Pedagogy of diversity and data analytics: Theory to practice
A course in probability is a requirement in Baccalaureate Programs in Electrical and Computer Engineering. Students view this course as conceptual with little connection to real world problems. Efforts have been undertaken to mitigate this issue and connect the conceptual topics to data analytics to...
Gespeichert in:
Veröffentlicht in: | Computer applications in engineering education 2019-09, Vol.27 (5), p.1277-1285 |
---|---|
1. Verfasser: | |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 1285 |
---|---|
container_issue | 5 |
container_start_page | 1277 |
container_title | Computer applications in engineering education |
container_volume | 27 |
creator | Shankar, P. Mohana |
description | A course in probability is a requirement in Baccalaureate Programs in Electrical and Computer Engineering. Students view this course as conceptual with little connection to real world problems. Efforts have been undertaken to mitigate this issue and connect the conceptual topics to data analytics to make the course relevant. Data analytics based exercises are now integral to the course. Several demos were created to link statistical concepts with practice through analysis of data. In this study, a demo created to illustrate the concept of diversity to improve the performance of a machine vision system is described. It incorporates concepts of Bayes’ rule, single and multiple random variables, goodness fit tests, random number simulation and data analytics to illustrate the pedagogy of diversity and associated data processing. Student survey results suggest that these demos enhance that the learning experience in the engineering probability course. |
doi_str_mv | 10.1002/cae.22151 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2283882514</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2283882514</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2971-b2105bd963c285328c796812e8d3852a2be1ce2462cbc78763d3b6c846da40013</originalsourceid><addsrcrecordid>eNp1kD1PwzAQhi0EEqUw8A8sMTGk9Z0Tx0EsVVU-pEowlNlybLekCrjYKVX-PS5hZbpXp-c-9BByDWwCjOHUaDdBhAJOyAhYVWWsyPH0mAVkvCz5ObmIccsYqwSvRuT-1Vm98Zue-jW1zbcLsel6qj8ttbrTKei27xoT7-jq3fnQ087TXdAm9dwlOVvrNrqrvzombw-L1fwpW748Ps9ny8xgVUJWI7CitumgQVlwlKashAR00nJZoMbagXGYCzS1KWUpuOW1MDIXVueMAR-Tm2HvLvivvYud2vp9SJ9FhSi5lFhAnqjbgTLBxxjcWu1C86FDr4CpoxyV5KhfOYmdDuyhaV3_P6jms8Uw8QOGHmNl</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2283882514</pqid></control><display><type>article</type><title>Pedagogy of diversity and data analytics: Theory to practice</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>Shankar, P. Mohana</creator><creatorcontrib>Shankar, P. Mohana</creatorcontrib><description>A course in probability is a requirement in Baccalaureate Programs in Electrical and Computer Engineering. Students view this course as conceptual with little connection to real world problems. Efforts have been undertaken to mitigate this issue and connect the conceptual topics to data analytics to make the course relevant. Data analytics based exercises are now integral to the course. Several demos were created to link statistical concepts with practice through analysis of data. In this study, a demo created to illustrate the concept of diversity to improve the performance of a machine vision system is described. It incorporates concepts of Bayes’ rule, single and multiple random variables, goodness fit tests, random number simulation and data analytics to illustrate the pedagogy of diversity and associated data processing. Student survey results suggest that these demos enhance that the learning experience in the engineering probability course.</description><identifier>ISSN: 1061-3773</identifier><identifier>EISSN: 1099-0542</identifier><identifier>DOI: 10.1002/cae.22151</identifier><language>eng</language><publisher>Hoboken: Wiley Subscription Services, Inc</publisher><subject>Analytics ; Computer simulation ; Data analysis ; Data processing ; diversity ; Engineering education ; engineering probability ; Machine vision ; Matlab ; Pedagogy ; Performance enhancement ; Random numbers ; Random variables ; receiver operating characteristics curves ; Statistical analysis ; Vision systems</subject><ispartof>Computer applications in engineering education, 2019-09, Vol.27 (5), p.1277-1285</ispartof><rights>2019 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2971-b2105bd963c285328c796812e8d3852a2be1ce2462cbc78763d3b6c846da40013</citedby><cites>FETCH-LOGICAL-c2971-b2105bd963c285328c796812e8d3852a2be1ce2462cbc78763d3b6c846da40013</cites><orcidid>0000-0002-9719-9700</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fcae.22151$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fcae.22151$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Shankar, P. Mohana</creatorcontrib><title>Pedagogy of diversity and data analytics: Theory to practice</title><title>Computer applications in engineering education</title><description>A course in probability is a requirement in Baccalaureate Programs in Electrical and Computer Engineering. Students view this course as conceptual with little connection to real world problems. Efforts have been undertaken to mitigate this issue and connect the conceptual topics to data analytics to make the course relevant. Data analytics based exercises are now integral to the course. Several demos were created to link statistical concepts with practice through analysis of data. In this study, a demo created to illustrate the concept of diversity to improve the performance of a machine vision system is described. It incorporates concepts of Bayes’ rule, single and multiple random variables, goodness fit tests, random number simulation and data analytics to illustrate the pedagogy of diversity and associated data processing. Student survey results suggest that these demos enhance that the learning experience in the engineering probability course.