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View all- Lang C(2020)Learner-Context Modelling: A Bayesian ApproachArtificial Intelligence in Education10.1007/978-3-030-52240-7_28(152-156)Online publication date: 30-Jun-2020
Opportunities for personalization in modeling students as Bayesian learners
Pages 41 - 45
Abstract
The following paper is a proof-of-concept demonstration of a novel Bayesian framework for making inferences about individual students and the context in which they are learning. It has implications for both efforts to automate personalized instruction and to probabilistically model educational context. By modelling students as Bayesian learners, individuals who weigh their prior belief against current circumstantial data to reach conclusions, it becomes possible to both generate estimates of performance and the impact of the educational environment in probabilistic terms. This framework is tested through a Bayesian algorithm that can be used to characterize student prior knowledge in course material and predict student performance. This is demonstrated using both simulated data. The algorithm generates estimates that behave qualitatively as expected on simulated data and predict student performance substantially better than chance. A discussion of the results and the conceptual benefits of the framework follow.
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Published In
March 2017
631 pages
ISBN:9781450348706
DOI:10.1145/3027385
- General Chairs:
- Alyssa Wise,
- Phillip H. Winne,
- Grace Lynch,
- Program Chairs:
- Xavier Ochoa,
- Inge Molenaar,
- Shane Dawson,
- Publications Chair:
- Marek Hatala
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Published: 13 March 2017
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LAK '17
LAK '17: 7th International Learning Analytics and Knowledge Conference
March 13 - 17, 2017
British Columbia, Vancouver, Canada
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LAK '17 Paper Acceptance Rate 36 of 114 submissions, 32%;
Overall Acceptance Rate 236 of 782 submissions, 30%
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View all- Lang C(2020)Learner-Context Modelling: A Bayesian ApproachArtificial Intelligence in Education10.1007/978-3-030-52240-7_28(152-156)Online publication date: 30-Jun-2020
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