Research on Individual Trait Clustering and Development Pathway Adaptation Based on the K-means Algorithm

TL;DR

This paper applies the K-means clustering algorithm to categorize college students based on their traits and academic data, enabling personalized career guidance to improve employment outcomes.

Contribution

It introduces a clustering-based approach to tailor career guidance for students using multiple characteristic variables, filling a gap in existing prediction-focused methods.

Findings

Students with different traits are suited for different careers.

Clustering improves personalized career guidance effectiveness.

Method provides a scientific basis for employment support.

Abstract

With the development of information technology, the application of artificial intelligence and machine learning in the field of education shows great potential. This study aims to explore how to utilize K-means clustering algorithm to provide accurate career guidance for college students. Existing methods mostly focus on the prediction of career paths, but there are fewer studies on the fitness of students with different combinations of characteristics in specific career directions. In this study, we analyze the data of more than 3000 students on their CET-4 scores, GPA, personality traits and student cadre experiences, and use the K-means clustering algorithm to classify the students into four main groups. The K-means clustering algorithm groups students with similar characteristics into one group by minimizing the intra-cluster squared error, ensuring that the students within the same cluster are highly similar in their characteristics, and that differences between different clusters are maximized. Based on the clustering results, targeted career guidance suggestions are provided for each group. The results of the study show that students with different combinations of characteristics are suitable for different career directions, which provides a scientific basis for personalized career guidance and effectively enhances students' employment success rate. Future research can further improve the precision of clustering and the guidance effect by expanding the sample size, increasing the feature variables and considering external factors.