This paper considers the ways in which Web Science educationcan benefit from an analysis method used to gauge disciplinary representation. Three key contributions are identified:1) driving development of the Web Science curriculum; 2) teaching WebScience, i.e. considering its evolution over time and using the method to foster comparisons of Web Science with other like fields; 3) teaching the analysis method itself as an example of amixed methods, Web Science method.This paper addresses topic #1 of the Web Science Educationactivities (Web Science education programmes design)

Hooper, Clare

English

Southampton (e-Prints Soton)

The Role of Disciplinary Analysis in  Web Science Education   Clare Hooper IT Innovation Centre  University of Southampton, UK cjh@it-innovation.soton.ac.uk    ABSTRACT This paper considers the ways in which Web Science education can benefit from an analysis method used to gauge disciplinary representation. Three key contributions are identified: 1) driving development  of  the  Web  Science  curriculum;  2)  teaching  Web Science,  i.e.  considering  its  evolution  over  time  and  using  the method  to  foster  comparisons  of  Web  Science  with  other  like fields; 3) teaching the analysis method itself as an example of a mixed methods, Web Science method. This  paper  addresses  topic  #1  of  the  Web  Science  Education activities (Web Science education programmes design). Categories and Subject Descriptors K.3.2 [Computers and Education]: Computer and Information Science  Education  –  computer  science  education,  curriculum, information systems education General Terms Human Factors Keywords Web  Science;  Web  Science  education;  disciplinary  analysis; methods 1.  INTRODUCTION The interdisciplinary nature of Web Science is both well-known and inherent to this field of research and practice. Previous work has  examined  the  nature  of  Web  Science’s  interdisciplinarity, most  notably  via  empirical  mixed  methods  analyses  of disciplinary representation  in Web Science publications [1] [2]. The previous work was motivated by various goals: a)  ground dialogue about disciplinary representation with data, identifying which disciplines are more or less represented, b)  identify problems in need of action regarding discipline diversity (i.e. identify missing types of research or the kinds of collaboration that we may wish to encourage), and  c)  improve our communication as a community and our ability to reach out to communities with whom we wish to engage. This paper asks the question: what lessons can be gleaned for the Web Science Education community? This paper identifies three ways in which disciplinary analysis contributes to Web Science Education. Firstly, insights into which disciplines are more or less present  in  the  Web  Science  community  can  help  drive  the development  of  Web  Science  curricula.  Secondly,  the  analysis provides a way to help Web Science students understand not only the current disciplinary make-up of the community, but how this has evolved over time, enabling discussion of the implications of this. Thirdly, disciplinary analysis is itself a sound Web Science method, drawing on mixed methods from multiple disciplines. This paper responds to the goals of the Web Science Education workshop  by  discussing  the  relevance  of  disciplinary  analysis outputs for Web Science Education and drawing on experiences of teaching disciplinary analysis to Web Science students. Section 2 provides a brief introduction to the disciplinary analysis method, while Section 3 discusses the three ways in which the outputs  of  this  method  can  support  Web  Science  Education. Section 4 provides conclusions. 2.  DISCIPLINARY ANALYSIS Disciplinary analysis was trialled in 2012 [1] and refined in 2013 [2]. It consists of four stages: gathering data, conducting Natural Language Processing to extract topics, visualising the results, and conducting a substantial expert survey to interpret the results. 1.  Data gathering: materials from the field at hand are collected. For Web Science case, sources include Web Science conference proceedings, Foundations & Trends in Web Science, journal.webscience.org, and other key papers. 2.  Natural Language Processing: this was done with Saffron, an application to understand research communities [3]. Saffron uses information extracted from unstructured documents with Natural Language Processing techniques. It yielded a set of ranked extracted terms.  3.  Graphing and visualisation: a network graph tool, Gephi, is used to build a graph showing links between terms: nodes are extracted terms and arcs are papers that link them. This let us identify clusters of closely related terms. Detected communities can be interpreted as application contexts ranging from technologies (i.e. semantic web) to disciplines (i.e. elearning) and topic areas (i.e. social networking).  4.  Expert survey: to map highly ranked terms with disciplines, an expert survey is used. Web Science experts are provided with the top ranked terms, and asked to map these terms to disciplines. This mapping is done as an expert survey to avoid issues of subjectivity and individual bias that impact the mapping if it is done by one or two individuals. 3.  DISCIPLINARY ANALYSIS FOR EDUCATION 3.1  Contribution 1: Driving Curricula Development  of  the  Web  Science  curriculum  has  quite  rightly been given great attention over recent years, evidenced not only by  first  class  papers  [4]  but  also  the  prestigious  Web  Science  Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not  made  or  distributed  for  profit  or  commercial  advantage  and  that copies bear this notice and the full citation on the first page. To copy otherwise,  or  republish,  to  post  on  servers  or  to  redistribute  to  lists, requires prior specific permission and/or a fee. Conference’10, Month 1–2, 2010, City, State, Country. Copyright 2010 ACM 1-58113-000-0/00/0010 …$15.00.  