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David W. Jackson; Helen Zhang; Fahd Abdus-Sabur – TechTrends: Linking Research and Practice to Improve Learning, 2025
During young adolescence, many youth develop strong identities in relation to science, technology, engineering and mathematics with computing (STEM + C). One way to design for student engagement in STEM + C is to create project-based units that leverage students' interests. We created one such unit, called the "smart greenhouse project".…
Descriptors: Instructional Design, Computer Uses in Education, STEM Education, Student Projects
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Yin-Chan Liao; G. Sue Kasun; Nozipho Moyo – Contemporary Issues in Technology and Teacher Education (CITE Journal), 2025
This study examined the impact of a U.S. federal teacher professional learning (PL) Fulbright program on computational literacy and artificial intelligence (AI) education for K-12 teachers (n = 21) from resource-constrained countries. Occurring shortly after the rise of generative AI in November 2023, the program may have further accentuated AI's…
Descriptors: Artificial Intelligence, Computer Literacy, Computer Science Education, Teacher Attitudes
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Eglash, Ron; Bennett, Audrey; Cooke, Laquana; Babbitt, William; Lachney, Michael – ACM Transactions on Computing Education, 2021
Students' lives, both in and out of school, are full of different forms of value. Wealthy students enjoy value in the form of financial capital; their fit to hegemonic social practices; excellent health care and so on. Low-income students, especially those from African American, Native American, and Latinx communities, often lack access to those…
Descriptors: Computer Science Education, African American Community, Cultural Influences, Community Development
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Duran, Rodrigo; Sorva, Juha; Seppälä, Otto – ACM Transactions on Computing Education, 2021
We propose a framework for identifying, organizing, and communicating learning objectives that involve program semantics. In this framework, detailed learning objectives are written down as rules of program behavior (RPBs). RPBs are teacher-facing statements that describe what needs to be learned about the behavior of a specific sort of programs.…
Descriptors: Behavioral Objectives, Computer Science Education, Programming, Evaluation Criteria
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Mertala, Pekka – British Journal of Educational Technology, 2021
While computing has been (re)introduced into the basic education curricula in various countries, its actual implementation appears to be inconsistent. There are schools in which computing education is commonplace, while the implementation seems to be lagging behind in others. There is emerging evidence that some teachers do not consider computing…
Descriptors: Multiple Literacies, Computer Science Education, Elementary Secondary Education, Teaching Methods
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Shi, Yang; Mao, Ye; Barnes, Tiffany; Chi, Min; Price, Thomas W. – International Educational Data Mining Society, 2021
Automatically detecting bugs in student program code is critical to enable formative feedback to help students pinpoint errors and resolve them. Deep learning models especially code2vec and ASTNN have shown great success for "large-scale" code classification. It is not clear, however, whether they can be effectively used for bug…
Descriptors: Artificial Intelligence, Program Effectiveness, Coding, Computer Science Education
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Hao, Xiaoxin; Xu, Zhiyi; Guo, Mingyue; Hu, Yuzheng; Geng, Fengji – International Journal of STEM Education, 2023
Background: Coding has become an integral part of STEM education. However, novice learners face difficulties in processing codes within embedded structures (also termed nested structures). This study aimed to investigate the cognitive mechanism underlying the processing of embedded coding structures based on hierarchical complexity theory, which…
Descriptors: Cognitive Processes, Difficulty Level, Programming, Computer Science Education
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Miedema, Daphne; Fletcher, George; Aivaloglou, Efthimia – ACM Transactions on Computing Education, 2023
Prior studies in the Computer Science education literature have illustrated that novices make many mistakes in composing SQL queries. Query formulation proves to be difficult for students. Only recently, some headway was made towards understanding why SQL leads to so many mistakes, by uncovering student misconceptions. In this article, we shed new…
Descriptors: Computer Science Education, Novices, Misconceptions, Programming Languages
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Collins, Jazmin; Ford, Vitaly – Journal of Cybersecurity Education, Research and Practice, 2023
The use of the Capture the Flag (CTF)-style competitions has grown popular in a variety of environments as a method to improve or reinforce cybersecurity techniques. However, while these competitions have shown promise in student engagement, enjoyment, and the teaching of essential workforce cybersecurity concepts, many of these CTF challenges…
Descriptors: Computer Security, Computer Science Education, Coding, Competition
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Johnson, Donald M.; Pate, Michael L.; Estepp, Christopher M.; Wardlow, George W. – Journal of Research in Technical Careers, 2023
A replication study was conducted to determine the effectiveness of an instructional treatment based on self-efficacy theory when used with novice Arduino microcontroller users. Students (n = 32) in an introductory university agricultural systems technology course participated in a lesson on Arduino microcontrollers, circuit breadboarding, and…
Descriptors: Self Efficacy, Computer Science Education, Instructional Effectiveness, Novices
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Pan, Zexuan; Cui, Ying; Leighton, Jacqueline P.; Cutumisu, Maria – Applied Cognitive Psychology, 2023
This systematic review examines 35 empirical studies featuring the use of think-aloud interviews in computational thinking (CT) research. Findings show that think-aloud interviews (1) are typically conducted in Computer Science classrooms and with K-12 students; (2) are usually combined with other exploratory CT assessment tools; (3) have the…
Descriptors: Computation, Thinking Skills, Protocol Analysis, Literature Reviews
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Araos, Andrés; Damsa, Crina; Gaševic, Dragan – Journal of Computer Assisted Learning, 2023
Background: The surge of online platforms has generated interest in how specialized platforms support formal and informal learning in various disciplinary domains. Knowledge is still limited regarding how undergraduate students navigate and use platforms to learn. Objectives: This study explores computer and software engineering students' learning…
Descriptors: Computer Science Education, Computer Software, Learning Activities, Undergraduate Students
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Xu, Weiqi; Wu, Yajuan; Ouyang, Fan – International Journal of Educational Technology in Higher Education, 2023
Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students' discourses, behaviors, and socio-emotions, it is of critical importance to examine…
Descriptors: Cooperative Learning, Problem Solving, Computer Science Education, Programming
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Vasconcelos, André; Monsores, Jomar; Almeida, Tania; Quadros, Laura; Ogasawara, Eduardo; Quadros, João – Education and Information Technologies, 2023
The use of information technology in the academic environment has grown. Building different didactic techniques to help students learn and practice with Information Technology (IT) resources is common. However, applying these techniques does not necessarily mean that students may acquire knowledge. The differential idea of this work is to create…
Descriptors: Foreign Countries, Elementary Education, Elementary School Students, Computer Science Education
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Hladik, Stephanie; Sengupta, Pratim; Shanahan, Marie-Claire – Cognition and Instruction, 2023
In this paper, we emphasize the importance of looking beyond technology itself and including interactional and experiential elements in our research gaze in informal computing education in science museums. We argue that, in these contexts, facilitation can be understood as "design work" that is both complex and challenging. We identify…
Descriptors: Museums, Facilitators (Individuals), Science Teaching Centers, Informal Education
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