<![CDATA[This study investigates the effectiveness of Mastery Learning supported by adaptive quizzes in improving students’ understanding of backpropagation, a key concept in machine learning. The problem addressed is the difficulty students face in mastering complex topics like backpropagation and the limitations of traditional lecture-based teaching methods in facilitating deep learning. A pretest-posttest experimental design was employed with 60 undergraduate students. The experimental group (n=30) used Mastery Learning and adaptive quizzes, while the control group (n=30) followed traditional lecture-based instruction. Data was collected via pretests, posttests, and a survey assessing perceived learning. Paired t-tests and independent t-tests were used for statistical analysis. The experimental group showed a significant improvement in posttest scores (mean = 85.4) compared to the control group (mean = 65.3). The effect size was large (Cohen’s d = 1.2), indicating substantial improvements in learning outcomes. Survey results showed that 94% of students in the experimental group found the adaptive quizzes helpful in understanding backpropagation and expressed a preference for continuing with this method. The study demonstrates the potential of combining Mastery Learning with adaptive quizzes in enhancing learning in machine learning courses. Limitations include the small sample size and the focus on a single topic (backpropagation). The results may not be generalized to other topics or populations. This study contributes to the literature by showing the effectiveness of adaptive learning systems in technical subjects like machine learning. Future research could explore the scalability of this method and its applicability to other machine learning concepts or fields.]]>