Education workshop series, part of the Web Science conference from the first conference in 2009. Production of any curriculum, let alone one for a cutting-edge interdisciplinary area such as Web Science, is no easy task. The  outputs  of  disciplinary  analysis  can  support  Web  Science educators  by  providing  insights  into  two  areas:  Web  Science application contexts and disciplinary representation. Regarding application contexts, past work has identified four key Web  Science  application  contexts:  information  retrieval;  social networking; semantic web; personalised learning [2]. As can be seen,  these  range  from  technologies  (i.e.  semantic  web)  to disciplines (i.e. elearning) and topic areas (i.e. social networking). These application contexts suggest key areas to be covered when producing curricula. Regarding disciplinary representation, recent work has identified the relative prevalence of disciplines [2]. This has yielded a list of more  present  disciplines  (computer  science,  communications, psychology,  sociology)  and  a  list  of  less  present  disciplines (economics,  philosophy,  law).  Again,  these  insights  can  help educators make informed decisions when building curricula. It can be seen that the outputs of a disciplinary analysis of Web Science support production of curricula by letting educators assess the correspondence between a) highly-ranked topics and expert-identified  disciplines  from  Web  Science  publications  with  b) topics and disciplines that are actively taught. Of course, many factors can and should be considered when building a curriculum, but disciplinary presence in the Web Science community is one of a  number  of  valuable  inputs  that  can  inform  the  Web  Science curriculum. 3.2  Contribution 2: Teaching Web Science The outputs of the disciplinary analysis can also be used when teaching students about the nature and history of Web Science. Application contexts and the current disciplinary make-up of the community  provide  insight  into  active  areas  of  research  and collaboration. It is possible to examine trends in disciplinary representation over time, facilitating insights into the evolution of Web Science and discussions of possible future trends. For example, the first three years  of  the  Web  Science  conference  show  relatively  stable acceptance rates (21%, 26% and 15% in 20009, 2010 and 2011 respectively) and term diversity (only a shift from 609 to 708 of the same top 1000 terms being included in the conferences over all three years). Nonetheless, interesting variations were revealed by an analysis of  ‘peak’  terms, terms to occur in five or  more publications that ‘peak’ in a given year (a difference of more than 5 papers in different years) in both papers and posters. 2 peaks were found in 2009, 10 in 2010 and 1 in 2011 [2]: •  2009: machine learning; real world •  2010:  available  online;  information  exchange; information  retrieval;  information  sharing;  natural language;  RDF  graph;  real  time;  semantic  web;  share information; SPARQL query •  2011: social media. Finally, disciplinary analysis can be applied in other contexts such as Network  Science  or  Internet  Science,  enabling  Web Science students  to  compare  these  fields.  This  can  prompt  a  greater understanding of Web Science and its broader context. 3.3  Contribution 3: A Web Science Method Finally, disciplinary analysis is a sound Web Science method of its  own  right,  drawing  on  mixed  methods  from  multiple disciplines. It combines quantitative Computer Science methods (natural language processing), quantitative and qualitative sense-making  methods  from  Network  Science  (graphing  and visualisation),  and  non-discipline  specific  qualitative  and quantitative methods (expert survey). Indeed, disciplinary analysis has  already  been  taught  to  Web  Science  students  studying  at Master degree level1. 4.  CONCLUSIONS Looking ahead, the Web Science Education workshop offers an ideal forum to discuss practical uses of disciplinary analysis and the possibility of conducting a more detailed analysis for insight into the history of Web Science. Web  Science  Education  is  a  key  part  of  the  Web  Science community, essential to the healthy growth and continuation of the  discipline.  By  integrating  lessons  learned  from  disciplinary analysis  –  both  in  terms  of  informing  curricula  and  teaching students about the  make-up of Web Science – it is possible to strengthen the theory and practice of Web Science Education. Last but  not  least,  disciplinary  analysis  serves  as  an  exemplar  of  a mixed  methods,  interdisciplinary  method  that  is  relevant  to  the heart of Web Science. 5.  ACKNOWLEDGEMENTS The research leading to these results received funding from the EU FP7 EINS under grant agreement No 288021. 6.  REFERENCES 1 Hooper,  C.J.,  Marie,  N.,  and  Kalampokis,  E.  Dissecting  the Butterfly: Representation of Disciplines Publishing at the Web Science  Conference  Series.  In  Proceedings  of  ACM  Web Science 2012 (Chicago, USA 2012), ACM, 137-140. 2 Hooper, Clare J., Bordea, G., and Buitelaar, P. Web Science and the  Two  (Hundred)  Cultures:  Representation  of  Disciplines Publishing  in  Web  Science.  In  Proceedings  of  ACM  Web Science 2013 (Paris, France 2013), ACM, 162-171. 3 Monaghan,  Fergal,  Bordea,  Georgeta,  Samp,  Krystian,  and Buitelaar,  Paul.  Exploring  Your  Research:  Sprinkling  some Saffron  on  Semantic  Web  Dog  Food.  In  Semantic  Web Challenge  at  the  International  Semantic  Web  Conference (Shanghai, China 2010). 4 White,  Su,  Croitoru,  Madalina,  Bazan,  Stéphane  et  al. Negotiating  the  Web  Science  Curriculum  through  Shared Educational  Artefacts.  In  ACM  Web  Science  (Koblenz, Germany 2011), ACM.                                                                       1  COMP6048:  Interdisciplinary  Thinking  (2013-2014) http://www.ecs.soton.ac.uk/module/COMP6048  

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The role of disciplinary analysis in web science education

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