研究生: |
鄧舒帆 Deng, Shu-Fan |
---|---|
論文名稱: |
以科技接受模式探討數位學習專班學生之自我調整學習能力與數位學習成效關係之研究 A Study on the Relationship of Self-Regulated Learning Ability and Learning Outcomes in E-learning Master Programs based on Technology Acceptance Model |
指導教授: |
李懿芳
Lee, Yi-Fang |
口試委員: |
黃政傑
Hwang, Jenq-Jye 宋修德 Sung, Hsiu-Te 李懿芳 Lee, Yi-Fang |
口試日期: | 2023/07/25 |
學位類別: |
碩士 Master |
系所名稱: |
工業教育學系技職教育行政碩士在職專班 Department of Industrial Education_Continuing Education Master's Program of Administration in Technological-Vocational Education |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 137 |
中文關鍵詞: | 數位學習 、數位學習專班 、科技接受模式 、自我調整學習 、數位學習成效 |
英文關鍵詞: | e-Learning, e-Learning Master Program, Technology Acceptance Model, Self-Regulated Learning, e-Learning Outcomes |
研究方法: | 調查研究 |
DOI URL: | http://doi.org/10.6345/NTNU202301124 |
論文種類: | 學術論文 |
相關次數: | 點閱:237 下載:4 |
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本研究旨在探討數位學習專班學生使用學習平臺修習數位課程後,感知的自我調整學習能力、科技接受程度與數位學習成效之關係,並驗證影響數位學習專班學生數位學習成效模型,以了解影響數位學習成效之關鍵因素。本研究採調查研究法,以111學年度全臺數位專班在學學生為樣本,使用便利叢集取樣,透過以研究者編修之「數位學習平臺使用感知與學習成效調查問卷」進行資料蒐集。回收有效問卷共205份,所得數據使用描述性統計、結構方程模型等方法進行統計分析與驗證。研究結果包含:一、數位學習專班學生之自我調整學習能力佳,對學習平臺的科技接受程度與數位學習成效的感知程度良好。二、數位學習專班學生之自我調整學習能力、科技接受程度與數位學習成效各變項之間,均具有正向關係。三、數位學習專班學生之自我調整學習能力與科技接受程度可有效預測數位學習成效。依據研究結果,本研究對辦理數位學習專班之學校、行政人員、教師及學生提出具體建議,期以提升辦學及學習成效:一、宜強化數位學習專班學生自我學習策略的實施與監控能力。二、宜持續優化數位學習平臺,提升操作的便利性及友善性,以増加數位學習者對平臺的接受程度。三、數位學習專班教師及行政人員,宜同時關注學生的自我調整學習能力及對數位平臺的接受程度,以有效提升學生數位學習成效。
This study aimed to explore the relationship between students' self-regulated learning abilities, perceived technological acceptance, and e-learning outcomes in a e-learning master program. The research also sought to validate a model that identifies key factors influencing students' e-learning outcomes. A survey research design was employed, using a clustered sampling method, with students enrolled in the e-learning master program during the 111 academic year as the sample. Data were collected through a researcher-designed "Perceived Usage of Learning Platforms and E-Learning Outcomes Survey." A total of 205 valid responses were obtained, and the data were analyzed using descriptive statistics and structural equation modeling techniques. The major findings were: First, students demonstrated strong self-regulated learning abilities and had a positive perception of the technological acceptance and e-learning outcomes. Secondly, there was a positive relationship among the variables of self-regulated learning abilities, technological acceptance, and e-learning outcomes. Thirdly, both self-regulated learning abilities and technological acceptance among students were found to effectively predict their e-learning outcomes. Based on the results, several suggestions were generated for schools, administrators, teachers, and students implementing in e-learning master program, aiming to enhance the effectiveness of education and learning. These recommendations include: 1) Students of e-learning master program are advised to sharpen their implementation and monitoring of self-learning strategies, 2) Continuous improvement of the learning platform was recommended to enhance its usability and user-friendliness, thus increasing students' acceptance of the learning platform, and 3) Teachers and administrators of the e-learning master program were encouraged to pay attention to students' self-regulated learning abilities and their acceptance of learning platform to effectively improve students' e-learning outcomes.
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