Investigating Epistemic Stances in Game Play with Data Mining

In this paper, techniques of statistical computing were applied to data logs to investigate the patterns in students' play of The Fuzzy Chronicles, and how these patterns relate to learning outcomes with regards to Newtonian kinematics. This paper has two goals. The first goal is to investigate the basic claims of the proposed Two-System Framework for Game-Based Learning (or 2SM) (Martinez-Garza & Clark, 2016) that may serve as part of a general-use explanatory framework for educational gaming. The second goal is to explore and demonstrate the use of automatically collected log files of student play as evidence through educational data mining techniques. These techniques could also find general use, and this paper offers a demonstration of plausible methods and processes that are suited for game play data. These goals were pursued via two research questions. The first research question examines whether students playing The Fuzzy Chronicles showed evidence of dichotomous fast/slow modes of solution. The 2SM theorizes that slow modes of solution will correlate to higher learning gains. Congruent with the 2SM, students who use mainly fast iterative solution strategies achieved lower learning gains than students who preferred slow, elaborate solutions, or a more balanced mix of the two. A second research question investigates the connection between conceptual understanding and student performance in conceptually-laden challenges. The finding was that students generally improve their performance in these challenges as gameplay progresses, but that this improvement is strongly moderated by their prior knowledge of physics. Implications of these findings in terms of educational game design, analysis of gameplay logs, and further refinement of the 2SM are discussed