NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 3 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Rebeckah K. Fussell; Emily M. Stump; N. G. Holmes – Physical Review Physics Education Research, 2024
Physics education researchers are interested in using the tools of machine learning and natural language processing to make quantitative claims from natural language and text data, such as open-ended responses to survey questions. The aspiration is that this form of machine coding may be more efficient and consistent than human coding, allowing…
Descriptors: Physics, Educational Researchers, Artificial Intelligence, Natural Language Processing
Peer reviewed Peer reviewed
Direct linkDirect link
Peter Hu; Yangqiuting Li; Chandralekha Singh – Physical Review Physics Education Research, 2024
Quantum information science and engineering (QISE) is a rapidly developing field that leverages the skills of experts from many disciplines to utilize the potential of quantum systems in a variety of applications. It requires talent from a wide variety of traditional fields, including physics, engineering, chemistry, and computer science, to name…
Descriptors: Quantum Mechanics, Computer Science Education, Inquiry, Teaching Methods
Peer reviewed Peer reviewed
Direct linkDirect link
John Pace; John Hansen; John Stewart – Physical Review Physics Education Research, 2024
Machine learning models were constructed to predict student performance in an introductory mechanics class at a large land-grant university in the United States using data from 2061 students. Students were classified as either being at risk of failing the course (earning a D or F) or not at risk (earning an A, B, or C). The models focused on…
Descriptors: Artificial Intelligence, Identification, At Risk Students, Physics