A reinforcement learning approach for sequential mastery testing

El Sayed M. El-Alfy

doi:10.1109/ADPRL.2011.5967390

A reinforcement learning approach for sequential mastery testing

El Sayed M. El-Alfy^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper explores a novel application for reinforcement learning (RL) techniques to sequential mastery testing. In such systems, the goal is to classify each examined person, using the minimal number of test items, as master or non-master. Using RL, an intelligent agent autonomously learns from interactions to administer more informative and effective variable-length tests. Empirical results are also provided to evaluate the performance of the proposed approach as compared to two common approaches for variable-length testing (Bayesian decision and sequential probability ratio test) as well as to the fixed-length testing.

Original language	English
Title of host publication	IEEE SSCI 2011
Subtitle of host publication	Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning
Pages	295-301
Number of pages	7
DOIs	https://doi.org/10.1109/ADPRL.2011.5967390
State	Published - 2011

Publication series

Name	IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

Keywords

Bayesian decision theory
intelligent tutoring
reinforcement learning
sequential mastery testing
sequential probability ratio test

ASJC Scopus subject areas

Computer Science Applications
Software

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10.1109/ADPRL.2011.5967390

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El-Alfy, E. S. M. (2011). A reinforcement learning approach for sequential mastery testing. In IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (pp. 295-301). Article 5967390 (IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning). https://doi.org/10.1109/ADPRL.2011.5967390

El-Alfy, El Sayed M. / A reinforcement learning approach for sequential mastery testing. IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning. 2011. pp. 295-301 (IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning).

@inproceedings{eeaf88f99bd145c1b6c781f7808ecaed,

title = "A reinforcement learning approach for sequential mastery testing",

abstract = "This paper explores a novel application for reinforcement learning (RL) techniques to sequential mastery testing. In such systems, the goal is to classify each examined person, using the minimal number of test items, as master or non-master. Using RL, an intelligent agent autonomously learns from interactions to administer more informative and effective variable-length tests. Empirical results are also provided to evaluate the performance of the proposed approach as compared to two common approaches for variable-length testing (Bayesian decision and sequential probability ratio test) as well as to the fixed-length testing.",

keywords = "Bayesian decision theory, intelligent tutoring, reinforcement learning, sequential mastery testing, sequential probability ratio test",

author = "El-Alfy, \{El Sayed M.\}",

year = "2011",

doi = "10.1109/ADPRL.2011.5967390",

language = "English",

isbn = "9781424498888",

series = "IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning",

pages = "295--301",

booktitle = "IEEE SSCI 2011",

}

El-Alfy, ESM 2011, A reinforcement learning approach for sequential mastery testing. in IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning., 5967390, IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, pp. 295-301. https://doi.org/10.1109/ADPRL.2011.5967390

A reinforcement learning approach for sequential mastery testing. / El-Alfy, El Sayed M.
IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning. 2011. p. 295-301 5967390 (IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper explores a novel application for reinforcement learning (RL) techniques to sequential mastery testing. In such systems, the goal is to classify each examined person, using the minimal number of test items, as master or non-master. Using RL, an intelligent agent autonomously learns from interactions to administer more informative and effective variable-length tests. Empirical results are also provided to evaluate the performance of the proposed approach as compared to two common approaches for variable-length testing (Bayesian decision and sequential probability ratio test) as well as to the fixed-length testing.

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KW - sequential probability ratio test

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M3 - Conference contribution

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El-Alfy ESM. A reinforcement learning approach for sequential mastery testing. In IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning. 2011. p. 295-301. 5967390. (IEEE SSCI 2011: Symposium Series on Computational Intelligence - ADPRL 2011: 2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning). doi: 10.1109/ADPRL.2011.5967390

A reinforcement learning approach for sequential mastery testing

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Keywords

ASJC Scopus subject areas

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