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Tracking Context and Attention Metadata for Multilingual Technology Enhanced Learning

This paper explores the use of tracking context and attention metadata in multilingual Technology Enhanced Learning (TEL) environments. It investigates the role of social tagging and the potential to improve system behavior by understanding user context. The paper also examines the gathering of usage and attention metadata and its application in TEL.

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Tracking Context and Attention Metadata for Multilingual Technology Enhanced Learning

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  1. Tracking context withusage and attention metadata in multilingual Technology Enhanced Learning Riina Vuorikari European Schoolnet / Open Univ. of the Netherlands Bettina Berendt KU Leuven www.berendt.de

  2. „Learning Object Repositories foster re-use and improve learning“ • Really? • Ochoa (2008), Vuorikari & Koper (2009): re-use ~ 20%

  3. A basic challenge: context Researching for a term paper in ... Biology Political Science

  4. Motivation(application goals) • Use case • Technology Enhanced Learning / Learning Object Repositories • teachers from different linguistic and country backgrounds • Goal: foster re-use of resources • Assumption: understanding user context can improve system behaviour • Approach: • consider UAM as part of context measurement, specifically: • Investigate different roles of social tagging • Profit from parallel research in different areas

  5. Motivation (general - workshop) • To exploit usage and attention metadata: need to • define what properties of usage to use and why • how to gather data on that property • what to do with that information

  6. Motivation(general – our work) • Automatically gathered indicators of context, attention, ... • What‘s common across fields? • How can we communicate better? • What‘s specific in T.E. learning? • How can tags help learning & teaching? • How to bridge (e.g. lingual) barriers? • What are the best research methods for answering all these questions?

  7. Types of context in TEL

  8. What is context? • “Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves.” (Dey, 2001) • Any information not explicitly carried by the “surface level“ of an interaction with a computational system • Surface level: • user issues a query, • user accesses a resource, ... • We will distinguish between • Macro-context • Micro-context

  9. Macro-context • educational level • formal and informal learning • delivery setting (distance, blended, ...) • intended users and user roles • ...

  10. Micro-context • User • trait • state • Interaction • atomic vs. activity structure • implicit vs. explicit interest indicators • Background knowledge / „semantic enrichment“; often w.r.t. material (Berendt, 2007)

  11. Tags and context

  12. Background knowledge What‘s in a tag? item user • material • user • interaction • „writing“ • „reading“ tag

  13. Tags as interest indicators (1) – or: measuring and using interaction-context • Comparative search efficiency: • Search option [A|B|...]  a target LO • Click-through rate aka confidence (fraction of successful searches) • Path length • In LOR: Search with ... • explicit search (for resources w/o tags or ratings): 4.4 searches / resource • community browsing (tagclouds, lists, pivot browsing): 3.9 searches / resources • social information (community browsing or explict search with interest indicators (tags, ratings)): 2.8 searches / resource  Recommendation of search options?!

  14. Tags provide context to better utilize background knowledge • Can tags be used for enriching existing metadata of educational resources in a multilingual context? • 30% of tag applications matched with descriptors from a multilingual thesaurus, which had also been used to index these learning resources (Vuorikari et al., 2009)  “Thesaurus tags” • Thesaurus tags provide for • adding properties to tags (e.g., relation to a concept in a multilingual thesaurus, language) • linking resources to multilingual thesaurus descriptors  can support retrieval of resources in a multilingual context • Cf. convergence and quality tendencies in tagging in general (Bollen & Halpin, 2009; Hayes et al., 2007)

  15. What is user context – the case of language • User models have many different variables • Language (first language, further languages, proficiency, preferences): an important descriptor of users • Language situation (interaction in a first or second language): an important descriptor  Linguistic trait and state context variables!

