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

Published: 16 March 2015 Publication 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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Cited By

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  • (2023)How well do contemporary knowledge tracing algorithms predict the knowledge carried out of a digital learning game?Educational technology research and development10.1007/s11423-023-10218-z71:3(901-918)Online publication date: 29-Mar-2023
  • (2017)A Priori Knowledge in Learning AnalyticsLearning Analytics: Fundaments, Applications, and Trends10.1007/978-3-319-52977-6_7(199-227)Online publication date: 18-Feb-2017
  • (2015)Tracking student progress in a game-like learning environment with a Monte Carlo Bayesian knowledge tracing modelProceedings of the Fifth International Conference on Learning Analytics And Knowledge10.1145/2723576.2723608(166-170)Online publication date: 16-Mar-2015

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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
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 16 March 2015

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      Author Tags

      1. Bayesian knowledge tracing
      2. game-based learning
      3. physics learning

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      LAK '15 Paper Acceptance Rate 20 of 74 submissions, 27%;
      Overall Acceptance Rate 236 of 782 submissions, 30%

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      View all
      • (2023)How well do contemporary knowledge tracing algorithms predict the knowledge carried out of a digital learning game?Educational technology research and development10.1007/s11423-023-10218-z71:3(901-918)Online publication date: 29-Mar-2023
      • (2017)A Priori Knowledge in Learning AnalyticsLearning Analytics: Fundaments, Applications, and Trends10.1007/978-3-319-52977-6_7(199-227)Online publication date: 18-Feb-2017
      • (2015)Tracking student progress in a game-like learning environment with a Monte Carlo Bayesian knowledge tracing modelProceedings of the Fifth International Conference on Learning Analytics And Knowledge10.1145/2723576.2723608(166-170)Online publication date: 16-Mar-2015

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