TY - JOUR
T1 - Advances in assessment of students' intuitive understanding of physics through gameplay data
AU - Martinez-Garza, Mario M.
AU - Clark, Douglas
AU - Nelson, Brian
N1 - Funding Information:
The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through grant R305A110782, and the National Science Foundation through grant 1119290. The opinions expressed are those of the authors and do not represent views of the Institute, the U.S. Department of Education, or the National Science Foundation.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Assessment
KW - Game play data
KW - Hidden Markov modeling
KW - Qualitative analysis
KW - Science learning
KW - Sequential pattern analysis
KW - Video games
UR - http://www.scopus.com/inward/record.url?scp=84903145160&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84903145160&partnerID=8YFLogxK
U2 - 10.4018/ijgcms.2013100101
DO - 10.4018/ijgcms.2013100101
M3 - Article
AN - SCOPUS:84903145160
SN - 1942-3888
VL - 5
SP - 1
EP - 16
JO - International Journal of Gaming and Computer-Mediated Simulations
JF - International Journal of Gaming and Computer-Mediated Simulations
IS - 4
ER -