本研究主要探究在數位學習環境中，認知失衡、學業情緒(困惑與焦慮)、自我效能、問題解決策略及學習表現之影響。由於目前只有非常少數研究建議西方臉部困惑與焦慮情緒，但至今仍未有華人之學業困惑與焦慮情緒之相關研究。因此本研究嘗試使用「深度學習臉部情緒辨識系統」(FEAT)六大情緒(生氣、厭惡、恐懼、愉悅、傷心、驚訝)之價向-激發與臉部肌肉動作(AU)來探測困惑與焦慮情緒，並進一步以FEAT系統、專家人工編碼(FACS)及情緒量表之相關來交互驗證困惑與焦慮情緒之可信度與合理性。研究對象為80位大學生，除了辨識系統數據、專家人工編碼外，並使用統計分析來探測認知失衡、學業情緒(困惑與焦慮) 、自我效能及問題解決策略間之關係及影響。

研究結果顯示，就臉部表情辨識學業困惑與焦慮情緒而言，華人的困惑與焦慮情緒皆位於Valence-Arousal中的第二象限中，其又與西方所定義的困惑與焦慮之情緒落點相似。就系統數據與人工編碼在困惑與焦慮情緒之一致性檢測，系統困惑與人工困惑之情緒編碼相似度達93.72%，系統焦慮與人工焦慮之情緒編碼相似度亦達94.35%；而無論在困惑或焦慮情緒，系統數據、人工編碼與學習者問卷感受皆呈顯著相關，某種程度驗證了本研究所探測之困惑與焦慮情緒具有相當信度與合理性。此外，本研究結果亦顯示，在數位學習環境中：(1)認知失衡對學業困惑與焦慮(系統、人工及問卷)皆具有顯著正向預測力。(2) 學業困惑對學業焦慮具有顯著正向預測力。(3) 學業困惑(人工及問卷)與自我效能呈負相關；學業焦慮(系統、人工及問卷)則與自我效能呈負相關。(4)學業困惑與焦慮對問題解決策略具有顯著負向預測力。(5)問題解決策略對學習表現具有顯著正向預測力。最後本研究依據結果進行討論，並針對教師教學、深度學習辨識系統及未來研究提出相關建議。

The purpose of this study was to investigate the roles of cognitive disequilibrium, academic emotions (i.e. academic confusion, anxiety), self-efficacy, problem-solving strategies, and performance in digital learning. Although there were few types of research on Western facial emotions of confusion and anxiety, there was no such finding on Chinese academic emotions of confusion and anxiety. This study thus attempted to use the “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 anxiety emotions and their corresponding AUs. In addition, this study also used expert coding by using the “Facial Action Coding System” (FACS) to examine the reliability (i.e., consistency) of the detection of academic confusion and anxiety of the FEAT system. Finally, this study further used the correlation of FEAT data, expert coding, and individuals’ perceptions of confusion and anxiety questionnaires to cross-validate the new detection of academic confusion and anxiety. Eighty college students participated in this study. Over 6000 photos were used to analyze by FEAT system and expert coding. The statistical analysis (e.g., cluster analysis, regression, correlations) were applied in this study.

The results indicated that The Chinese academic confusion and anxiety emotions were mostly in the second quadrant (negative and high arousal), and both emotions were similar to the Western analysis of confusion and anxiety emotions. The results also indicated that there was good reliability between FEAT data and expert coding on academic confusion and anxiety, as well as their corresponding AUs. The consistency between FEAT confusion and expert coding on confusion was up to 93.72 percent, while the consistency between FEAT anxiety and expert coding on anxiety was up to 94.35 percent. Finally, the results showed that there were significant correlations among FEAT data, expert coding, and individuals’ perceptions of emotion questionnaires on academic confusion and anxiety, which further cross-validated the new detection of facial expression of Chinese academic confusion and anxiety by this study.

The results also indicated that, in the digital learning environment, (1). cognitive disequilibrium positively predicted both academic confusion and anxiety on all data sources, including FEAT data, expert coding, and questionnaire of confusion and anxiety. (2). academic confusion significantly predicted anxiety on all data sources. (3). academic confusion(expert coding, and questionnaire data) negatively correlated with self-efficacy, while academic anxiety of all data sources also negatively correlated with self-efficacy. (4). academic confusion and anxiety of all data sources negatively predicted problem-solving strategies. (5). problem-solving strategies positively predicted performance. Finally, the implications and suggestions for teacher instruction, FEAT system, and future research were provided.

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