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...
Gespeichert in:
Veröffentlicht in: | Electronics (Basel) 2023-02, Vol.12 (3), p.572 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 3 |
container_start_page | 572 |
container_title | Electronics (Basel) |
container_volume | 12 |
creator | Zhu, Huirong Zheng, Xuxu 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. |
doi_str_mv | 10.3390/electronics12030572 |
format | Article |
fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2774855877</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A743140538</galeid><sourcerecordid>A743140538</sourcerecordid><originalsourceid>FETCH-LOGICAL-c311t-9fb14e03ee756fffbfb27951156536f485229462a1c08655a735c9e11acab7553</originalsourceid><addsrcrecordid>eNptkc9qGzEQxkVoIMHNE-Qi6Nmp_qxW1tG4blJI6aHpedFqR_aEteRKWoNvfY328fokVbo59FAxoA_N7_tAM4TccnYnpWHvYQRXUgzoMhdMMqXFBbkWTJulEUa8-UdfkZucn1k9hsuVZNfk1zrY8Zwx0-jpE1i3h5R___hJN3EXsOAJ6LrHEcuZ2jDMBIYd_RxrzxaMgdYq-4o5O8ABXRV7hBMcIJSX0K9lGqrMFAN9wF3Np9thcrP3hJZuD8cxnv_iH2yx9H7CwQYHb8mlt2OGm9d7Qb593D5tHpaPX-4_bdaPSyc5L0vje94AkwBatd773vdCG8W5apVsfbNSQpimFZY7tmqVsloqZ4Bz62yvlZIL8m7OPab4fYJcuuc4pTqW3Amtq1-ttK7U3Uzt7AgdBh9Lqgmvn44BPNb3tW4kb5iqw10QORtcijkn8N0x4cGmc8dZ97K47j-Lk38A6jmQ4A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2774855877</pqid></control><display><type>article</type><title>Analysis of Teachers’ Cognitive Ability and Teaching Motivation on the Academic Achievement of Students in Higher Education via Employment Data Guidance</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Zhu, Huirong ; Zheng, Xuxu ; Zhao, Leina</creator><creatorcontrib>Zhu, Huirong ; Zheng, Xuxu ; Zhao, Leina</creatorcontrib><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.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics12030572</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>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</subject><ispartof>Electronics (Basel), 2023-02, Vol.12 (3), p.572</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c311t-9fb14e03ee756fffbfb27951156536f485229462a1c08655a735c9e11acab7553</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Zhu, Huirong</creatorcontrib><creatorcontrib>Zheng, Xuxu</creatorcontrib><creatorcontrib>Zhao, Leina</creatorcontrib><title>Analysis of Teachers’ Cognitive Ability and Teaching Motivation on the Academic Achievement of Students in Higher Education via Employment Data Guidance</title><title>Electronics (Basel)</title><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.</description><subject>Academic achievement</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Colleges & universities</subject><subject>Data analysis</subject><subject>Decision analysis</subject><subject>Decision making</subject><subject>Decision trees</subject><subject>Education</subject><subject>Education, Higher</subject><subject>Employment</subject><subject>Learning</subject><subject>Motivation in education</subject><subject>Rough set models</subject><subject>Students</subject><subject>Teacher morale</subject><subject>Teachers</subject><subject>Teaching</subject><subject>Tree generating algorithms</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNptkc9qGzEQxkVoIMHNE-Qi6Nmp_qxW1tG4blJI6aHpedFqR_aEteRKWoNvfY328fokVbo59FAxoA_N7_tAM4TccnYnpWHvYQRXUgzoMhdMMqXFBbkWTJulEUa8-UdfkZucn1k9hsuVZNfk1zrY8Zwx0-jpE1i3h5R___hJN3EXsOAJ6LrHEcuZ2jDMBIYd_RxrzxaMgdYq-4o5O8ABXRV7hBMcIJSX0K9lGqrMFAN9wF3Np9thcrP3hJZuD8cxnv_iH2yx9H7CwQYHb8mlt2OGm9d7Qb593D5tHpaPX-4_bdaPSyc5L0vje94AkwBatd773vdCG8W5apVsfbNSQpimFZY7tmqVsloqZ4Bz62yvlZIL8m7OPab4fYJcuuc4pTqW3Amtq1-ttK7U3Uzt7AgdBh9Lqgmvn44BPNb3tW4kb5iqw10QORtcijkn8N0x4cGmc8dZ97K47j-Lk38A6jmQ4A</recordid><startdate>20230201</startdate><enddate>20230201</enddate><creator>Zhu, Huirong</creator><creator>Zheng, Xuxu</creator><creator>Zhao, Leina</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20230201</creationdate><title>Analysis of Teachers’ Cognitive Ability and Teaching Motivation on the Academic Achievement of Students in Higher Education via Employment Data Guidance</title><author>Zhu, Huirong ; Zheng, Xuxu ; Zhao, Leina</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c311t-9fb14e03ee756fffbfb27951156536f485229462a1c08655a735c9e11acab7553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Academic achievement</topic><topic>Algorithms</topic><topic>Analysis</topic><topic>Colleges & universities</topic><topic>Data analysis</topic><topic>Decision analysis</topic><topic>Decision making</topic><topic>Decision trees</topic><topic>Education</topic><topic>Education, Higher</topic><topic>Employment</topic><topic>Learning</topic><topic>Motivation in education</topic><topic>Rough set models</topic><topic>Students</topic><topic>Teacher morale</topic><topic>Teachers</topic><topic>Teaching</topic><topic>Tree generating algorithms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Huirong</creatorcontrib><creatorcontrib>Zheng, Xuxu</creatorcontrib><creatorcontrib>Zhao, Leina</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhu, Huirong</au><au>Zheng, Xuxu</au><au>Zhao, Leina</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of Teachers’ Cognitive Ability and Teaching Motivation on the Academic Achievement of Students in Higher Education via Employment Data Guidance</atitle><jtitle>Electronics (Basel)</jtitle><date>2023-02-01</date><risdate>2023</risdate><volume>12</volume><issue>3</issue><spage>572</spage><pages>572-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics12030572</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2079-9292 |
ispartof | Electronics (Basel), 2023-02, Vol.12 (3), p.572 |
issn | 2079-9292 2079-9292 |
language | eng |
recordid | cdi_proquest_journals_2774855877 |
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 |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T14%3A16%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Analysis%20of%20Teachers%E2%80%99%20Cognitive%20Ability%20and%20Teaching%20Motivation%20on%20the%20Academic%20Achievement%20of%20Students%20in%20Higher%20Education%20via%20Employment%20Data%20Guidance&rft.jtitle=Electronics%20(Basel)&rft.au=Zhu,%20Huirong&rft.date=2023-02-01&rft.volume=12&rft.issue=3&rft.spage=572&rft.pages=572-&rft.issn=2079-9292&rft.eissn=2079-9292&rft_id=info:doi/10.3390/electronics12030572&rft_dat=%3Cgale_proqu%3EA743140538%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2774855877&rft_id=info:pmid/&rft_galeid=A743140538&rfr_iscdi=true |