BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration Article Swipe
James Sharpnack
,
Kuangrong Hao
,
Phoebe Mulcaire
,
Klinton Bicknell
,
Geoffrey T. LaFlair
,
Kevin Yancey
,
Alina von Davier
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.21033
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.21033
In this paper, we present a complete framework for quickly calibrating and administering a robust large-scale computerized adaptive test (CAT) with a small number of responses. Calibration - learning item parameters in a test - is done using AutoIRT, a new method that uses automated machine learning (AutoML) in combination with item response theory (IRT), originally proposed in [Sharpnack et al., 2024]. AutoIRT trains a non-parametric AutoML grading model using item features, followed by an item-specific parametric model, which results in an explanatory IRT model. In our work, we use tabular AutoML tools (AutoGluon.tabular, [Erickson et al., 2020]) along with BERT embeddings and linguistically motivated NLP features. In this framework, we use Bayesian updating to obtain test taker ability posterior distributions for administration and scoring. For administration of our adaptive test, we propose the BanditCAT framework, a methodology motivated by casting the problem in the contextual bandit framework and utilizing item response theory (IRT). The key insight lies in defining the bandit reward as the Fisher information for the selected item, given the latent test taker ability from IRT assumptions. We use Thompson sampling to balance between exploring items with different psychometric characteristics and selecting highly discriminative items that give more precise information about ability. To control item exposure, we inject noise through an additional randomization step before computing the Fisher information. This framework was used to initially launch two new item types on the DET practice test using limited training data. We outline some reliability and exposure metrics for the 5 practice test experiments that utilized this framework.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.21033
- https://arxiv.org/pdf/2410.21033
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404317996
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Raw OpenAlex JSON
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https://openalex.org/W4404317996Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.21033Digital Object Identifier
- Title
-
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item CalibrationWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-10-28Full publication date if available
- Authors
-
James Sharpnack, Kuangrong Hao, Phoebe Mulcaire, Klinton Bicknell, Geoffrey T. LaFlair, Kevin Yancey, Alina von DavierList of authors in order
- Landing page
-
https://arxiv.org/abs/2410.21033Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2410.21033Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2410.21033Direct OA link when available
- Concepts
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Computerized adaptive testing, Calibration, Computer science, Machine learning, Artificial intelligence, Statistics, Mathematics, PsychometricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.psychometric | 191 |
| abstract_inverted_index.administering | 12 |
| abstract_inverted_index.distributions | 120 |
| abstract_inverted_index.item-specific | 75 |
| abstract_inverted_index.randomization | 215 |
| abstract_inverted_index.administration | 122, 126 |
| abstract_inverted_index.discriminative | 196 |
| abstract_inverted_index.linguistically | 103 |
| abstract_inverted_index.non-parametric | 65 |
| abstract_inverted_index.characteristics | 192 |
| abstract_inverted_index.(AutoGluon.tabular, | 93 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |