A study on the influence of teacher certification concept on the construction of art majors in universities based on big data analysis

Abstract

The development of the data era has put higher requirements for college art courses, but the imperfect evaluation system of practical teaching of college art courses has become a stumbling block to the flourishing development of college art. Based on the decision tree model, this paper proposes constructing the optimal decision tree using pruning techniques. The optimized decision tree is adopted to explore the differences in the construction of art majors in universities under the condition of normal major certification and feedback on the influence of normal major certification on art majors through the evaluation results. From the analysis of the three index examples, the mean values of cognitive evaluation, pedagogical evaluation, and effectiveness evaluation of art courses were 2.527, 4.756, and 3.267, respectively, indicating the best evaluation in the pedagogical evaluation of art courses, but the cognitive evaluation was slightly lower. Normal major certification significantly influences the construction of art majors in colleges and universities. The decision tree model can also guide the innovation direction of college art courses more intuitively through data so that the evaluation system of college art courses can enjoy the development dividend of the new technology era.

Keywords

• decision tree model
• college art
• big data
• construction of art major
• normal professional certification
• 97b20