Discussion Skills Evaluation and Training

  • Katashi NagaoEmail author


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.


Discussion ability Speaking ability Listening ability Evaluation of discussion skills Training of discussion skills Gamification 


  1. R. Barzilay, M. Lapata, Modeling local coherence: an entity-based approach. Comput. Linguist. 34(1), 1–34 (2008)CrossRefGoogle Scholar
  2. 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
  3. J. McGonigal, Reality Is Broken: Why Games Make Us Better and How They Can Change the World (Penguin Books, 2011)Google Scholar
  4. 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
  5. 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

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Nagoya UniversityNagoyaJapan

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