A Typical Model Evaluation System for Rural Vocational Education Against Poverty is Based on a Decision Tree Mining Algorithm

Abstract

This paper presents an in-depth study and analysis of the evaluation of typical models of rural vocational education in combating poverty, employing a decision tree mining algorithm, and utilizing it to develop a practical evaluation system. The paper delves into various aspects such as teaching quality, education scale, teaching methods, and governmental policy support and financial input towards local agriculture-related vocational education. Additionally, it discusses the educational challenges contributing to the dearth of rural talent. The concept of educational data mining is introduced, followed by a description of several common decision tree algorithms including the ID3, C4.5, CART, and SLIQ algorithms, highlighting their connections and differences. Subsequently, the concept of multi-valued decision tables and decision trees is thoroughly explored, along with the decision tree analysis method for multi-valued decision tables, primarily based on the core idea of dynamic programming and the proposed algorithm for minimizing the decision tree size and extracting valuable information. Given the considerable size of generated decision trees, a recursive algorithm for merging identical subtrees and leaf nodes to form a decision graph is provided, resulting in a reduced storage space without redundant nodes. The primary causes identified for these challenges include weak government support for rural vocational education, low social recognition of rural vocational education, and the limited infrastructure of rural vocational colleges.