Data-Driven Teacher Assessment with Random Forests and SVM for Effective Pedagogical Management

Financial and human resource management, as well as supervision of instruction and learning, are growingly significant responsibilities of school administrators as school autonomy expands. Programs for professional development and evaluation of teachers are two aspects of human resource management that school administrators in many countries oversee. Pedagogical management is enhanced when school leaders are able to assess teachers effectively, provide them constructive criticism, and link professional development with institutional objectives. Word segmentation, data cleaning, and annotation are employed in this work. Features are extracted using TF -IDF. The suggested approach boosts F1-score, recall, accuracy, and precision by merging SVM and RF classification techniques. The research compares the hybrid SVRF model to several machine learning approaches to evaluating educators. The SVRF model achieves a higher accuracy rate than competing classification approaches, coming in at 99.46%. Recall, accuracy, and F1-score, which are evaluation metrics, were all enhanced by the model. The study emphasises the use of modern machine learning approaches to assess educators, leading to more effective school management. Using hybrid categorisation systems, school leaders can enhance the quality of instruction and the accuracy of evaluations. These results show that in order for teachers to have meaningful professional development, data-driven evaluations are necessary.