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

Published:16 March 2015Publication History

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.

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        cover image ACM Other conferences
        LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge
        March 2015
        448 pages
        ISBN:9781450334174
        DOI:10.1145/2723576

        Copyright © 2015 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 16 March 2015

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        LAK '15 Paper Acceptance Rate20of74submissions,27%Overall Acceptance Rate236of782submissions,30%

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