Papers by Nigel Fernandez
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. |
SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction (2025.emnlp-main)
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| Challenge: | Traditionally, estimating item difficulties requires real students to respond to items . a cold-start approach cannot be applied to previously unseen items either . |
| Approach: | They propose a method for aligning simulated students with instructed ability to predict difficulty of open-ended items. |
| Outcome: | The proposed method outperforms existing methods on two real-world student responses. |
Automated Knowledge Component Generation and Interpretable Knowledge Tracing in Coding Problems (2026.findings-acl)
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Zhangqi Duan, Nigel Fernandez, Arun Balajiee Lekshmi Narayanan, Mohammad Hassany, Rafaella Sampaio de Alencar, Peter Brusilovsky, Bita Akram, Andrew Lan
| Challenge: | Existing solutions to automate KC generation and tagging for open-ended programming problems are highly labor-intensive and prone to bias and errors. |
| Approach: | They propose an automated pipeline for KC generation and tagging for open-ended programming problems using large language models. |
| Outcome: | The proposed method outperforms existing ones and outperfies human-written KCs on future student response prediction. |
SyllabusQA: A Course Logistics Question Answering Dataset (2024.acl-long)
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| Challenge: | We introduce Fact-QA, an LLM-based evaluation metric to evaluate the factuality of predicted answers. |
| Approach: | They propose to use an open-source dataset to analyze logistics-related question-answer pairs in a logistics-based course. |
| Outcome: | The proposed approach performs close to humans on traditional metrics of textual similarity, but there is a significant gap between them and humans in terms of fact precision. |
KASER: Knowledge-Aligned Student Error Simulator for Open-Ended Coding Tasks (2026.acl-long)
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| Challenge: | coding tasks that provide detailed insights into student knowledge are challenging to train . open-ended tasks often suffer from mode collapse and fail to capture student errors . |
| Approach: | They propose a method that aligns errors with student knowledge by using a hybrid reward system. |
| Outcome: | The proposed method outperforms baselines on code and error prediction and error coverage and simulated code diversity on two real-world datasets. |