An Explainable Learning Technique for Financial Professional Teaching Evaluation Process

The assessment of teaching quality in financial education is important for the development of future professionals armed with both theoretical competency and practical financial knowledge. Prevailing assessment methods usually manual marking, questionnaires, and static performance appraisals are subject to subjectivity, rigidity, and incapacity for the processing of large amounts and variability of educational data. These drawbacks limit the precision and reliability of assessments and, consequently, impede instructional development and student performance. To bridge these gaps, this research recommends an intelligent assessment system based on an enhanced deep learning architecture that unifies structured data analysis and textual feedback understanding. The hybrid suggested model incorporates for processing unstructured data textual data. Feature refinement modules and attention mechanisms are used to segregate key assessment indicators like teaching clarity, structure of the course, and student satisfaction. Explainability is provided through attention visualization and interpretability layers so that educators can see which features have the most influence on the final evaluation score. Experimental validation was performed on actual academic datasets gathered from financial departments of applied universities. The suggested model exhibited great performance with 98.5% accuracy, 98.8% precision, 98.5% recall, and 98.6% F1-score, surpassing conventional machine learning and rule-based assessment systems.