Deep Learning-Based Correlation Analysis between the Evaluation Score of English Teaching Quality and the Knowledge...

Area of Science:

• Educational Technology
• Applied Linguistics

Background:

• Improving English teaching quality is crucial for colleges and universities.
• Existing research lacks focus on the correlation between vocabulary, grammar, and cloze test scores in high school students.
• This study addresses the gap by examining English knowledge points and their relation to teaching quality.

Purpose of the Study:

• To explore the relationship between English teaching quality and specific knowledge points (vocabulary, grammar, reading, cloze, composition).
• To develop and validate a novel evaluation model for classroom English teaching quality.
• To analyze the correlation between teachers' quality and students' learning effects and knowledge mastery.

Main Methods:

• Empirical research across vocabulary, grammar, reading comprehension, cloze tests, and composition.
• Development of a combined model using decision trees and knowledge point rule association.
• Analytic Hierarchy Process (AHP) for determining indicator weights.
• Association rule analysis for model validation.

Main Results:

• A comprehensive evaluation model for students' English learning ability in the classroom was constructed.
• The model effectively reflects teachers' teaching levels and analyzes correlations between teacher quality and student outcomes.
• Validation through association rule analysis confirmed the model's effectiveness in assessing student mastery.

Conclusions:

• The developed model provides a robust framework for evaluating English teaching quality and student learning.
• Understanding the correlation between knowledge points is key to improving teaching effectiveness.
• The study highlights the importance of integrating digital teaching requirements into evaluation systems.