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A Linked-Data-Driven Web Portal for Learning Analytics:Data Enrichment, Interactive Visualization, and Knowledge
Discovery
Yingjie HuSTKO Lab
University of California, SantaBarbara
Grant McKenzieSTKO Lab
University of California, SantaBarbara
Jiue-An YangSTKO Lab
San Diego State University /University of California, Santa
Song GaoSTKO Lab
University of California, SantaBarbara
Amin AbdallaSTKO Lab
Vienna University ofTechnology
Krzysztof JanowiczSTKO Lab
University of California, SantaBarbara
ABSTRACTThis paper presents a Linked-Data-driven Web portal for thefield of learning analytics. The portal allows users to browsethe linked datasets and explore data about researchers, con-ferences, and publications. Additionally, users can interactwith various dynamic visualization applications and performanalysis, e.g., study temporal change of research trends.Based on the provided datasets on Learning Analytics andKnowledge (LAK) and Educational Data Mining (EDM),we enriched the data with geospatial locations of researchinstitutes, topics extracted from papers, and the expertiseof researchers. The interactive modules of the Web por-tal are then designed and implemented using the enrichedRDF data. The implemented modules can be divided intotwo groups. The first group is concerned with providingdynamic and interactive visualization of the data, such asthe modules of Conference Participants and Reference Map.The modules in the second group are designed for more ad-vanced analysis and discovery of new knowledge, such as themodules of Scholar Similarity and Reviewer Recommenda-tion. The modules have been designed following a looselycoupled, modular infrastructure, and can be easily migratedand reused in other projects.
Categories and Subject DescriptorsH.2.8 [Database Applications]: Scientific Databases—bibliography database; H.4 [Information Systems Appli-cations]: Miscellaneous
General TermsManagement, Design, Measurement
KeywordsLinked scientometrics, semantics, topic modeling, interac-tive visualization, Linked Data, learning analytics, data min-ing
1. INTRODUCTION AND MOTIVATIONThe Learning Analytics and Knowledge (LAK) dataset pro-vides a rich collection of data extracted from publications inthe field of learning analytics and structured in a machine-readable manner [7]. It provides a great opportunity forscientometrics research. For instance, one can investigatethe topic trends in this emerging research field, study thenetwork of collaborative researchers, examine the topicalsimilarity among researchers, explore the spatio-temporalspread of LAK-related ideas, and check the relation betweenconference locations and affiliations of the contributing re-searchers. In this paper, we present a Linked-Data-drivenWeb portal for scientometrics based on the LAK dataset.It is designed to facilitate the exploration, enrichment, vi-sualization, and analysis of the data. While part of theportal is based on a previous project which implements asemantically-enabled and Linked-Data-driven platform [5],we have designed and developed 11 new scientometric mod-ules for the LAK challenge. We are especially interestedin the novel area of spatio-temporal scientometrics [2, 3].While this includes geographic space, we are also interestedin more general spaces. All modules are based on a com-bination of various technologies, such as Linked Data, Se-mantic Web reasoning, geocoding, D3 (Data-Driven Docu-ments) library, GeoJSON, and a variety of KDD techniquessuch as Latent Dirichlet Allocation-based topic modeling,Multi-Dimensional Scaling, and so forth. Specifically, theWeb portal provides the following features:
1. Enriched data. The existing LAK dataset has beenenriched with extracted research topics, key concepts,
institute locations, citations, and researcher expertise.We also integrated the data from two prominent sci-entometric services, Microsoft Academic Search1 andArnetMiner2, to provide more comprehensive analysis.
2. An intuitive user interface. We provide four accessesfor the data: conferences, researchers, publications andanalytics. Users can start browsing the data fromany of these access points using either keyword-basedsearch or follow your nose exploration. From a partic-ular data item (e.g, a conference), users can also checkits related items (e.g., researchers and publications inthis conference) by following the hyperlinks. Users canalso use the analytic modules to perform analysis basedon the entire LAK dataset instead of exploring subsetssequentially.
3. A set of dynamic and interactive (geospatial) visual-izations and animations. Modules, such as the Coau-thor Treemap and the Academic Network, enable usersto interact with the visualization and explore details.This portal especially features a group of geospatial vi-sualizations, such as the Reference Map, Citation Map,and Participants Map, which present the geospatialdistribution of researchers, institutions, citations, andreferences.
