Papers by Simon Woodhead
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions (2024.emnlp-main)
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| Challenge: | a new variational approach to distractors in multiple-choice questions is needed . high-quality distractors are crucial to the assessment and pedagogical value of MCQs . a variational method that learns the error behind distractors is more effective . |
| Approach: | They propose a variational approach that learns an interpretable representation of errors behind distractors in math MCQs. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on distractors in math MCQs. |
PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues (2025.emnlp-main)
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| Challenge: | Understanding and improving affective learning strategies continues to be one of computing's primary contributions to education research. |
| Approach: | They propose a framework for PII anonymization that leverages knowledge of the data context to simplify the PI I detection problem. |
| Outcome: | The proposed framework simplifies the detection problem by leveraging knowledge of the data context. |
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models (2024.findings-naacl)
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Wanyong Feng, Jaewook Lee, Hunter McNichols, Alexander Scarlatos, Digory Smith, Simon Woodhead, Nancy Ornelas, Andrew Lan
| Challenge: | Multiple-choice questions (MCQs) are easy to administer and grade . but crafting high-quality distractors remains labor-intensive and limited scalability . |
| Approach: | They propose to automate the generation of distractors in math MCQs by using large language models to generate distractors. |
| Outcome: | The proposed methods can generate valid distractors, but they are less adept at anticipating common errors or misconceptions among real students. |
Simulated Students in Tutoring Dialogues: Substance or Illusion? (2026.acl-long)
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| Challenge: | evaluating the effectiveness of new technology requires real students, which is time-consuming and hard to scale up. |
| Approach: | They propose to define the student simulation task and benchmark a wide range of student simulation methods on these metrics. |
| Outcome: | The proposed evaluation metrics show that prompting strategies perform poorly on a real-world tutoring dialogue dataset. |