Reflections on Serious Games

  • Arthur C. GraesserEmail author
Part of the Advances in Game-Based Learning book series (AGBL)


This chapter comments on the contributions in this edited volume and identifies some challenges for future research on serious games. The contributors used rigorous experimental methods to systematically assess the impact of many components of serious games on learning and motivation. The games are serious because there is alignment with relevant instructional content in educational curricula and there is an assessment of associated knowledge, skills, and strategies. The chapters report learning gains for the games compared to comparison conditions, as well as the added value of several game features, such as multimedia, realism, challenge, adaptivity, feedback, interactivity, modeling, collaboration, competition, reflection, fantasy, narrative, and so on. These features are highly correlated in most games so it is difficult to assign credit to particular features when they are implemented in conjunction with many other features. Additional challenges emerge when the games target deep learning of difficult material: (1) game features imposing extraneous cognitive load on working memory, (2) incompatibilities in the timing of feedback to optimize deep learning versus motivation, and (3) control struggles between the game agenda and students’ self-regulated learning. It is argued that researchers could be more involved in the building of games under the guidance of scientific principles even though there are difficulties in the design process and in attempts to scale up researcher-designed serious games. The chapter ends with a quandary in assessing psychological constructs in serious games that are adaptive to the learner.


Deep learning Game design 



The serious games developed in the Institute for Intelligent Systems at the University of Memphis were support by the National Science Foundation (ITR 0325428, DRK-12-0918409, and DRK-12-1108845) and the Institute of Education Sciences (R305B070349; R305C120001). The opinions, findings, and conclusions do not reflect the views of the funding agencies, cooperating institutions, or other individuals.