</description><subject>Analytics</subject><subject>Computer simulation</subject><subject>Data analysis</subject><subject>Data processing</subject><subject>diversity</subject><subject>Engineering education</subject><subject>engineering probability</subject><subject>Machine vision</subject><subject>Matlab</subject><subject>Pedagogy</subject><subject>Performance enhancement</subject><subject>Random numbers</subject><subject>Random variables</subject><subject>receiver operating characteristics curves</subject><subject>Statistical analysis</subject><subject>Vision systems</subject><issn>1061-3773</issn><issn>1099-0542</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kD1PwzAQhi0EEqUw8A8sMTGk9Z0Tx0EsVVU-pEowlNlybLekCrjYKVX-PS5hZbpXp-c-9BByDWwCjOHUaDdBhAJOyAhYVWWsyPH0mAVkvCz5ObmIccsYqwSvRuT-1Vm98Zue-jW1zbcLsel6qj8ttbrTKei27xoT7-jq3fnQ087TXdAm9dwlOVvrNrqrvzombw-L1fwpW748Ps9ny8xgVUJWI7CitumgQVlwlKashAR00nJZoMbagXGYCzS1KWUpuOW1MDIXVueMAR-Tm2HvLvivvYud2vp9SJ9FhSi5lFhAnqjbgTLBxxjcWu1C86FDr4CpoxyV5KhfOYmdDuyhaV3_P6jms8Uw8QOGHmNl</recordid><startdate>201909</startdate><enddate>201909</enddate><creator>Shankar, P. Mohana</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-9719-9700</orcidid></search><sort><creationdate>201909</creationdate><title>Pedagogy of diversity and data analytics: Theory to practice</title><author>Shankar, P. Mohana</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2971-b2105bd963c285328c796812e8d3852a2be1ce2462cbc78763d3b6c846da40013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Analytics</topic><topic>Computer simulation</topic><topic>Data analysis</topic><topic>Data processing</topic><topic>diversity</topic><topic>Engineering education</topic><topic>engineering probability</topic><topic>Machine vision</topic><topic>Matlab</topic><topic>Pedagogy</topic><topic>Performance enhancement</topic><topic>Random numbers</topic><topic>Random variables</topic><topic>receiver operating characteristics curves</topic><topic>Statistical analysis</topic><topic>Vision systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shankar, P. Mohana</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computer applications in engineering education</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shankar, P. Mohana</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pedagogy of diversity and data analytics: Theory to practice</atitle><jtitle>Computer applications in engineering education</jtitle><date>2019-09</date><risdate>2019</risdate><volume>27</volume><issue>5</issue><spage>1277</spage><epage>1285</epage><pages>1277-1285</pages><issn>1061-3773</issn><eissn>1099-0542</eissn><abstract>A course in probability is a requirement in Baccalaureate Programs in Electrical and Computer Engineering. Students view this course as conceptual with little connection to real world problems. Efforts have been undertaken to mitigate this issue and connect the conceptual topics to data analytics to make the course relevant. Data analytics based exercises are now integral to the course. Several demos were created to link statistical concepts with practice through analysis of data. In this study, a demo created to illustrate the concept of diversity to improve the performance of a machine vision system is described. It incorporates concepts of Bayes’ rule, single and multiple random variables, goodness fit tests, random number simulation and data analytics to illustrate the pedagogy of diversity and associated data processing. Student survey results suggest that these demos enhance that the learning experience in the engineering probability course.</abstract><cop>Hoboken</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/cae.22151</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-9719-9700</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1061-3773 |
ispartof | Computer applications in engineering education, 2019-09, Vol.27 (5), p.1277-1285 |
issn | 1061-3773 1099-0542 |
language | eng |
recordid | cdi_proquest_journals_2283882514 |
source | Wiley Online Library Journals Frontfile Complete |
subjects | Analytics Computer simulation Data analysis Data processing diversity Engineering education engineering probability Machine vision Matlab Pedagogy Performance enhancement Random numbers Random variables receiver operating characteristics curves Statistical analysis Vision systems |
title | Pedagogy of diversity and data analytics: Theory to practice |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-29T03%3A27%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Pedagogy%20of%20diversity%20and%20data%20analytics:%20Theory%20to%20practice&rft.jtitle=Computer%20applications%20in%20engineering%20education&rft.au=Shankar,%20P.%20Mohana&rft.date=2019-09&rft.volume=27&rft.issue=5&rft.spage=1277&rft.epage=1285&rft.pages=1277-1285&rft.issn=1061-3773&rft.eissn=1099-0542&rft_id=info:doi/10.1002/cae.22151&rft_dat=%3Cproquest_cross%3E2283882514%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2283882514&rft_id=info:pmid/&rfr_iscdi=true |