Multidimensional Data Analysis and Algorithm Optimization in Workshop Teaching Effect Evaluation

Workshop teaching has experienced three stages from exploration to steady development, and has become an important mode to improve the quality of education and promote educational reform. This study integrates multidimensional data such as curriculum characteristics, teachers' characteristics, teaching behavior, students' characteristics and learning effectiveness, and uses cluster analysis and association rule mining technology to deeply analyze the complex relationship between learners' characteristics and teaching effectiveness. Utilize the K-means clustering algorithm to categorize student cohorts and discern diverse learning methodologies. Employ the Apriori algorithm to investigate potential correlations between learning behaviors and instructional efficacy. In addition, this study also implemented algorithm optimization strategies such as adaptive learning rate adjustment and model pruning to improve the efficiency and accuracy of evaluation. The empirical study selects $\mathbf{1 2 0}$ college students, and collects data through questionnaires, online platform records and classroom observation. The results show that cluster analysis effectively reveals different learning styles, while association rule mining directly relates learning behavior and teaching effectiveness. Algorithm optimization significantly improves the performance of the algorithm, especially in the case of large amount of data, the average running time is reduced by about 30 %. The research results show that the method of combining multidimensional data analysis and algorithm optimization can effectively evaluate the teaching effect of workshop and provide scientific basis for teaching improvement.