Student development direction prediction method and system based on K-Means clustering algorithm

Abstract

The invention provides a student development direction prediction method and system based on a K-Means clustering algorithm, and the method comprises the steps: collecting student learning condition analysis data of higher vocational colleges, and taking the data as training data; processing the training data by using a K-Means clustering algorithm, optimizing the K-Means clustering algorithm by using a feature selection algorithm, and finding out main factors affecting the development direction of higher vocational students; an RBF neural network model is constructed, main factors influencing the development direction of higher vocational students are input into the RBF neural network model for optimization training, and a trained student development direction prediction model is obtained; and analyzing the processed to-be-predicted student learning condition data by using the trained student development direction prediction model, and predicting and outputting a prediction result of the future development direction of the student. According to the invention, learning condition analysis data is fully utilized to predict the future development direction of students, and feasible suggestions are provided for making student learning plans and professional development plans.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a method and system for predicting student development direction based on K-Means clustering algorithm. Background technique [0002] Vocational education has been paid more and more attention by the country and is in a period of vigorous development. With the importance of vocational education becoming more and more prominent, the enrollment rate of higher vocational education is also constantly increasing. [0003] With the development of higher vocational colleges, the number of higher vocational students is also growing rapidly. In higher vocational colleges, a large number of students' academic situation analysis data are stored, and this important academic situation analysis data is in many colleges and universities. It has not been fully utilized. How to make full use of these valuable academic situation analysis data to train students in higher vocational college...

AI Technical Summary

Problems solved by technology

[0004] In view of this, this application proposes a method and system for predicting students' development direction based on K-Means clustering algorithm, which solves the problem that the prior art does not make full use of the academic situation analysis data to predict the future development direction of students , to provide feasible suggestions for formulating student study plans and career development plans

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