研究生: |
黃詩婷 Shih-Ting Huang |
---|---|
論文名稱: |
以AI深度學習臉部情緒辨識系統,輔測線上學習環境之控制信念、學業情緒、心流狀態對專注力、線上搜尋策略與學習表現之影響 The Role of Locus of Control, Academic Emotions, Flow State on Concentration, On-line Searching Strategy and Performance in the On-line Learning Environment: Using Facial Emotion Analysis System on Deep Learning Techniques |
指導教授: |
王淑玲
Shu-Ling Wang |
口試委員: |
林珊如
San-Ju Lin 王嘉瑜 Chia-Yu Wang |
學位類別: |
碩士 Master |
系所名稱: |
人文社會學院 - 數位學習與教育研究所 Graduate Institute of Digital Learning and Education |
論文出版年: | 2021 |
畢業學年度: | 109 |
語文別: | 中文 |
論文頁數: | 200 |
中文關鍵詞: | 臉部情緒辨識 、人工智慧 、深度學習 、FACS 、學業無聊 、心流狀態 、學業焦慮 、線上學習環境 、控制信念 、專注力 、眨眼頻率 、線上搜尋策略 |
外文關鍵詞: | Academic boredom, Flow state,, Academic anxiety, Academic concentration, On-line searching strategy |
相關次數: | 點閱:994 下載:0 |
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本研究主要探討線上學習環境中,控制信念、學業情緒(學業無聊及學業焦慮)、心流狀態對專注力、線上搜尋策略(深入潛進、表面搜尋策略及搜尋歷程品質)與學習表現之影響,且本研究使用「AI 深度學習臉部情緒辨識系統」與「FACS人工編碼」進行情緒辨識與臉部肌肉動作評測,並嘗試交互驗證辨識系統與人工編碼間之關係。本研究對象為80位大學生,採用質與量的研究方法進行分析。在質化方面,採用內容分析法,瞭解學生實際搜尋歷程與學習表現。在量化方面,本研究採用「AI 深度學習臉部情緒辨識系統」與「FACS人工編碼」方式,以瞭解受試者之影片學習臉部情緒與表情肌肉動作;採用眨眼頻率量測法,瞭解學生之影片學習專注力;採用問卷調查法,瞭解學生之控制信念、學習情緒、專注力及線上搜尋策略,並探測各變項間之影響。
針對臉部情緒辨識數據之研究結果顯示,(1) 系統辨識之傷心與本研究無聊、系統愉悅與本研究心流、系統生氣與本研究焦慮,兩者間皆具顯著正相關。(2) 系統情緒辨識(六大基本情緒)與人工情緒編碼具顯著正相關;系統AU辨識(14個AU)與人工AU編碼亦具顯著正相關。(3) 系統辨識傷心與人工編碼無聊之AU編碼出現組合相似度達78.5%(共重複11個AU編碼);系統辨識愉悅與人工編碼心流之AU編碼出現組合相似度達66.6%(共重複6個AU編碼);系統辨識生氣與人工編碼焦慮之AU編碼出現組合相似度達69.2%(共重複9個AU編碼)。此外,本研究結果顯示,在線上學習環境中, (4) 內在控制信念對心流狀態有顯著正向預測力;外在控制信念亦對學業無聊有顯著正向預測力,但對學業焦慮具負向預測力。此外,不同先備知識程度學生(高中低知識組),亦會產生不同的學業情緒差異。(5) 正向學業情緒(心流)對於學業專注力有顯著正向預測力,對眨眼頻率有顯著負向預測力;而負向學業情緒(無聊與焦慮)對於學業專注力有顯著負向預測力,對眨眼頻率有顯著正向預測力。(6) 學業專注力對線上搜尋策略(深入潛進)與搜尋歷程品質有顯著正向預測力,對表面搜尋策略有顯著負向預測力;眨眼頻率對深入潛進搜尋策略與搜尋歷程品質有顯著負向預測力,但對表面搜尋策略有顯著正向預測力。(7) 深入潛進搜尋策略與搜尋歷程品質對學習表現有顯著正向預測力,而表面搜尋策略對學習表現有顯著負向預測力。最後,根據研究結果對於教師教學、AI臉部情緒辨識系統及未來研究提出相關建議。
The study attempts to investigate the role of locus of control, academic emotion (i.e. academic boredom and academic anxiety), flow state on academic concentration, on-line searching strategy (i.e. deep diving, fast surfing and on-line searching process), and performance in the on-line learning environment. In addition, this study also attempts to cross validate the analysis of “Facial Emotion Analysis System” and “Facial Action Coding System” on facial emotion and facial muscle movements. A total of 80 college students participated in this study. This study applied both qualitative and quantitative methods for data analysis. “Facial Emotion Analysis System” and “Facial Action Coding System” were used to analyze the participants’ facial emotion and facial muscle movements. The blink rate of participants was used to analyze the participant’s academic concentration during the video learning. The content analysis was used to analyze the participants’ on-line searching process and performance, while questionnaires were used to investigate the participants’ locus of control, academic emotion, concentration, on-line searching strategy. The results indicated that, (1) a positive correlation between sadness and boredom, joy and flow state, as well as angry and anxiety from the analyses of Facial Emotion Analysis System and Facial Action Coding System. (2) Both a positive correlation for six basic emotions and 14 AUs between Facial Emotion Analysis System and Facial Action Coding System. (3) The similarity between sadness and boredom is up to 78.5 percent from the analyses of Facial Emotion Analysis System and Facial Action Coding System;the similarity between joy and flow state is up to 66.6 percent from the analyses of Facial Emotion Analysis System and Facial Action Coding System;the similarity between angry and anxiety is up to 69.2 percent from the analyses of Facial Emotion Analysis System and Facial Action Coding System. (4) Internal locus of control positively predicted flow state, while external locus of control positively predicted academic boredom, and negatively predicted academic anxiety. In addition, participants with different levels of prior knowledge also have different academic emotions. (5) Positive academic emotion (flow state) positively predicted academic concentration, and negatively predicted blink rate. However, negative academic emotions, such as boredom and anxiety, negatively predicted academic concentration, and positively predicted blink rate. (6) Academic concentration positively predicted deep diving searching strategy, as well as searching quality, and negatively predicted fast surfing searching strategy. Furthermore, blink rate negatively predicted deep diving searching strategy and searching quality, while positively predicted fast surfing searching strategy. (7) Deep diving strategy and searching quality significantly predicted learning performance, while fast surfing strategy negatively predicted learning performance. Finally, the implications and suggestions for teaching, facial emotion analysis system and future research were provided.
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