Some practical item selection algorithms in cognitive diagnostic computerized adaptive testing -- smart diagnosis for smart learning

The current studies represent an effort to advance the feasibility of cognitive diagnostic computerized adaptive testing (CD-CAT), an intelligent educational measurement tool that was envisioned as enhancing individualized learning over twenty years ago. Several new selection algorithms are proposed for addressing two important issues in CD-CAT: measurement efficiency and item exposure control. The posterior-weighted CDM discrimination index (PWCDI) and posterior-weighted attribute-level CDM discrimination index (PWACDI) are computationally affordable and highly efficient alternatives to other information index-based algorithms. The binary stratification algorithm offers an elegant solution to item exposure control in both fixed-length and variable-length CD-CAT, compared with the restrictive stochastic methods for fixed-length CD-CAT and SHTVOR for variable-length CD-CAT.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Educational Psychology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Zheng, Chanjin

Contributors dc:contributor

• Douglas, Jeffrey A.
• Culpepper, Steven A.
• Chang, Hua-Hua
• Anderson, Carolyn J.
• Zhang, Jinming

• cognitive diagnostic computerized adaptive testing (CD-CAT)
• item selection algorithms

Statement dc:rights

• Copyright 2015 Chanjin Zheng

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/78750

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/78750