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Title page for ETD etd-01242016-180919


Type of Document Dissertation
Author Martinez Garza, Mario Manuel
Author's Email Address mario.m.martinez@vanderbilt.edu
URN etd-01242016-180919
Title Coevolution of Theory and Data Analytics of Digital Game-Based Learning
Degree PhD
Department Learning, Teaching and Diversity
Advisory Committee
Advisor Name Title
Douglas B. Clark Committee Chair
Daniel T. Levin Committee Member
Melissa S. Gresalfi Committee Member
Rogers P. Hall Committee Member
Keywords
  • data mining
  • learning analytics
  • educational games
  • science learning
Date of Defense 2016-01-15
Availability restricted
Abstract
Learning theory and educational data analytics can be said to coevolve, that is, to refine and improve each other reciprocally, each aspect providing a necessary element for the growth and advancement of the other. In this three-paper dissertation, I explore this process of coevolution between learning theory and data analytics in the context of digital game-based learning. From the theoretical side, I describe a framework based on a general theory of cognition (the two-system or dual-system model) that can be applied to digital game environments. The main hypothesis in this framework is that certain patterns of action in the game-space indicate the use of certain epistemic stances that have analogues within the two-system model. The proposed Two Stance/Two Model Framework (2SM) provides (a) improved explanatory power regarding intrapersonal variation in learning from games, (b) more complete theory regarding individual needs, goals, and agency, (c) a more extensive account of collaboration and community, and (d) improved perspective on knowledge-rich interactions in online affinity spaces. From the methodological side, I applied techniques of statistical computing (affinity clustering and sequence mining) to detect the stances of the 2SM as they appear in a physics learning game. The 2SM theorized that slow modes of solution would correlate to higher learning gains; students who use mainly fast iterative solution strategies did achieve lower learning gains than students who preferred slow, elaborated solutions. A second finding was that, as play progresses, students generally improve their performance in game areas that highlight physics concepts, but that this improvement is strongly moderated by their prior knowledge of physics. This dissertation further contributes to the existing knowledge of digital game-based learning by demonstrating how an analysis of the collected actions of players can be applied in a reliable and comprehensive fashion to research questions that are otherwise challenging to investigate.
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