  16. Measures / indicators of user language • self-profiling that explicitly addresses the question (“your mother tongue?”) • IP address • browser settings • language of the currently used interface • language of search terms and tags • known or inferred language of tagged resources • NB: indicator of the language situation: compare user language with interface/resource language (e.g., Berendt & Kralisch, 2009)

  17. Tags as interest indicators (2) – or: tags for measuring (re-)use • bookmarking and personal collections of digital learning resources as a proxy for the use and re-use of resources • When coupled with user and resource location / language: • become proxy for (re-)use across national and language boundaries (e.g., Vuorikari & Koper, 2009)

  18. Why support trans-lingual re-use? (1): Situation on the Web • What would be an „ideal“ language situation on the Web? • One H0: ~ amount of materials proportional to number of first-language speakers of a language • Not the case – non-English languages are severely under-represented on the Web due to • Resource-creation behaviour • Link-setting behaviour • Link-following behaviour • Attitudes towards resources in different languages (Berendt & Kralisch, 2009)

  19. Why support trans-lingual re-use? (2): User preferences • Theoretical reasons (cognitive effort), supported by implicit and explicit user preferences: • Clear tendency to access materials in one‘s mother tongue in a medical information system • 17% of users of a LOR had saved only content in their native language in their Favourites • LORs: pragmatic reasons (curricula) • Search-engine give better results when queried with linguistically correct (rather than Latinized) spellings • But: individual differences depending on proficiency in English! (Berendt & Kralisch, 2009; Vuorikari & Koper, 2009; Blanco & Lioma, 2009)

  20. Using context for improving trans-lingual re-use: How? (1) • better-organised search result lists • first a ranked list in the preferred language, • then a ranked list in a second language, etc. • done: Google for general search results and by LeMill (lemill.net) for learning resources. • organise tag clouds by language or country as in LRE (lreforschools.eun.iorg)

  21. Using context for improving trans-lingual re-use: How? (2) • for users with weaker language preferences; for „unknown“ users; ...“: • Make exceptions to the foregoing by recommendations using • travel-well tags: • terms with the same or similar spelling in most languages • ex.: technical terms like “mathematics”, place and person names • How to identify them? • via multi-lingual thesaurus or similar • tag has been assigned by users from different languages • Tag has been assigned to resources in different languages • travel-well resources: • Have been bookmarked by people from different languages, country contexts, … (Vuorikari & Ochoa, 2009)

  22. Using context for improving trans-lingual re-use: How? (3) • Direct recommendations 1 (see above): • Recommend content or tags in a preferred language • Indirect recommendations: • E.g., recommend bookmark lists of other users with a similar “language preference profile” • Profile similarity: “extensional” and/or “intensional”: degree of tolerance for mixed-language resources, results, etc. • Direct recommendations 2: • repositories could specifically encourage users who are competent in “smaller” languages also to author content in their language.

  23. Outlook

  24. Some questions ... and now yours! (Thx!) • How to best couple „content“ and „architecture“ research • How to bring together research from Web analytics / e-Commerce, search-engine research, digital libraries, TEL, ... • How to complement the exploratory research with experimental research effectively and efficiently • How to best combine different methods (validate that the dependent variables used mean something!) • Which privacy/security questions arise? • What is specific for TEL?

  25. References • Most references can be found in the paper, available at www.cs.kuleuven.be/~berendt/Papers/TEL_context_attention_vuorikari_berendt.pdf • In addition: • Bollen, D., & Halpin, H. (2009). An Experimental Analysis of Suggestions in Collaborative Tagging. In Proc. Of WI-IAT’09, Milan, Italy, 15-18 Sept 2009. IEEE Computer Society Press. • Hayes, C. et al. (2007). ... In Berendt, B., Hotho, A., Mladeni\v{c}, D., & Semeraro, G. (Eds.) (2007). From Web to Social Web: Discovering and deploying user and content profiles. Workshop on Web Mining, WebMine 2006, Berlin, Germany, September 18, 2006, Revised Selected and Invited Papers. LNAI 4737. Berlin etc.: Springer.

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