數位學習已成為當前數位時代的主流學習方法，隨著人工智慧和深度學習技術的快速發展，學習分析也為數位學習提供了包括學習評量、學習成效預測、個人化學習等支持和應用。然而，當前的學習模式多為一次性學習與總結性評量，無法逐次推動學習的改善；其次，由於學習成效的影響因素複雜，也隨學習情境的異動而改變，傳統結果導向的總結性評量，無法針對學習過程的影響因素，提供相應的學習改善策略。
本研究參考系統化OPDCA運作模式，提出一具持續優化機制的學習模式，同時參考資料科學分析框架，設計學習評量架構，再運用深度學習技術開發學習成效預測與原因分析模型。依據學習模式，學生進行多項學習任務與每項學習任務後的評量，包含成效評比與預測，以及透過注意力機制的可解釋性，識別學習的關鍵影響因素，以掌握學習成效與潛在的學習失敗以及原因，據此提供下一階段學習改善建議，從而實現學習策略的階段性動態調整與持續優化。
本研究針對分析模型進行準確性評量。結果顯示，所開發的CNN結合注意力機制模型於學習成效預測的表現優傳統機器學習方法，準確率達93%，F1-Score為92.46，且隨著資料量增加，準確度不斷提升。同時，本研究依所提學習模式，開發一數位閱讀理解素養培養平台，進行持續學習評量機制之應用有效性評量。實驗顯示接受學習評量之實驗組學生的學習成效，優於未提供學習評量之對照組學生的表現，其期末考平均T分數提高8.8分，且兩者具顯著差異（p=0.000）。
本研究所開發之學習成效評量方法具創新性與準確性，且於所提之學習模式的應用上，亦透過持續的階段性評量掌握變動的學習影響因素，能有效推動學習的持續改善。

Digital learning has become the mainstream method in the current digital era. With rapid advancements in AI and deep learning, learning analytics now supports digital learning through assessment, performance prediction, and personalized learning. However, current models often focus on one-time learning and summative assessments, failing to promote continuous improvement. Moreover, due to the complexity of factors influencing learning outcomes, which change with context, traditional result-oriented assessments cannot provide strategies to address these factors.
This study proposes a learning model with continuous optimization based on the OPDCA cycle and data science frameworks, using deep learning for performance prediction and causal analysis. Students undergo multi-stage learning with assessments after each stage, including performance comparisons and predictions. Through an attention mechanism, key factors influencing learning are identified to understand effectiveness, potential failures, and their causes, providing improvement suggestions for the next stage.
The study first evaluates the model's accuracy. Results show the developed CNN model with attention mechanism outperforms traditional methods in predicting learning effectiveness, achieving 93% accuracy and 92.46 F1-score, improving with increased data. A digital reading comprehension platform was developed to evaluate the model's effectiveness. Experiments show significantly improved performance in the test group, with a 8.8 increase in final exam scores (p=0.000).
This innovative assessment method offers accuracy and, through continuous phased evaluations, effectively promotes ongoing learning improvement by capturing changing influential factors.

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