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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