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Shao-Heng Ko; Kristin Stephens-Martinez – ACM Transactions on Computing Education, 2025
Background: Academic help-seeking benefits students' achievement, but existing literature either studies important factors in students' selection of all help resources via self-reported surveys or studies their help-seeking behavior in one or two separate help resources via actual help-seeking records. Little is known about whether computing…
Descriptors: Computer Science Education, College Students, Help Seeking, Student Behavior
Fatima Abu Deeb; Timothy Hickey – Computer Science Education, 2024
Background and Context: Auto-graders are praised by novice students learning to program, as they provide them with automatic feedback about their problem-solving process. However, some students often make random changes when they have errors in their code, without engaging in deliberate thinking about the cause of the error. Objective: To…
Descriptors: Reflection, Automation, Grading, Novices
Gao, Zhikai; Erickson, Bradley; Xu, Yiqiao; Lynch, Collin; Heckman, Sarah; Barnes, Tiffany – International Educational Data Mining Society, 2022
In computer science education timely help seeking during large programming projects is essential for student success. Help-seeking in typical courses happens in office hours and through online forums. In this research, we analyze students coding activities and help requests to understand the interaction between these activities. We collected…
Descriptors: Computer Science Education, College Students, Programming, Coding
Karnalim, Oscar; Simon; Chivers, William; Panca, Billy Susanto – ACM Transactions on Computing Education, 2022
To help address programming plagiarism and collusion, students should be informed about acceptable practices and about program similarity, both coincidental and non-coincidental. However, current approaches are usually manual, brief, and delivered well before students are in a situation where they might commit academic misconduct. This article…
Descriptors: Computer Science Education, Programming, Plagiarism, Formative Evaluation
Dan Sun; Chee-Kit Looi; Yan Li; Chengcong Zhu; Caifeng Zhu; Miaoting Cheng – Educational Technology Research and Development, 2024
In the current era where computational literacy holds significant relevance, a growing number of schools across the globe have placed emphasis on K-12 programming education. This field of education primarily comprises two distinct modalities--the block-based programming modality (BPM) and the text-based programming modality (TPM). Previous…
Descriptors: Programming, Student Behavior, Thinking Skills, Computation
Dan Sun; Chengcong Zhu; Fan Xu; Yan Li; Fan Ouyang; Miaoting Cheng – Journal of Educational Computing Research, 2024
Although previous research has provided some insights into the effects of block-based and text-based programming modalities, there is a dearth of a detailed, multi-dimensional analysis of the transition process from different introductory programming modalities to professional programming learning. This study employed a quasi-experimental design…
Descriptors: Programming, Secondary School Students, Computation, Thinking Skills
Yin-Rong Zhang; Zhong-Mei Han; Tao He; Chang-Qin Huang; Fan Jiang; Gang Yang; Xue-Mei Wu – Journal of Computer Assisted Learning, 2025
Background: Collaborative programming is important and challenging for K12 students. Scaffolding is a vital method to support students' collaborative programming learning. However, conventional scaffolding that does not fade may lead students to become overly dependent, resulting in unsatisfactory programming performance. Objectives: This study…
Descriptors: Middle School Students, Grade 8, Scaffolding (Teaching Technique), Programming
Slaviša Radovic; Niels Seidel; Joerg M. Haake; Regina Kasakowskij – Journal of Computer Assisted Learning, 2024
Background: Self-assessment serves to improve learning through timely feedback on one's solution and iterative refinement as a way to improve one's competence. However, the complexity of the self-assessment process is widely recognized, as well as that students can benefit from it only if their assessment is accurate enough. Objectives: In order…
Descriptors: Self Evaluation (Individuals), Distance Education, Student Behavior, Accuracy
Gordon, Chelsea L.; Lysecky, Roman; Vahid, Frank – IEEE Transactions on Education, 2023
Computer science textbooks with lengthy text explanations of concepts are often considered thorough and rigorous, so lengthy textbooks (and class notes) are commonplace. Some, however, suggest text should be concise because people tend to skim lengthy text. This article takes advantage of modern digital textbooks that measure reading time to…
Descriptors: Computer Science Education, Electronic Books, Textbooks, Student Behavior
Morales-Trujillo, Miguel Ehecatl; Galster, Matthias; Gilson, Fabian; Mathews, Moffat – IEEE Transactions on Education, 2022
Background: Peer evaluation in software engineering (SE) project courses enhances the learning experience of students. It also helps instructors monitor and assess both teams and individual students. Peer evaluations might influence the way individual students and teams work; therefore, the quality of the peer evaluations should be tracked through…
Descriptors: Undergraduate Students, Computer Software, Programming, Peer Evaluation
Hopcan, Sinan; Polat, Elif; Albayrak, Ebru – Journal of Educational Computing Research, 2022
The pair programming approach is used to overcome the difficulties of the programming process in education environments. In this study, the interaction sequences during the paired programming of preservice teachers was investigated. Lag sequential analysis were used to explore students' behavioral patterns in pair programming. The participants of…
Descriptors: Cooperative Learning, Student Behavior, Programming, Computer Science Education
Sonsoles López-Pernas; Mohammed Saqr; Aldo Gordillo; Enrique Barra – Interactive Learning Environments, 2023
Learning analytics methods have proven useful in providing insights from the increasingly available digital data about students in a variety of learning environments, including serious games. However, such methods have not been applied to the specific context of educational escape rooms and therefore little is known about students' behavior while…
Descriptors: Learning Analytics, Educational Games, Student Behavior, Computer Uses in Education
Du, Xiaoming; Ge, Shilun; Wang, Nianxin – International Journal of Information and Communication Technology Education, 2022
In the context of education big data, it uses data mining and learning analysis technology to accurately predict and effectively intervene in learning. It is helpful to realize individualized teaching and individualized teaching. This research analyzes student life behavior data and learning behavior data. A model of student behavior…
Descriptors: Prediction, Data, Student Behavior, Academic Achievement
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
Kim, ChanMin; Vasconcelos, Lucas; Belland, Brian R.; Umutlu, Duygu; Gleasman, Cory – International Journal of Educational Technology in Higher Education, 2022
It is critical to teach all learners to program and think through programming. But to do so requires that early childhood teacher candidates learn to teach computer science. This in turn requires novel pedagogy that can both help such teachers learn the needed skills, but also provide a model for their future teaching. In this study, we examined…
Descriptors: Programming, Error Correction, Student Behavior, Early Childhood Teachers