Interconnecting and Enriching Higher Education Programsusing Linked Data

Fouad ZablithOlayan School of Business

American University of BeirutPO Box 11-0236 Riad El Solh 1107 2020

Beirut Lebanonfouadzablithaubedulb

ABSTRACTOnline environments are increasingly used as platforms tosupport and enhance learning experiences In higher edu-cation students enroll in programs that are usually formedof a set of courses and modules Such courses are designedto cover a set of concepts and achieve specific learning ob-jectives that count towards the related degree However weobserve that connections among courses and the way theyconceptually interlink are hard to exploit This is normal ascourses are traditionally described using text in the form ofdocuments such as syllabi and course catalogs We believethat linked data can be used to create a conceptual layeraround higher education programs to interlink courses in agranular and reusable manner We present in this paper ourwork on creating a semantic linked data layer to conceptu-ally connect courses taught in a higher education programWe highlight the linked data model we created to be collab-oratively extended by course instructors and students usinga semantic Mediawiki platform We also present two ap-plications that we built on top of the data to (1) showcasehow learning material can now float around courses throughtheir interlinked concepts in eLearning environments (we usemoodle as a proof of concept) and (2) to support the processof higher education program reviews

Categories and Subject DescriptorsE1 [Data] Data StructuresmdashGraphs and networks J1[Computer Applications] Administrative Data Process-ingmdashEducation

General TermsDesign Management

KeywordsCurriculum data management education graph learninganalytics learning environments linked data semantic web

Copyright is held by the International World Wide Web Conference Com-mittee (IW3C2) IW3C2 reserves the right to provide a hyperlink to theauthorrsquos site if the Material is used in electronic mediaWWW 2015 Companion May 18ndash22 2015 Florence ItalyACM 978-1-4503-3473-01505httpdxdoiorg10114527409082741740

1 INTRODUCTIONWith the advancements of Internet and Web technologies

higher education is undergoing major changes in the waycourses and programs are delivered For example we are wit-nessing new teaching methodologies the explosion of openeducation in the form of massive online courses (MOOCs)and new means of interaction between teachers and studentsThis move from an offline delivery mode to blended and on-line modes is opening up new opportunities for educators toexplore

In higher education most of the programs are designedaround courses that follow a certain sequence This se-quence is usually guided by the knowledge covered in certaincourses which are entry points to further knowledge cov-ered in subsequent courses Traditionally coursesrsquo syllabiand program catalogs are used as the main source of coursedescription The problem is that this description is usuallyat a high level prohibiting course designers the ability tobuild a holistic representation of what a course covers andhow the knowledge in a course connects to the knowledgecovered in other courses This lack of knowledge represen-tation creates boundaries around courses that are hard tobreak even in online settings

We observe that online environments are not being ex-ploited to their full extent in higher education We believethat the Web of data can be used to break the knowledgeislands in courses delivered in higher education We presentin this paper our work on conceptually interlinking courseswithin a higher education program We propose a datamodel that goes down to the concepts taught in coursesand we use the concepts to cross connect the courses andlearning material We use a semantic Mediawiki [6] plat-form as an entry point to collaboratively build this linkeddata graph around the higher education program We im-plement this wiki environment at the Olayan School of Busi-ness of the American University of Beirut We then exploitthe linked data generated in two pilot applications the firstis used to demonstrate how the taught concepts in coursescan be used to cross connect moodle pages and enrich themwith relevant learning material while the second includes anew unprecedented view of how courses interlink and shareconcepts which proved to be useful in the program reviewexercise of the school

We present in Section2 an overview of the related workThen we discuss the linked data model in Section 3 We laterpresent in Section 4 how we generated the linked data layerby focusing on the deployment of the semantic Mediawiki

711

and the steps we followed in building the knowledge graphIn Section 5 we highlight the two applications we developedon top of the data followed by our concluding notes andfuture work in Section 6

