Get 20M+ Full-Text Papers For Less Than $1.50/day. Subscribe now for You or Your Team.

Learn More →

Serious Games AnalyticsSerious Games Analytics to Measure Implicit Science Learning

Serious Games Analytics: Serious Games Analytics to Measure Implicit Science Learning [Evidence Centered Game Design (ECgD) is an increasingly popular model used for stealth game assessments employing education data mining techniques for the measurement of learning within serious (and other) games (GlassLab, Psychometric considerations in game-based assessment. Institute of Play. Retrieved July 1, 2014, from http://www.instituteofplay.org/work/projects/glasslab-research/). There is a constant tension in ECgD between how pre-defined the learning outcomes and measures need to be, and how much important, but unanticipated, learning can be detected in gameplay. The EdGE research team is employing an emergent approach to developing a game-based assessment mechanic that starts empirically from what the players do in a well-crafted game and detects patterns that may indicate implicit understanding of salient phenomena. Implicit knowledge is foundational to explicit knowledge (Polanyi, The tacit dimension. University of Chicago Press, Chicago, IL,1966), yet is largely ignored in education because of the difficulty measuring knowledge that a learner has not yet formalized. This chapter describes our approach to measuring implicit science learning in the game, Impulse, designed to foster an implicit understanding of Newtonian mechanics using a combination of video analysis, game log analyses, and comparisons with pre-post assessment results. This research demonstrates that it is possible to reliably detect strategies that demonstrate an implicit understanding of fundamental physics using data mining techniques on user-generated data.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Serious Games AnalyticsSerious Games Analytics to Measure Implicit Science Learning

Part of the Advances in Game-Based Learning Book Series
Editors: Loh, Christian Sebastian; Sheng, Yanyan; Ifenthaler, Dirk
Serious Games Analytics — Mar 13, 2015

Loading next page...
 
/lp/springer-journals/serious-games-analytics-serious-games-analytics-to-measure-implicit-aoPZ53bHtT

References (42)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2015
ISBN
978-3-319-05833-7
Pages
343 –360
DOI
10.1007/978-3-319-05834-4_15
Publisher site
See Chapter on Publisher Site

Abstract

[Evidence Centered Game Design (ECgD) is an increasingly popular model used for stealth game assessments employing education data mining techniques for the measurement of learning within serious (and other) games (GlassLab, Psychometric considerations in game-based assessment. Institute of Play. Retrieved July 1, 2014, from http://www.instituteofplay.org/work/projects/glasslab-research/). There is a constant tension in ECgD between how pre-defined the learning outcomes and measures need to be, and how much important, but unanticipated, learning can be detected in gameplay. The EdGE research team is employing an emergent approach to developing a game-based assessment mechanic that starts empirically from what the players do in a well-crafted game and detects patterns that may indicate implicit understanding of salient phenomena. Implicit knowledge is foundational to explicit knowledge (Polanyi, The tacit dimension. University of Chicago Press, Chicago, IL,1966), yet is largely ignored in education because of the difficulty measuring knowledge that a learner has not yet formalized. This chapter describes our approach to measuring implicit science learning in the game, Impulse, designed to foster an implicit understanding of Newtonian mechanics using a combination of video analysis, game log analyses, and comparisons with pre-post assessment results. This research demonstrates that it is possible to reliably detect strategies that demonstrate an implicit understanding of fundamental physics using data mining techniques on user-generated data.]

Published: Mar 13, 2015

Keywords: Implicit learning; Science learning; Assessment; Educational data mining

There are no references for this article.