PERC 2024 Abstract Detail Page
Previous Page | New Search | Browse All
| Abstract Title: | Mapping the literature landscape of artificial intelligence and machine learning in physics education research |
|---|---|
| Abstract Type: | Symposium Talk |
| Abstract: | With the emergence of computational approaches, engagement with educational data has undergone significant changes. This shift has been mainly driven by the advancements in machine learning (ML) and artificial intelligence (AI). In this talk, I will outline the landscape of the physics education research literature surrounding the use of AI and ML. I particularly focus on the articles published in Physical Review Physics Education Research journal and proceedings of the Physics Education Research Conference. Emergent trends and their implications on future results are also discussed. |
| Footnote: | Supported in part by U.S. National Science Foundation grant 230065. |
| Parallel Session: | Leveraging Artificial Intelligence in Teaching and Learning of Physics |
| Contributed Paper Record: | Contributed Paper Information |
| Contributed Paper Download: | Download Contributed Paper |
Author/Organizer Information | |
| Primary Contact: |
Amir Bralin Purdue University West Lafayette, IN 47907 Phone: 7657720524 |
| Co-Author(s) and Co-Presenter(s) |
Amogh Sirnoorkar, Center for Advancing the Teaching and Learning of STEM (CATALYST), Purdue University. Yiyuan Zhang, Department of Computer Science, Purdue University. N. Sanjay Rebello, Department of Curriculum and Instruction, and Department of Physics and Astronomy, Purdue University. |
