Keywords: Metacognition Self-regulated learning Scientific reasoning Game-based learning Sequence mining Process data
Log files

What’s the Big Idea: To assess how metacognitive monitoring and scientific reasoning impacted the efficiency of game completion.

Terms to get familiar with: Metacognitive Monitoring, Self-regulated learning (SRL), game-based learning environments (GBLEs), advanced learning technologies (ALTs), non-player characters (NPCs), cognitive, affective, metacognitive, and motivational (CAMM) processes

Research questions/Hypothesis, methods used in research/conclusions: Interesting article, I'm still reading it. 

Research Papers to read from this article:
Mayer, R. E. (Ed.). (2014). Computer games for learning: An evidence-based approach.
Cambridge, MA: MIT Press.

What’s the Big Idea: Engaging students in solving complex, ill-structured problems without the provision of support structures can be a productive exercise in failure.

Research questions/Hypothesis, methods used in research/conclusions: Eleventh-grade students were asked to solve problems on Newtonian kinematics. Two groups were compared: one who initially received ill-structured problems as a group, followed by well-structured problems individually, the other had both well-structured problems as a group and individually.  After this, all students were given ill-structured problems and those who initially received those outperformed those who always had well-structured problems.

​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.

Keywords: Experimentation, Human Factors, Video games, education, learning, literacy

What’s the Big Idea: Learning principles applied to video games make good games people want to play.

Research questions/Hypothesis, methods used in research/conclusions: Gee discusses that he has 36 principles in the book of the same title as this paper, and this paper describes a dozen or so principles and shows how modern games apply those principles. He argues that games might be a better place for preparing workers for modern workspaces than traditional schools. People can achieve recreation and deep learning at the same time.

​Keywords: Game design, game design principles, game design frameworks

What’s the Big Idea: Describes the design of a game aimed at teaching argumentation skills to college students. The paper analyzes the game design using Gee and Clark and Meyer.

Terms to get familiar with: Initial design, play testing, iterative design, learning testing, Gee principles (31 and 13 condensed), Clark/Meyer principles, endogenous and exogenous games.

Research questions/Hypothesis, methods used in research/conclusions: By evaluating the principles laid out by predecessors (Gee, Clark/Meyer), improve the design of the game Advisor to the King, or AttK. The paper evaluates the game on how well it meets the 13 principles from Gee that non-educational video games follow to support learning in three conceptual areas: Empowered Learners, Problem Solving, and Understanding. It was also evaluated against Clark and Meyer’s e-learning effectiveness in five major areas.  The conclusion was that even though the two sets of principles have different origins and intentions, the different frameworks largely agree, and the AttK game design was validated by the study.

Research Papers to read from this article:
[3] E. Arnott, P. Hastings, and D. Allbritton. Research methods tutor: Evaluation of a dialogue-based tutoring system in the classroom. Behavior Research Methods, 40(3):694– 672, 2008.
[4] A. Britt, P. Wiemer-Hastings, A. Larson, and C. Perfetti. Using intelligent feedback to improve sourcing and integration in students’ essays. International Journal of Artificial Intelligence in Education, 14:359–374, 2004.
[5] M. A. Britt and A. Larson. Construction of argument representations during on-line reading. Journal of Memory and Language, 48(4):749–810, 2003.
[6] M. A. Britt, C. Kurby, S. Dandotkar, and C. Wolfe. I agreed with what? Memory for simple argument claims. Discourse Processes, 45(1):52–84, 2008.
[7] J. Gee. Learning by design: Good video games as learning machines. e-Learning, 2005.
[8] R. Clark and R. Mayer. e-Learning and the Science of Instruction, chapter Simulations
and Games in e-Learning. Pfeiffer, 2008.
[9] J. Gee. What video games have to teach us about learning and literacy. Palgrave Macmil-
lan, 2003.

https://futureoflife.org/data/documents/research_priorities.pdf?x93895

What’s the Big Idea: Watch out for unintended consequences with AI. Beware of the benefits and pitfalls.

Research questions/Hypothesis, methods used in research/conclusions: As AI gets more widely used and smarter, some of the systems built with good intention will cause negative effects.