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Oscar Karnalim; Simon; William Chivers – Computer Science Education, 2024
Background and Context: To educate students about programming plagiarism and collusion, we introduced an approach that automatically reports how similar a submitted program is to others. However, as most students receive similar feedback, those who engage in plagiarism and collusion might feel inadequately warned. Objective: When students are…
Descriptors: Teaching Methods, Plagiarism, Computer Science Education, Programming
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Kristina Litherland; Anders Kluge – Computer Science Education, 2024
Background and Context: We explore the potential for understanding the processes involved in students' programming based on studying their behaviour and dialogue with each other and "conversations" with their programs. Objective: Our aim is to explore how a perspective of inquiry can be used as a point of departure for insights into how…
Descriptors: Programming, Programming Languages, Secondary School Students, Computer Science Education
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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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Christopher Petrie – Computer Science Education, 2024
Background and Context: The Domain-Specific Programming (DSP) platforms EarSketch and TunePad are being used widely in schools for coding novices. Existing studies on both platforms have mainly concentrated on attitudinal changes, leaving a gap in the literature. Objective: The purpose of this research was to advance our understanding of two…
Descriptors: Computer Software, Mental Computation, Programming, Interdisciplinary Approach
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Renske Weeda; Sjaak Smetsers; Erik Barendsen – Computer Science Education, 2024
Background and Context: Multiple studies report that experienced instructors lack consensus on the difficulty of programming tasks for novices. However, adequately gauging task difficulty is needed for alignment: to select and structure tasks in order to assess what students can and cannot do. Objective: The aim of this study was to examine…
Descriptors: Novices, Coding, Programming, Computer Science Education
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Zachary M. Savelson; Kasia Muldner – Computer Science Education, 2024
Background and Context: Productive failure (PF) is a learning paradigm that flips the order of instruction: students work on a problem, then receive a lesson. PF increases learning, but less is known about student emotions and collaboration during PF, particularly in a computer science context. Objective: To provide insight on students' emotions…
Descriptors: Student Attitudes, Psychological Patterns, Fear, Failure
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Anna van der Meulen; Mijke Hartendorp; Wendy Voorn; Felienne Hermans – Computer Science Education, 2024
Background and Context: In order to fully include learners with visual impairments in early programming education, it is necessary to gain insight into specificities regarding their experience of and approach to abstract computational concepts. Objective: In this study, we use the model of the layers of abstraction to explore how learners with…
Descriptors: Blindness, Visual Impairments, Students with Disabilities, Programming
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Hugo G. Lapierre; Patrick Charland; Pierre-Majorique Léger – Computer Science Education, 2024
Background and Context: Current programming learning research often compares novices and experienced programmers, leaving early learning stages and emotional and cognitive states under-explored. Objective: Our study investigates relationships between cognitive and emotional states and learning performance in early stage programming learners with…
Descriptors: Programming, Computer Science Education, Psychological Patterns, Cognitive Processes
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Michael Lachney; Madison C. Allen Kuyenga; Jada Phelps; Aman Yadav; Matt Drazin – Computer Science Education, 2024
Background & context: Inspired by the nature-cultures of belonging from Black hair care, we conducted a design experiment to bridge computer science (CS) education, urban gardening, and cosmetology in a culturally responsive computing (CRC) library program. Objective: The design was oriented around a small-scale aquaponics system to grow mint…
Descriptors: Middle School Students, High School Students, Librarians, Library Services
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Zhanxia Yang; Marina Bers – Computer Science Education, 2024
Background and Context: Historically, women have been underrepresented in computer science. To address this gender gap, researchers advocate for high-quality computer science programs for early childhood. Objectives: This study examines gender differences in coding performance before and after implementing a 24-lesson visual programming curriculum…
Descriptors: Gender Differences, Grade 1, Elementary School Students, Programming
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Teresa M. Ober; Ying Cheng; Meghan R. Coggins; Paul Brenner; Janice Zdankus; Philip Gonsalves; Emmanuel Johnson; Tim Urdan – Computer Science Education, 2024
Background and Context: Differences in children's and adolescents' initial attitudes about computing and other STEM fields may form during middle school and shape decisions leading to career entry. Early emerging differences in career interest may propagate a lack of diversity in computer science and programming fields. Objective: Though middle…
Descriptors: Middle School Students, Student Attitudes, Computer Science Education, STEM Education