Summary of Banditcat and Autoirt: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration, by James Sharpnack et al.
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration
by James Sharpnack, Kevin Hao, Phoebe Mulcaire, Klinton Bicknell, Geoff LaFlair, Kevin Yancey, Alina A. von Davier
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive framework for calibrating and administering large-scale computerized adaptive tests (CATs) using a small number of responses. The AutoIRT method combines automated machine learning (AutoML) with item response theory (IRT), training non-parametric models to learn item parameters. The approach utilizes tabular AutoML tools, BERT embeddings, and linguistically motivated NLP features. Bayesian updating is used to obtain test taker ability posterior distributions for administration and scoring. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a big step forward in making computerized tests more efficient. It shows how to calibrate and administer large-scale tests using just a few answers. The new method, called AutoIRT, uses AI to learn how items work and then combines that with special math to get accurate scores. |
Keywords
» Artificial intelligence » Bert » Machine learning » Nlp
