Knowledge Structures Based Adaptive Testing Model
Xia Li, Hao Zhang, Huali Yang, Guixin Xing
Available Online August 2016.
- https://doi.org/10.2991/cset-16.2016.13How to use a DOI?
- Knowledge structure, adaptive test, intelligent selection, state of knowledge, error correction
- 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.
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 -