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Shang Shanshan; Geng Sen – Journal of Computer Assisted Learning, 2024
Background: Artificial intelligence-generated content (AIGC) has stepped into the spotlight with the emergence of ChatGPT, making effective use of AIGC for education a hot topic. Objectives: This study seeks to explore the effectiveness of integrating AIGC into programming learning through debugging. First, the study presents three levels of AIGC…
Descriptors: Artificial Intelligence, Educational Technology, Technology Integration, Programming
Hsiao-Ping Hsu – TechTrends: Linking Research and Practice to Improve Learning, 2025
The advancement of large language model-based generative artificial intelligence (LLM-based GenAI) has sparked significant interest in its potential to address challenges in computational thinking (CT) education. CT, a critical problem-solving approach in the digital age, encompasses elements such as abstraction, iteration, and generalisation.…
Descriptors: Programming, Prompting, Computation, Thinking Skills
Shu-Jie Chen; Xiaofen Shan; Ze-Min Liu; Chuang-Qi Chen – Educational Technology & Society, 2025
The introduction of programming education in K-12 schools to promote computational thinking has attracted a great deal of attention from scholars and educators. Debugging code is a central skill for students, but is also a considerable challenge when learning to program. Learners at the K-12 level often lack confidence in programming debugging due…
Descriptors: Programming, Coding, Elementary School Students, Secondary School Students
Hua-Xu Zhong; Jui-Hung Chang; Chin-Feng Lai; Pei-Wen Chen; Shang-Hsuan Ku; Shih-Yeh Chen – Education and Information Technologies, 2024
Artificial intelligence (AI) education is becoming an advanced learning trend in programming education. However, AI subjects can be difficult to understand because they require high programming skills and complex knowledge. This makes it challenging to determine how different departments of students are affected by them. This study draws on…
Descriptors: Undergraduate Students, Artificial Intelligence, Programming, STEM Education
Jian Liao; Linrong Zhong; Longting Zhe; Handan Xu; Ming Liu; Tao Xie – IEEE Transactions on Learning Technologies, 2024
ChatGPT has received considerable attention in education, particularly in programming education because of its capabilities in automated code generation and program repairing and scoring. However, few empirical studies have investigated the use of ChatGPT to customize a learning system for scaffolding students' computational thinking. Therefore,…
Descriptors: Scaffolding (Teaching Technique), Thinking Skills, Computation, Artificial Intelligence
Naya-Varela, Martin; Guerreiro-Santalla, Sara; Baamonde, Tamara; Bellas, Francisco – IEEE Transactions on Learning Technologies, 2023
This article presents the Robobo SmartCity model, an educational resource to introduce students to computational intelligence (CI) topics using educational robotics as the core learning technology. Robobo SmartCity allows educators to train learners in artificial intelligence (AI) fundamentals from a feasible and practical perspective, following…
Descriptors: Robotics, Educational Technology, Technology Uses in Education, Computation
Blanke, Tobias; Colavizza, Giovanni; van Hout, Zarah – Education for Information, 2023
The article presents an open educational resource (OER) to introduce humanities students to data analysis with Python. The article beings with positioning the OER within wider pedagogical debates in the digital humanities. The OER is built from our research encounters and committed to computational thinking rather than technicalities. Furthermore,…
Descriptors: Open Educational Resources, Data Analysis, Programming Languages, Humanities
Aydin, Muharrem; Karal, Hasan; Nabiyev, Vasif – Education and Information Technologies, 2023
This study aims to examine adaptability for educational games in terms of adaptation elements, components used in creating user profiles, and decision algorithms used for adaptation. For this purpose, articles and full-text papers in Web of Science, Google Scholar, and Eric databases between 2000-2021 were searched using the keywords…
Descriptors: Educational Games, Game Based Learning, Programming, Physics
Hao-Yue Jin; Maria Cutumisu – Education and Information Technologies, 2024
Computational thinking (CT) is considered to be a critical problem-solving toolkit in the development of every student in the digital twenty-first century. Thus, it is believed that the integration of deeper learning in CT education is an approach to help students transfer their CT skills beyond the classroom. Few literature reviews have mapped…
Descriptors: Computation, Thinking Skills, Problem Solving, Artificial Intelligence
Siu-Cheung Kong; Wei Shen – Interactive Learning Environments, 2024
Logistic regression models have traditionally been used to identify the factors contributing to students' conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected…
Descriptors: Student Characteristics, Predictor Variables, Comprehension, Computation
Pang, Bo; Nijkamp, Erik; Wu, Ying Nian – Journal of Educational and Behavioral Statistics, 2020
This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models…
Descriptors: Artificial Intelligence, Regression (Statistics), Models, Classification
Jui-Hung Chang; Chi-Jane Wang; Hua-Xu Zhong; Hsiu-Chen Weng; Yu-Kai Zhou; Hoe-Yuan Ong; Chin-Feng Lai – Educational Technology Research and Development, 2024
Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students' learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence are important research topics. This study…
Descriptors: Artificial Intelligence, Technological Advancement, Student Evaluation, Models
Hsu, Ting-Chia; Abelson, Hal; Van Brummelen, Jessica – International Review of Research in Open and Distributed Learning, 2022
The purpose of this study was to design a curriculum of artificial intelligence (AI) application for secondary schools. The learning objective of the curriculum was to allow students to learn the application of conversational AI on a block-based programming platform. Moreover, the empirical study actually implemented the curriculum in the formal…
Descriptors: Secondary School Students, Experiential Learning, Curriculum Design, Artificial Intelligence
Ramon Mayor Martins; Christiane Gresse Von Wangenheim – Informatics in Education, 2024
Information technology (IT) is transforming the world. Therefore, exposing students to computing at an early age is important. And, although computing is being introduced into schools, students from a low socio-economic status background still do not have such an opportunity. Furthermore, existing computing programs may need to be adjusted in…
Descriptors: Information Technology, Socioeconomic Status, Social Class, Computer Literacy
Thrall, Elizabeth S.; Lee, Seung Eun; Schrier, Joshua; Zhao, Yijun – Journal of Chemical Education, 2021
Techniques from the branch of artificial intelligence known as machine learning (ML) have been applied to a wide range of problems in chemistry. Nonetheless, there are very few examples of pedagogical activities to introduce ML to chemistry students in the chemistry education literature. Here we report a computational activity that introduces…
Descriptors: Undergraduate Students, Artificial Intelligence, Man Machine Systems, Science Education
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