Publication Date
| In 2026 | 0 |
| Since 2025 | 18 |
| Since 2022 (last 5 years) | 89 |
| Since 2017 (last 10 years) | 197 |
| Since 2007 (last 20 years) | 272 |
Descriptor
| Programming | 285 |
| Undergraduate Students | 285 |
| Computer Science Education | 271 |
| Foreign Countries | 112 |
| Teaching Methods | 108 |
| Student Attitudes | 92 |
| Introductory Courses | 71 |
| Computer Software | 65 |
| Programming Languages | 52 |
| Instructional Effectiveness | 50 |
| Problem Solving | 49 |
| More ▼ | |
Source
Author
| Barnes, Tiffany | 6 |
| Karnalim, Oscar | 3 |
| Menon, Pratibha | 3 |
| Price, Thomas W. | 3 |
| Ayub, Mewati | 2 |
| Bishop-Clark, Cathy | 2 |
| Bowman, Nicholas A. | 2 |
| Cheers, Hayden | 2 |
| Chi, Min | 2 |
| Culver, K. C. | 2 |
| Dong, Yihuan | 2 |
| More ▼ | |
Publication Type
Education Level
Audience
| Teachers | 5 |
| Administrators | 1 |
| Media Staff | 1 |
| Researchers | 1 |
Location
| Turkey | 23 |
| Australia | 6 |
| Brazil | 6 |
| Malaysia | 6 |
| United Kingdom | 6 |
| China | 5 |
| Pennsylvania | 5 |
| Finland | 4 |
| Italy | 4 |
| Japan | 4 |
| Taiwan | 4 |
| More ▼ | |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textJesper Dannath; Alina Deriyeva; Benjamin Paaßen – International Educational Data Mining Society, 2025
Research on the effectiveness of Intelligent Tutoring Systems (ITSs) suggests that automatic hint generation has the best effect on learning outcomes when hints are provided on the level of intermediate steps. However, ITSs for programming tasks face the challenge to decide on the granularity of steps for feedback, since it is not a priori clear…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Science Education, Undergraduate Students
Direct linkCindy Royal – Journalism and Mass Communication Educator, 2025
Artificial intelligence (AI) has taken the forefront in discussions of the future of media and education. Although there are valid concerns, AI has the potential to be useful in learning new skills, particularly those related to computer programming. This case study depicts the ways AI was introduced to assist in teaching coding, specifically in a…
Descriptors: Artificial Intelligence, Coding, Programming, Computer Science Education
Anshul Shah; Thomas Rexin; Fatimah Alhumrani; William G. Griswold; Leo Porter; Gerald Soosai Raj – ACM Transactions on Computing Education, 2025
Objectives: The traditional, instructor-led form of live coding has been extensively studied, with findings showing that this form of live coding imparts similar learning to static-code examples. However, a concern with Traditional Live Coding is that it can turn into a passive learning activity for students as they simply observe the instructor…
Descriptors: Computer Science Education, Advanced Courses, Active Learning, Programming
Yingbin Zhang; Yafei Ye; Luc Paquette; Yibo Wang; Xiaoyong Hu – Journal of Computer Assisted Learning, 2024
Background: Learning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an…
Descriptors: Learning Analytics, Learning Processes, Test Reliability, Psychometrics
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
Karnalim, Oscar; Simon; Chivers, William – IEEE Transactions on Learning Technologies, 2023
We have recently developed an automated approach to reduce students' rationalization of programming plagiarism and collusion by informing them about the matter and reporting uncommon similarities to them for each of their submissions. Although the approach has benefits, it does not greatly engage students, which might limit those benefits. To…
Descriptors: Gamification, Programming, Plagiarism, Cooperative Learning
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)
Joe Michael Allen – ProQuest LLC, 2021
A well-run introductory CS1 course is essential for all students within CS education. CS1 is necessary to keep students in the major and important to attract non-majors to the CS field. Unfortunately, there are many well-known issues that most CS1 courses have in common: high drop rates, low retention, high student stress, student struggle,…
Descriptors: Undergraduate Students, Computer Science Education, Computer Science, Required Courses
Dan Sun; Fan Xu – Journal of Educational Computing Research, 2025
Real-time collaborative programming (RCP), which allows multiple programmers to work concurrently on the same codebase with changes instantly visible to all participants, has garnered considerable popularity in higher education. Despite this trend, little work has rigorously examined how undergraduates engage in collaborative programming when…
Descriptors: Cooperative Learning, Programming, Computer Science Education, Undergraduate Students
Haley A. Delcher; Enas S. Alsatari; Adeyeye I. Haastrup; Sayema Naaz; Lydia A. Hayes-Guastella; Autumn M. McDaniel; Olivia G. Clark; Devin M. Katerski; Francois O. Prinsloo; Olivia R. Roberts; Meredith A. Shaddix; Bridgette N. Sullivan; Isabella M. Swan; Emily M. Hartsell; Jeffrey D. DeMeis; Sunita S. Paudel; Glen M. Borchert – Biochemistry and Molecular Biology Education, 2025
Today, due to the size of many genomes and the increasingly large sizes of sequencing files, independently analyzing sequencing data is largely impossible for a biologist with little to no programming expertise. As such, biologists are typically faced with the dilemma of either having to spend a significant amount of time and effort to learn how…
Descriptors: Artificial Intelligence, Technology Uses in Education, Training, Teaching Methods
Pruthikrai Mahatanankoon; James R. Wolf – Journal of Information Systems Education, 2025
Advances in information and communication technologies (ICT) coupled with artificial intelligence have made computer programming skills indispensable for IT majors and for an increasing number of other science, technology, engineering, and mathematics (STEM) disciplines. Like any hands-on skill, mastering computer programming requires dedicated…
Descriptors: Measures (Individuals), Programming, Undergraduate Students, Computer Science Education
Ruijie Zhou; Chong Xie; Xiuling He; Yangyang Li; Qiong Fan; Ying Yu; Zhonghua Yan – Journal of Educational Computing Research, 2024
Computational thinking (CT), an essential competency for comprehending and addressing intricate issues in the digital world, has been incorporated into curriculum planning as a goal for programming education. This study introduced flow design into programming curricula to investigate its impact on undergraduates 'CT skills during pair work. Two…
Descriptors: Undergraduate Students, Thinking Skills, Computation, Programming
Ting-Ting Wu; Hsin-Yu Lee; Pei-Hua Chen; Wei-Sheng Wang; Yueh-Min Huang – Journal of Computer Assisted Learning, 2025
Background: Conventional reflective learning methodologies in programming education often lack structured guidance and individualised feedback, limiting their pedagogical effectiveness. Whilst computational thinking (CT) offers a systematic problem-solving framework with decomposition, pattern recognition, abstraction, and algorithm design, its…
Descriptors: Computation, Thinking Skills, Educational Diagnosis, Diagnostic Tests
Zhang, Yingbin; Paquette, Luc; Pinto, Juan D.; Liu, Qianhui; Fan, Aysa Xuemo – Education and Information Technologies, 2023
It is widely recognized that debugging is challenging for novice programmers and, as such, computing educators and researchers have called for explicit debugging instruction. Debugging requires various knowledge and skills, and different students may show different strengths and weaknesses. An understanding of such individual differences is…
Descriptors: Undergraduate Students, Programming, Novices, Troubleshooting
Cheers, Hayden; Lin, Yuqing; Yan, Weigen – Informatics in Education, 2023
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, most of these tools only measure the similarity between assignment submissions, and do not actually identify which are suspicious of plagiarism. This work…
Descriptors: Plagiarism, Assignments, Computer Software, Computer Science Education
