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Using Epistemic Networks with Automated Codes to Understand Why Players Quit Levels in a Learning Game
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Using Epistemic Networks with Automated Codes to Understand Why Players Quit Levels in a Learning Game

Shamya Karumbaiah, Ryan S. Baker, Amanda Barany and Valerie Shute
Advances in Quantitative Ethnography, pp 106-116
11 Oct 2019
DOI: https://doi.org/10.1007/978-3-030-33232-7_9

Abstract

Automated codes Epistemic network analysis Interaction log Learning game Quitting behavior
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.

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20 citations in Scopus

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