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Award ID contains: 2226601

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  1. ; ; ; (, ASEE Conferences)
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  3. ; ; ; (, ASEE 2022 Annual Conference)
    This work-in-progress paper presents a joint effort by engineering education and machine learning researchers to develop automated methods for analyzing student responses to challenging conceptual questions in mechanics. These open-ended questions, which emphasize understanding of physical principles rather than calculations, are widely used in large STEM classes to support active learning strategies that have been shown to improve student outcomes. Despite their benefits, written justifications are not commonly used, largely because evaluating them is time-consuming for both instructors and researchers. This study explores the potential of large pre-trained generative sequence-to-sequence language models to streamline the analysis and coding of these student responses. 
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