以TAM探討國小學習障礙學童使用Deepfake技術整合數位學習之意向

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Title:	以TAM探討國小學習障礙學童使用Deepfake技術整合數位學習之意向
Other Titles:	Applying Technology Acceptance Model to Explore the Intention of Using Deepfake-based Digital Learning for Elementary Students with Learning Disabilities
Authors:	卓宜蔚 CHO, I-WEI
Contributors:	資訊管理學系陳宗義 CHEN, TSUNG-YI
Keywords:	科技接受模式;深偽技術;數位學習;學習障礙 Technology acceptance model;deepfake;digital learning;learning disabilities
Date:	2021
Issue Date:	2022-08-18 13:57:23 (UTC+8)
Abstract:	在108課綱強調「素養導向」學習以及新冠肺炎疫情爆發影響下，學生使用數位學習平臺「自主學習」的能力，顯得格外的重要。而學習障礙學生普遍因為「習得無助感」造成學習動機低落，缺乏「自主學習」的能力。因此，本研究以科技接受模式為架構，學生的資訊背景能力為外部變項，來探討國小學習障礙學童使用深偽（deepfake）技術整合應用於數位學習之意向。本研究採敘述性分析、單因子變異數分析、Scheffe事後分析、及迴歸分析等方法來進行分析。研究分析發現：學習障礙學生使用深偽技術整合於數位學習之使用意願會受到「知覺易用性」、「知覺有用性」及「知覺趣味性」正向的影響。本研究所得之細部結果將能提供教育單位及數位教材開發商，針對學習障礙之學生，於未來設計研發數位新型態的教學策略及數位學習平臺之參考。更期許未來以深偽技術為基的數位學習平臺能有效支援特教老師的教學活動，以改善目前學習障礙學生，普遍學習動機低落的現象，增進學生「自主學習」的能力。　　Literacy-oriented learning emphasized by the 2019 Curriculum Guidelines and the outbreak of the COVID-19 pandemic have rendered students' ability to apply digital learning platforms for autonomous learning particularly crucial. Because of learned helplessness, students with learning disabilities generally have low learning motivation and lack autonomous learning ability. Using the technology acceptance model as the framework and information background of students as the external variable, this study explored the intention of elementary schoolchildren with learning disabilities to use Deepfake for digital learning. Statistical methods adopted in this study were descriptive analysis, one-way analysis of variance, Scheffe's post hoc test, and regression analysis. This study revealed that the intention of elementary schoolchildren with learning disabilities to use Deepfake for digital learning was positively affected by perceived ease-of-use, perceived usefulness, and perceived playfulness. The findings of this study may serve as references for educational institutions and software developers to design and develop innovative digital teaching strategies and digital learning platforms for students with learning disabilities. In the future, Deepfake-based digital learning platforms may effectively support special education teaching activities, improve the learning motivation, which is generally low, among students with learning disabilities, and enhance their autonomous learning ability.
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