Deep learning-based strategies for evaluating and enhancing university teaching quality

Abstract

The education sector currently faces several challenges, including the subjectivity of evaluation methods, uniformity of data, and a lack of real-time feedback. This study aims to address these issues by leveraging deep learning techniques, specifically Convolutional Neural Networks (CNNs), to accurately assess and enhance the quality of university teaching. In contrast to traditional teaching quality assessment methods, which often lack rigor and comprehensiveness, this study introduces a precise and thorough evaluation framework. By integrating deep learning algorithms, the study seeks to improve the objectivity and accuracy of evaluations, facilitate personalized feedback, and foster innovation in teaching methodologies. The research process involves multiple complex stages, including data collection, preprocessing, feature extraction, model construction, training, validation, and results analysis. Multi-source data—comprising student performance data, teacher evaluations, course content, and student feedback—are used to create a robust dataset. Data encoding, standardization, and feature engineering techniques are employed to enhance model input. Experimental results demonstrate that the CNN model achieves prediction accuracies of 92% for “Excellent,” 88% for “Good,” 85% for “Average,” and 80% for “Poor” in the test set. These results underscore the model's high performance in classification tasks, particularly in accurately identifying high-quality teaching, with both high precision and recall. This study not only addresses a gap in the field by utilizing multi-source data for comprehensive evaluation but also validates the effectiveness of deep learning models in assessing teaching quality. Additionally, the study provides a foundation for developing targeted teaching improvement strategies.