Abstract
In this paper, we propose a tool for supporting consensus-building in conversations with multiple participants. We call it “Discussion Map with Assistant (DMA)”. It consists of nodes and links. We classify the nodes into two types; alternatives and criteria. Alternatives represent what the participants are choosing between. Criteria are used to judge the alternatives. Each criterion contains an importance value. Each link between nodes also contains an importance value. The system estimates a ranking list of alternatives among participants from each map. We introduce a forgetting function to the model. The system also supports the decision-making process by using discussion maps from participants. It generates sentences and charts that describe the current state of the discussion. We evaluate the effectiveness of the discussion map system with DMA in a decision-making task experimentally.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note that not all alternatives have a link. It depends on the participant that creates the DM.
- 2.
The initial memory level is 100 in this formulation.
- 3.
Note that three of them are hidden in this figure.
- 4.
The difference of the score is 5% or less.
- 5.
This is based on the average ranks among participants.
- 6.
As conditions for the final decision, each discussion needs more than four alternatives and more than two criteria.
- 7.
The assistant function, DMA, becomes active in five minutes although groups with our system can use the DM system from the start.
- 8.
For instance, for G1, the number of alternatives with our system was 9 (19/2.11) while that without our system was 7 (21/3).
References
Alonso, S., Herrera-Viedma, E., Cabrerizo, F.J., Chiclana, F., Herrera, F.: Visualizing consensus in group decision making situations. In: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2007, pp. 1–6 (2007)
Bautista, J., Carenini, G.: An integrated task-based framework for the design and evaluation of visualizations to support preferential choice. In: Proceedings of AVI 2006, pp. 217–224 (2006)
Ebbinghaus, H.: Memory: A Contribution to Experimental Psychology. Dover Publications, New York (1885)
El-Assady, M., Hautli-Janisz, A., Gold, V., Butt, M., Holzinger, K., Keim, D.: Interactive visual analysis of transcribed multi-party discourse. In: Proceedings of ACL 2017, System Demonstrations, pp. 49–54 (2017)
Gratzl, S., Lex, A., Gehlenborg, N., Pfister, H., Streit, M.: LineUp: visual analysis of multi-attribute rankings. IEEE Trans. Vis. Comput. Graph. 19(12), 2277–2286 (2013)
Hmelo-Silver, C.E.: Problem-based learning: what and how do students learn? Educ. Psychol. Rev. 16, 235–266 (2004)
Ito, T., Imi, Y., Ito, T., Hideshima, E.: COLLAGREE: a facilitator-mediated large-scale consensus support system. In: Proceedings of the 2nd Collective Intelligence Conference (2014)
Ito, T., Imi, Y., Sato, M., Ito, T., Hideshima, E.: Incentive mechanism for managing large-scale internet-based discussions on COLLAGREE. In: Proceedings of the 3rd Collective Intelligence Conference (2015)
Katsura, Y., Okada, S., Nitta, K.: Dynamic argumentation support tool using argument diagram. In: Proceedings of The 29th Annual Conference of the Japanese Society for Artificial Intelligence (2015). (in Japanese)
Masukawa, H.: Development of the reflective collaboration note: ReCoNote. In: Proceedings of the 29th Annual Conference of JSET (2013). (in Japanese)
Miyake, N., Shirouzu, H.: The dynamic jigsaw: repeated explanation support for collaborative learning of cognitive science. In: The Meeting of the 27th Annual Meeting of the Cognitive Science Society (2005)
Nagao, K.: Meeting analytics: creative activity support based on knowledge discovery from discussions. In: Proceedings of the 51st Hawaii International Conference on System Sciences, pp. 820–829 (2018)
Nagao, K., Kaji, K., Yamamoto, D., Tomobe, H.: Discussion mining: annotation-based knowledge discovery from real world activities. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3331, pp. 522–531. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30541-5_64
Sakaguchi, K., Shimada, K.: Cooperation level estimation of pair work using top-view image. In: Kim, S., Jung, J.-W., Kubota, N. (eds.) Soft Computing in Intelligent Control. AISC, vol. 272, pp. 77–87. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05570-1_9
Scardamalia, M., Bransford, J., Kozma, B., Quellmalz, E.: New assessments and environments for knowledge building. In: Griffin, P., McGaw, B., Care, E. (eds.) Assessment and Teaching of 21st Century Skills, pp. 231–300. Springer, Dordrecht (2012). https://doi.org/10.1007/978-94-007-2324-5_5
Shiota, T., Yamamura, T., Shimada, K.: Analysis of facilitators’ behaviors in multi-party conversations for constructing a digital facilitator system. In: Proceedings of the 10th International Conference on Collaboration Technologies (2018)
Suzuki, H., Funaoi, H., Kubota, Y.: Supporting “assemble & disperse” style collaborative learning using tablet terminals. Technical report of IEICE-ET2013-26, pp. 41–46 (2013). (in Japanese)
Takagi, H., Shimada, K.: Understanding level estimation using discussion maps for supporting consensus-building. Procedia Comput. Sci. 35, 786–793 (2014)
Villalon, J.J., Calvo, R.A.: Concept map mining: a definition and a framework for its evaluation. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 2008, pp. 357–360 (2008)
Yamasaki, K., Fukuda, H., Hirashima, T., Funaoi, H.: Kit-build concept map and its preliminary evaluation. In: Proceedings of The 18th International Conference on Computers in Education, ICCE 2010, pp. 290–294 (2010)
Acknowledgment
This work was supported by JSPS KAKENHI Grant Number 17H01840.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Kirikihira, R., Shimada, K. (2018). Discussion Map with an Assistant Function for Decision-Making: A Tool for Supporting Consensus-Building. In: Egi, H., Yuizono, T., Baloian, N., Yoshino, T., Ichimura, S., Rodrigues, A. (eds) Collaboration Technologies and Social Computing. CollabTech 2018. Lecture Notes in Computer Science(), vol 11000. Springer, Cham. https://doi.org/10.1007/978-3-319-98743-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-98743-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98742-2
Online ISBN: 978-3-319-98743-9
eBook Packages: Computer ScienceComputer Science (R0)