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Leah Bidlake; Eric Aubanel; Daniel Voyer – ACM Transactions on Computing Education, 2025
Research on mental model representations developed by programmers during parallel program comprehension is important for informing and advancing teaching methods including model-based learning and visualizations. The goals of the research presented here were to determine: how the mental models of programmers change and develop as they learn…
Descriptors: Schemata (Cognition), Programming, Computer Science Education, Coding
Muntasir Hoq; Ananya Rao; Reisha Jaishankar; Krish Piryani; Nithya Janapati; Jessica Vandenberg; Bradford Mott; Narges Norouzi; James Lester; Bita Akram – International Educational Data Mining Society, 2025
In Computer Science (CS) education, understanding factors contributing to students' programming difficulties is crucial for effective learning support. By identifying specific issues students face, educators can provide targeted assistance to help them overcome obstacles and improve learning outcomes. While identifying sources of struggle, such as…
Descriptors: Computer Science Education, Programming, Misconceptions, Error Patterns
Mehmet Basaran; Ömer Faruk Vural; Sermin Metin; Sabiha Tamur – International Journal of Early Childhood, 2025
This study investigates ChatGPT's perspectives on coding education for preschool children to provide a comprehensive understanding that is valuable for educators in early childhood education. An instrumental case study approach was employed, utilizing qualitative research design and case study methods. Data were gathered using a structured…
Descriptors: Preschool Education, Computer Science Education, Coding, Artificial Intelligence
Diana Franklin; Paul Denny; David A. Gonzalez-Maldonado; Minh Tran – Cambridge University Press & Assessment, 2025
Generative AI is a disruptive technology that has the potential to transform many aspects of how computer science is taught. Like previous innovations such as high-level programming languages and block-based programming languages, generative AI lowers the technical expertise necessary to create working programs, bringing the power of computation…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Science Education, Expertise
Ishaya Gambo; Faith-Jane Abegunde; Omobola Gambo; Roseline Oluwaseun Ogundokun; Akinbowale Natheniel Babatunde; Cheng-Chi Lee – Education and Information Technologies, 2025
The current educational system relies heavily on manual grading, posing challenges such as delayed feedback and grading inaccuracies. Automated grading tools (AGTs) offer solutions but come with limitations. To address this, "GRAD-AI" is introduced, an advanced AGT that combines automation with teacher involvement for precise grading,…
Descriptors: Automation, Grading, Artificial Intelligence, Computer Assisted Testing
Davi Bernardo Silva; Deborah Ribeiro Carvalho; Carlos N. Silla – IEEE Transactions on Learning Technologies, 2024
Throughout a programming course, students develop various source code tasks. Using these tasks to track students' progress can provide clues to the strengths and weaknesses found in each learning topic. This practice allows the teacher to intervene in learning in the first few weeks of class and maximize student gains. However, the biggest…
Descriptors: Computation, Models, Ability Grouping, Programming
Cindy 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
Zifeng Liu; Wanli Xing; Xinyue Jiao; Chenglu Li; Wangda Zhu – Education and Information Technologies, 2025
The ability of large language models (LLMs) to generate code has raised concerns in computer science education, as students may use tools like ChatGPT for programming assignments. While much research has focused on higher education, especially for languages like Java and Python, little attention has been given to K-12 settings, particularly for…
Descriptors: High School Students, Coding, Artificial Intelligence, Electronic Learning
Yuhan Lin – ProQuest LLC, 2024
Block-based programming environments have become increasingly commonplace in computer science education. Despite a rapidly expanding ecosystem of block-based programming environments, text-based languages remain the dominant programming paradigm outside of educational contexts, motivating the transition from block-based to text-based programming.…
Descriptors: Computer Science Education, Programming, Coding, Scaffolding (Teaching Technique)
Pavlos Toukiloglou; Stelios Xinogalos – Education and Information Technologies, 2024
Hour of Code is a widely recognized global event that aims to introduce programming to novice users and integrate computer science into education. This paper presents an analysis of the effectiveness of the support system and user interface of Minecraft Adventurer, a serious game designed for the Hour of Code global event. Although previous…
Descriptors: Novices, Programming, Coding, Computer Science Education
Priti Oli – ProQuest LLC, 2024
This dissertation focuses on strategies and techniques to enhance code comprehension skills among students enrolled in introductory computer science courses (CS1 and CS2). We propose a novel tutoring system, "DeepCodeTutor," designed to improve the code comprehension abilities of novices. DeepCodeTutor employs scaffolded self-explanation…
Descriptors: Reading Comprehension, Tutoring, Scaffolding (Teaching Technique), Automation
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
Andrew Millam; Christine Bakke – Journal of Information Technology Education: Innovations in Practice, 2024
Aim/Purpose: This paper is part of a multi-case study that aims to test whether generative AI makes an effective coding assistant. Particularly, this work evaluates the ability of two AI chatbots (ChatGPT and Bing Chat) to generate concise computer code, considers ethical issues related to generative AI, and offers suggestions for how to improve…
Descriptors: Coding, Artificial Intelligence, Natural Language Processing, Computer Software
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
Tuba Ketenci; Brendan Calandra; Jonathan Cohen; Maggie Renken; Nurjamal Chonoeva – Journal of Research on Technology in Education, 2023
The goal of this study was to examine how two groups of middle school students' self-efficacy, interest, goal orientation, and prior experience related to evidence of their building upon existing ideas and code in digital artifacts they created using MIT's App Inventor, a computational practice that Brennan and Resnick (2012) identified as…
Descriptors: Middle School Students, Coding, Computer Science Education, Self Efficacy

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