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Online Embedded Assessment for Dragoon, Intelligent Tutoring System

abstract: Embedded assessment constantly updates a model of the student as the student works on instructional tasks. Accurate embedded assessment allows students, instructors and instructional systems to make informed decisions without requiring the student to stop instruction and take a test. This thesis describes the development and comparison of several student models for Dragoon, an intelligent tutoring system. All the models were instances of Bayesian Knowledge Tracing, a standard method. Several methods of parameterization and calibration were explored using two recently developed toolkits, FAST and BNT-SM that replaces constant-valued parameters with logistic regressions. The evaluation was done by calculating the fit of the models to data from human subjects and by assessing the accuracy of their assessment of simulated students. The student models created using node properties as subskills were superior to coarse-grained, skill-only models. Adding this extra level of representation to emission parameters was superior to adding it to transmission parameters. Adding difficulty parameters did not improve fit, contrary to standard practice in psychometrics. / Dissertation/Thesis / Masters Thesis Computer Science 2015

Identiferoai:union.ndltd.org:asu.edu/item:36515
Date January 2015
ContributorsGrover, Sachin (Author), VanLehn, Kurt (Advisor), Walker, Erin (Committee member), Shiao, Ihan (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeMasters Thesis
Format66 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved

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