Method for detecting teacher sub-health states based on classification and regression tree

Abstract

The invention provides a method for detecting teacher sub-health states based on a classification and regression tree. The traditional sub-health assessment for college teachers lacks timeliness, objectivity and efficiency. In the big data environment, the machine learning technology can be used for establishing a sub-health assessment model more effectively, so as to support prediction and early-warning of the sub-health states of the college teachers. The method for detecting teacher sub-health states based on the classification and regression tree comprises the steps of: firstly, performingmulti-dimensional analysis and conceptual modeling on college teacher sub-health influencing factors; secondly, performing analysis and data pre-processing on features of sample data; on this basis,utilizing a classification and regression tree algorithm for giving out a detailed process of modeling a sub-health decision-making model, and analyzing assessment indexes; and finally, utilizing a Spark distributed computing framework to give out parallel implementation of model construction. The method provided by the invention is more efficient and objective, can immediately reflect the sub-health states of the teachers, and supports the prediction and early-warning of the sub-health states of the college teachers.

Description

technical field [0001] The invention belongs to the field of data mining, and in particular relates to a sub-health decision-making model for teachers constructed based on a classification regression tree algorithm to detect the sub-health status of teachers. Background technique [0002] Sub-health is a borderline state between health and disease. Under the heavy tasks of teaching and scientific research, teachers work without time and space boundaries. At the same time, social and family pressures such as family, life, professional title promotion, and competition make sub-health conditions have a great impact on the physical and mental health of teachers. [0003] Traditional research methods use self-assessment scales and questionnaires to make statistics and evaluate teachers' sub-health status and influencing factors. This method often can only start from a macro perspective and carry out investigations in stages. Therefore, from the perspective of prediction and ea...

AI Technical Summary

Problems solved by technology

However, there is no research using the method of classification and regression trees to provide a complete and comprehensive analysis and modeling for the detection of teachers' sub-health status.

Images