Analysis of Teachers’ Cognitive Ability and Teaching Motivation on the Academic Achievement of Students in Higher Education via Employment Data Guidance

Teachers and students are the two basic elements in educational activities. Students are educated but are not exactly passive recipients of education. With subjective initiative, all educational impacts must be through the initiative of students to achieve the desired effect. Therefore, all activiti...

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Veröffentlicht in:Electronics (Basel) 2023-02, Vol.12 (3), p.572
Hauptverfasser: Zhu, Huirong, Zheng, Xuxu, Zhao, Leina
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Zhao, Leina
description Teachers and students are the two basic elements in educational activities. Students are educated but are not exactly passive recipients of education. With subjective initiative, all educational impacts must be through the initiative of students to achieve the desired effect. Therefore, all activities of education must start from mobilizing students’ initiative and motivation so that they have sufficient motivation to learn actively and well. The effective analysis of employment data, at the statistical level of data analysis, is a favorable basis to support the influence of teachers on students. However, most of the previous methods are C4.5 algorithms, decision tree generation algorithms based on rough sets, etc., which are commonly used for employment data analysis. None of them can sufficiently deal with the problem of different decision accuracy requirements and noise adaptability. In this paper, we analyze the employment data of a university in 2012 as an example and compare the analysis results with those of the C4.5 algorithm and decision tree generation algorithm based on a rough set. The results show that the decision tree algorithm based on the multiscale rough set model generates a simple decision tree structure. In addition, our methods do not have indistinguishable datasets and are fast in terms of computing. This study provides an effective guide to the relevance of teachers’ cognitive abilities and teaching motivations for students’ employment.
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Students are educated but are not exactly passive recipients of education. With subjective initiative, all educational impacts must be through the initiative of students to achieve the desired effect. Therefore, all activities of education must start from mobilizing students’ initiative and motivation so that they have sufficient motivation to learn actively and well. The effective analysis of employment data, at the statistical level of data analysis, is a favorable basis to support the influence of teachers on students. However, most of the previous methods are C4.5 algorithms, decision tree generation algorithms based on rough sets, etc., which are commonly used for employment data analysis. None of them can sufficiently deal with the problem of different decision accuracy requirements and noise adaptability. 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source MDPI - Multidisciplinary Digital Publishing Institute; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Academic achievement
Algorithms
Analysis
Colleges & universities
Data analysis
Decision analysis
Decision making
Decision trees
Education
Education, Higher
Employment
Learning
Motivation in education
Rough set models
Students
Teacher morale
Teachers
Teaching
Tree generating algorithms
title Analysis of Teachers’ Cognitive Ability and Teaching Motivation on the Academic Achievement of Students in Higher Education via Employment Data Guidance
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