Higher Education Course Evaluation Based on Deep Learning Model

Abstract

Higher education has completed a revolutionary transition stage from traditional elite education to mass quality education, and the improvement of higher education quality has increasingly become the focus of social attention. Courses are the most basic unit of university teaching, and the quality of courses directly affects the quality of personnel training. The expansion of higher education has resulted in insufficient teaching resources and a decline in the quality of courses. Improving quality is the eternal theme of higher education reform. How to formulate scientific curriculum quality standards and objectively evaluate curriculum quality is the most concerned issue of the society. Therefore, in-depth analysis of the factors that affect the quality of the curriculum and the establishment of the curriculum quality evaluation system and method has certain practical significance for improving the quality of the curriculum. This work combines the evaluation of higher education courses with deep learning models and proposes a network MSACNN for quality evaluation of higher education courses. This work proposes to use an attention-based multiscale network to explicitly learn the relationship between the qualities of various higher education courses. By using parallel networks with different convolution kernels, it combines different scale features at the same spatial location to better learn the relationship between the quality features of higher education courses. This work conducts sufficient experiments on the higher education curriculum quality dataset, and the experimental results demonstrate that the multiscale network based on the attention mechanism exhibits superior performance. MSACNN surpasses other machine learning methods in both precision and recall metrics.