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A dynamic bayesian network for inference of learners' algebraic knowledge

Published: 24 March 2014 Publication History

Abstract

An Intelligent Tutoring System (ITS) is an educational software that provides personal assistance for students, allowing them to learn at their own pace. This is possible because ITSs are able to map the learners' knowledge to create a student model. Most of the tutors use a Bayesian Network (BN) to perform this task, due to their ability to deal with uncertain data. However, classic static BNs are unable to model data, such as the student's knowledge, that changes over time. Dynamic Bayesian Networks (DBN) are an interesting option in this case, because they are a special type of BN that reasons over time. This paper presents an architecture of DBN that aims at inferring student's algebraic knowledge. This network was constructed based on a concept map, which was developed with the goal of structuring the algebraic knowledge, i. e. defining relationships among concepts. The proposed DBN was evaluated with the help of an expert in order to verify the ability of the network to predict the student's knowledge on the application of operations to solve 1st degree equations. This DBN is being integrated into an web-based ITS for algebra learning.

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Cited By

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  • (2019)Learner modelling: systematic review of the literature from the last 5 yearsEducational Technology Research and Development10.1007/s11423-018-09644-167:5(1105-1143)Online publication date: 8-Jan-2019
  • (2019)Predicting Academic Performance: A Bootstrapping Approach for Learning Dynamic Bayesian NetworksArtificial Intelligence in Education10.1007/978-3-030-23204-7_3(26-36)Online publication date: 21-Jun-2019
  • (2016)Modelling Students' Algebraic Knowledge with Dynamic Bayesian Networks2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT.2016.96(44-48)Online publication date: Jul-2016

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  1. A dynamic bayesian network for inference of learners' algebraic knowledge

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      cover image ACM Conferences
      SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
      March 2014
      1890 pages
      ISBN:9781450324694
      DOI:10.1145/2554850
      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 the author(s) 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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      Publication History

      Published: 24 March 2014

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      Author Tags

      1. algebra
      2. dynamic bayesian network
      3. intelligent tutoring system
      4. student model

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      SAC 2014: Symposium on Applied Computing
      March 24 - 28, 2014
      Gyeongju, Republic of Korea

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      SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
      Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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      Cited By

      View all
      • (2019)Learner modelling: systematic review of the literature from the last 5 yearsEducational Technology Research and Development10.1007/s11423-018-09644-167:5(1105-1143)Online publication date: 8-Jan-2019
      • (2019)Predicting Academic Performance: A Bootstrapping Approach for Learning Dynamic Bayesian NetworksArtificial Intelligence in Education10.1007/978-3-030-23204-7_3(26-36)Online publication date: 21-Jun-2019
      • (2016)Modelling Students' Algebraic Knowledge with Dynamic Bayesian Networks2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT.2016.96(44-48)Online publication date: Jul-2016

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