Higher Education Course Evaluation Based on Deep Learning Model

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