The slides I presented at the 2014 Web Science Education workshop, based on my accepted paper. The abstract of the paper follows:
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).
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Presentation of a JISC research project into e-textbook publishing processes at a university. The context is explained and some lessons-learned are listed.
Social Network Analysis as a Technology-Enhanced Learning tool - overview of ...VocTEL Conference
Social Network Analysis as a Technology-Enhanced Learning tool - overview of learning and research practices at Varna Free University | Antonina Ivanova, Galina Momcheva | VocTEL 2015
Presentation by Andras Szucs, EDEN at the 2019 European Distance Learning Week's first-day webinar on "Why is digital learning relevant for curriculum transformation in Higher Education? " - 11 November 2019
Recording of the discussion is available: https://eden-online.adobeconnect.com/p6n1qh9zz2kf/ & https://www.youtube.com/watch?v=JQhG87aNGqQ
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A New Curriculum for Information Literacy: JISC-RSC, York, Oct 2011 Emma Coonan
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The Role of Disciplinary Analysis in Web Science Education
1. The Role of Disciplinary Analysis in
Web Science Education
23 June 2014
Clare Hooper, IT Innovation Centre
cjh@it-innovation.soton.ac.uk
Web Science Education at ACM Web Science 2014
I will introduce disciplinary analysis and discuss its use in Web Science education for:
Driving curriculum development
Teaching about Web Science’s state and evolution
An example of a mixed methods approach
Quality: Contractor has shown collaborator diversity impacts quality of work
Credibility: What if CS and Sociology are the only contributors?
Insight: identify over- and under-representation, take action if needed.
Communication: to externals about what InternetSci is, and internally too
Much of this work was done in conjunction with DERI
Natural language processing: computer science and linguistics about understanding natural human languages.
Work developed over 3 years. Test w/69 papers; 500 papers 13 person survey; bigger corpus 123 person survey
Application contexts as key areas
Disciplinary representation as one of a number of inputs to inform the curriculum
‘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.
• 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.
