Computerized Adaptive Testing Item Selection in Computerized Adaptive Learning Systems [chapter]

Theo J.H.M. Eggen
Psychometrics in practice at RCEC  
Item selection methods traditionally developed for computerized adaptive testing (CAT) are explored for their usefulness in item-based computerized adaptive learning (CAL) systems. While in CAT Fisher information-based selection is optimal, for recovering learning populations in CAL systems item selection based on Kullback-Leibner information is an alternative.
doi:10.3990/3.9789036533744.ch2 fatcat:4vmp4npudbafjmxv42rnkep5gi