2 RELATED WORKLinked data is getting increasingly used in various con-

texts higher education is no exception In their survey Di-etze et al [7] highlight the growing adoption of linked databy various universities Various platforms have emergedwhere linked data is made available for direct consump-tion and reuse This includes for example the OUrsquos linkedopen data platform (httpdataopenacuk) the Univer-sity of Muenster (httpdatauni-muensterde) the Uni-versity of Oxford (httpdataoxacuk) the Universityof Southampton (httpdatasouthamptonacuk) amongothers

Methodologies and frameworks have been proposed to trans-form existing data sources into linked data [2 8 9] Inthis context available organizational data was transformedusing pre-programmed transformation patterns into linkeddata Following the trend and success of social and knowl-edge graphs functionalities provided by Facebook1 and Goo-gle2 there are discussions around the value of having an ed-ucation graph3 [4] Heath et al [4] proposed in their work tocreate an education graph by processing courses informationand learning material from various universities in the UK Intheir approach they mainly rely on bibliographical data ofmaterial repositories to identify links to course resources [4]

In our context the aim is to interlink courses within thesame institution at the level of concepts covered in coursetopics To our knowledge there is no existing vocabularythat covers this granular information about courses infor-mation To achieve our objective we had to go at lower(ie more specific) conceptual levels by enabling users tohave a direct impact on reshaping how courses interconnectamong them and with learning resources This controlledenvironment is necessary for aligning the conceptual cover-age of courses delivered by more than one instructor Wealso aimed to have direct input from students to connectto and expand the graph around the education programFor example when students find an interesting material on-line (eg video or article) relevant to a specific course wewanted to enable them to connect it back to the course byextending the graph and creating the appropriate links tothe course

3 LINKED DATA GRAPHWe present in this section the data model we adopted and

followed to capture coursesrsquo related information We usedthe information included in the coursesrsquo syllabi template asa guide to what information to represent A typical coursesyllabus follows a predefined template which includes thecourse details such as course number description prerequi-sites textbook topical coverage and others Furthermore

1httpnewsroomfbcomNews562Introducing-Graph-Search-Beta2httpwwwgooglecominsidesearchfeaturessearchknowledgehtml3httphackeducationcom20111110is-there-an-education-graph

each course has well defined learning objectives We visual-ize our proposed data model in Figure 1

We focus in this part on the description of specific as-pects that guided the development of the elements that arenot covered in the syllabi content Based on the need forconceptually connecting courses (ie beyond the analysis oftopics in common) we captured in the graph concepts thatare taught at the level of every course topic This design of-fers many advantages First we are getting a more granularview of what is covered in each course Second such con-cepts can be used as anchors between learning material andcourses This will enable course designers to know whereexactly each piece of material fits in the course and thisenables learning material to float around not only coursetopics but also around the program as a whole In otherwords an interesting article that can be useful in one coursecan also be used in other courses if the concepts are sharedbetween the courses

Following the linked data principles [3] we aimed to reusethe available ontologies that are relevant to our contextFor example the CourseWare ontology4 was reused to rep-resent information related to course types student interac-tion types number of credits assessment methods and oth-ers The AIISO5 was adopted to capture the coursesrsquo uniquecodes We have also used the Dublin Core Terms6 to repre-sent generic properties such as descriptions In cases whereno vocabularies were found we created a local vocabularyused in our context

4 LINKED DATA GENERATIONWe followed a collaborative process to build the graph

around the higher education program This involved profes-sors students and program coordinators As courses are de-livered by different professors we required to have a systemthat supports collaboration through which each professor orinvolved person can see expand and modify content as seenappropriate Another requirement was to enable studentsto also enrich and add to the graph in a quick and easy wayonline material that they find interesting In addition tothe above another needed feature was the ability to controllinked data vocabularies In this section we discuss how wegenerated linked data around our higher education programusing the semantic Mediawiki and a semantic bookmarkletfor linking online resources to the courses

41 Semantic Mediawiki DeploymentWe have put to the test a semantic Mediawiki available

at httplinkedaubedulbcollab For controlling thedata generated and vocabularies used we created severalforms that are automatically loaded when new content is tobe created In the case of creating new courses for examplethe course form7 is automatically loaded8 The advantageof using such forms is that the user is guided around what

