中国科学院心理研究所机构知识库

In the stage of online education quality evaluation, due to multiple influencing factors and complex indicator parameters, the final evaluation results are prone to significant deviations from the actual situation. In order to improve the accuracy of online education quality evaluation, this article proposes a machine learning model based online education quality evaluation method for vocational e-commerce courses. We designed an education data structure based on BOM and conducted qualitative and quantitative analysis on the factors and parameters that affect the quality of online education. In the construction stage of the evaluation index system, comprehensive indicators are designed from four aspects: school management quality, teacher teaching process, student learning behavior, and academic quality. In the stage of education quality evaluation, the SVM algorithm in machine learning is used to optimize PSO, establish an evaluation optimization model, and train iteratively through parameter optimization to achieve network education quality evaluation. The test results show that the evaluation results of the design method on the quality of online education differ significantly from the actual situation, with a specific error range of 0.02, which improves the accuracy of the evaluation.