Exploring the Process Re-engineering and Effectiveness Enhancement Path of Academic Affairs Management in Colleges and Universities Empowered by Digital Transformation

Abstract

Academic affairs management is the core of all management work in colleges and universities, and all colleges and universities are actively promoting the scientific and standardized management mode, and the management means are also moving towards digitalization and automation. In this paper, an efficient and accurate transformation prediction method for the digital transformation of academic affairs management is constructed based on the XGBoost algorithm, and the results of the students' academic early warning are divided into three categories. At the same time, feature selection as well as uneven data processing are performed on the data. The accuracy rate and F1 score were used as evaluation indexes to compare the XGBoost prediction model with SVM, RF and other algorithmic models, and SHAP was used to analyze the interpretability of the model. Data analysis showed that XGBoost predictive performance was good (F1 value of 0.879) and better than SVM, RF and other models. The influence of data accuracy, first semester grades, and educational support on the energy efficiency of digital transformation of academic affairs management is more obvious, and there is a significant difference in the influencing factors for the digital transformation of academic affairs management at different levels of transformation effectiveness.