Proceedings of the 2016 International Conference on Computer Science and Electronic Technology

Knowledge Structures Based Adaptive Testing Model

Authors
Xia Li, Hao Zhang, Huali Yang, Guixin Xing
Corresponding Author
Xia Li
Available Online August 2016.
DOI
https://doi.org/10.2991/cset-16.2016.13How to use a DOI?
Keywords
Knowledge structure, adaptive test, intelligent selection, state of knowledge, error correction
Abstract
How to know the students' overall cognitive situation quickly and accurately by testing has been a hot issue in teaching. Based on the relevant research at home and abroad over the adaptive testing, and in view of the current adaptive testing is not intelligent enough, this paper presents a personalized intelligent selection method which combines the traditional dichotomy and knowledge state boundary method. In test error correction process, this paper proposed the recent knowledge state compatible set theory, by using this theory, this paper tries to use probabilistic approach to rationalize the irrational state of knowledge to achieve the maximum "real" of the level of output. Examples of verification found that the proposed adaptive test model could quickly and accurately measure the true level of students' cognitive structure, and greatly improve test efficiency.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Computer Science and Electronic Technology
Part of series
Advances in Computer Science Research
Publication Date
August 2016
ISBN
978-94-6252-213-8
ISSN
2352-538X
DOI
https://doi.org/10.2991/cset-16.2016.13How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xia Li
AU  - Hao Zhang
AU  - Huali Yang
AU  - Guixin Xing
PY  - 2016/08
DA  - 2016/08
TI  - Knowledge Structures Based Adaptive Testing Model
BT  - 2016 International Conference on Computer Science and Electronic Technology
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/cset-16.2016.13
DO  - https://doi.org/10.2991/cset-16.2016.13
ID  - Li2016/08
ER  -