4httpcoursewarerkbexplorercom5httpvocaborgaiisoschema6httppurlorgdcterms7httplinkedaubedulbcollabindexphpSpecialFormEditCourseNew_Course8For a filled form example check the Foundations of Infor-mation Systems course at the following linkhttplinkedaubedulbcollabindexphptitle=INFO200_-_Foundations_of_Information_Systemsampaction=formedit

712

and the steps we followed in building the knowledge graphIn Section 5 we highlight the two applications we developedon top of the data followed by our concluding notes andfuture work in Section 6

2 RELATED WORKLinked data is getting increasingly used in various con-

texts higher education is no exception In their survey Di-etze et al [7] highlight the growing adoption of linked databy various universities Various platforms have emergedwhere linked data is made available for direct consump-tion and reuse This includes for example the OUrsquos linkedopen data platform (httpdataopenacuk) the Univer-sity of Muenster (httpdatauni-muensterde) the Uni-versity of Oxford (httpdataoxacuk) the Universityof Southampton (httpdatasouthamptonacuk) amongothers

Methodologies and frameworks have been proposed to trans-form existing data sources into linked data [2 8 9] Inthis context available organizational data was transformedusing pre-programmed transformation patterns into linkeddata Following the trend and success of social and knowl-edge graphs functionalities provided by Facebook1 and Goo-gle2 there are discussions around the value of having an ed-ucation graph3 [4] Heath et al [4] proposed in their work tocreate an education graph by processing courses informationand learning material from various universities in the UK Intheir approach they mainly rely on bibliographical data ofmaterial repositories to identify links to course resources [4]

In our context the aim is to interlink courses within thesame institution at the level of concepts covered in coursetopics To our knowledge there is no existing vocabularythat covers this granular information about courses infor-mation To achieve our objective we had to go at lower(ie more specific) conceptual levels by enabling users tohave a direct impact on reshaping how courses interconnectamong them and with learning resources This controlledenvironment is necessary for aligning the conceptual cover-age of courses delivered by more than one instructor Wealso aimed to have direct input from students to connectto and expand the graph around the education programFor example when students find an interesting material on-line (eg video or article) relevant to a specific course wewanted to enable them to connect it back to the course byextending the graph and creating the appropriate links tothe course

3 LINKED DATA GRAPHWe present in this section the data model we adopted and

followed to capture coursesrsquo related information We usedthe information included in the coursesrsquo syllabi template asa guide to what information to represent A typical coursesyllabus follows a predefined template which includes thecourse details such as course number description prerequi-sites textbook topical coverage and others Furthermore

1httpnewsroomfbcomNews562Introducing-Graph-Search-Beta2httpwwwgooglecominsidesearchfeaturessearchknowledgehtml3httphackeducationcom20111110is-there-an-education-graph

each course has well defined learning objectives We visual-ize our proposed data model in Figure 1

We focus in this part on the description of specific as-pects that guided the development of the elements that arenot covered in the syllabi content Based on the need forconceptually connecting courses (ie beyond the analysis oftopics in common) we captured in the graph concepts thatare taught at the level of every course topic This design of-fers many advantages First we are getting a more granularview of what is covered in each course Second such con-cepts can be used as anchors between learning material andcourses This will enable course designers to know whereexactly each piece of material fits in the course and thisenables learning material to float around not only coursetopics but also around the program as a whole In otherwords an interesting article that can be useful in one coursecan also be used in other courses if the concepts are sharedbetween the courses

Following the linked data principles [3] we aimed to reusethe available ontologies that are relevant to our contextFor example the CourseWare ontology4 was reused to rep-resent information related to course types student interac-tion types number of credits assessment methods and oth-ers The AIISO5 was adopted to capture the coursesrsquo uniquecodes We have also used the Dublin Core Terms6 to repre-sent generic properties such as descriptions In cases whereno vocabularies were found we created a local vocabularyused in our context

