本研究主要探討線上學習環境中，學業情緒(困惑)與自我效能對影片觀看調整行為(詳細觀看、重複觀看與跳過觀看)、後設認知策略、回饋(簡單對錯、正確答案與精緻化)與學習表現之相互影響。本研究主要使用「深度學習臉部情緒辨識系統(FEAT)」之六大情緒之價向-激發，與臉部肌肉動作(AU)來探測困惑情緒，之後並輔以「專家編碼FACS」進行困惑情緒辨識與臉部肌肉動作單元(AUs)評測，以探測系統困惑數據與人工編碼困惑之一致性。最後並以FEAT系統、專家編碼與情緒問卷之相關來交互檢測本研究所研探之困惑情緒之可信度與合理性。本研究對象為107位北部大專院校學生。在資料處理分析方面，本研究除了以「深度學習臉部情緒辨識系統」與「專家編碼」，並使用行為序列分析學生觀看影片的行為，統計分析（如集群分析、迴歸、行為序列分析、ｔ檢定）將會應用於本研究之資料分析。
研究結果顯示，就臉部表情辨識學業困惑情緒而言，本研究探究出華人困惑情緒乃位在系統Valence-Arousal落點的第二象限，且AU4與AU7為明顯臉部困惑表情，與西方困惑相似。研究還發現一般困惑AUs包含AU1、2、4、7、10、17、23與25；典型困惑AUs包含AU4、7、10、17與23。本研究結果亦顯示，系統數據與人工編碼皆具顯著相關，且在學習單階段之系統數據、人工編碼，及學習者問卷感受亦皆顯著相關，某種程度支持此困惑情緒之可信度與合理性。此外，本研究結果亦顯示，在線上學習環境中：(1)就個人層面因素間之關係，困惑問卷皆與自我效能在任何階段皆呈負相關。(2)就個人層面對行為與表現而言，系統困惑與人工困惑對影片觀看調整行為具顯著預測力，但困惑對後設認知策略則無顯著預測力，而高困惑者較常採用回放至不理解處並按下暫停(SB→PA)、播放後遇到複雜處放慢影片速度(PL→RS)、影片速度調快後按下暫停(RF→PA)以釐清概念。自我效能對影片觀看調整行為則無顯著預測力，但對後設認知策略與學習表現皆具正向預測力，且高自我效能者較常採用回放至不理解處並按下暫停(SB→PA)、調整影片速度(RS↔RF)。(3)就行為層面因素間關係及對表現而言: 就影片觀看調整行為與後設認知策略關係，在影片觀看階段，重複觀看者較常使用後設認知策略，相較於詳細觀看與跳過觀看者；在接收回饋後，再次觀看者比未再觀看者較常使用後設認知策略；就行為對表現而言，在影片觀看階段，跳過觀看者的學習表現比詳細觀看者佳，但接收回饋後，有無再次觀看影片者的學習表現則無顯著差異。而後設認知策略對學習表現在接收回饋前有正向預測力，但在接收回饋後則無預測力。(4)就環境層面之接收回饋對個人層面與行為而言:接收到正確答案與精緻化回饋者，比接收簡單對錯者的問卷困惑感受較低，自我效能也較高；而在接收正確答案者較少回去再次觀看與後設認知策略，相較於接收到簡單對錯與精緻化回饋者。最後根據研究結果進行討論，並對教師教學、教材設計、AI臉部情緒辨識系統與未來研究提出相關建議。

The purpose of this study was to investigate the reciprocal relationships among academic emotion (i.e. confusion), self-efficacy, video-viewing behavior (i.e. one time detail, repetitive, zapping), metacognitive strategies, feedback (i.e. knowledge of response, knowledge of the correct response, elaborated feedback) and performance in the online learning environment. Although there was few research on Western facial emotions of confusion, there was no such finding on Chinese academic emotion of confusion. This study thus attempted to use “Facial Emotion Analysis Tool” (FEAT) based on its Valence-Arousal dimensional model of six emotions, and 14 Action Units (AU) of facial muscle movement to detect academic confusion and its corresponding AUs. In addition, this study also used expert coding by using “Facial Action Coding System” (FACS) to examine the reliability (i.e., consistency) of the detection of academic confusion of FEAT system. Finally, this study attempted to use the correlation of FEAT data, expert coding and individual’s perceptions of confusion questionnaires to cross validate the new detection of academic confusion. There were one hundred and seven college students from a vocational university in northern Taiwan participated in this study. About 20000 photos of all three learning stages were used to analyze by FEAT system, and expert coding. The statistical analysis (e.g., cluster analysis, regression, behavior sequence analysis, correlations) were applied for data analysis in this study.
The results showed that Chinese academic confusion was located in the second quadrant of the Valence-Arousal dimension of FEAT system. AU4 and AU7 were the most evident AUs for academic confusion of facial expressions, which is similar to Westerners. The study also found that typical academic confusion consisted of AU4, 7, 10, 17 and 23, while the general academic confusion included AU1, 2, 4, 7, 10, 17, 23 and 25. The results also showed significant correlations between FEAT data and expert coding on academic confusion at all three learning stages, while a significant correlation among Feat data, expert coding and student’s perceptions of confusion questionnaire at the third learning stage, which to some extent validated the reliability of the new detection of academic confusion by this study.
The results indicated that, in online reasoning learning, (1) For the relationship between personal factors: academic confusion of questionnaire was negatively correlated to self-efficacy at all the three learning stages. (2) For the role of personal factors in behavior and performance: FEAT data and expert coding on academic confusion both predicted video viewing regulating behaviors, but academic confusion did not predict the use of metacognitive strategies; Self-efficacy did not predict video-viewing regulating behaviors, but it significantly predicted metacognitive strategies and academic performance. The behavior sequence analysis also indicated that that both high academic confusion and self-efficacy individuals have different video-viewing regulating behaviors as compared to low confusion and low self-efficacy counterparts. (3) For the role of behaviors in learning and performance: Individuals with repetitive video-viewing behaviors used more metacognitive strategies than those with one time detailed and zapping viewing behaviors at the video-viewing stage, while after receiving feedback individuals who re-viewing the video used more metacognitive strategies than those who did not view the video again. For the roles of behaviors in performance, individuals with zapping video-viewing behaviors had better performance than those with one time detailed video-viewing behaviors, while after receiving feedback, there was no difference in performance. Metacognitive strategies positively predicted performance, but it did not predict performance after receiving feedback. (4) For the role of receiving feedback (environmental influence) in personal and behavioral influences: Receiving the feedback of correct response (KCR) and elaborative feedback (EF) had lower academic confusion and higher self-efficacy than those receiving simple feedback of knowledge of response (KR). Individuals who receive feedback of correct response (KCR) seldom viewed the video again, and used less metacognitive strategies than those receiving simple (KR) and elaborative feedback (EF). Finally, implications and suggestions for teacher instruction, instructional material design, facial expression analysis system and future research were provided.

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