Graphical Model Based Computer Adaptive Testing

Russell G. Almond, Robert J. Mislevy
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:11-22, 1997.

Abstract

This paper synthesizes ideas from the fields of graphical modelling and eductational testing, particularly Item Response Theory (IRT) applied to Computerized Adaptive Testing (CAT). Graphical modelling can offer IRT a language for describing multifaceted skills and knowledge and disentangling evidence from com- plex performances. IRT-CA T can offer graphical modellers several ways of treating sources of variability other than including more variables in the model. In particular, variables can enter into the modelling pro- cess at several levels: (1) in validity studies (but not in the ordinarily used model), (2) in task construction (in particular, in defining link parameters), (3) in test or model assembly (blocking and randomization con- straints in selecting tasks or other model pieces), (4) in response characterization (i.e. as part of task models which characterize a response) or (5) in the main (student) model. The paper describes an implementation of these ideas in a fielded application: HYDRIVE, a tutor for hydraulics diagnosis

Cite this Paper

BibTeX
@InProceedings{pmlr-vR1-almond97b, title = {Graphical Model Based Computer Adaptive Testing}, author = {Almond, Russell G. and Mislevy, Robert J.}, booktitle = {Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics}, pages = {11--22}, year = {1997}, editor = {Madigan, David and Smyth, Padhraic}, volume = {R1}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r1/almond97b/almond97b.pdf}, url = {https://proceedings.mlr.press/r1/almond97b.html}, abstract = {This paper synthesizes ideas from the fields of graphical modelling and eductational testing, particularly Item Response Theory (IRT) applied to Computerized Adaptive Testing (CAT). Graphical modelling can offer IRT a language for describing multifaceted skills and knowledge and disentangling evidence from com- plex performances. IRT-CA T can offer graphical modellers several ways of treating sources of variability other than including more variables in the model. In particular, variables can enter into the modelling pro- cess at several levels: (1) in validity studies (but not in the ordinarily used model), (2) in task construction (in particular, in defining link parameters), (3) in test or model assembly (blocking and randomization con- straints in selecting tasks or other model pieces), (4) in response characterization (i.e. as part of task models which characterize a response) or (5) in the main (student) model. The paper describes an implementation of these ideas in a fielded application: HYDRIVE, a tutor for hydraulics diagnosis}, note = {Reissued by PMLR on 30 March 2021.} }
Endnote
%0 Conference Paper %T Graphical Model Based Computer Adaptive Testing %A Russell G. Almond %A Robert J. Mislevy %B Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1997 %E David Madigan %E Padhraic Smyth %F pmlr-vR1-almond97b %I PMLR %P 11--22 %U https://proceedings.mlr.press/r1/almond97b.html %V R1 %X This paper synthesizes ideas from the fields of graphical modelling and eductational testing, particularly Item Response Theory (IRT) applied to Computerized Adaptive Testing (CAT). Graphical modelling can offer IRT a language for describing multifaceted skills and knowledge and disentangling evidence from com- plex performances. IRT-CA T can offer graphical modellers several ways of treating sources of variability other than including more variables in the model. In particular, variables can enter into the modelling pro- cess at several levels: (1) in validity studies (but not in the ordinarily used model), (2) in task construction (in particular, in defining link parameters), (3) in test or model assembly (blocking and randomization con- straints in selecting tasks or other model pieces), (4) in response characterization (i.e. as part of task models which characterize a response) or (5) in the main (student) model. The paper describes an implementation of these ideas in a fielded application: HYDRIVE, a tutor for hydraulics diagnosis %Z Reissued by PMLR on 30 March 2021.
APA
Almond, R.G. & Mislevy, R.J.. (1997). Graphical Model Based Computer Adaptive Testing. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R1:11-22 Available from https://proceedings.mlr.press/r1/almond97b.html. Reissued by PMLR on 30 March 2021.

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