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Ronit Shmallo; Adi Katz – Computer Science Education, 2024
Background and Context: Gender research shows that women are better at reading comprehension. Other studies indicate a lower tendency in women to choose STEM professions. Since data modeling requires reading skills and also belongs in the areas of information systems and computer science (STEM professions), these findings provoked our curiosity.…
Descriptors: Gender Differences, Transfer of Training, Databases, Models
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Odden, Tor Ole B.; Silvia, Devin W.; Malthe-Sørenssen, Anders – Journal of Research in Science Teaching, 2023
This article reports on a study investigating how computational essays can be used to help students in higher education STEM take up disciplinary epistemic agency--cognitive control and responsibility over one's own learning within the scientific disciplines. Computational essays are a genre of scientific writing that combine live, executable…
Descriptors: Computation, Essays, Undergraduate Students, STEM Education
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Hsiao, Hsien-Sheng; Chen, Jyun-Chen; Chen, Jhen-Han; Chien, Yu-Hung; Chang, Chung-Pu; Chung, Guang-Han – Educational Technology Research and Development, 2023
Since the late twentieth century, with the development of the Internet of Things (IoT), the IoT covers the application of comprehensive knowledge and technology in the fields of circuitry, physics, mechanics, and information, making it a suitable topic for hands-on science, technology, engineering, and mathematics (STEM) activities. The IoT covers…
Descriptors: Gamification, Models, High School Students, Programming
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Dickes, Amanda Catherine; Farris, Amy Voss; Sengupta, Pratim – Journal of Science Education and Technology, 2020
In recent years, the field of education has challenged researchers and practitioners to incorporate computing as an essential focus of K-12 STEM education. Integrating computing within K-12 STEM supports learners of all ages in codeveloping and using computational thinking in existing curricular contexts alongside practices essential for…
Descriptors: Elementary School Science, Coding, STEM Education, Computer Science Education
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Hutchins, Nicole M.; Biswas, Gautam; Zhang, Ningyu; Snyder, Caitlin; Lédeczi, Ákos; Maróti, Miklós – International Journal of Artificial Intelligence in Education, 2020
Driven by our technologically advanced workplaces and the surge in demand for proficiency in the computing disciplines, it is becoming imperative to provide computational thinking (CT) opportunities to all students. One approach for making computing accessible and relevant to learning and problem-solving in K-12 environments is to integrate it…
Descriptors: Computer Assisted Instruction, Problem Solving, Computation, Thinking Skills
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Emara, Mona; Hutchins, Nicole M.; Grover, Shuchi; Snyder, Caitlin; Biswas, Gautam – Journal of Learning Analytics, 2021
The integration of computational modelling in science classrooms provides a unique opportunity to promote key 21st century skills including computational thinking (CT) and collaboration. The open-ended, problem-solving nature of the task requires groups to grapple with the combination of two domains (science and computing) as they collaboratively…
Descriptors: Cooperative Learning, Self Management, Metacognition, Computer Science Education
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Clark, Douglas B.; Sengupta, Pratim – Interactive Learning Environments, 2020
This paper situates a critical review of studies that we have conducted within the broader research literature to analyze the affordances of integrating modeling within disciplinarily-integrated games from computational thinking and science as practice perspectives. Across the studies, the analyses pursue two themes: (a) the role of agent-based…
Descriptors: Game Based Learning, Thinking Skills, Computer Games, Science Education
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Maruyama, Ryoga; Ogata, Shinpei; Kayama, Mizue; Tachi, Nobuyuki; Nagai, Takashi; Taguchi, Naomi – International Association for Development of the Information Society, 2022
This study aims to explore an educational learning environment that supports students to learn conceptual modelling with the unified modelling language (UML). In this study, we call the describing models "UML programming." In this paper, we show an educational UML programming environment for science, technology, engineering, art, and…
Descriptors: Case Studies, Programming Languages, Learning Processes, Models
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Byrne, Jake Rowan; O'Sullivan, Katriona; Sullivan, Kevin – IEEE Transactions on Education, 2017
This paper explores the use of a constructivist 21st-century learning model to implement a week-long workshop, delivered as a "hackathon," to encourage preuniversity teenagers to pursue careers in STEM, with a particular emphasis on computer science. For Irish preuniversity students, their experience of computing can vary from word…
Descriptors: Teaching Methods, Workshops, Models, Adolescents
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Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
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Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
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Sampson, Demetrios G., Ed.; Ifenthaler, Dirk, Ed.; Isaías, Pedro, Ed. – International Association for Development of the Information Society, 2018
The aim of the 2018 International Association for Development of the Information Society (IADIS) Cognition and Exploratory Learning in the Digital Age (CELDA) conference was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There have been advances in both cognitive…
Descriptors: Learning Processes, Teaching Methods, Educational Technology, Technology Uses in Education
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Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction
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Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection