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ERIC Number: EJ983221
Record Type: Journal
Publication Date: 2012-Oct
Pages: 19
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0033-3123
Uncovering the Best Skill Multimap by Constraining the Error Probabilities of the Gain-Loss Model
Anselmi, Pasquale; Robusto, Egidio; Stefanutti, Luca
Psychometrika, v77 n4 p763-781 Oct 2012
The Gain-Loss model is a probabilistic skill multimap model for assessing learning processes. In practical applications, more than one skill multimap could be plausible, while none corresponds to the true one. The article investigates whether constraining the error probabilities is a way of uncovering the best skill assignment among a number of alternatives. A simulation study shows that this approach allows the detection of the models that are closest to the correct one. An empirical application shows that it allows the detection of models that are entirely derived from plausible assumptions about the skills required for solving the problems. (Contains 5 tables, 5 figures, and 2 footnotes.)
Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A