Papers by Suin Kim
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction (2025.acl-long)
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| Challenge: | Multiple-choice questions (MCQs) are critical for identifying misconceptions and gaps in knowledge and accurately assessing students' understanding. |
| Approach: | They propose to train a model to generate distractors that are more likely to be selected by students by a pairwise ranker and a distractor generator via Direct Preference Optimization. |
| Outcome: | The proposed model outperforms baseline models and performs comparable to humans in various metrics including pairwise rank accuracy and distractor plausibility. |
Knowledge Tracing in Programming Education Integrating Students’ Questions (2025.acl-long)
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| Challenge: | Existing knowledge tracing models that ignore student questions are suboptimal for programming education because of the complexity of coding tasks and the diverse methods students use to solve problems. |
| Approach: | They propose a model that leverages students' questions and automatically extracted skill information to enhance the accuracy of predicting students' performance on subsequent problems. |
| Outcome: | The proposed model improves student performance across Python programming courses of differing difficulty levels. |