4 LINKED DATA GENERATIONWe followed a collaborative process to build the graph

around the higher education program This involved profes-sors students and program coordinators As courses are de-livered by different professors we required to have a systemthat supports collaboration through which each professor orinvolved person can see expand and modify content as seenappropriate Another requirement was to enable studentsto also enrich and add to the graph in a quick and easy wayonline material that they find interesting In addition tothe above another needed feature was the ability to controllinked data vocabularies In this section we discuss how wegenerated linked data around our higher education programusing the semantic Mediawiki and a semantic bookmarkletfor linking online resources to the courses

41 Semantic Mediawiki DeploymentWe have put to the test a semantic Mediawiki available

at httplinkedaubedulbcollab For controlling thedata generated and vocabularies used we created severalforms that are automatically loaded when new content is tobe created In the case of creating new courses for examplethe course form7 is automatically loaded8 The advantageof using such forms is that the user is guided around what

4httpcoursewarerkbexplorercom5httpvocaborgaiisoschema6httppurlorgdcterms7httplinkedaubedulbcollabindexphpSpecialFormEditCourseNew_Course8For a filled form example check the Foundations of Infor-mation Systems course at the following linkhttplinkedaubedulbcollabindexphptitle=INFO200_-_Foundations_of_Information_Systemsampaction=formedit

712

Course

Learning Material

Subject

Description

Course Learning Goal

Business Learning Goal

Description

Number of Credit Hours

Specific Learning Objective

Description

Course Description

Course Number

Taught Concept

Topic

Assessment Method

Instructional Methodology

Organization

Required Academic Standing

Sophomore

Junior

Senior

Learning Material Type

Article

Book

Video

Description

URL

Description

Wikipedia Reference

DescriptionGrade Percentage

Description

Description

hasLearningGoalhasCreditHours

hasSubject

hasPerformanceEvaluation

hasInstructionalMethodology

hasStandingPrerequisite

hasSpecificLearningGoal

coversTopicisTaughtAt

hasDescription

hasCourseNumber

hasDescription

fulfillsBusinessLearningGoalhasDescription

hasDescription

includesTaughtConcept

hasDescription

hasWikipediaReference

hasDescription

hasDescriptionhasGradePercentage

hasDescription

coversConcept

hasURL

hasDescription

hasLearningMaterialType

Figure 1 The Linked Data Graph

fields to fill and the vocabularies used can be predefined inthe form when the RDF is generated

Linking data is done by controlling the field content in thewiki By adopting the wiki forms the fields can guide usersin reusing existing concepts from the wiki For examplewhen specifying the course prerequisite relation the user isprompted with the list of courses available in the knowledgespace that can be chosen from This is a core feature usedfor the interlinking process Another example is at the levelof concepts covered in course topics The same concept canbe covered in one or more course topic creating the linksneeded

42 Steps Followed for Building the LinkedData Graph

Building the linked data graph was done in three phasesIn the first phase course syllabi are used as entry pointswhere the high level course information is entered In thesecond phase textbook materials used in the course are pro-cessed by the teaching assistants (TAs) to identify the con-cepts covered in the course In the third phase new ex-ternal materials are added to the graph by instructors andstudents using a semantic bookmarklet

Phase 1 Creating Course Information This phasewas straight forward as course syllabi follow a predefinedstructure Some difficulties were faced at the level of identi-fying learning goals as some of coursesrsquo learning goals weredescribed as text without a clear structure Another com-plexity at this level was that course learning goals were attwo levels (as depicted in Figure 1) courses with specificlearning goals which are linked to a broader list of busi-ness learning goals identified within our School of Business

Another challenge faced at this level is when the topicsof courses change with time due for example to changesin textbook editions or in the delivery of content acrosssemesters For instance in the Foundations of InformationSystems course the Social Media topic covered in the ldquoEx-periencing MISrdquo [5] textbook has changed from the 3rd to 4th

edition In this case while the topical coverage has changedsome of the concepts that were covered in the previous edi-tion were still there We handled such cases by archivingtopics and removing the link to the course This way wewere able to preserve the concepts related to the old topicand reuse them if needed in the new topic The evolution ofchanges at this level can be better managed in the futureOne possible improvement can be done through capturingtemporal changes coupled with the type of changes per-formed (eg adding or removing concepts from the conceptgraph) While tracing such evolution patterns can be doneat the data entry level a post analysis of changes occurringon the data graph can be possibly performed Such featuresare beneficial for analytics applications and can be furtherexplored as part of our future research We have processedso far the 19 core courses offered at the School of Businessleaving the elective courses to be represented at a later stage

