Loading…
Network models for mapping educational data
Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable lear...
Saved in:
Published in: | Design Science 2017, Vol.3, Article e18 |
---|---|
Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c364t-544a303e146cb688d04a4afebd322927a949dd92299c4bbc13f38296fc8474ae3 |
---|---|
cites | cdi_FETCH-LOGICAL-c364t-544a303e146cb688d04a4afebd322927a949dd92299c4bbc13f38296fc8474ae3 |
container_end_page | |
container_issue | |
container_start_page | |
container_title | Design Science |
container_volume | 3 |
creator | Willcox, Karen E. Huang, Luwen |
description | Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable learning analytics. For example, a curriculum map is usually a table, where relationships among curricular elements are represented implicitly in the rows of the table. The proposed network modeling approach overcomes this limitation through explicit modeling of these relationships in a graph structure, which in turn unlocks the ability to perform scalable analyses on the dataset. The paper presents network models for educational use cases, with concrete examples in curriculum mapping, accreditation mapping and concept mapping. Illustrative examples demonstrate how the formal modeling approach enables visualization and learning analytics. The analysis provides insight into learning pathways, supporting design of adaptive learning systems. It also permits gap analysis of curriculum coverage, supporting student advising, student degree planning and curricular design at scales ranging from an entire institution to an individual course. |
doi_str_mv | 10.1017/dsj.2017.18 |
format | article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_e4fd151444404f05bc2e5cd6b4a6012b</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_e4fd151444404f05bc2e5cd6b4a6012b</doaj_id><sourcerecordid>1991077088</sourcerecordid><originalsourceid>FETCH-LOGICAL-c364t-544a303e146cb688d04a4afebd322927a949dd92299c4bbc13f38296fc8474ae3</originalsourceid><addsrcrecordid>eNpNkEtLA0EQhAdRMMSc_AMLHsPGnsfOzhwl-AgEveh56J1H2HWTiTMbxH_vxojYly6a4muqCLmmsKBA61uXuwUbxYKqMzJhUPFS1EDP_-lLMsu5AwAqpa4kn5D5sx8-Y3ovttH5PhchpmKL-3272xTeHSwObdxhXzgc8IpcBOyzn_3uKXl7uH9dPpXrl8fV8m5dWi7FUFZCIAfuqZC2kUo5ECgw-MZxxjSrUQvtnB61tqJpLOWBK6ZlsErUAj2fktWJ6yJ2Zp_aLaYvE7E1P4eYNgbT0NreGy-CoxUV44AIUDWW-co62QiUQFkzsm5OrH2KHwefB9PFQxoTZUO1plDXoNTomp9cNsWckw9_XymYY7lmLNccyzVU8W_OpWo3</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1991077088</pqid></control><display><type>article</type><title>Network models for mapping educational data</title><source>Cambridge Journals Online</source><source>ProQuest - Publicly Available Content Database</source><creator>Willcox, Karen E. ; Huang, Luwen</creator><creatorcontrib>Willcox, Karen E. ; Huang, Luwen</creatorcontrib><description>Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable learning analytics. For example, a curriculum map is usually a table, where relationships among curricular elements are represented implicitly in the rows of the table. The proposed network modeling approach overcomes this limitation through explicit modeling of these relationships in a graph structure, which in turn unlocks the ability to perform scalable analyses on the dataset. The paper presents network models for educational use cases, with concrete examples in curriculum mapping, accreditation mapping and concept mapping. Illustrative examples demonstrate how the formal modeling approach enables visualization and learning analytics. The analysis provides insight into learning pathways, supporting design of adaptive learning systems. It also permits gap analysis of curriculum coverage, supporting student advising, student degree planning and curricular design at scales ranging from an entire institution to an individual course.</description><identifier>ISSN: 2053-4701</identifier><identifier>EISSN: 2053-4701</identifier><identifier>DOI: 10.1017/dsj.2017.18</identifier><language>eng</language><publisher>Cambridge: Cambridge University Press</publisher><subject>Accreditation ; Adaptive systems ; Analytics ; Concept mapping ; Core curriculum ; curriculum design ; curriculum mapping ; Datasets ; Design ; Education ; educational mapping ; Engineering ; Health education ; Learning ; learning analytics ; Modelling ; Pharmaceutical sciences</subject><ispartof>Design Science, 2017, Vol.3, Article e18</ispartof><rights>Copyright © The Author(s) 2017 Distributed as Open Access under a CC-BY 4.0 license (http://creativecommons.org/licenses/by/4.0/)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c364t-544a303e146cb688d04a4afebd322927a949dd92299c4bbc13f38296fc8474ae3</citedby><cites>FETCH-LOGICAL-c364t-544a303e146cb688d04a4afebd322927a949dd92299c4bbc13f38296fc8474ae3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1991077088/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1991077088?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,4024,25753,27923,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Willcox, Karen E.</creatorcontrib><creatorcontrib>Huang, Luwen</creatorcontrib><title>Network models for mapping educational data</title><title>Design Science</title><description>Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable learning analytics. For example, a curriculum map is usually a table, where relationships among curricular elements are represented implicitly in the rows of the table. The proposed network modeling approach overcomes this limitation through explicit modeling of these relationships in a graph structure, which in turn unlocks the ability to perform scalable analyses on the dataset. The paper presents network models for educational use cases, with concrete examples in curriculum mapping, accreditation mapping and concept mapping. Illustrative examples demonstrate how the formal modeling approach enables visualization and learning analytics. The analysis provides insight into learning pathways, supporting design of adaptive learning systems. It also permits gap analysis of curriculum coverage, supporting student advising, student degree planning and curricular design at scales ranging from an entire institution to an individual course.