高中生人工智慧課程學習參與量表之發展與效化

The Development and Validation of the Learning Engagement Scale for High School Students’ Artificial Intelligence Courses

科技發展日新月異，未來以人工智慧為首之相關新興科技課程的學習愈顯重要。高中生在新興科技課程的學習參與經驗將會影響其未來在相關領域的投入，而新興科技課程在國內仍屬開創的階段。因此，本研究的主要目的是探討高中生在人工智慧課程學習參與量表之發展與效化。在第一階段計266位高中生（男性214位、女性52位）參與本研究的新興課程學習並進行學習參與量表的填寫。於施測後利用結構方程模型統計方法逐一進行量表之信、效度檢驗及驗證性因素分析，遂得認知參與、情緒參與、行為參與三個構面共計14個子題目，經模型競爭比較之檢驗確認本量表以二階三因子架構為最佳配適模型。發展量表後，再蒐集295位高中生參與覆核效化之驗證，經分析後確認本模型之效度延展性良好。最後，為避免發生測量模型錯誤設定導致量表工具失真之情事，再採驗證式四分差分析進行本量表之測驗模型查驗，結果顯示本量表所有構面向度之測量題項皆為反映性指標。本學習參與量表所建構之測量題項、構面因子間的路徑關係及模型架構無論在理論發展或是實務應用上皆具有其價值性，可供人工智慧課程教師與相關研究之參考。

With the rapid development of science and technology, learning of relevant emerging science and technology courses that are led by artificial intelligence has become increasingly important. High school students’ experience of the new science and technology courses involving artificial intelligence is very important. The purpose of this study focuses on developing learning engagement questionnaire of artificial intelligence course for high school students. In the first stage, 266 senior high school students were enrolled in our course. After the course, they filled in the questionnaire. The accuracy and validity of the questionnaire were tested by using the statistical method of covariance based structural equation modeling. Fourteen sub-topics were identified in a three aspect structure including cognitive engagement, emotional engagement, and behavioral engagement. A second-order three-factor model was confirmed to be the best for fitting the scale after a competitive comparison of different models. In the second stage, 295 high school students to validate the model of the questionnaire. The model was confirmed to have accurate prediction capability. Finally, this study used confirmatory tetrad analysis to check the scale model. The results showed that all the measurement items of each aspect were reflective indicators. The questionnaire and model developed in this study provide a useful tool for evaluating engagement in artificial intelligence courses, benefiting both theoretical development and practical applications for teachers and educational institutions.