Phase 2 Identifying Concepts Taught in CoursesThis phase was the most extensive and time consumingphase TAs were trained to identify concepts covered inthe topics of the courses Based on the model we created(cf Figure 1) concepts are linked to the topics of a courseand not directly to the course itself This choice of designenables grouping concepts by topic rather than by courseThis has a practical implication in filtering concepts coveredin specific topics (as we discuss later in the paper when we in-

713

tegrate data in moodle) Another implication is at the levelof program analytics Overlap among course topics can behighlighted easily which proved to be useful for the programreview and design exercises At this level the TAs were go-ing into each topic covered in the textbook to identify themain concepts adding definitions from the book and linkingto a Wikipedia9 reference when found When applicable theTAs were instructed to reuse existing concepts in the repos-itory The concepts field in the topic form automaticallyprovides the TAs with the list of existing concepts to choosefrom One challenge at this level is when concepts from dif-ferent topics share the same name however are semanticallydifferent For example the Optimization concept covered inthe Managerial Decision Making (ie operations research)is semantically different than the optimization concept inthe Managerial Economics field This is where Wikipedia isused as an external reference for disambiguating such casesWhile linking to the concepts to Wikipedia entries is cur-rently done manually this task can be performed (semi)-automatically by querying DBpedia [1] and aiming to findoverlap between the conceptsrsquo definition and the descriptionin the DBpedia page In addition to text matching thegraph of the concepts can be used as a context to highlightthe degree of matching Then the user can browse the pro-posed matching concepts to select the most appropriate oneCurrently concepts identified are not related through an ex-plicit relation We are planning to capture in the futurerelations such as sub-class and prerequisite relations amongconcepts This can also have an impact on the analysis ofcourse sequencing in the program So far we have identified2680 concepts covered in the core courses of the School ofBusiness

Phase 3 Semantically Anchoring Learning Mate-rial to Courses While the previous phases were mainlyfocusing on reorganizing internal knowledge sources thisphase is more about linking new materials to the programby anchoring them to concepts covered in courses Here theaim was to enable students and instructors to link any inter-esting online material to the graph We developed a simplebookmarklet that can be installed in any browser When theuser links a learning material clicking the bookmarklet willautomatically extract the page link title and descriptionThe user is then prompted to a page pointing to our wikiplatform where the concepts covered in the material canbe entered by reusing existing concepts from the graph10As mentioned earlier links between a learning material andthe program are done through the concepts This some-how enables educators and students to think more aroundthe relevance of the material around concepts For examplewe witnessed a student who bookmarked an article relatedto Big Data11 relevant to the Foundations of InformationSystems course she was enrolled in While anchoring thisarticle she reused concepts12 from the graph that indirectlyspread to two other courses Starting from an information

9wwwwikipediaorg10A video tutorial on how to use the bookmarklet can heaccessed at httplinkedaubedulbdocstutorial_material_bookmark

11httpwwwcapgeminicomresourcesthe-deciding-factor-big-data-decision-making

12The full list of concepts highlighted by the student can befound in Figure 2

systemsrsquo related material the student indirectly linked to amanagement and operations management courses

5 CONSUMPTION OF LINKED DATAWe present in this section two scenarios where we used

the linked data generated We accessed the data throughthe semantic search feature of the wiki endpoint that pro-vides querying functionalities with different output formatsThe queries were formulated and passed through php to thewiki query endpoint and results were returned in JSON forprocessing

51 Interlinking Course Learning Material onMoodle

The data generated was used to enrich and connect themoodle course pages Moodle is extensively used at theSchool of Business as a way to communicate course relatedinformation and to interact around course deliverables

