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Jinshui Wang; Shuguang Chen; Zhengyi Tang; Pengchen Lin; Yupeng Wang – Education and Information Technologies, 2025
Mastering SQL programming skills is fundamental in computer science education, and Online Judging Systems (OJS) play a critical role in automatically assessing SQL codes, improving the accuracy and efficiency of evaluations. However, these systems are vulnerable to manipulation by students who can submit "cheating codes" that pass the…
Descriptors: Programming, Computer Science Education, Cheating, Computer Assisted Testing
Atharva Naik; Jessica Ruhan Yin; Anusha Kamath; Qianou Ma; Sherry Tongshuang Wu; R. Charles Murray; Christopher Bogart; Majd Sakr; Carolyn P. Rose – British Journal of Educational Technology, 2025
The relative effectiveness of reflection either through student generation of contrasting cases or through provided contrasting cases is not well-established for adult learners. This paper presents a classroom study to investigate this comparison in a college level Computer Science (CS) course where groups of students worked collaboratively to…
Descriptors: Cooperative Learning, Reflection, College Students, Computer Science Education
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
Rico-Juan, Juan Ramon; Sanchez-Cartagena, Victor M.; Valero-Mas, Jose J.; Gallego, Antonio Javier – IEEE Transactions on Learning Technologies, 2023
Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an…
Descriptors: Artificial Intelligence, Models, Student Behavior, Feedback (Response)
Nicolas Pope; Juho Kahila; Henriikka Vartiainen; Matti Tedre – IEEE Transactions on Learning Technologies, 2025
The rapid advancement of artificial intelligence and its increasing societal impacts have turned many computing educators' focus toward early education in machine learning (ML). Limited options for educational tools for teaching novice learners about the mechanisms of ML and data-driven systems presents a recognized challenge in K-12 computing…
Descriptors: Artificial Intelligence, Computer Oriented Programs, Computer Science Education, Grade 4
Roosa Wingström; Johanna Hautala; Riina Lundman – Creativity Research Journal, 2024
Artificial intelligence (AI) has breached creativity research. The advancements of creative AI systems dispute the common definitions of creativity that have traditionally focused on five elements: actor, process, outcome, domain, and space. Moreover, creative workers, such as scientists and artists, increasingly use AI in their creative…
Descriptors: Creativity, Artificial Intelligence, Computer Science, Scientists
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
Han Wan; Hongzhen Luo; Mengying Li; Xiaoyan Luo – IEEE Transactions on Learning Technologies, 2024
Automatic program repair (APR) tools are valuable for students to assist them with debugging tasks since program repair captures the code modification to make a buggy program pass the given test-suite. However, the process of manually generating catalogs of code modifications is intricate and time-consuming. This article proposes contextual error…
Descriptors: Programming, Computer Science Education, Introductory Courses, Assignments
Rui Wang; Haili Ling; Jie Chen; Huijuan Fu – International Journal of Distance Education Technologies, 2025
This study adopted the Latent Dirichlet Allocation (LDA) to extract learners' needs based on 70,145 reviews from online course designed for software design and development in China and then applied Quality Function Deployment (QFD) to map learners' differentiated needs into quality attributes. Taking national first-class courses as the…
Descriptors: Educational Improvement, Student Needs, Computer Science Education, Foreign Countries
Nontachai Samngamjan; Pakawat Phettom; Kajohnsak Sa-ngunsat; Wudhijaya Philuek – Shanlax International Journal of Education, 2024
In the realm of education, the integration of AI literacy into computer science teaching is becoming increasingly crucial (Walsh et al., 2023; Voulgari et al., 2022; Velander et al., 2023). Teachers play a pivotal role in bridging the gap between research and practical knowledge transfer of AIrelated skills, necessitating a solid foundation in…
Descriptors: Artificial Intelligence, Technological Literacy, Foreign Countries, Student Teachers
Dai, Yun; Liu, Ang; Qin, Jianjun; Guo, Yanmei; Jong, Morris Siu-Yung; Chai, Ching-Sing; Lin, Ziyan – Journal of Engineering Education, 2023
Background: The recent discussion of introducing artificial intelligence (AI) knowledge to K-12 students, like many engineering and technology education topics, has attracted a wide range of stakeholders and resources for school curriculum development. While teachers often have to directly interact with external stakeholders out of the public…
Descriptors: Artificial Intelligence, Technology Education, Curriculum Development, Computer Science Education
David M. Woods; Andrea Hulshult – Information Systems Education Journal, 2025
IT/IS educators continue to work to develop content and activities for teaching Agile practices, processes, and methodologies to their courses to ensure students have the skills expected by businesses. Given the wide range of tools and technologies that fall under the umbrella of Agile and the wide range of places where Agile is applied, educators…
Descriptors: Information Technology, Information Science Education, Computer Science Education, Teaching Methods
Madhav Sharma; Roger McHaney – Decision Sciences Journal of Innovative Education, 2025
Many management information systems (MIS) faculty have adopted a project-oriented approach in their systems analysis and design courses. In these courses, students use a software development methodology to create a web or mobile application project, which can be based on a predefined case or developed for an external stakeholder. Because most…
Descriptors: Computer Software, Artificial Intelligence, Instructional Design, Computer Science Education
Xiaojing Duan; Bo Pei; G. Alex Ambrose; Arnon Hershkovitz; Ying Cheng; Chaoli Wang – Education and Information Technologies, 2024
Providing educators with understandable, actionable, and trustworthy insights drawn from large-scope heterogeneous learning data is of paramount importance in achieving the full potential of artificial intelligence (AI) in educational settings. Explainable AI (XAI)--contrary to the traditional "black-box" approach--helps fulfilling this…
Descriptors: Academic Achievement, Artificial Intelligence, Prediction, Models
Bambang Sulistio; Arief Ramadhan; Edi Abdurachman; Muhammad Zarlis; Agung Trisetyarso – Education and Information Technologies, 2024
Computer science development, especially machine learning, is a thriving innovation essential for education. It makes the process of teaching and learning more accessible and manageable and also promotes equality. The positive influence of machine learning can also be felt in Islamic studies, particularly in Hadith studies. This literature review…
Descriptors: Electronic Learning, Artificial Intelligence, Computer Uses in Education, Islam