Purpose: The proposed project, Explanation and Prediction Increasing Gains and Metacognition (EPIGAME), will integrate metacognitive research on prediction and explanation into the design of a physics-based digital game environment to scaffold students’ understanding of formal physics concepts. The design of the EPIGAME environment will support pilot and future research on games for learning through (a) randomized assignment of players to multiple configurations of parameters within the game and (b) embedded computer adaptive assessment and data log analysis functionality to support sophisticated analytics and data collection. The goal is for teachers to use EPIGAME software to scaffold middle school students (and potentially older students) in bridging intuitive understandings with explicit articulated core concepts of Newtonian mechanics.

Project Activities: The EPIGAME team will develop and pilot test a game environment called Cup Racer that incorporates different approaches to integrating prediction and explanation into the fabric of a digital game. The game environment will be based on popular game-play mechanics and will support randomized trials within the design of the environment. It can be integrated into middle school curricula that feature an exploration of Newtonian mechanics, a central feature of middle school and high school standards nationwide. The EPIGAME Cup Racer environment will thus provide a platform both for students learning core science ideas and researchers studying design principles for games. Development and piloting will be organized into three primary stages of the project, each of which will comprise two full cycles of planning, development, and testing phases.

Products: Products developed in this project will include the Cup Racer game and peer-reviewed publications.

STRUCTURED ABSTRACT

Setting: The development and pilot work will be conducted in Nashville, Tennessee.

Population: The study sample includes middle school students across multiple school contexts.

Intervention: EPIGAME will develop and pilot a game environment called Cup Racer that can (a) support randomized trials investigating the different approaches to integrating prediction and explanation into a digital game based on popular game-play mechanics and (b) become a core part of the curriculum in middle schools for exploring Newtonian mechanics. The EPIGAME Cup Racer environment will thus provide a platform both for students learning of core science ideas and researchers studying design principles for games. A larger curriculum including Cup Racer will be developed so that researchers can assess not only the direct learning within Cup Racer, but also the preparation for future learning.

Research Design and Methods: The research carried out by this team will be organized into three, one-year stages, with each stage including two full cycles of planning, development, and pilot testing phases. The first stage will focus on developing and piloting the core Cup Racer game, including variants of the navigation interface for piloting. The second stage will focus on developing and piloting the explanation variants of Cup Racer and refining the core game and prediction variants from the first stage. The third stage will focus on developing and piloting integrations between the prediction and explanation functionality and the computer-adaptive functionality to tailor a player’s experiences to his or her needs. Concurrently, the team will be developing embedded assessment systems. Development of these systems will progress across all three stages, beginning with item development and piloting during the first stage, the integration of the items into a computer adaptive framework within Cup Racer during the second stage, and the refinement and integration of the computer-adaptive functionality with the prediction and explanation functionality in the third stage.

Embedded within the system itself, the team will use randomized, controlled comparisons to investigate: (a) prediction approaches ranging from none (real-time navigation interfaces) to high-prediction navigation interfaces; (b) explanation approaches ranging from none (control) through variants of didactic explanation and self-explanation; (c) combinations of prediction functionality, didactic explanation functionality, and self-explanation functionality to explore interactions and synergies among them; and (d) game-based versions, non-game simulation variants, and non-computer-based traditional curricula to establish overall baselines of the potential of digital games for science learning. The above-mentioned factors will be combined into various configurations for game play. This will then allow the researchers to make data-driven inferences about the configuration(s) that lead to best performance.

Control Condition: Control conditions will vary as a function of experimental manipulation.

Key Measures: For Cup Racer, researchers will develop and validate their own measures with a focus on establishing: (a) a full gradient of item difficulties, (b) items with highly accessible literacy levels, and (c) conceptually important and accurate physics foci. Key measures will focus primarily on multiple-choice style physics assessments developed and validated in conjunction with physicists and assessment researchers. Researchers will embed the computer-adaptive assessment format within the dialog of the game as conversation choices. The research team will also (a) develop analytic tools leveraging students’ gameplay data logs and (b) conduct surveys, interviews, and think-aloud protocols to collect formative data. The key learning outcomes focus on Newton’s First and Second Laws and associated kinematics and representations central to national and state standards for middle school science.

