Abstract
Understanding why students quit a level in a learning game could inform the design of appropriate and timely interventions to keep students motivated to persevere. In this paper, we study student quitting behavior in Physics Playground (PP) – a Physics game for secondary school students. We focus on student cognition that can be inferred from their interaction with the game. PP logs meaningful and crucial student behaviors relevant to physics learning in real time. The automatically generated events in the interaction log are used as codes for quantitative ethnography analysis. We study epistemic networks from five levels to study how the temporal interconnections between the events are different for students who quit the game and those who did not. Our analysis revealed that students who quit over-rely on nudge actions and tend to settle on a solution more quickly than students who successfully complete a level, often failing to identify the correct agent and supporting objects to solve the level.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Shute, V.J., Ventura, M., Kim, Y.J.: Assessment and learning of qualitative physics in newton’s playground. J. Educ. Res. 106(6), 423–430 (2013)
Lomas, D., Patel, K., Forlizzi, J.L., Koedinger, K.R.: Optimizing challenge in an educational game using large-scale design experiments. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 89–98. ACM, New York (2013)
Karumbaiah, S., Rahimi, S., Baker, R.S, Shute, V.J., D’Mello, S.: Is student frustration in learning games more associated with game mechanics or conceptual understanding? In: Kay, J., Luckin, R. (eds.) 13th International Conference of Learning Sciences, London, UK, vol. 3, pp. 1385–1386 (2018)
Baker, R.S.J., Mitrović, A., Mathews, M.: Detecting gaming the system in constraint-based tutors. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 267–278. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13470-8_25
Shaffer, D.W.: Quantitative Ethnography. Cathcart Press (2017)
Owen, V.E.: Capturing in-game learner trajectories with ADAGE (assessment data aggregator for game environments): a cross-method analysis. Doctoral dissertation, University of Wisconsin-Madison, Madison, WI (2014)
Karumbaiah, S., Baker, R.S., Shute, V.: Predicting quitting in students playing a learning game. In: Boyer, K.E., Yudelson, M. (eds.) Proceedings of the 11th International Conference on Educational Data Mining, pp. 167–176 (2018)
Shaffer, D.W., Collier, W., Ruis, A.R.: A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. J. Learn. Anal. 3(3), 9–45 (2016)
Arastoopour, G., Shaffer, D.W., Swiecki, Z., Ruis, A.R., Chesler, N.C.: Teaching and assessing engineering design thinking with virtual internships and epistemic network analysis. Int. J. Eng. Educ. 32(2) (2016)
Knight, S., Arastoopour, G., Williamson Shaffer, D., Buckingham Shum, S., Littleton, K.: Epistemic networks for epistemic commitments. In: Polman, J.L., et al. (eds.) Learning and Becoming in Practice: The International Conference of the Learning Sciences, Boulder, CO, vol. 1, pp. 150–157 (2014)
Beck, J.E., Gong, Y.: Wheel-spinning: students who fail to master a skill. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 431–440. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39112-5_44
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Karumbaiah, S., Baker, R.S., Barany, A., Shute, V. (2019). Using Epistemic Networks with Automated Codes to Understand Why Players Quit Levels in a Learning Game. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds) Advances in Quantitative Ethnography. ICQE 2019. Communications in Computer and Information Science, vol 1112. Springer, Cham. https://doi.org/10.1007/978-3-030-33232-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-33232-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33231-0
Online ISBN: 978-3-030-33232-7
eBook Packages: Computer ScienceComputer Science (R0)