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Molly Domino; Bob Edmison; Stephen H. Edwards; Rifat Sabbir Mansur; Alexandra Thompson; Clifford A. Shaffer – Computer Science Education, 2025
Background and Context: Self-regulated learning (SRL) skills are critical aspect of learning to program and are predictive of academic success. Early college students often struggle to use these skills, but can improve when given targeted instruction. However, it is not yet clear what skills are best to prioritize. Objective: We seek to create a…
Descriptors: Metacognition, Programming, Computer Science Education, College Students
Hawlitschek, Anja; Berndt, Sarah; Schulz, Sandra – Computer Science Education, 2023
Background and Context: Pair programming is an important approach to fostering students' programming and collaborative learning skills. However, the empirical findings on pair programming are mixed, especially concerning effective instructional design. Objective: The objective of this literature review is to provide lecturers with systematic…
Descriptors: Cooperative Learning, Programming, Computer Science Education, College Students
Cheers, Hayden; Lin, Yuqing – Computer Science Education, 2023
Background and Context: Source code plagiarism is a common occurrence in undergraduate computer science education. Many source code plagiarism detection tools have been proposed to address this problem. However, such tools do not identify plagiarism, nor suggest what assignment submissions are suspicious of plagiarism. Source code plagiarism…
Descriptors: Plagiarism, Programming, Computer Science Education, Identification
W. Monty Jones; Katherine Hansen; Douglas Lusa Krug; Michael L. Schad; Nakisha Whittington; Xun Liu – Computer Science Education, 2025
Background and Context: Efforts to engage adult learners in computer science in the United States have been largely unsuccessful. While research examining the use of music for teaching computer programming with K-12 learners is emerging, little research with adult learners exists. Objective: This study evaluates the effect of computer coding…
Descriptors: Musical Composition, Computer Software, Adult Students, Student Attitudes
Christina Glasauer; Martin K. Yeh; Lois Anne DeLong; Yu Yan; Yanyan Zhuang – Computer Science Education, 2025
Background and Context: Feedback on one's progress is essential to new programming language learners, particularly in out-of-classroom settings. Though many study materials offer assessment mechanisms, most do not examine the accuracy of the feedback they deliver, nor give evidence on its validity. Objective: We investigate the potential use of a…
Descriptors: Novices, Computer Science Education, Programming, Accuracy
Shindler, Michael; Pinpin, Natalia; Markovic, Mia; Reiber, Frederick; Kim, Jee Hoon; Carlos, Giles Pierre Nunez; Dogucu, Mine; Hong, Mark; Luu, Michael; Anderson, Brian; Cote, Aaron; Ferland, Matthew; Jain, Palak; LaBonte, Tyler; Mathur, Leena; Moreno, Ryan; Sakuma, Ryan – Computer Science Education, 2022
Background and Context: We replicated and expanded on previous work about how well students learn dynamic programming, a difficult topic for students in algorithms class. Their study interviewed a number of students at one university in a single term. We recruited a larger sample size of students, over several terms, in both large public and…
Descriptors: Misconceptions, Programming, Computer Science Education, Replication (Evaluation)
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
Pelánek, Radek; Effenberger, Tomáš – Computer Science Education, 2022
Background and Context: Block-based programming is a popular approach to teaching introductory programming. Block-based programming often works in the context of microworlds, where students solve specific puzzles. It is used, for example, within the Hour of Code event, which targets millions of students. Objective: To identify design guidelines…
Descriptors: Programming, Computer Science Education, Puzzles, Problem Solving
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
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
von Hausswolff, Kristina – Computer Science Education, 2022
Background and Context: Research in programming education seems to show that hands-on writing at the keyboard is beneficial for learning, but we lack an explanation of why that is and an underlying theory to anchor that explanation. Objective: The first objective is to lay out a theoretical foundation for understanding the learning situation when…
Descriptors: Programming, Computer Science Education, Novices, Student Experience
Coto, Mayela; Mora, Sonia; Grass, Beatriz; Murillo-Morera, Juan – Computer Science Education, 2022
Background and context: Emotions are ubiquitous in academic settings and affect learning strategies, motivation to persevere, and academic outcomes, however they have not figured prominently in research on learning to program at the university level. Objective: To summarize the current knowledge available on the effect of emotions on students…
Descriptors: Programming, Computer Science Education, Psychological Patterns, Emotional Response
Fowler, Max; Smith, David H., IV; Hassan, Mohammed; Poulsen, Seth; West, Matthew; Zilles, Craig – Computer Science Education, 2022
Background and Context: Lopez and Lister first presented evidence for a skill hierarchy of code reading, tracing, and writing for introductory programming students. Further support for this hierarchy could help computer science educators sequence course content to best build student programming skill. Objective: This study aims to replicate a…
Descriptors: Programming, Computer Science Education, Correlation, Introductory Courses
Vandenberg, Jessica; Lynch, Collin; Boyer, Kristy Elizabeth; Wiebe, Eric – Computer Science Education, 2023
Background and Context: Students' self-efficacy toward computing affect their participation in related tasks and courses. Self-efficacy is likely influenced by students' initial experiences and exposure to computer science (CS) activities. Moreover, student interest in a subject likely informs their ability to effectively regulate their learning…
Descriptors: Elementary School Students, Cooperative Learning, Programming, Network Analysis
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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