEM (Formerly IEEE Games Innovation Conference (IGIC)), Toronto, ON, pp. 1–6 (2014)
Whitaker, E., et al.: The effectiveness of intelligent tutoring on training in a video game. In: IEEE International Games Innovation Conference (IGIC), Vancouver, BC, pp. 267–274 (2013)
Whitaker, E., Trewhitt, E., Veinott, E.S.: Intelligent tutoring design alternatives in a serious game. In: Sottilare, R., Schwarz, J. (Eds.) First International Conference for Adaptive Instructional Systems as Part of HCII 2019, pp. 151–165. Springer, Cham (2019)
Wylie, R., Chi, M.: The self-explanation principle in multimedia learning. In: The Cambridge Handbook of Multimedia Learning, Cambridge, UK, pp. 413–432 (2014)
Acknowledgement
This work is part of a larger team effort in which the Virtual Heroes Division of ARA developed the serious game, and Georgia Technology Research Institute developed the intelligent tutoring system embedded in the game. We would like to thank our sponsor. This research was supported by Intelligence Advanced Research Projects Activity (IARPA) via Air Force Research Laboratory Contract #FA8650-11-C-7177 to ARA, Inc. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, AFRL, or the U.S. Government.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Veinott, E.S., Whitaker, E. (2019). Leaving Hints: Using Player In-Game Hints to Measure and Improve Learning. In: Stephanidis, C., Antona, M. (eds) HCI International 2019 – Late Breaking Posters. HCII 2019. Communications in Computer and Information Science, vol 1088. Springer, Cham. https://doi.org/10.1007/978-3-030-30712-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-30712-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30711-0
Online ISBN: 978-3-030-30712-7
eBook Packages: Computer ScienceComputer Science (R0)
