Advances in assessment of students' intuitive understanding of physics through gameplay data

Mario M. Martinez-Garza, Douglas Clark, Brian Nelson

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


In this paper, the authors present advances in analyzing gameplay data as evidence of learning outcomes using computational methods of statistical analysis. These analyses were performed on data gathered from the SURGE learning environment (Martinez-Garza, Clark, & Nelson, 2010). SURGE is a digital game designed to help students articulate their intuitive concepts of motion physics and organize them toward a more normative scientific understanding. Various recurring issues of assessment, which pervade assessment of learning in games more generally, prompted the authors to consider whether gameplay (actions of learners in the context of the game) can be analyzed to produce evidence of learning. The authors describe their approach to the analysis of game play in terms of qualitative assessment that the authors believe may lay the groundwork for the application of similar computationally-intensive techniques in other educational game contexts.

Original languageEnglish (US)
Pages (from-to)1-16
Number of pages16
JournalInternational Journal of Gaming and Computer-Mediated Simulations
Issue number4
StatePublished - 2013


  • Assessment
  • Game play data
  • Hidden Markov modeling
  • Qualitative analysis
  • Science learning
  • Sequential pattern analysis
  • Video games

ASJC Scopus subject areas

  • Computer Science Applications


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