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Josh Tenenberg; Donald Chinn – Computer Science Education, 2025
Background and context: We address the question of what computer science students take the discipline to be. How students conceive the discipline can influence whether a student pursues computer science, what particular area within computer science they focus on and whether they persist in the discipline. In this paper, we examine the epistemic…
Descriptors: Computer Science Education, Epistemology, Student Attitudes, Intellectual Disciplines
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
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)
Hege Annette Olstad; Birgit Rognebakke Krogstie; Xiaomeng Su; Rune Hjelsvold – Computer Science Education, 2025
Background and Context: While higher education in Norway intends to ensure students can translate academic learning to real-world settings, a gap persists in computing students' perceptions of their acquired competencies. Objective: This study aims to understand the gap and explore how formative assessment with ePortfolios can help students…
Descriptors: Foreign Countries, Formative Evaluation, Computer Science Education, College Students
Kathleen J. Lehman; Julia Rose Karpicz; Tomoko M. Nakajima; Linda J. Sax; Veronika Rozhenkova – Computer Science Education, 2024
Department chairs play a key role in efforts to diversify higher education, particularly in fields like computer science that face long-standing gender and racial/ethnic gaps. This study considers the role of computer science department chairs in guiding broadening participation efforts and how they make sense of external dynamics that influence…
Descriptors: Department Heads, Influences, Student Participation, Computer Science Education
Schulz, Sandra; Berndt, Sarah; Hawlitschek, Anja – Computer Science Education, 2023
Background and Context: Collaborative and cooperative learning is important to prepare students for their future work and to increase their learning performance in university courses. Several studies have shown promising results regarding team activities, such as pair programming. However, there is little information on how teamwork is currently…
Descriptors: Cooperative Learning, Computer Science Education, Higher Education, Foreign Countries
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
Tianxiao Yang; Jongpil Cheon – Computer Science Education, 2025
Background and context: There were few studies indicating if students' computational thinking (CT) self-efficacy and their CT performance were aligned with each other. Objectives: The study was to investigate if there was a discrepancy between students' CT self-efficacy and their CT performance. Method: Involving 104 non-CS undergraduate students…
Descriptors: Self Efficacy, Computer Science Education, Prediction, Teacher Expectations of Students
Steve Balady; Cynthia Taylor – Computer Science Education, 2024
Background and Context: Computer Science has traditionally had poor student retention, especially among women. Prior work has found that student attitudes are a key factor to retention, especially with "weedout" courses such as Calculus. Objective: To determine how student attitudes towards CS 1 and Calculus change over active-learning…
Descriptors: Student Attitudes, Calculus, Computer Science Education, Academic Persistence
Jill Denner; Heather Bell; David Torres; Emily Green – Computer Science Education, 2024
Background and context: High school students' interest in computing fields is not always sustained in community college due to a disconnect between institutions. Objective: To understand how cross-sector collaborations can align institutional pathways in computing. Research questions: What cross-sector practices can be used to build a computing…
Descriptors: Computer Science Education, Guided Pathways, High Schools, Community Colleges
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

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