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W. Brian Lane; Terrie M. Galanti; X. L. Rozas – Journal for STEM Education Research, 2023
Integrating computational thinking (CT) into STEM disciplines requires secondary teachers to develop their pedagogical content knowledge of computing and content integration. Experienced teachers who choose to integrate CT in their secondary STEM courses may struggle in the same ways as novice teachers as they learn about programming and its…
Descriptors: Physics, Teaching Methods, Grounded Theory, Capacity Building
Thompson, JaCoya; Arastoopour Irgens, Golnaz – Journal of Statistics and Data Science Education, 2022
Data science is a highly interdisciplinary field that comprises various principles, methodologies, and guidelines for the analysis of data. The creation of appropriate curricula that use computational tools and teaching activities is necessary for building skills and knowledge in data science. However, much of the literature about data science…
Descriptors: Data Analysis, Middle School Students, Statistics Education, Student Centered Learning
Neelima Bhatnagar; Victoria Causer; Michael J. Lucci; Michael Pry; Dorothy M. Zilic – Information Systems Education Journal, 2024
Data analytics is a rapidly growing field that plays a crucial role in extracting valuable insights from large volumes of data. A data analytics practicum course provides students with hands-on experience in applying data analytics techniques and tools to real-world scenarios. This practicum is intended to serve as a bridge between the student's…
Descriptors: Statistics Education, Data Analysis, Practicums, Education Work Relationship
Mashood, K. K.; Khosla, Kamakshi; Prasad, Arjun; V., Sasidevan; Ashefas CH, Muhammed; Jose, Charles; Chandrasekharan, Sanjay – Physical Review Physics Education Research, 2022
Recent educational policies advocate a radical revision of science curricula and pedagogy, to support interdisciplinary practices, a distinguishing feature of contemporary science. Computational modeling (CM) is a core methodology of interdisciplinary science, as such models allow intertwining of data and theoretical perspectives from multiple…
Descriptors: Teaching Methods, Undergraduate Students, Science Instruction, Science Curriculum
Petrie, Christopher – Computer Science Education, 2022
Background and Context: Computational Thinking (CT) has been recently integrated into new and revised Digital Technologies content (DTC) in the Technology learning area of the New Zealand School Curriculum. Objective: To aid this change, this research examined how CT supports learning outcomes in both music and programming with the Sonic Pi…
Descriptors: Interdisciplinary Approach, Outcomes of Education, Computer Science Education, Programming
Solin, Pavel; Roanes-Lozano, Eugenio – International Journal for Technology in Mathematics Education, 2020
Many mathematics educators are not aware of a strong connection that exists between the education of computer programming and mathematics. The reason may be that they have not been exposed to computer programming. This connection is worth exploring, given the current trends of automation and Industry 4.0. Therefore, in this paper we take a closer…
Descriptors: Computer Science Education, Mathematics Education, Programming Languages, Interdisciplinary Approach
Finch, Lila; Moreno, Celeste; Shapiro, R. Benjamin – Cognition and Instruction, 2021
Creating learning environments that integrate arts, sciences, and computing in education can improve learning in these disciplines. In particular, transdisciplinary integrations of these disciplines can lead to expansive alterations or dissolutions of epistemological, ideological, and methodological boundaries. We wish to support teachers in the…
Descriptors: Interdisciplinary Approach, Learning Processes, Thinking Skills, Epistemology

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