Generating Feedback Reports for Adults Taking Basic Skills Tests | SpringerLink
Skip to main content

Generating Feedback Reports for Adults Taking Basic Skills Tests

  • Conference paper
Applications and Innovations in Intelligent Systems XIII (SGAI 2005)

Abstract

SkillSum is an Artificial Intelligence (AI) and Natural Language Generation (NLG) system that produces short feedback reports for people who are taking online tests which check their basic literacy and numeracy skills. In this paper, we describe the SkillSum system and application, focusing on three challenges which we believe are important ones for many systems which try to generate feedback reports from Web-based tests: choosing content based on very limited data, generating appropriate texts for people with varied levels of literacy and knowledge, and integrating the web-based system with existing assessment and support procedures.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 17159
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 21449
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. J Burstein, M Chodorow, C Leacock (2003) CriterionSM Online Essay Evaluation: An Application for Automated Evaluation of Student Essays. In Proceedings of IAAI 2003, pages 3–10

    Google Scholar 

  2. S Carey, S Law, J Hansbro (1997). Adult Literacy in Britain. Office of (UK) National Statistics.

    Google Scholar 

  3. L Carlson, D Marcu, and M Okurowski (2002). Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory. In Current Directions in Discourse and Dialogue, J. van Kuppevelt and R. Smith eds., Kluwer Academic Publishers.

    Google Scholar 

  4. Fitzgibbon and E. Reiter (2003). Memories for life: Managing information over a human lifetime. Grand Challenge proposal, published by UK Computing Research Committee (UKCRC).

    Google Scholar 

  5. P Kotler, N Roberto, N Lee (2002). Social Marketing: Improving the Quality of Life (2nd Ed). Sage.

    Google Scholar 

  6. B Lavoie and O Rainbow (1997). A Fast and Portable Realizer for Text Generation Systems. In Proceedings of ANLP-1997, pages 265–268

    Google Scholar 

  7. C Moser et al (1999) Improving Literacy and Numeracy: A Fresh Start. Available at http://www.lifelonglearning.co.uk/mosergroup/

    Google Scholar 

  8. M O’Donnell, C Mellish, J Oberlander, A Knott (2001) ILEX: An architecture for a dynamic hypertext generation system. Journal of Natural Language Engineering, 7:225–250.

    Article  Google Scholar 

  9. C Perfetti (1994). Psycholinguistics and Reading Ability. In M Gernsbacher (ed), Handbook of Psycholinguistics. Academic Press.

    Google Scholar 

  10. K Rayner and A Pollatsek (1989). The Psychology of Reading. Prentice Hall.

    Google Scholar 

  11. E Reiter and R Dale (2000). Building Natural Language Generation Systems. Cambridge University Press.

    Google Scholar 

  12. E Reiter, R Robertson, and L Osman (2003). Lessons from a Failure: Generating Tailored Smoking Cessation Letters. Artificial Intelligence 144:41–58.

    Article  Google Scholar 

  13. E Reiter and S Sripada (2002). Human Variation and Lexical Choice. Computational Linguistics 28:545–553

    Article  Google Scholar 

  14. E Reiter, S Sripada, and R Robertson (2003). Acquiring Correct Knowledge for Natural Language Generation. Journal of Artificial Intelligence Research 18:491–516.

    MATH  Google Scholar 

  15. S Williams (2004). Natural Language Generation of Discourse Relations for Different Reading Levels. PhD thesis, Dept of Computing Science, University of Aberdeen.

    Google Scholar 

  16. S Williams and E Reiter (2005a). Deriving content selection rules from a corpus of non-naturally occurring documents for a novel NLG application. In Proceedings of Corpus Linguistics 2005 workshop on using Corpora for NLG, pages 41–48.

    Google Scholar 

  17. S Williams and E Reiter (2005b). Generating readable texts for readers with low basic skills. In Proceedings of the 2005 European Natural Language Generation Workshop, pages 140–147.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag London Limited

About this paper

Cite this paper

Reiter, E., Williams, S., Crichton, L. (2006). Generating Feedback Reports for Adults Taking Basic Skills Tests. In: Macintosh, A., Ellis, R., Allen, T. (eds) Applications and Innovations in Intelligent Systems XIII. SGAI 2005. Springer, London. https://doi.org/10.1007/1-84628-224-1_5

Download citation

  • DOI: https://doi.org/10.1007/1-84628-224-1_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-223-2

  • Online ISBN: 978-1-84628-224-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics