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Important Things to Know Before Developing Artificial Intelligence-Based Drone Learning Systems: From the Experience of Educational Practice
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Author(s): Ted Yuan-Yen Huang (Office of Sustainable Development and University Social Responsibility, National Central University, Taiwan), Eric Zhi-Feng Liu (Graduate institute of Learning and Instruction, National Central University, Taiwan)and Harry Hung-Yu Sang (Graduate Institute of Learning and Instruction, National Central University, Taiwan)
Copyright: 2025
Volume: 15
Issue: 1
Pages: 14
Source title:
International Journal of Online Pedagogy and Course Design (IJOPCD)
Editor(s)-in-Chief: Chia-Wen Tsai (Ming Chuan University, Taiwan)and Pei-Di Shen (Ming Chuan University, Taiwan)
DOI: 10.4018/IJOPCD.376343
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Abstract
This study explored drone-based learning in educational contexts using a mixed-method design to identify key learning attributes. After completing researcher-developed drone tasks, 73 learners demonstrated a significantly improved understanding of drone concepts and proficiency in Blockly coding. However, learners perceived self-efficacy as significantly lower than other self-regulated strategies in drone activities. Task-based drone activities, facilitated by group settings, encouraged learners to develop metacognition through collective scaffolding methods, such as peer discussions and team testing. The identified learning attributes provide valuable insights for educators in designing assessments for collaborative drone problem-solving. Additionally, the interplay among effort regulation, problem-solving, and cooperativity observed in this study offers essential references for the future development of distributed expertise systems.
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