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Developing an Interdisciplinary Hands-On Learning Activity with the 6E Model to Improve Students' STEM Knowledge, Learning Motivation and Creativity
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Jyun-Chen Chen; Chia-Yu Liu – Journal of Computer Assisted Learning, 2025
Background: Based on the embodied cognition perspective, interdisciplinary hands-on learning combines several disciplines, such as science, technology, engineering and mathematics (STEM), to improve students' capacity to solve real-world problems. Despite the popularity of interdisciplinary hands-on learning, particularly the six-phase 6E model,…
Descriptors: Interdisciplinary Approach, Experiential Learning, STEM Education, Problem Solving
Engagement, Academic Emotions, and Artificial Intelligence Application Creativity in the Metaverse: A Study of Facial Expressions
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Yu-Shan Chang – Journal of Computer Assisted Learning, 2025
Background: Due to the rapid development of artificial intelligence (AI) and the widespread adoption of online learning post-COVID-19, the metaverse has become an important strategy for innovative teaching. Objectives: This study aimed to investigate the impact of the metaverse on learning engagement, learning emotions, and creative performance in…
Descriptors: Learner Engagement, Psychological Patterns, Emotional Response, Artificial Intelligence
An Investigation of Teachers' Perceptions of Using ChatGPT as a Supporting Tool for Teaching and Learning in the Digital Era
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Areej ElSayary – Journal of Computer Assisted Learning, 2024
Background: The widespread use of information and communication technology (ICT) has led to significant changes in societal aspects, resulting in the emergence of a "knowledge society." However, students and teachers have faced challenges in adapting to this digitalization. In the United Arab Emirates (UAE), transitioning to a…
Descriptors: Teacher Attitudes, Artificial Intelligence, Information Technology, Barriers
Knowledge (Co-)Construction among Artificial Intelligence, Novice Teachers, and Experienced Teachers in an Online Professional Learning Community
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Fangzhou Jin; Xiangmei Peng; Lanfang Sun; Zicong Song; Keyi Zhou; Chin-Hsi Lin – Journal of Computer Assisted Learning, 2025
Background: There are various challenges to teachers' use of generative artificial intelligence (GenAI) for professional learning. Although GenAI is expected to play a transformative role in teachers' learning, its impact on them remains subtle. Objectives: Guided by community of practice, this paper examines the integration of GenAI into an…
Descriptors: Artificial Intelligence, Communities of Practice, Technology Uses in Education, Experienced Teachers
Effect of Groups Size on Students' Learning Achievement, Motivation, Cognitive Load, Collaborative Problem-Solving Quality, and In-Class Interaction in an Introductory AI Course
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Zhan, Zehui; He, Guoqing; Li, Tingting; He, Luyao; Xiang, Siyu – Journal of Computer Assisted Learning, 2022
Background: Group size is one of the important factors that affect collaborative learning, however, there is no consensus in the literature on how many students should the groups be composed of during the problem-solving process. Objectives: This study investigated the effect of group size in a K-12 introductory Artificial Intelligence course by…
Descriptors: Cognitive Ability, High School Students, Cooperative Learning, Artificial Intelligence
A Simple and Effective Remedial Learning System with a Fuzzy Expert System
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Lin, C.-C.; Guo, K.-H.; Lin, Y.-C. – Journal of Computer Assisted Learning, 2016
This study aims at implementing a simple and effective remedial learning system. Based on fuzzy inference, a remedial learning material selection system is proposed for a digital logic course. Two learning concepts of the course have been used in the proposed system: number systems and combinational logic. We conducted an experiment to validate…
Descriptors: Remedial Instruction, Artificial Intelligence, Intelligent Tutoring Systems, Electronic Learning