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Md. Yunus Naseri; Caitlin Snyder; Katherine X. Perez-Rivera; Sambridhi Bhandari; Habtamu Alemu Workneh; Niroj Aryal; Gautam Biswas; Erin C. Henrick; Erin R. Hotchkiss; Manoj K. Jha; Steven Jiang; Emily C. Kern; Vinod K. Lohani; Landon T. Marston; Christopher P. Vanags; Kang Xia – IEEE Transactions on Education, 2025
Contribution: This article discusses a research-practice partnership (RPP) where instructors from six undergraduate courses in three universities developed data science modules tailored to the needs of their respective disciplines, academic levels, and pedagogies. Background: STEM disciplines at universities are incorporating data science topics…
Descriptors: Data Science, Courses, Research and Development, Theory Practice Relationship
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Cornelis J. C. Vertegaal; Cecilia Martinez; Ramiro Serra; Prem Sundaramoorthy; Mark J. Bentum – IEEE Transactions on Education, 2025
Contribution: This study identifies the types of interaction that contribute to student learning with student-led tutorials (SLTs). The quality of these interactions include peer discussion, student tutor presentation, joint reasoning, and constructive feedback. Background: The introduction of SLTs in an advanced electromagnetics bachelor course…
Descriptors: Undergraduate Students, Physics, Science Curriculum, Science Instruction
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Tan, Kok-Kiong; Tang, Kok-Zuea; Ng, Vivian; Tay, Arthur; Yen, Shih-Cheng; Lee, Tong-Heng – IEEE Transactions on Education, 2010
Physics has been an important part of the science curriculum in high schools. Without the appropriate high school physics background, it is difficult for a student subsequently to pursue an electronics engineering program at the university level since a good understanding of many concepts in physics is required to comprehend the material covered…
Descriptors: Foreign Countries, Science Curriculum, Physics, Electronics
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Georgiopoulos, M.; DeMara, R. F.; Gonzalez, A. J.; Wu, A. S.; Mollaghasemi, M.; Gelenbe, E.; Kysilka, M.; Secretan, J.; Sharma, C. A.; Alnsour, A. J. – IEEE Transactions on Education, 2009
This paper presents an integrated research and teaching model that has resulted from an NSF-funded effort to introduce results of current Machine Learning research into the engineering and computer science curriculum at the University of Central Florida (UCF). While in-depth exposure to current topics in Machine Learning has traditionally occurred…
Descriptors: Undergraduate Students, Undergraduate Study, Student Research, Science Curriculum