4. Data mining and knowledge discovery. The ActiveScholar module, for instance, can help identify themost active researchers in a conference, while theScholar Similarity module can detect the similarityamong scholars based on their publications. The TopicTrending module explores the popular topics for theEDM conference from 2008 to 20133, while the KeyConcept module enables the user to grasp the gist ofa paper.
5. Linked-Data-driven analytic tools for the LAK com-munity. Based on the LAK dataset, two useful toolshave been designed. The Reviewer Recommendationtool recommends reviewers for a paper (related to thetopics presented in LAK) based on the existing publi-cations of researchers in this community while at thesame time excluding co-authors based on data pro-vided from the network module. The Potential Col-laborator tool can help researchers find potential col-laborators who have very similar research interests butmay have not collaborated before.
6. A modular and self-contained design paradigm. Allfunctionalities in the presented portal have been de-signed as self-contained modules. Therefore, suchfunctionalities can be easily migrated and reused infuture projects. Queries are based on RDF, SPARQL,and ontologies, which make storing part of the portal’sbusiness logic directly with the data possible.
1http://academic.research.microsoft.com/2http://arnetminer.org/3This is the only conference in the dataset that offers anextensive history worth analyzing. In the future, more con-ferences can be added.
The presented Web portal (called DEKDIV4) can be ac-cessed at: http://stko-exp.geog.ucsb.edu/lak/.
In the following sections, we briefly discuss the data enrich-ment, visualization, and knowledge discovery from the LAKdata.
2. DATASET DESCRIPTION AND EN-RICHMENT
The LAK dataset contains information about publicationsand researchers in the Learning Analytics and Knowledge(LAK) conference series (from 2011 to 2013) and the Edu-cational Data Mining (EDM) conference series (from 2008to 2013). This dataset also contains a special issue from theJournal of Educational Technology and Society on learninganalytics and knowledge. In contrast to other publicationdata hubs (e.g., DBLP5 and CiteSeer6) on the Linked OpenData (LOD) cloud, the LAK dataset provides full texts andfull references in addition to the typical bibliographic data.As a result, it is not only possible to explore the collabora-tion relations (e.g., via co-authorships), but also to mine keyconcepts, research topics, and citation networks. This facil-itate a more holistic understanding of the learning analyticsand educational data mining research fields.
In this work, we further enrich the provided LAK datasetwith key concepts and topics extracted from papers, geo-graphic locations of authors’ affiliations, as well as the ex-pertise of researchers and paper citations imported fromArnetminer and Microsoft Academic Search (MAS). Whilethese additional data have been imported into our own triplestore7, they can also be merged with the existing LAKdataset. In addition, a customized daemon can be developedto dynamically enrich the data whenever new publications(e.g., papers from the LAK 2014 conference) are added. Infact, this is the strategy that has already been adopted in ourprevious Linked Data portal for the Semantic Web Journalto synchronize the information about new publications [5].In the following subsections, we discuss the three types ofdata that have been added and linked to the existing LAKdataset.
2.1 Key Concepts and Research TopicsBased on the full text of each publication in the LAKdataset, we extracted important concepts using the AlchemyAPI8. Alchemy is a Web service that provides automatic nat-ural language processing functions. Each of the extractedconcepts (key phrases) is associated with a relevance valuewhich indicates the term’s importance in relation to the en-tire paper. The extracted concepts were used in the KeyConcepts module to give users an overview of a paper’s con-tent. These concepts were also used to display the trend ofresearch topics in the Topic Trending module. Additionally,we use a Latent Dirichlet allocation (LDA) model to extract
4Short for Data Enrichment, Knowledge Discovery andInteractive Visualization.5http://datahub.io/dataset/fu-berlin-dblp6http://thedatahub.org/dataset/rkb-explorer-citeseer7http://stko-exp.geog.ucsb.edu/pubby8http://www.alchemyapi.com/
latent topics from the full text data. LDA is an unsuper-vised, generative probabilistic model used to infer the latenttopics in a textual corpus [1]. In contrast to the key conceptsextracted using Alchemy, LDA describes each topic using amixture of keywords, and therefore can help discover hid-den relations among key concepts. The topics presented byLDA were used for the functions in Scholar Similarity andReviewer Recommendation.
