A learning diagnosis architecture with a Bayesian network approach

Ho Chuan Huang; Tsui-Ying Wang

doi:10.1109/ICALT.2005.10

A learning diagnosis architecture with a Bayesian network approach

Ho Chuan Huang, Tsui-Ying Wang

Department of Occupational Therapy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

In this paper, we present a knowledge learning diagnosis approach to supporting a computer-supported collaborative learning environment. Bayesian network technique is used here to diagnose the misconception about learning knowledge and to reason potential misconception for individual learners. After learners have made a test, the system using probabilistic reasoning will automatically create learning communities based on the learners' characteristics and test results. This work is motivated by diagnosing learning performance of learners for both instructors and learners to understand concept comprehension of learners in a computer-supported collaborative learning (CSCL) environment.

Original language	English
Title of host publication	Proceedings - 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005
Pages	33-34
Number of pages	2
DOIs	https://doi.org/10.1109/ICALT.2005.10
Publication status	Published - 2005 Dec 1
Event	5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005 - Kaohsiung, Taiwan Duration: 2005 Jul 5 → 2005 Jul 8

Publication series

Name	Proceedings - 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005
Volume	2005

Other

Other	5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005
Country	Taiwan
City	Kaohsiung
Period	05-07-05 → 05-07-08

All Science Journal Classification (ASJC) codes

Engineering(all)

Access to Document

10.1109/ICALT.2005.10

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Huang, H. C., & Wang, T-Y. (2005). A learning diagnosis architecture with a Bayesian network approach. In Proceedings - 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005 (pp. 33-34). [1508599] (Proceedings - 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005; Vol. 2005). https://doi.org/10.1109/ICALT.2005.10

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title = "A learning diagnosis architecture with a Bayesian network approach",

abstract = "In this paper, we present a knowledge learning diagnosis approach to supporting a computer-supported collaborative learning environment. Bayesian network technique is used here to diagnose the misconception about learning knowledge and to reason potential misconception for individual learners. After learners have made a test, the system using probabilistic reasoning will automatically create learning communities based on the learners' characteristics and test results. This work is motivated by diagnosing learning performance of learners for both instructors and learners to understand concept comprehension of learners in a computer-supported collaborative learning (CSCL) environment.",

author = "Huang, {Ho Chuan} and Tsui-Ying Wang",

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note = "5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005 ; Conference date: 05-07-2005 Through 08-07-2005",

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Huang, HC & Wang, T-Y 2005, A learning diagnosis architecture with a Bayesian network approach. in Proceedings - 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005., 1508599, Proceedings - 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005, vol. 2005, pp. 33-34, 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005, Kaohsiung, Taiwan, 05-07-05. https://doi.org/10.1109/ICALT.2005.10

A learning diagnosis architecture with a Bayesian network approach. / Huang, Ho Chuan; Wang, Tsui-Ying.

Proceedings - 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005. 2005. p. 33-34 1508599 (Proceedings - 5th IEEE International Conference on Advanced Learning Technologies, ICALT 2005; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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