  1. Adams, D. M., Mayer, R. E., McNamara, A., Koenig, A., & Wainess, R. (2012). Narrative games for learning: Testing the discovery and narrative hypotheses. Journal of Educational Psychology, 104(1), 235–249.CrossRefGoogle Scholar
  2. Cai, Z., Graesser, A. C., Forsyth, C., Burkett, C., Millis, K., Wallace, P., et al. (2011). Trialog in ARIES: User input assessment in an intelligent tutoring system. In W. Chen & S. Li (Eds.), Proceedings of the 3rd IEEE International Conference on Intelligent Computing and Intelligent Systems (pp. 429–433). Guangzhou: IEEE Press.Google Scholar
  3. Calvo, R. A., & D’Mello, S. K. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1, 18–37.CrossRefGoogle Scholar
  4. Clark, D., Tanner-Smith, E., & Killingsworth, S. (2014). Digital games, design and learning: A systematic review and meta-Analysis. Menlo Park, CA: SRI International.Google Scholar
  5. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper-Row.Google Scholar
  6. D’Mello, S., & Graesser, A. C. (2010). Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-Adapted Interaction, 20, 147–187.CrossRefGoogle Scholar
  7. D’Mello, S. K., & Graesser, A. C. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22, 145–157.CrossRefGoogle Scholar
  8. Dede, C. (2015). Data-intensive research in education: Current work and next steps. Computer Research Association. Retrieved from
  9. Forsyth, C. M., Graesser, A. C., Pavlik, P., Millis, K., & Samei, B. (2014). Discovering theoretically grounded predictors of shallow vs. deep- level learning. In J. Stamper, Z. Pardos, M. Mavrikis, & B. M. McLaren (Eds.), Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014) (pp. 229–232). Honolulu, Hawaii: International Educational Data Mining Society.Google Scholar
  10. Gee, J. P. (2003). What video games have to teach us about language and literacy. New York: Macmillan.Google Scholar
  11. Graesser, A. C., Chipman, P., Leeming, F., & Biedenbach, S. (2009). Deep learning and emotion in serious games. In U. Ritterfeld, M. Cody, & P. Vorderer (Eds.), Serious games: Mechanisms and effects (pp. 81–100). New York, London: Routledge, Taylor & Francis.Google Scholar
  12. Graesser, A.C., & D’Mello, S. (2012). Emotions during the learning of difficult material. In. B. Ross (Ed.), The psychology of learning and motivation (Vol. 57, pp. 183–225). Amsterdam, The Netherlands: Elsevier.Google Scholar
  13. Graesser, A. C., & Hu, X. (2011). Commentary on causal prescriptive statements. Educational Psychology Review, 23, 279–285.CrossRefGoogle Scholar
  14. Graesser, A. C., Hu, X., Nye, B., & Sottilare, R. (2016). Intelligent tutoring systems, serious games, and the Generalized Intelligent Framework for Tutoring (GIFT). In H. F. O’Neil, E. L. Baker, & R. S. Perez (Eds.), Using games and simulation for teaching and assessment (pp. 58–79). Routledge: Abingdon, Oxon.Google Scholar
  15. Graesser, A. C., Li, H., & Forsyth, C. (2014). Learning by communicating in natural language with conversational agents. Current Directions in Psychological Science, 23, 374–380.CrossRefGoogle Scholar
  16. Graesser, A. C., & McNamara, D. S. (2010). Self-regulated learning in learning environments with pedagogical agents that interact in natural language. Educational Psychologist, 45, 234–244.CrossRefGoogle Scholar
  17. Hacker, D. J., Dunlosky, J., & Graesser, A. C. (Eds.). (2009). Handbook of metacognition in education. Mahwah, NJ: Erlbaum/Taylor & Francis.Google Scholar
  18. Halpern, D. F., Millis, K., Graesser, A. C., Butler, H., Forsyth, C., & Cai, Z. (2012). Operation ARA: A computerized learning game that teaches critical thinking and scientific reasoning. Thinking Skills and Creativity, 7, 93–100.CrossRefGoogle Scholar
  19. Jackson, G. T., & McNamara, D. S. (2013). Motivation and performance in a game-based intelligent tutoring system. Journal of Educational Psychology, 105, 1036–1049.CrossRefGoogle Scholar
  20. Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757–798.CrossRefGoogle Scholar
  21. Lepper, M. R., & Henderlong, J. (2000). Turning “play” into “work” and “work” into “play”: 25 years of research on intrinsic versus extrinsic motivation. In C. Sansone & J. M. Harackiewicz (Eds.), Intrinsic and extrinsic motivation: The search for optimal motivation and performance (pp. 257–307). San Diego, CA: Academic.CrossRefGoogle Scholar
  22. Mayer, R. E. (2011). Multimedia learning and games. In S. Tobias & J. D. Fletcher (Eds.), Computer games and instruction (pp. 281–305). Charlotte, NC: Information Age.Google Scholar
  23. McNamara, D. S., Jackson, G. T., & Graesser, A. C. (2010). Intelligent tutoring and games (ITaG). In Y. K. Baek (Ed.), Gaming for classroom-based learning: Digital role-playing as a motivator of study (pp. 44–65). Hershey, PA: IGI Global.CrossRefGoogle Scholar
  24. Millis, K., Forsyth, C., Butler, H., Wallace, P., Graesser, A., & Halpern, D. (2011). Operation ARIES! A serious game for teaching scientific inquiry. In M. Ma, A. Oikonomou, & J. Lakhmi (Eds.), Serious games and edutainment applications (pp. 169–196). London, UK: Springer.CrossRefGoogle Scholar
  25. Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19, 309–326.CrossRefGoogle Scholar
  26. O’Neil, H. F., Baker, E. L., & Perez, R. S. (Eds.), Using games and simulation for teaching and assessment. Routledge: Abingdon, Oxon.Google Scholar
  27. O’Neil, H. F., & Perez, R. S. (Eds.). (2008). Computer games and team and individual learning. Amsterdam, The Netherlands: Elsevier.Google Scholar
  28. Ritterfeld, U., Cody, M., & Vorderer, P. (2009) (Eds.), Serious games: Mechanisms and effects. New York and London: Routledge, Taylor & Francis.Google Scholar
  29. Rowe, J. P., Shores, L. R., Mott, B. W., & Lester, J. C. (2011). Integrating learning, problem solving, and engagement in narrative-centered learning environments. International Journal of Artificial Intelligence in Education, 21, 115–133.Google Scholar
  30. Salen, K., & Zimmerman, E. (2004). Rules of play: Game design fundamentals. Cambridge: MIT Press.Google Scholar
  31. Shaffer, D. W. (2007). How computer games help children learn. New York, NY: Palgrave.Google Scholar
  32. Shute, V. J., & Ventura, M. (2013). Measuring and supporting learning in games: Stealth assessment. Cambridge, MA: The MIT Press.Google Scholar
  33. Tobias, S., & Fletcher, J. D. (2011). Computer games and instruction. Charlotte, NC: Information Age.Google Scholar
  34. VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16, 227–265.Google Scholar
  35. Wouters, P., van Nimwegen, C., van Oostendorp, H., & van der Spek, E. D. (2013). A meta-analysis of the cognitive and motivational effects of serious games. Journal of Educational Psychology, 105, 249–265.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Department of Psychology and Institute for Intelligent SystemsUniversity of MemphisMemphisUSA

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