</description><subject>Accreditation</subject><subject>Adaptive systems</subject><subject>Analytics</subject><subject>Concept mapping</subject><subject>Core curriculum</subject><subject>curriculum design</subject><subject>curriculum mapping</subject><subject>Datasets</subject><subject>Design</subject><subject>Education</subject><subject>educational mapping</subject><subject>Engineering</subject><subject>Health education</subject><subject>Learning</subject><subject>learning analytics</subject><subject>Modelling</subject><subject>Pharmaceutical sciences</subject><issn>2053-4701</issn><issn>2053-4701</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkEtLA0EQhAdRMMSc_AMLHsPGnsfOzhwl-AgEveh56J1H2HWTiTMbxH_vxojYly6a4muqCLmmsKBA61uXuwUbxYKqMzJhUPFS1EDP_-lLMsu5AwAqpa4kn5D5sx8-Y3ovttH5PhchpmKL-3272xTeHSwObdxhXzgc8IpcBOyzn_3uKXl7uH9dPpXrl8fV8m5dWi7FUFZCIAfuqZC2kUo5ECgw-MZxxjSrUQvtnB61tqJpLOWBK6ZlsErUAj2fktWJ6yJ2Zp_aLaYvE7E1P4eYNgbT0NreGy-CoxUV44AIUDWW-co62QiUQFkzsm5OrH2KHwefB9PFQxoTZUO1plDXoNTomp9cNsWckw9_XymYY7lmLNccyzVU8W_OpWo3</recordid><startdate>2017</startdate><enddate>2017</enddate><creator>Willcox, Karen E.</creator><creator>Huang, Luwen</creator><general>Cambridge University Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PADUT</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>DOA</scope></search><sort><creationdate>2017</creationdate><title>Network models for mapping educational data</title><author>Willcox, Karen E. ; Huang, Luwen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c364t-544a303e146cb688d04a4afebd322927a949dd92299c4bbc13f38296fc8474ae3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accreditation</topic><topic>Adaptive systems</topic><topic>Analytics</topic><topic>Concept mapping</topic><topic>Core curriculum</topic><topic>curriculum design</topic><topic>curriculum mapping</topic><topic>Datasets</topic><topic>Design</topic><topic>Education</topic><topic>educational mapping</topic><topic>Engineering</topic><topic>Health education</topic><topic>Learning</topic><topic>learning analytics</topic><topic>Modelling</topic><topic>Pharmaceutical sciences</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Willcox, Karen E.</creatorcontrib><creatorcontrib>Huang, Luwen</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest research library</collection><collection>Research Library (Corporate)</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Research Library China</collection><collection>ProQuest - 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><collection>ProQuest Central Basic</collection><collection>Directory of Open Access Journals</collection><jtitle>Design Science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Willcox, Karen E.</au><au>Huang, Luwen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Network models for mapping educational data</atitle><jtitle>Design Science</jtitle><date>2017</date><risdate>2017</risdate><volume>3</volume><artnum>e18</artnum><issn>2053-4701</issn><eissn>2053-4701</eissn><abstract>Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable learning analytics. For example, a curriculum map is usually a table, where relationships among curricular elements are represented implicitly in the rows of the table. The proposed network modeling approach overcomes this limitation through explicit modeling of these relationships in a graph structure, which in turn unlocks the ability to perform scalable analyses on the dataset. The paper presents network models for educational use cases, with concrete examples in curriculum mapping, accreditation mapping and concept mapping. Illustrative examples demonstrate how the formal modeling approach enables visualization and learning analytics. The analysis provides insight into learning pathways, supporting design of adaptive learning systems. It also permits gap analysis of curriculum coverage, supporting student advising, student degree planning and curricular design at scales ranging from an entire institution to an individual course.</abstract><cop>Cambridge</cop><pub>Cambridge University Press</pub><doi>10.1017/dsj.2017.18</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2053-4701 |
ispartof | Design Science, 2017, Vol.3, Article e18 |
issn | 2053-4701 2053-4701 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_e4fd151444404f05bc2e5cd6b4a6012b |
source | Cambridge Journals Online; ProQuest - Publicly Available Content Database |
subjects | Accreditation Adaptive systems Analytics Concept mapping Core curriculum curriculum design curriculum mapping Datasets Design Education educational mapping Engineering Health education Learning learning analytics Modelling Pharmaceutical sciences |
title | Network models for mapping educational data |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T02%3A37%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Network%20models%20for%20mapping%20educational%20data&rft.jtitle=Design%20Science&rft.au=Willcox,%20Karen%20E.&rft.date=2017&rft.volume=3&rft.artnum=e18&rft.issn=2053-4701&rft.eissn=2053-4701&rft_id=info:doi/10.1017/dsj.2017.18&rft_dat=%3Cproquest_doaj_%3E1991077088%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c364t-544a303e146cb688d04a4afebd322927a949dd92299c4bbc13f38296fc8474ae3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1991077088&rft_id=info:pmid/&rfr_iscdi=true |