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Model-based Highlighting to Support Selective Reading on the Web

Model-based Highlighting to Support Selective Reading on the Web. Ion Juvina ijuvina@andrew.cmu.edu. About... . ... me Information Science @ UU.NL. Aknowledgements . Herre van Oostendorp Eelco Herder Ellert van den Broek Vincent van der Linden Koen Buurman Poyan Karbor Brian Pauw

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Model-based Highlighting to Support Selective Reading on the Web

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  1. Model-based Highlighting to Support Selective Reading on the Web Ion Juvina ijuvina@andrew.cmu.edu

  2. About... • ... me • Information Science @ UU.NL

  3. Aknowledgements • Herre van Oostendorp • Eelco Herder • Ellert van den Broek • Vincent van der Linden • Koen Buurman • Poyan Karbor • Brian Pauw • Arnaud Lek • Martijn Abbing • Richard van Yperen

  4. Contents • Introduction • Study “Individual differences” • Cognitive model • Study “Voice suggestions” • Study “Graphical suggestions” • Study “VIP” • Conclusion and discussion

  5. Introduction • Navigation is a major part of user experience on the web (Lazar, 2003) • Web interfaces • Structure vs. content • Revisits • Post valued recall • Case: using the web by visually impaired persons (VIP) via screen readers

  6. Study “Individual differences” • Objective: • Identify determinants of performance and satisfaction in web-assisted tasks

  7. Activities • Morrison, Pirolli and Card (2001) • Types of activities users perform on the Web. • 2188usable responses. • Example: I accessed Netscape's financial site to check my creditcard balance and how long it would take to pay it off.I'm now MUCH more fiscally aware of my spendinghabits and am trying to pay off my balance moreactively.

  8. Tasks

  9. Hypothetical factors • Domain expertise • Working memory capacity • Episodic memory • Cognitive style • Spatial ability • Age • Gender

  10. Navigation metrics

  11. Results

  12. Results (cont’d)

  13. Results (cont’d)

  14. Cognitive models • SNIF-ACT • CoLiDeS • MESA • Value and limitations: • Information scent • Common • Too narrowly defined • Semantics prevail over syntax • Static background knowledge • Common user prevails over individual differences

  15. CoLiDeS+

  16. Models vs. data

  17. Study “Voice suggestions”

  18. Study “Graphical suggestions”

  19. Study “Graphical suggestions” (cont’d)

  20. Study “VIP” • Manipulation • Screen switched off • Highlighting by increasing reading priority • Suggestions generated on-the-fly • No look ahead • Results: • Common mechanism • Spatial ability – task performance • Weak suggestion mechanism • Position: 5 -> 4 • Breaks of coherence

  21. Conclusions • Support for selective reading is: • Needed • Possible • Not easy • Empirical research and cognitive modeling could help

  22. Discussion points • Ecological validity vs. accuracy ? • Scalability of cognitive models ? • Ontologies vs. semantic spaces ? • Goals known in advance ? • Static goal representations ?

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