6 citations · 8 across the 8 of their papers we have counts for
8 papers
CodeGENCAT: Generative Computerized Adaptive Testing for Open-ended Coding Problems
Wanyong Feng, Alexander Scarlatos, Ruochen Sun +1
Existing Computerized Adaptive Testing (CAT) frameworks typically select questions based on the predicted likelihood that the student will answer correctly. This design ignores inf…
Reasoning and Sampling-Augmented MCQ Difficulty Prediction via LLMs
Wanyong Feng, Peter Tran, Stephen Sireci +1
The difficulty of multiple-choice questions (MCQs) is a crucial factor for educational assessments. Predicting MCQ difficulty is challenging since it requires understanding both th…
From Text to Visuals: Using LLMs to Generate Math Diagrams with Vector Graphics
Jaewook Lee, Jeongah Lee, Wanyong Feng +1
Advances in large language models (LLMs) offer new possibilities for enhancing math education by automating support for both teachers and students. While prior work has focused on…
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank
Alexander Scarlatos, Wanyong Feng, Digory Smith +2
Multiple-choice questions (MCQs) are commonly used across all levels of math education since they can be deployed and graded at a large scale. A critical component of MCQs is the d…
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models
Wanyong Feng, Jaewook Lee, Hunter McNichols +5
Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices.…
Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning
Hunter McNichols, Wanyong Feng, Jaewook Lee +4
Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable form of assessment. An important aspe…
Balancing Test Accuracy and Security in Computerized Adaptive Testing
Wanyong Feng, Aritra Ghosh, Stephen Sireci +1
Computerized adaptive testing (CAT) is a form of personalized testing that accurately measures students' knowledge levels while reducing test length. Bilevel optimization-based CAT…
A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing
Nischal Ashok Kumar, Wanyong Feng, Jaewook Lee +3
In this paper, we take a preliminary step towards solving the problem of causal discovery in knowledge tracing, i.e., finding the underlying causal relationship among different ski…