An empirical study on the innovation of vocational education technology to construct a new classroom teaching mode in the context of new media

Abstract

In this paper, the convergence and generalization performance of Random Forest is used to improve the classification accuracy of target variables, and the robustness and classification accuracy of Random Forest is dramatically improved by conditional Random Forest, which is trained to generate a Random Forest model for head pose estimation. The improved random forest algorithm is designed using logistic regression, and a new classroom teaching model for vocational education is constructed using the improved random forest algorithm. Taking the students of secondary school A in G city as the research object, the teaching model constructed in this study is applied to the classroom of “Information Technology Teaching Literacy” and empirically analyzed from three aspects: the cognitive level of learners, the emotional state and the comparison of students’ performance. The results show that compared with the achievement before teaching, after the teaching is finished, the student’s achievement improves greatly by 0.2542, and the average score is 86.49, which is 18.28. It shows that the teaching practice of the new classroom teaching design of vocational education in this paper has significant results, which can improve the student’s learning achievement and effectively enhance learning efficiency.

Keywords

• random forest
• head posture estimation
• logistic regression
• novel classroom teaching
• 97u80