Keywords: Video game, Productive failure, Virology (the study of viruses), Gameplay analyses
What’s the Big Idea: How failure influences thinking skills in an educational video game. Does in-game failure lead to learning gains? What’s the relationship between in-game failure and game-related discourse?
Terms to get familiar with: productive failure (Kapur, 2008)
Research questions/Hypothesis, methods used in research/conclusions: A video game Virulent was played by 88 middle school students. It teaches virology to investigate the role of level failures in learning. Productive failure is explored. A pre-test was administered to gather information on the participants knowledge, and during gameplay, the Assessment Data Aggregator for Game Environments (ADAGE) system was used to gather data. Discourse data was taken via audio recording of the players, transcribed, then completed transcripts were analyzed using MAXQDA (v. 12) software examining negative statements and the relation to in-game failure. Conclusion: failed attempts before success, total attempts at main levels, and pre-assessment scores were significant predictors of post-assessment scores. Game progression, total time playing the game, and total failures at main levels including attempts after initial success were found to be non-significant.
Memorable quotes:
“To ensure players are kept at an appropriate challenge level, many games employ features that adjust the difficulty to fit the player’s skill level.”
Research Papers to read from this article:
Gee, J. P. (2005). Learning by design: Good video games as learning machines. E-Learning and Digital Media, 2(1), 5–16.
Hunicke, R. (2005). The case for dynamic difficulty adjustment in games. Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology, Valencia, Spain (pp. 429–433). New York, NY: ACM.
Juul, J. (2013). The art of failure: An essay on the pain of playing video games. Cambridge, MA: MIT Press.
Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.
Kapur, M. (2010). Productive failure in mathematical problem solving. Instructional Science, 38(6), 523–550.
Kapur, M. (2011). A further study of productive failure in mathematical problem solving: Unpacking the design components. Instructional Science, 39(4), 561–579. Kapur, M. (2014a). Comparing learning from productive failure and vicarious failure. Journal of the Learning Sciences, 23(4), 651–677.
Kapur, M. (2014b). Productive failure in learning math. Cognitive Science, 38(5), 1008–1022.
Kapur, M. (2015). Learning from productive failure. Learning: Research and Practice, 1(1), 51–65. http://dx.doi.org/10.1080/23735082.2015.1002195.
Kapur, M., & Bielaczyc, K. (2012). Designing for productive failure. The Journal of the Learning Sciences, 21(1), 45–83.
Owen, V. E., & Halverson, A. N. D. R. (2013). ADAGE (Assessment Data Aggregator for Game Environments): A click-stream data framework for assessment of learning in
play. Proceedings of the 9th games+learning+society conference-GLS 9 (pp. 248–254). Pittsburgh, PA: ETC Press.
Takeuchi, L. M., & Vaala, S. (2014). Level up learning: A national survey on teaching with digital games. Joan Ganz Cooney Center. Retrieved from http://
joanganzcooneycenter.org/wp-content/uploads/2014/10/jgcc_leveluplearning_final.pdf.
What’s the Big Idea: How failure influences thinking skills in an educational video game. Does in-game failure lead to learning gains? What’s the relationship between in-game failure and game-related discourse?
Terms to get familiar with: productive failure (Kapur, 2008)
Research questions/Hypothesis, methods used in research/conclusions: A video game Virulent was played by 88 middle school students. It teaches virology to investigate the role of level failures in learning. Productive failure is explored. A pre-test was administered to gather information on the participants knowledge, and during gameplay, the Assessment Data Aggregator for Game Environments (ADAGE) system was used to gather data. Discourse data was taken via audio recording of the players, transcribed, then completed transcripts were analyzed using MAXQDA (v. 12) software examining negative statements and the relation to in-game failure. Conclusion: failed attempts before success, total attempts at main levels, and pre-assessment scores were significant predictors of post-assessment scores. Game progression, total time playing the game, and total failures at main levels including attempts after initial success were found to be non-significant.
Memorable quotes:
“To ensure players are kept at an appropriate challenge level, many games employ features that adjust the difficulty to fit the player’s skill level.”
Research Papers to read from this article:
Gee, J. P. (2005). Learning by design: Good video games as learning machines. E-Learning and Digital Media, 2(1), 5–16.
Hunicke, R. (2005). The case for dynamic difficulty adjustment in games. Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology, Valencia, Spain (pp. 429–433). New York, NY: ACM.
Juul, J. (2013). The art of failure: An essay on the pain of playing video games. Cambridge, MA: MIT Press.
Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.
Kapur, M. (2010). Productive failure in mathematical problem solving. Instructional Science, 38(6), 523–550.
Kapur, M. (2011). A further study of productive failure in mathematical problem solving: Unpacking the design components. Instructional Science, 39(4), 561–579. Kapur, M. (2014a). Comparing learning from productive failure and vicarious failure. Journal of the Learning Sciences, 23(4), 651–677.
Kapur, M. (2014b). Productive failure in learning math. Cognitive Science, 38(5), 1008–1022.
Kapur, M. (2015). Learning from productive failure. Learning: Research and Practice, 1(1), 51–65. http://dx.doi.org/10.1080/23735082.2015.1002195.
Kapur, M., & Bielaczyc, K. (2012). Designing for productive failure. The Journal of the Learning Sciences, 21(1), 45–83.
Owen, V. E., & Halverson, A. N. D. R. (2013). ADAGE (Assessment Data Aggregator for Game Environments): A click-stream data framework for assessment of learning in
play. Proceedings of the 9th games+learning+society conference-GLS 9 (pp. 248–254). Pittsburgh, PA: ETC Press.
Takeuchi, L. M., & Vaala, S. (2014). Level up learning: A national survey on teaching with digital games. Joan Ganz Cooney Center. Retrieved from http://
joanganzcooneycenter.org/wp-content/uploads/2014/10/jgcc_leveluplearning_final.pdf.