The suggested design of moodle course pages at the Amer-ican University of Beirut is to subdivide the page using thecovered topics in the course and add topic related materialassignments and others under each section Course instruc-tors design their own page at the beginning of each semesteror reuse an existing one if the course was already taught bythe instructor One trend that is observed is that instruc-tors of the same course tend to share interesting materialsthat could be used in classrooms However such insights arenot usually captured and have to be re-shared whenevera new instructor teaches the course In addition to shar-ing constraints another bottleneck observed is that coursesare designed (even on moodle) in isolation However ma-terial relevant to some courses can potentially be relevantto others (as perceived with the Big Data article discussedearlier)

The aim of this application is to break out of the static andisolated nature of content shared within a course topic onmoodle The application offers the functionality to dynami-cally enrich moodle pages without leaving it with materialrelevant to the topics of the course

A video tutorial was used as a guide for students andinstructors to follow13 This application can be launchedby pressing a bookmarklet in the browser and the followingsequence of steps is performed

1 Scan the topics available in the moodle page the exe-cuted code first will launch a javascript that will scanthe moodle page for the topics header

2 Enable the buttons on the moodle page the buttonsare dynamically added to the moodle page with thecorresponding topic and course code embedded insidethe button links

3 Send query to the wiki endpoint when the user pressesthe button a query is passed to the wiki endpoint withthe topic and course code As per our model in Fig-ure 1 the link between the course and material is doneat two levels through the topic and then through thetaught concepts So the query is built to first fetch theconcepts covered in a topic and then filter the learningmaterial based on the concepts in focus

13httplinkedaubedulbdocstutorial_extract_material

714

tegrate data in moodle) Another implication is at the levelof program analytics Overlap among course topics can behighlighted easily which proved to be useful for the programreview and design exercises At this level the TAs were go-ing into each topic covered in the textbook to identify themain concepts adding definitions from the book and linkingto a Wikipedia9 reference when found When applicable theTAs were instructed to reuse existing concepts in the repos-itory The concepts field in the topic form automaticallyprovides the TAs with the list of existing concepts to choosefrom One challenge at this level is when concepts from dif-ferent topics share the same name however are semanticallydifferent For example the Optimization concept covered inthe Managerial Decision Making (ie operations research)is semantically different than the optimization concept inthe Managerial Economics field This is where Wikipedia isused as an external reference for disambiguating such casesWhile linking to the concepts to Wikipedia entries is cur-rently done manually this task can be performed (semi)-automatically by querying DBpedia [1] and aiming to findoverlap between the conceptsrsquo definition and the descriptionin the DBpedia page In addition to text matching thegraph of the concepts can be used as a context to highlightthe degree of matching Then the user can browse the pro-posed matching concepts to select the most appropriate oneCurrently concepts identified are not related through an ex-plicit relation We are planning to capture in the futurerelations such as sub-class and prerequisite relations amongconcepts This can also have an impact on the analysis ofcourse sequencing in the program So far we have identified2680 concepts covered in the core courses of the School ofBusiness

Phase 3 Semantically Anchoring Learning Mate-rial to Courses While the previous phases were mainlyfocusing on reorganizing internal knowledge sources thisphase is more about linking new materials to the programby anchoring them to concepts covered in courses Here theaim was to enable students and instructors to link any inter-esting online material to the graph We developed a simplebookmarklet that can be installed in any browser When theuser links a learning material clicking the bookmarklet willautomatically extract the page link title and descriptionThe user is then prompted to a page pointing to our wikiplatform where the concepts covered in the material canbe entered by reusing existing concepts from the graph10As mentioned earlier links between a learning material andthe program are done through the concepts This some-how enables educators and students to think more aroundthe relevance of the material around concepts For examplewe witnessed a student who bookmarked an article relatedto Big Data11 relevant to the Foundations of InformationSystems course she was enrolled in While anchoring thisarticle she reused concepts12 from the graph that indirectlyspread to two other courses Starting from an information

9wwwwikipediaorg10A video tutorial on how to use the bookmarklet can heaccessed at httplinkedaubedulbdocstutorial_material_bookmark

11httpwwwcapgeminicomresourcesthe-deciding-factor-big-data-decision-making

12The full list of concepts highlighted by the student can befound in Figure 2

systemsrsquo related material the student indirectly linked to amanagement and operations management courses

