Evaluation of College English Teaching Quality Based on Improved BT-SVM Algorithm.

Area of Science:

• Educational Technology
• Machine Learning
• Data Science

Background:

• Colleges and universities are reforming teaching evaluation programs to enhance educational quality.
• There is a growing need for robust teaching quality evaluation systems within academic institutions.
• Support Vector Machines (SVM) are effective machine learning algorithms for statistical learning with limited data.

Purpose of the Study:

• To develop an optimized teaching quality evaluation system for higher education.
• To adapt and improve existing multiple classification algorithms for educational assessment.
• To leverage the capabilities of Support Vector Machines (SVM) in evaluating teaching quality.

Main Methods:

• Optimization of a multiple classification algorithm using a binary tree structure.
• Implementation of a binary tree Support Vector Machine (SVM) classification algorithm.
• Design and execution of comparative experiments to validate the proposed model.

Main Results:

• The proposed evaluation model demonstrates strong generalization ability.
• The model achieves higher classification accuracy compared to existing methods.
• The classification efficiency of the new model is significantly improved.

Conclusions:

• The optimized binary tree SVM offers a superior approach for college teaching quality evaluation.
• The developed system provides a reliable method for pre-evaluating institutional teaching quality.
• This research contributes to the advancement of machine learning applications in educational assessment.