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A Comparison of Generative AI Solutions and Textbook Solutions in an Introductory Programming Course
Ernst Bekkering; Patrick Harrington – Information Systems Education Journal, 2025
Generative AI has recently gained the ability to generate computer code. This development is bound to affect how computer programming is taught in higher education. We used past programming assignments and solutions for textbook exercises in our introductory programming class to analyze how accurately one of the leading models, ChatGPT, generates…
Descriptors: Higher Education, Artificial Intelligence, Programming, Textbook Evaluation
Xin Gong; Shufan Yu; Jie Xu; Ailing Qiao; Han Han – Education and Information Technologies, 2024
Tangible programming combines the advantages of object manipulation with programmable hardware, which plays an essential role in improving programming skills. As a tool for ensuring the quality of projects and improving learning outcomes, the PDCA cycle strategy is conducive to cultivating reflective thinking. However, there is still a lack of…
Descriptors: Programming, Computer Science Education, Outcomes of Education, Reflection
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
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
Gerard Dummer; Elwin Savelsbergh; Paul Drijvers – Informatics in Education, 2025
Programmed control systems are ubiquitous in the present-day world. In current educational practice, however, these systems are hardly being addressed, and little is known about children's spontaneous understandings about such systems. Therefore, we explored pupils' understandings prior to instruction in three concrete settings: a car park, an…
Descriptors: Elementary School Students, Grade 3, Grade 6, Computer Science Education
Dagyeom Lee; Youngjun Lee – Informatics in Education, 2024
As our society has advanced in the era of digital transformation, education has been transformed from knowledge-centered to competency-centered to solve future problems in the light of unpredictable changes and events in our lives. Programming education provides the basic knowledge needed, and fosters higher-order thinking skills in the process of…
Descriptors: Problem Solving, Computer Science Education, Programming, Thinking Skills
Guozhu Ding; Xiangyi Shi; Shan Li – Education and Information Technologies, 2024
In this study, we developed a classification system of programming errors based on the historical data of 680,540 programming records collected on the Online Judge platform. The classification system described six types of programming errors (i.e., syntax, logical, type, writing, misunderstanding, and runtime errors) and their connections with…
Descriptors: Programming, Computer Science Education, Classification, Graphs
Lin Zhang; Qiang Jiang; Weiyan Xiong; Wei Zhao – Journal of Educational Computing Research, 2025
This study seeks to deepen the understanding of the direct and indirect effects of human-computer dialogic interaction programming activities, facilitated by ChatGPT, on student engagement. Data were collected from 109 Chinese high school students who engaged in programming tasks using either ChatGPT-driven dialogic interaction or traditional pair…
Descriptors: Artificial Intelligence, Computer Software, Computer Science Education, Programming
Yuan-Chen Liu; Tzu-Hua Huang; Chien-Chia Huang – Interactive Learning Environments, 2024
In this study, an interactive programming learning environment was built with two types of error prompt functions: 1) the key prompt and 2) step-by-step prompt. A quasi-experimental study was conducted for five weeks, in which 75 sixth grade students from disadvantaged learning environments in Taipei, Taiwan, were divided into three groups: 1) the…
Descriptors: Programming, Computer Science Education, Cues, Grade 6
Dan Sun; Fan Ouyang; Yan Li; Chengcong Zhu; Yang Zhou – Journal of Computer Assisted Learning, 2024
Background: With the development of computational literacy, there has been a surge in both research and practice application of text-based and block-based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major…
Descriptors: Computer Science Education, Programming, Computer Literacy, Comparative Analysis
Guangrui Fan; Dandan Liu; Rui Zhang; Lihu Pan – International Journal of STEM Education, 2025
Purpose: This study investigates the impact of AI-assisted pair programming on undergraduate students' intrinsic motivation, programming anxiety, and performance, relative to both human-human pair programming and individual programming approaches. Methods: A quasi-experimental design was conducted over two academic years (2023-2024) with 234…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Programming
Meier, Heidi; Lepp, Marina – Journal of Educational Computing Research, 2023
Especially in large courses, feedback is often given only on the final results; less attention is paid to the programming process. Today, however, some programming environments, e.g., Thonny, log activities during programming and have the functionality of replaying the programming process. This information can be used to provide feedback, and this…
Descriptors: Programming, Introductory Courses, Computer Science Education, Teaching Methods
Haipeng Wan; Xue Zhang; Xinxue Yang; Shan Li – Education and Information Technologies, 2024
This study investigated the impact of problematization-oriented scaffolding and structuring-oriented scaffolding, incorporated within instructional videos, on students' computational thinking and their performance in programming education. We recruited 86 participants from three senior classes at a high school. Each of the three classes was…
Descriptors: Scaffolding (Teaching Technique), Instructional Design, Thinking Skills, Computer Science Education
Dan Sun; Azzeddine Boudouaia; Chengcong Zhu; Yan Li – International Journal of Educational Technology in Higher Education, 2024
ChatGPT, an AI-based chatbot with automatic code generation abilities, has shown its promise in improving the quality of programming education by providing learners with opportunities to better understand the principles of programming. However, limited empirical studies have explored the impact of ChatGPT on learners' programming processes. This…
Descriptors: Computer Science Education, Computer Software, Feedback (Response), Artificial Intelligence
Merino-Armero, José Miguel; González-Calero, José Antonio; Cózar-Gutiérrez, Ramón – Journal of Research on Technology in Education, 2022
The interest in computational thinking development at pre-university education stage is increasing. In this study, a meta-analysis was conducted to address two main objectives: (a) to analyze the effectiveness of empirical interventions in K-12 education for the development of Computational Thinking (CT); and (b) to identify and evaluate the…
Descriptors: Meta Analysis, Computer Science Education, Thinking Skills, Intervention