PERC 2024 Abstract Detail Page
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| Abstract Title: | Developing Coupled, Multiple-Response Assessments by Leveraging Generative-AI in Physics |
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| Abstract Type: | Symposium Talk |
| Abstract: | Assessments are central to academic practice, particularly in Science, Technology, Engineering, and Mathematics (STEM) courses. In this talk, I will present an approach to developing an assessment format called "Coupled Multiple-Response (CMR)". This format entails multiple-choice and multiple-response formats paired together to facilitate students to both committing to a claim along with selecting options that align with their reasoning. In addition to facilitating streamlined scoring, this format captures subtle nuances beyond correctness of solutions. The assessment is developed by augmenting student data from an ongoing physics course along with Generative-AI. An approach to leveraging AI in developing this assessment is discussed. |
| Parallel Session: | Leveraging Artificial Intelligence in Teaching and Learning of Physics |
| Session Time: | Parallel Sessions Cluster 2 |
| Room: | Burroughs |
Author/Organizer Information | |
| Primary Contact: |
Ravishankar Chatta Subramanium Purdue University |
