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Ezberci-Çevik, Ebru; Kurnaz, Mehmet Altan – Malaysian Online Journal of Educational Technology, 2022
In this study, it is aimed to reveal the models related to star subject as one of the concepts of astronomy of prospective science teachers before and after the current instruction through model analysis. This modeling situation is expressed as a Grounded Mental Model (GMM), since there will be a mental modeling that is revealed according to what…
Descriptors: Schemata (Cognition), Astronomy, Science Teachers, Preservice Teachers
Sy-Miin Chow; Jungmin Lee; Jonathan Park; Prabhani Kuruppumullage Don; Tracey Hammel; Michael N. Hallquist; Eric A. Nord; Zita Oravecz; Heather L. Perry; Lawrence M. Lesser; Dennis K. Pearl – Journal of Statistics and Data Science Education, 2024
Personalized educational interventions have been shown to facilitate successful and inclusive statistics, mathematics, and data science (SMDS) in higher education through timely and targeted reduction of heterogeneous training disparities caused by years of cumulative, structural challenges in contemporary educational systems. However, the burden…
Descriptors: Individualized Instruction, Instructional Design, Science Education, Higher Education
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
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
Hope E. Lackey; Rachel L. Sell; Gilbert L. Nelson; Thomas A. Bryan; Amanda M. Lines; Samuel A. Bryan – Journal of Chemical Education, 2023
The methodology and mathematical treatment of several classic multivariate methods for the analysis of spectroscopic data is demonstrated in a straightforward way that can be used as a basis for teaching an undergraduate introductory course on chemometric analysis. The multivariate techniques of classical least-squares (CLS), principal component…
Descriptors: Chemistry, Data Analysis, Optics, Lighting