Skip to main content

Discussion Skills Evaluation and Training

  • Chapter
  • First Online:
  • 1229 Accesses

Abstract

There must be as many concrete indicators as possible in education, which will become signposts. People will not be confident about their learning and will become confused with tenuous instruction. It is necessary to clarify what they can do and what kinds of abilities they can improve. This paper describes a case of evidence-based education that acquires educational data from students’ study activities and not only uses the data to enable instructors to check the students’ levels of understanding but also improve their levels of performance. Whether a meeting is executed smoothly and effectively depends on the discussion ability of the participants. Evaluating participants’ statements in a meeting and giving them feedback can effectively help them improve their discussion skills. We developed a system for improving the discussion skills of participants in a meeting by automatically evaluating statements in the meeting and effectively feeding back the results of the evaluation to them. To evaluate the skills automatically, the system uses both acoustic features and linguistic features of statements. It evaluates the way a person speaks, such as their “voice size,” on the basis of the acoustic features, and it also evaluates the contents of a statement, such as the “consistency of context,” on the basis of linguistic features. These features can be obtained from meeting minutes. Since it is difficult to evaluate the semantic contents of statements such as the “consistency of context,” we built a machine learning model that uses the features of minutes such as speaker attributes and the relationship of statements. We implemented the discussion evaluation system and used it in seminars in our laboratory. We also confirmed that the system is effective for improving the discussion skills of meeting participants. Furthermore, with regard to skills that are difficult to evaluate automatically, we adopted a mechanism that enables participants to mutually evaluate each other by applying a gamification method. In this chapter, I will also describe the mechanism in detail.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   159.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  • R. Barzilay, M. Lapata, Modeling local coherence: an entity-based approach. Comput. Linguist. 34(1), 1–34 (2008)

    Article  Google Scholar 

  • K. Kurihara, M. Goto, J. Ogata, Y. Matsusaka, T. Igarashi, Presentation Sensei: A Presentation Training System using Speech and Image Processing, in Proceeding of ICMI 2007, pp.358–365 (2007)

    Google Scholar 

  • J. McGonigal, Reality Is Broken: Why Games Make Us Better and How They Can Change the World (Penguin Books, 2011)

    Google Scholar 

  • K. Nagao, M. P. Tehrani, J. T. B. Fajardo, Tools and evaluation methods for discussion and presentation skills training, SpringerOpen J. Smart Learn. Environ. 2(5) (2015)

    Google Scholar 

  • T. Tsuchida, S. Ohira, K. Nagao, Creation of contents and visualization of metadata during face-to-face meetings. Transac. Inf. Process. Soc. Jpn. 51(2), 404–416, 2010. (in Japanese)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Katashi Nagao .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Nagao, K. (2019). Discussion Skills Evaluation and Training. In: Artificial Intelligence Accelerates Human Learning. Springer, Singapore. https://doi.org/10.1007/978-981-13-6175-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6175-3_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6174-6

  • Online ISBN: 978-981-13-6175-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics