English Teaching Quality Evaluation Model for Higher Education Based on Educational Data Mining Technology

Constructing teaching quality evaluation systems for higher vocational education is beneficial for schools, serving as the cornerstone for developing abilities and enabling precise education management. In this publication, a Dual Relational Graph Attention Network (ETQE-DRGAN)-Based Educational Data Mining Technology English Teaching Quality Evaluation Model for Higher Education is proposed. In three ways, this study assesses and defends the system. An overview of the system's primary components, such as the user and data administration modules, data mining, online assessment, and inquiry modules, is given in the first section. Frequent item sets are divided into two stages and the development of invalid candidate sets is limited to avoid the formation of negative association rules. The Tid-list Vertical Partitioning Query-Apriori Algorithm (TPQ-Apriori algorithm) is improved by these two approaches. The system analysis is covered in section three. In order to assess the success of instruction, this work incorporates a Dual Relational Graph Attention Networks method that takes into account several factors such as age, gender, education, teaching attitude, teaching topic, teaching technique, and teaching effectiveness. The proposed ETQE-DRGAN is implemented in MATLAB, using the School Database for calculation. Numerous performance metrics like response time, precision, accuracy, sensitivity, and F1-score are utilized to calculate the effectiveness of ETQE-DRGAN technique. The obtained outcomes indicate that the ETQE-DRGAN approach achieves the highest precision of 98.5%, Accuracy of 98% and fastest response time of 6.95swhen compared to existing methods like ETQE-AA, ETQE-DTA and ETQE-ML.