2 citations · 3 across the 5 of their papers we have counts for
5 papers
Learning Item Embeddings and Hyperparameters for IRT Calibration via Monte Carlo EM
James Sharpnack, Kai-Ling Lo
High-stakes computerized adaptive tests (CATs) must continually calibrate new items in their item bank. When an item is new, few responses are available, so item parameter estimate…
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration
James Sharpnack, Kevin Hao, Phoebe Mulcaire +4
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. Cal…
AI-assisted Gaze Detection for Proctoring Online Exams
Yong-Siang Shih, Zach Zhao, Chenhao Niu +3
For high-stakes online exams, it is important to detect potential rule violations to ensure the security of the test. In this study, we investigate the task of detecting whether te…
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning
James Sharpnack, Phoebe Mulcaire, Klinton Bicknell +2
Item response theory (IRT) is a class of interpretable factor models that are widely used in computerized adaptive tests (CATs), such as language proficiency tests. Traditionally,…
Improving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging
Xiawei Wang, James Sharpnack, Thomas C. M. Lee
Lung cancer is a major cause of cancer-related deaths, and early diagnosis and treatment are crucial for improving patients' survival outcomes. In this paper, we propose to employ…