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題名:人工智慧課程接受模型之發展與探討-以高中生為例
作者:曾衒銘
作者(外文):TSENG, HSUAN-MING
校院名稱:國立彰化師範大學
系所名稱:科學教育研究所
指導教授:段曉林
學位類別:博士
出版日期:2023
主題關鍵詞:人工智慧人工智慧課程科技接受模型學習參與artificial intelligenceartificial intelligence courselearning engagementtechnology acceptance model
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科技發展日新月異,傳統在校內所學習的科目與內容對於學生在面臨全球化的競爭挑戰下已不敷使用,未來相關新興科技的學習越顯重要。以人工智慧為首的新興科技相關課程在教育主管機關的主導下已逐漸被推廣到國民義務教育範疇。教師在教學現場進行課程教學時,學習者的參與狀況與接受該科目知識程度,及未來是否願意繼續投入心力與時間進行延伸學習等問題,是教育工作者與主管機關所應重視之議題。本研究期以人工智慧課程為例,透過在企業管理、資訊應用等領域中廣泛使用的科技接受模型做為模型藍圖,發展並效化「人工智慧課程接受模型」,盼以此模型來預測或解釋高中學生在面對相關課程時的決策因素。為達此研究目的,研究者逐一透過人工智慧課程內容與學習成就評量設計及發展課程學習參與量表、課程接受量表。透過信效度檢驗及效化複核分析等統計步驟,藉此確認模型之主體構面因子,包含知覺有用性、知覺易學性、行為意圖、認知參與、情緒參與及行為參與。最後以偏最小平方法結構方程分析進行人工智慧課程接受模型之探索發展,最終確認此模型無論是在測量模型的信效度或是結構模型的解釋能力及預測能力皆屬中高度以上,且各項統計指標亦達學術上的要求。據此,高中生人工智慧課程接受模型所建構之因果關係無論在理論發展或是實務應用上皆具有其相當的價值性,可供人工智慧課程教師與教育單位作為課程發展與政策擬定之重要工具。
With the rapid development of technology, the subjects and content traditionally learned in schools are no longer sufficient for students facing the challenges of global competition. Learning about emerging technologies, such as artificial intelligence (AI), is becoming increasingly important. Under the guidance of educational authorities, new technology-related courses, led by AI, are gradually being promoted in national compulsory education. When teachers teach these courses, they need to pay attention to the students' participation, their level of knowledge and willingness to invest time and effort in extended learning. In this study, an AI course acceptance model was developed and validated using a technology acceptance model widely used in the field of enterprise management and information application. The researcher designed and developed a course participation scale and a course acceptance scale based on the AI course content and learning achievements. Through statistical analysis procedures, such as validity and reliability testing, the researcher confirmed that the main factors of the model included perceived usefulness, perceived ease of learning, behavioral intention, cognitive participation, emotional participation, and behavioral participation. Finally, using the partial least squares method and structural equation modeling, the researcher explored the development of the emerging technology acceptance model. Ultimately, it was confirmed that this model has moderate to high levels of both reliability and validity in the measurement model, as well as explanatory and predictive capabilities in the structural model. Additionally, all statistical indicators met the academic requirements. Therefore, the causal relationship constructed by the high school students' AI course acceptance model has considerable value in theoretical development and practical application. It can serve as an important tool for emerging technology course teachers and educational units in developing courses and formulating policies.
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