Data Analytic Strategy: Data will be analyzed using uni-and multivariate analysis of variance, in addition to hidden Markov models. Researchers will also examine possible differences in outcomes by gender and self-reported experience playing digital games.

Products and Publications

Book chapter

Adams, D. M., Clark, D. B., & Virk, S. S. (2018). Worked examples in physics games: Challenges in integrating proven cognitive scaffolds into game mechanics. In Cvetkovic, D. (Ed.), Simulations and Gaming (pp. 61–73).InTech.

Martinez-Garza, M. M., & Clark, D. B. (2017). Two systems, two stances: A Novel theoretical framework for model-based learning in digital games. In Wouters P., van Oostendorp H. (Eds), Instructional techniques to facilitate learning and motivation of serious games (pp. 37–58). Springer, Cham.

Martinez-Garza, M., & Clark, D. B. (2019). Investigating Epistemic Stances in Game Play Through Learning Analytics. In B. Dubbels (Ed.), Exploring the Cognitive, Social, Cultural, and Psychological Aspects of Gaming and Simulations (pp. 87–140). Hershey, PA: IGI Global. doi:10.4018/978-1-5225-7461-3.ch004

Martinez-Garza, M., Clark, D.B., Killingsworth, S., and Adams, D. (2016). Beyond Fun: Pintrich, Motivation to Learn, and Games for Learning. In D. Russell, and J. Laffey (Eds.), Handbook of Research on Gaming Trends in P–12 Education (pp. 1–32). Hershey, PA: IGI Global Publishing.

Van Eaton, G., and Clark, D.B. (2016). Designing Digital Objects to Scaffold Learning. In D. Russell, and J. Laffey (Eds.), Handbook of Research on Gaming Trends in P–12 Education (pp. 237–252). Hershey, PA: IGI Global Publishing.

Journal article, monograph, or newsletter

Adams, D., and Clark, D.B. (2014). Integrating Self-Explanation Functionality Into a Complex Game Environment: Keeping Gaming in Motion. Computers and Education, 73: 149–159.

Clark, D.B., and Martinez-Garza, M. (2015). Deep Analysis of Nuances and Epistemic Frames Around Argumentation and Learning in Informal Learning Spaces. Computers in Human Behavior, 53: 617–620.

Clark, D.B., Sengupta, P., Brady, C., Martinez-Garza, M., and Killingsworth, S. (2015). Disciplinary Integration in Digital Games for Science Learning. International STEM Education Journal, 2(2): 1–21.

Clark, D. B., Virk, S. S., Barnes, J., & Adams, D. M. (2016). Self-explanation and digital games: Adaptively increasing abstraction. Computers & Education, 103, 28–43.

Clark, D.B., Virk, S., Sengupta, P., Brady, C., Martinez-Garza, M., Krinks, K., Killingsworth, S., Kinnebrew, J., Biswas, G., Barnes, J., Minstrell, J., Nelson, B., Slack, K., and D'Angelo, C. (2016). SURGE's Evolution Deeper Into Formal Representations: The Siren's Call of Popular Game-Play Mechanics. International Journal of Designs for Learning, 7(1).

Killingsworth, S., Clark, D.B., and Adams, D. (2015). Self-Explanation and Explanatory Feedback in Games: Individual Differences, Gameplay, and Learning. International Journal of Education in Mathematics, Science and Technology, 3(3): 162–186.

Martinez-Garza, M. M., & Clark, D. B. (2017). Investigating Epistemic Stances in Game Play with Data Mining. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 9(3), 1–40.

Martinez-Garza, M. M., Clark, D., & Nelson, B. (2013). Advances in assessment of students' intuitive understanding of physics through gameplay data. International Journal of Gaming and Computer-Mediated Simulations (IJGCMS), 5(4), 1–16.

Martinez-Garza, M., Clark, D. B., & Nelson, B. C. (2013). Digital games and the US National Research Council's science proficiency goals. Studies in Science Education, 49(2), 170–208.

Van Eaton, G., Clark, D.B., and Smith, B.E. (2015). Patterns of Physics Reasoning in Face-to-Face and Online Forum Collaboration Around a Digital Game. International Journal of Education in Mathematics, Science and Technology, 3(1): 1–13.