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ERIC Number: ED512650
Record Type: Non-Journal
Publication Date: 2010-Aug
Pages: 34
Abstractor: As Provided
Reference Count: 34
ISBN: N/A
ISSN: N/A
A Bayesian Network Approach to Modeling Learning Progressions and Task Performance. CRESST Report 776
West, Patti; Rutstein, Daisy Wise; Mislevy, Robert J.; Liu, Junhui; Choi, Younyoung; Levy, Roy; Crawford, Aaron; DiCerbo, Kristen E.; Chappel, Kristina; Behrens, John T.
National Center for Research on Evaluation, Standards, and Student Testing (CRESST)
A major issue in the study of learning progressions (LPs) is linking student performance on assessment tasks to the progressions. This report describes the challenges faced in making this linkage using Bayesian networks to model LPs in the field of computer networking. The ideas are illustrated with exemplar Bayesian networks built on Cisco Networking Academy LPs and tasks designed to obtain evidence in their terms. We briefly discuss challenges in the development of LPs, and then move to challenges with the implementation of Bayesian networks, including selection of the method, issues of model fit and confirmation, and grainsize. We conclude with a discussion of the challenges we face in ongoing work. IP Addressing Skills Progression is appended. (Contains 2 tables, 11 figures and 1 footnote.)
National Center for Research on Evaluation, Standards, and Student Testing (CRESST). 300 Charles E Young Drive N, GSE&IS Building 3rd Floor, Mailbox 951522, Los Angeles, CA 90095-1522. Tel: 310-206-1532; Fax: 310-825-3883; Web site: http://www.cresst.org
Publication Type: Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Department of Education (ED)
Authoring Institution: National Center for Research on Evaluation, Standards, and Student Testing