Abstract
With the rapid development of computer technology and artificial intelligence, research on machine learning-based decision-making in ideological and political education at the university level has become a crucial focus in the field of education. This research project aims to leverage machine learning methods and data analysis techniques to construct a machine learning-based analytical approach for decision-making in ideological and political education at universities, providing effective theoretical support for decision-making in this context.The project involves an in-depth investigation and analysis of the requirements for decision-making in ideological and political education at the university level. Through interviews and surveys of education decision-makers, the study aims to understand the challenges and needs they face in the decision-making process. Subsequently, a substantial amount of student learning data and relevant educational resource data will be collected, followed by data preprocessing and feature selection. Various machine learning methods and data mining techniques, such as Support Vector Machines (SVM), will then be employed for association analysis and modeling of the data.By mining student learning data, the research seeks to uncover latent information related to students’ learning performance and educational needs. Based on this exploration, a machine learning-based decision support system for ideological and political education at the university level will be constructed.
Keywords
Machine Learning; Ideological and Political Education; University Student Education; Multi-trait Network; Decision Model
References
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