2.2 Geospatial Reference DataScientific activities (e.g., co-publications, citations, and ref-erences) generally show geospatial patterns. However, suchpatterns have rarely been considered in traditional sciento-metric analysis which often focuses on numeric values (e.g.,the H-index) [4]. Recently, researchers began to pay moreattentions to spatial scientometrics [2] and a spatio-temporalframework for exploring the citation impact of publicationsand scientists has been proposed in a previous work [3].Based on this motivation, we first identified the affiliation ofeach author in the LAK dataset and geolocate these insti-tutes using a customized parser on top of the Google MapsReverse Geocoding API (the algorithm can be found in [3]).We also geolocated the affiliation of the first author in eachreference entry in order to reveal geospatial patterns in thereference data. These geospatial data are the foundation forthe Citation Map, Reference Map, Collaborative Institutes,and Conference Participants modules.
2.3 Researcher Expertise and External Cita-tion Data
In addition to the key phrases, topics, and geospatial loca-tions, we also integrated the scientometric data from twomajor Web services: Arnetminer and Microsoft AcademicSearch. Among the 853 authors in the LAK dataset, Ar-netminer contains data for 595 of them, and such dataincludes publications by these authors not only from theLAK dataset, but also from other journals and conferences.We imported the researcher expertise data (which were ex-tracted based on all publications of a given author) fromArnetminer into our local triple store, and used these exper-tise data for the Potential Collaborators module. MicrosoftAcademic Search is another source whose data have beenintegrated into the presented Web portal. MAS contains alarge amount of information about affiliations and citations.Such data have been used to find the affiliation of authors inthe reference data. The citation data from MAS have alsobeen integrated to show the external citations which are notstored in the existing LAK dataset.
3. INTERACTIVE VISUALIZATIONSThis section presents the functionality modules that servethe purpose of interactive visualization and animation. Vi-sualization plays an important role in data analysis, sincehumans can often easily recognize patterns visually (in con-trast to machines). When designing this Web portal, wepaid special attentions to each aspect of the visualizationscreated. For instance, we tried to ensure that each visu-alization fits the data being presented, and that the userinterface enables users to interact and understand the data.The subsections below describe some of these modules, andthe role that visualization plays in their conceptualization.
Collaborative Institutes & Conference Partici-pantsThe Collaborative Institutes module is designed to help usersexplore the spatial distribution of co-authorship. In orderto realize this, the number of co-authored conference papersbetween two institutes are displayed on a global map. Asshown in Figure 1, a link between two institutes indicates re-search collaborations between authors from these institutes.Interactions provided by this module include moving themouse over links and nodes to show additional details abouteach of the institutes and publication information. Usingthis geo-visualization, one can see spatial patterns of re-search collaborations. For example, some researchers preferto collaborate domestically for certain conferences, as op-posed to international collaborations. Previous studies [2]argued that the tendency of domestic co-publication mayindicate that a large number of researchers in this domain re-side within a single country. This is evident in the institute-collaboration pattern seen in the EDM 2013 dataset, whichshows primarily US-based collaborations. Conversely, con-ferences, such as LAK 2013, consist of more internationallyoriented authors.
Figure 1: Geo-locations of institutes of collaboratingauthors.
The Conference Participants module was developed to pro-vide an interactive visualization and animation of the geo-graphic distributions of EDM & LAK conference authors;see Figure 2. The size of the yellow circles represents thenumber of participants that attended the conference; thelarger the circle, the more attendees from the mapped in-stitute. Hovering the mouse over a circle on the map dis-plays the total number of authors who participated in theconference. From a purely visual perspective, it appearsthat the geographic distribution of participants is stronglyinfluenced by the region where the event took place. Forinstance, LAK 2013 was held in Leuven, Belgium, and itattracted many European researchers, while many partici-pants of EDM 2013, which was held in Memphis, USA, arefrom the United States.
Figure 2: The spatial distribution of LAK 2013 au-thors grouped by their institute’s location.
