Development of Improved Apriori Algorithm for an Effective Teaching Evaluation of Finance Majors in Applied Universities

Assessing the quality of teaching is crucial to enhancing learning outcomes, especially in applied universities where theoretical principles have to be successfully integrated with practical competence. Traditional assessment systems based on heavy reliance on subjective student opinions and traditional statistical methods usually fail to detect intricate, underlying patterns that characterize effective teaching. Such systems are prone to problems such as data redundancy, imprecision, and scalability limitations, which result in inaccurate or averaged evaluations. To counter these drawbacks, this work suggests a sophisticated assessment framework that relies on an improved Apriori Algorithm, which is combined with the Deep Learning algorithm. The suggested framework consists Improved Apriori model for an effective data mining, whereas gated recurrent neural is used for handling the time-series data for an effective evaluation of finance content. The proposed system overcomes the following weaknesses of prevalent strategies by additional noise filtering of high-confidence and non-redundant rules between pedagogical approaches and student performance indicators. By learning profound dependencies between teaching attributes and feedback trends, the system can discover interpretable, actionable knowledge often lost to older models. Experimental testing proves that the enhanced algorithm substantially enhances the accuracy and precision of teaching performance evaluations. Comparative study with baseline models exhibits a substantial improvement in classification performance, with an accuracy of 97.5% for teaching evaluation, along with associated improvements in precision, recall, and F1-score. These findings highlight the model's ability to support informed decision-making, providing educational institutions with means for making focused improvements in curriculum development and teaching delivery.