5 CONSUMPTION OF LINKED DATAWe present in this section two scenarios where we used

the linked data generated We accessed the data throughthe semantic search feature of the wiki endpoint that pro-vides querying functionalities with different output formatsThe queries were formulated and passed through php to thewiki query endpoint and results were returned in JSON forprocessing

51 Interlinking Course Learning Material onMoodle

The data generated was used to enrich and connect themoodle course pages Moodle is extensively used at theSchool of Business as a way to communicate course relatedinformation and to interact around course deliverables

The suggested design of moodle course pages at the Amer-ican University of Beirut is to subdivide the page using thecovered topics in the course and add topic related materialassignments and others under each section Course instruc-tors design their own page at the beginning of each semesteror reuse an existing one if the course was already taught bythe instructor One trend that is observed is that instruc-tors of the same course tend to share interesting materialsthat could be used in classrooms However such insights arenot usually captured and have to be re-shared whenevera new instructor teaches the course In addition to shar-ing constraints another bottleneck observed is that coursesare designed (even on moodle) in isolation However ma-terial relevant to some courses can potentially be relevantto others (as perceived with the Big Data article discussedearlier)

The aim of this application is to break out of the static andisolated nature of content shared within a course topic onmoodle The application offers the functionality to dynami-cally enrich moodle pages without leaving it with materialrelevant to the topics of the course

A video tutorial was used as a guide for students andinstructors to follow13 This application can be launchedby pressing a bookmarklet in the browser and the followingsequence of steps is performed

1 Scan the topics available in the moodle page the exe-cuted code first will launch a javascript that will scanthe moodle page for the topics header

2 Enable the buttons on the moodle page the buttonsare dynamically added to the moodle page with thecorresponding topic and course code embedded insidethe button links

3 Send query to the wiki endpoint when the user pressesthe button a query is passed to the wiki endpoint withthe topic and course code As per our model in Fig-ure 1 the link between the course and material is doneat two levels through the topic and then through thetaught concepts So the query is built to first fetch theconcepts covered in a topic and then filter the learningmaterial based on the concepts in focus

13httplinkedaubedulbdocstutorial_extract_material

714

InformationSystems

Management

Operations Management

BigDataOperations Decisions

Social MediaAutomated ProcessOrganizational Silos

DecisionInternet Services

Business Process ReengineeringIntuitive Decision Making

Learning Material Taught Concepts Moodle Courses

Figure 2 Learning Material Cross Connecting Courses

4 Parse and visualize query results the query results arereturned in a JSON output and parsed to identify thedifferent types of related material (so far we have videomaterial articles and books) Finally the results areused to populate the page where users can read articlesor play videos

Figure 2 highlights how the Big Data article mentionedearlier is linked to various courses through the commonlytaught concepts This design can place the material auto-matically under the right topic on the moodle page

52 Using Linked Data for Program ReviewAnother context where we used linked data was in the

program review process at the School of Business Everyfour years the curriculum has to be reviewed for changeswhere courses are studied to be added or removed from theprogram Another task that is part of the review processis the course sequencing To achieve this purpose tradi-tionally each course is studied on its own and compared toother courses and to the learning objectives in the programHowever it was not possible to perform an in-depth analysisbeyond the syllabi content and hence it was hard to knowexactly what is covered in each course and what are theconcepts that are repeated across different courses

Course mapping was highlighted to be one of the majortasks required for adjusting course sequencing and cover-age Our platform provided an unprecedented view of howcourses overlap going to the concept level We deployeda visualization showing how courses connect through topicsand taught concepts This visualization is dynamically gen-erated based on the wiki content A visualization examplearound the Foundations of Informations Systems course canbe accessed online at httplinkedaubedulbcollabindexphpLearning_Concepts_Graph This visualizationis created using the HyperGraph tool14 However this visu-alization proved to be a bit complex to analyze in a system-atic way

