Models for Conditional Probability Tables in Educational Assessment

Russell G. Almond, Lou DiBello, Frank Jenkins, Deniz Senturk, Robert J. Mislevy, Linda S. Steinberg, Duanli Yan
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:1-7, 2001.

Abstract

Experts in educational assessment can often identify the skills needed to provide a solution for a test item and which patterns of those skills pro duce better expected performance. The method described here combines judgements about the structure of the conditional probability table (e.g., conjunctive or compensatory) with Item Response Theory methods for partial credit scoring (Samejima, 1969) to produce a conditional probability table or a prior distribution for a learning algorithm. The structural judgements induce a projection of each configuration of parent skill variables onto a single latent response-propensity $\theta$. This is then used to calculate a probability for each cell in the table.

Cite this Paper


BibTeX
@InProceedings{pmlr-vR3-almond01a, title = {Models for Conditional Probability Tables in Educational Assessment}, author = {Almond, Russell G. and DiBello, Lou and Jenkins, Frank and Senturk, Deniz and Mislevy, Robert J. and Steinberg, Linda S. and Yan, Duanli}, booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics}, pages = {1--7}, year = {2001}, editor = {Richardson, Thomas S. and Jaakkola, Tommi S.}, volume = {R3}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r3/almond01a/almond01a.pdf}, url = {http://proceedings.mlr.press/r3/almond01a.html}, abstract = {Experts in educational assessment can often identify the skills needed to provide a solution for a test item and which patterns of those skills pro duce better expected performance. The method described here combines judgements about the structure of the conditional probability table (e.g., conjunctive or compensatory) with Item Response Theory methods for partial credit scoring (Samejima, 1969) to produce a conditional probability table or a prior distribution for a learning algorithm. The structural judgements induce a projection of each configuration of parent skill variables onto a single latent response-propensity $\theta$. This is then used to calculate a probability for each cell in the table.}, note = {Reissued by PMLR on 31 March 2021.} }
Endnote
%0 Conference Paper %T Models for Conditional Probability Tables in Educational Assessment %A Russell G. Almond %A Lou DiBello %A Frank Jenkins %A Deniz Senturk %A Robert J. Mislevy %A Linda S. Steinberg %A Duanli Yan %B Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2001 %E Thomas S. Richardson %E Tommi S. Jaakkola %F pmlr-vR3-almond01a %I PMLR %P 1--7 %U http://proceedings.mlr.press/r3/almond01a.html %V R3 %X Experts in educational assessment can often identify the skills needed to provide a solution for a test item and which patterns of those skills pro duce better expected performance. The method described here combines judgements about the structure of the conditional probability table (e.g., conjunctive or compensatory) with Item Response Theory methods for partial credit scoring (Samejima, 1969) to produce a conditional probability table or a prior distribution for a learning algorithm. The structural judgements induce a projection of each configuration of parent skill variables onto a single latent response-propensity $\theta$. This is then used to calculate a probability for each cell in the table. %Z Reissued by PMLR on 31 March 2021.
APA
Almond, R.G., DiBello, L., Jenkins, F., Senturk, D., Mislevy, R.J., Steinberg, L.S. & Yan, D.. (2001). Models for Conditional Probability Tables in Educational Assessment. Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R3:1-7 Available from http://proceedings.mlr.press/r3/almond01a.html. Reissued by PMLR on 31 March 2021.

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