Academic NetworkThe Academic Network module is constructed to show aninteractive visualization of an author’s academic networkbased on co-author links. A graph-node approach (oftenfound in social network analysis) has been implemented toconnect authors with each other through their LAK/EDMpublications. This technique views the academic networkas a set of relationships composed of nodes and links. Inthis module, each author is presented as a node in the net-work, and a link connects two nodes if co-authorship ex-ists between these two authors. The total number of co-authorships between two authors is recorded as an attributeof the link and is visually represented though the strokewidth of each link (Figure 3). In addition to the visualiza-tion of the academic network, measurements of the networkare also provided. For example, the Centrality of a node rep-resents its relative importance within a network, and fourtypes of centrality are visually presented in the AcademicNetwork module when a node is selected.
Figure 3: The Academic Network of a single re-searcher.
Reference MapBased on the enriched and geocoded data, we have designeda module called Reference Map. This module presents ananimated and interactive visualization demonstrating the
geographic distribution of a paper’s references. Using thereference data provided by the LAK dataset, we extractedthe names of the first authors in the reference records, anduse MAS to find the institutions of these authors. We thenvisualized these institutions as smaller bubbles on a globalmap, and created animated links from these bubbles to thelocation of the published paper, where a larger bubble iscreated (see Figure 4). The message that we are trying todeliver through this visualization is: scientific publications(i.e., the smaller bubbles) emerged at different locations ofthe world, and new papers (i.e., the bigger red bubble) wascreated and published when other researcher adopted theseideas (i.e., the animated linked). In this module, users cansee additional information about authors, institutions, andeach reference when hovering over the bubbles and links.
Figure 4: Geo-visualization of a paper’s references.
4. LAK DATA MINING AND KNOWL-EDGE DISCOVERY
The richness of the LAK/EDM dataset allows the develop-ment of a wide assortment of knowledge discovery tools. Inthis section, we present some of the modules which are de-veloped based on data mining techniques. These modulesutilize the enriched dataset, and offer informative metricsto facilitate the understanding of not only the dataset itselfbut also the LAK community. In addition, some of thesemodules provide useful services which, until now, may havenot been fully realized.
Active ScholarsThe module of Active Scholar is designed to discover themost productive researchers based on the provided dataset.We developed a SPARQL query which ranks authors in eachconference according to the number of their publications.The top 30 authors were then selected, and displayed inthe center of the visualization canvas (see Figure 5). Thenumber after the name of each author denotes the numberof papers that this specific author published in this confer-ence. While authors who have two or more publications aredisplayed at the top, we have to randomly select authorswho have a single publication in the conference due to thelimited visualization space. In addition to the most activescholars, we also identified and visualized the most populartopics based on the number of publications in this topic. Byhovering mouse on one of the authors, users can see the top-ics that are related to this author. Similarly, moving mouseon one topic will show all the authors who have publications
on this topic. Based on this module, we have discoveredsome very active scholars (e.g., Ryan S.J.d Baker) who of-ten publish more than 2 papers in the LAK conferences.
Figure 5: The Active Scholar Module.
Scholar SimilarityThe Scholar Similarity module measures the similarityamong authors in the LAK dataset using a MultidimensionalScaling (MDS) approach. The full text publications of eachauthor have been selected as input for a Latent Dirichletallocation (LDA) model consisting of 20 topics. LDA is anunsupervised, generative probabilistic model used to inferthe latent topics in a textual corpus [1]. Given these topics,each author can then be described as a distribution acrossthese topics which we term as signatures. Using the Jensen-Shannon divergence [6] method, each author in the datasetis compared to each other author producing a dissimilaritymeasure bounded between 0 and 1. This resulting matrix ofdissimilarity values is used as input to MDS which producesa 2D representation of the authors in space. Authors thatare closer together in space are more similar in their researchtopics compared with those further apart.
Reviewer RecommendationThe Reviewer Recommendation module (Figure 6) is builton the concept that researchers who have already publishedon a topic have the potential to become good reviewers fornew papers on the same topic. Similar to the Scholar Sim-ilarity module, this tool uses LDA to generate a set of top-ics from a corpus of full text publications. The authors ofthese publications are then defined as a distribution acrossthe topic space. Given a new submission (in the form ofan abstract), the topic distribution of this material can beinferred from the existing LDA topics. Again, using theJensen-Shannon divergence method, the topic distributionof the new material was compared with the topic distribu-tions of all authors, and authors were then ranked based onthe similarity value ( defined as 1 – JSD dissimilarity value).To avoid interest conflicts, we compared the list of potentialreviewers with the researcher’s previous co-authors. Thoseco-authors were highlighted in the visualization to reflect theconflicts.
