PP Resources for Teachers

Click on the links below to read a sampling of published papers about Physics Playground:

Shute, V. J., & Rahimi, S. (2021). Stealth assessment of creativity in a physics video gameComputers in Human Behavior, 116 1-13. https://doi.org/10.1016/j.chb.2020.106647

Rahimi, S., Shute, V. J., Kuba, R., Dai, C-P., Yang, X., Smith, G., & Alonso Fenandez, C. (2021). The effects of incentive systems on learning and performance in educational gamesComputers & Education, 165, 1-17. https://doi.org/10.1016/j.compedu.2021.104135

Shute, V. J., Rahimi S., Smith, G., Ke, F., Almond, R., Dai, C-P, Kamikabeya, R., Liu, Z., Yang, X., & Sun, C. (2020). Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity, and learning supports in Physics Playground. Journal of Computer-Assisted Learning (38 pages).

Spann, C. A., Shute, V. J., Rahimi, S., & D’Mello, S. K. (2019). The productive role of cognitive reappraisal in regulating frustration during game-based learning. Computers in Human Behavior. https://doi-org.proxy.lib.fsu.edu/10.1016/j.chb.2019.03.002

Shute, V. J., Rahimi, S., & Smith, G. (2019). Game-based learning analytics in Physics Playground. In M. Chang & A. Tlili (Eds.), Data analytics approaches in educational games and gamification systems. (pp. 69-93). New York, NY: Springer.

Shute, V. J., Ke, F., Almond, R. G., Rahimi, S., Smith, G., & Lu, X. (2019). How to increase learning while not decreasing the fun in educational games. In R. Feldman (Ed.), Learning Science: Theory, Research, and Practice (pp. 327-357). New York, NY: McGraw Hill.

Karumbaiah, S., Rahimi, S., Baker, R. S., Shute, V. J., & D’Mello, S. (2018). Is student frustration in learning games more associated with game mechanics or conceptual understanding?. In J. Kay, R. Luckin, M. Mavrikis, & K. Porayska-Pomsta (Eds.), International Conference of Learning Sciences (pp. 1-2). London, UK.

Kim, Y. J., Almond, R. G., & Shute, V. J. (2016). Applying Evidence-Centered Design for the development of game-based assessments in Physics Playground. International Journal of Testing, 16(2), 142-163.

Shute, V. J. & Wang, L. (2016). Assessing and supporting hard-to-measure constructs. In A. A. Rupp, & J. P. Leighton (Eds.), The handbook of cognition and assessment: Frameworks, methodologies, and application, (pp. 535-562). Hoboken, NJ: John Wiley & Sons, Inc.

Bosch, N., Chen, H., Baker, R., Shute, V. J., & D’Mello, S. (2015). Accuracy vs. availability heuristic in multimodal affect detection in the wild. Proceedings of the 17th International Conference on Multimodal Interaction (ICMI), Seattle, WA.

Kim, Y. J., & Shute, V. J. (2015). The interplay of game elements with psychometric qualities, learning, and enjoyment in gamebased assessment. Computers & Education, 87, 340-356.

Shute, V. J., D’Mello, S. K., Baker, R., Cho, K., Bosch, N., Ocumpaugh, J., Ventura, M., & Almeda, V. (2015). Modeling how incoming knowledge, persistence, affective states, and in-game progress influence student learning from an educational game. Computers & Education, 86, 224-235.

Shute, V. J., Ventura, M., & Kim, Y. J. (2013). Assessment and learning of qualitative physics in Newton’s Playground. The Journal of Educational Research, 106, 423-430.


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