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.