NDLI: How does Bayesian knowledge tracing model emergence of knowledge about a mechanical system?

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How does Bayesian knowledge tracing model emergence of knowledge about a mechanical system?

Content Provider	ACM Digital Library
Author	Pallant, Amy Lee, Hee-Sun Gweon, Gey-Hong Dorsey, Chad Lord, Trudi Tinker, Robert Damelin, Daniel Finzer, William Kimball, Nathan
Abstract	An interactive learning task was designed in a game format to help high school students acquire knowledge about a simple mechanical system involving a car moving on a ramp. This ramp game consisted of five challenges that addressed individual knowledge components with increasing difficulty. In order to investigate patterns of knowledge emergence during the ramp game, we applied the Monte Carlo Bayesian Knowledge Tracing (BKT) algorithm to 447 game segments produced by 64 student groups in two physics teachers' classrooms. Results indicate that, in the ramp game context, (1) the initial knowledge and guessing parameters were significantly highly correlated, (2) the slip parameter was interpretable monotonically, (3) low guessing parameter values were associated with knowledge emergence while high guessing parameter values were associated with knowledge maintenance, and (4) the transition parameter showed the speed of knowledge emergence. By applying the k-means clustering to ramp game segments represented in the three dimensional space defined by guessing, slip, and transition parameters, we identified seven clusters of knowledge emergence. We characterize these clusters and discuss implications for future research as well as for instructional game design.
Starting Page	171
Ending Page	175
Page Count	5
File Format	PDF
ISBN	9781450334174
DOI	10.1145/2723576.2723587
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Physics learning Game-based learning Bayesian knowledge tracing
Content Type	Text
Resource Type	Article

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