評估CAL認知學徒制對大小班規模教學的適用性與有效性研究（英文稿）

Evaluating the Applicability and Effectiveness of a CAL-Based Cognitive Apprenticeship System in Small and Large Class Instruction

本研究評估了不同班級規模的計算機概論課程中的認知學徒模式，以確定其對學生成績和教學實踐的影響。我們的方法包括分析小班和大班的期中考和期末考以及程式設計作業。主要研究結果表明小班制可以促進更深入的學習和程式設計技能的有效應用，從而顯著增強長期技能發展和深度學習機會。相較之下，小班和大班之間的考試成績沒有顯著差異，這表明對短期評估結果的影響有限。此外，定性回饋指出，小班學生感受到個人化關注和系統化的學習環境，而大班學生則感到分心，並對翻轉教室和評分系統持批評態度。這些分析結果強調班級規模在制定教育策略和學生參與程式設計課程的重要性。

This study evaluates the cognitive apprenticeship model in introductory computer science courses across different class sizes to determine its impact on student outcomes and instructional practices. The research methodology involved a comparative analysis of midterm and final examination results, as well as programming assignment performance, in both small- and large-class settings. Key findings indicate that smaller classes foster more in-depth learning and more effective application of programming skills, thereby contributing significantly to long-term skill development and opportunities for deep learning. In contrast, no statistically significant differences were noted in the exam scores between small and large classes, suggesting limited impact on short-term assessment outcomes. Additionally, qualitative feedback revealed that students in smaller classes appreciated the personalized attention and systematic learning environment provided by the cognitive apprenticeship system. Conversely, students in larger classes experienced distractions and were critical of the flipped classroom and the associated grading systems. These insights emphasize the importance of class size in shaping educational strategies and student engagement in programming courses.