Research Topic TrendsThis module demonstrates how research topics in the LAKor the EDM conferences trend over time (see Figure 7). In
Figure 6: The Reviewer Recommendation module.
order to implement this module, data preprocessing has beendone to extract the top 10 topics for each year ranked bytotal number of papers which contain the topic keywords.The EDM conferences, for instance, has 46 distinct topicsextracted from 2008 to 2013. Frequencies for all topics werecalculated across all years in a time-series format for fur-ther visualization and analysis. Using the interactive streamgraph, it is clear to see the decline of certain topics, emer-gence of new topics, as well as trend expansions throughtime. The user interface enables interaction through mousehover, reporting the chosen topic along with the number ofpapers associated with the topic in each year.
Figure 7: The stream graph visualization of theEDM hot topic trends.
Potential CollaboratorsThis analytical function is designed to find out the re-searchers who have similar research interests but may havenever co-authored a paper before (i.e., the researchers thatcan potentially become collaborators). The similarity ofresearchers was calculated using cosine similarity measurebased on expertise of the research imported from Arnet-miner. We then calculated the shortest network distancebased on the co-authorships in the module of Academic Net-work. After that, a metric (see equation (1)) was designed tofind authors who have the potential to become collaborators.
p = sim(a1 , a2 ) × (1 − 1/d) (1)
Where p is the score for collaboration potential, sim(a1 , a2 )is the cosine similarity between authors a1 and a2, and d isthe shortest network distance. Figure 8 shows a screenshotof the Potential Collaborator module, where the blue iconrepresents the current researcher and the surrounding greyicons display his/her potential collaborators. Researcherswho have already co-authored papers before will have a link,while no link indicates there exist no co-authored paper inthe LAK dataset.
Figure 8: The potential collaborator module.
5. OTHER FUNCTIONAL MODULESWhile we only highlight some selected modules here, thereare a list of other DEKDIV modules. Interested readers areencourages to explore these modules. Examples include:
• Conference Hot Topics: The most popular topics inthis conference.
• Coauthor Treemap: A treemap visualization of co-authors and their affiliations.
• Citation Map: The geospatial distribution of the cita-tions of authors and their publications.
• Key Concepts: Important key phrases of a given paperand their relevance.
6. CONCLUSIONSIn this paper, we presented a Linked-Data-driven sciento-metrics Web portal, called DEKDIV, for the LAK challenge.We enriched the initial LAK data with paper topics, geospa-tial locations, and author expertise. We published the datain our local triple store, and designed various interactivevisualization and analysis modules to facilitate the under-standing of research in the LAK community. We also devel-oped a set of more complex service modules on top of theenriched data. They can be used to recommend reviewers fornewly submitted papers, help researchers find potential col-laborators, detect trend changes in publication topics, andso forth. The developed Web portal is based on our previouswork for the Semantic Web journal, and the entire systemis highly modular, reusable, as well as free and open sourcesoftware.
7. REFERENCES[1] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent
dirichlet allocation. the Journal of machine Learningresearch, 3:993–1022, 2003.
[2] K. Frenken, S. Hardeman, and J. Hoekman. Spatialscientometrics: Towards a cumulative researchprogram. Journal of Informetrics, 3(3):222–232, 2009.
[3] S. Gao, Y. Hu, K. Janowicz, and G. McKenzie. Aspatiotemporal scientometrics framework for exploringthe citation impact of publications and scientists. InProceedings of the 21st ACM SIGSPATIALInternational Conference on Advances in GeographicInformation Systems, pages 204–213. ACM, 2013.
[4] J. E. Hirsch. An index to quantify an individual’sscientific research output. Proceedings of the Nationalacademy of Sciences of the United States of America,102(46):16569, 2005.
[5] Y. Hu, K. Janowicz, G. McKenzie, K. Sengupta, andP. Hitzler. A linked-data-driven andsemantically-enabled journal portal for scientometrics.In The Semantic Web–ISWC 2013, pages 114–129.Springer, 2013.
[6] J. Lin. Divergence measures based on the shannonentropy. Information Theory, IEEE Transactions on,37(1):145–151, 1991.
[7] D. Taibi and S. Dietze. Fostering analytics on learninganalytics research: the lak dataset. 2013.