Summary of Autoirt: Calibrating Item Response Theory Models with Automated Machine Learning, by James Sharpnack et al.
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning
by James Sharpnack, Phoebe Mulcaire, Klinton Bicknell, Geoff LaFlair, Kevin Yancey
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposes a novel multistage fitting procedure for item response theory (IRT) models in computerized adaptive tests (CATs), leveraging Automated Machine Learning (AutoML) tools. The approach combines Monte Carlo EM and two-stage inner loops to train non-parametric AutoML grade models using item features, followed by item-specific parametric models. This accelerates the modeling workflow for scoring tests. The authors demonstrate the effectiveness of their method on the Duolingo English Test, showing improved predictive performance, better calibration, and more accurate scores compared to existing IRT models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers developed a new way to score language proficiency tests like the one used by Duolingo. They created a process that uses machine learning tools to fit item response theory (IRT) models, which are important for adaptive tests. The method is faster and more accurate than previous approaches. The authors tested their approach on real test data and showed it works well. |
Keywords
» Artificial intelligence » Machine learning