14httphypergraphsourceforgenet

Another view that we built on top of the data was a tab-ular representation of the courses along with the topics andoverlapping concepts The table can be accessed online15and we show part of the table in Figure 3 The users wereable to see the list of courses (course codes are on the toprow of the table) and in each row we present the list oftopics and their corresponding concepts The ldquoXrdquo marks thenumber or occurrences of this concept in the whole programWhen more than one ldquoXrdquo is in the cell the user can roll themouse over the table cell to see where this concept occurs inother courses For example Figure 3 indicates that the con-cept Audit is mentioned in the Accounting 210 course andin the Marketing 222 course We also fetch the topics wherethe concepts are covered This tool proved to be useful inthe program review process as it enabled the program coor-dinators and instructors to highlight the parts of the coursesthat need adjustment

6 CONCLUSION AND FUTURE WORKWe presented in this paper our work on connecting higher

education program information at a conceptual level usinglinked data The data model we designed captures and con-nects courses information going down to the topical cov-erage and concepts taught The concepts are then used asanchors between learning materials and the higher educationprogram

We highlighted our continuous work on collaborativelybuilding this linked data graph by involving people arounda semantic Mediawiki The wiki offers a platform whereinstructors can reach a consensus around what is taught intheir courses by having a controlled environment to managethe reuse of existing knowledge and appropriate vocabulariesfor creating linkages

One advantage of linked data is the ease of building appli-cations that make use of the data generated In our work weshowed how we can bring learning materials in the contextof a course and place them under its corresponding topic

15httplinkedaubedulbappstablebrowsertablephp

715

Figure 3 View of the Table Listing Concepts Covered in Courses with Overlap Detection

The use of taught concepts as anchors between the materialsand courses gave us a great flexibility in fetching materialthat stretch the boundaries of courses delivery which canimprove the learning experience of students and highlighthow courses cross-connect in the degree program they areenrolled in

Another scenario where we applied the use of linked datais at the level of program analysis and review Program de-signers at the School of Business were offered a unique viewthat was not possible before on how courses conceptuallyconnect They were able to see how concepts are repeatedin courses enabling them to make better decisions aroundrequired changes in the program

We believe that this work is only a glimpse of what canbe done with this new linked data layer We are planningto evaluate the impact of having such data layer in learn-ing environments through a guided user study coupled withevaluation measures We are currently working on captur-ing social interactions around our education program Bymerging the social and education graphs we anticipate thatwe will be able to granularly analyze how teachers and stu-dents interact around concepts delivered during their highereducation journeys

7 ACKNOWLEDGMENTSThis work was supported by the University Research Board

grant at the American University of Beirut I would like tothank my colleagues and students who contributed to theextension of the graph through the wiki and supported mein the implementation of the tools I would also like to thankthe students who have used the bookmarklet to bookmarkthe interesting materials that triggered invaluable insightsand observations

8 REFERENCES[1] C Bizer J Lehmann G Kobilarov S Auer

C Becker R Cyganiak and S Hellmann DBpedia-acrystallization point for the web of data WebSemantics Science Services and Agents on the WorldWide Web 7(3)154ndash165 2009

[2] M drsquoAquin Linked data for open and distancelearning Commonwealth of learning report 2012

[3] T Heath and C Bizer Linked Data Evolving the Webinto a Global Data Space 2011

[4] T Heath R Singer N Shabir C Clarke andJ Leavesley Assembling and applying an educationgraph based on learning resources in universities InLinked Learning (LILE) Workshop 2012

[5] D Kroenke D Bunker and D N WilsonExperiencing mis Prentice Hall 2010

[6] M Krotzsch D Vrandecic and M Volkel Semanticmediawiki In The Semantic Web-ISWC 2006 pages935ndash942 Springer 2006

[7] Stefan Dietze Salvador Sanchez-Alonso Hannes EbnerHong Qing Yu Daniela Giordano Ivana Marenzi andBernardo Pereira Nunes Interlinking educationalresources and the web of data Program 47(1)60ndash91Feb 2013

[8] F Zablith M drsquoAquin S Brown andL Green-Hughes Consuming linked data within a largeeducational organization 2011

[9] F Zablith M Fernandez and M Rowe Productionand consumption of university linked data InteractiveLearning Environments 23(1)55ndash78 2015

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