skip to main content
research-article
Public Access

Evaluating MIDST, A System to Support Stigmergic Team Coordination

Published: 22 April 2021 Publication History

Abstract

Data science teams working on a shared analysis face coordination problems such as dividing up the work to be done, monitoring performance and integrating the pieces. Research on distributed software development teams has raised the potential of stigmergic coordination, that is, coordination through a shared work product in place of explicit communication. The MIDST system was developed to support stigmergic coordination by making individual contributions to a shared work product visible, legible and combinable. In this paper, we present initial studies of a total of 40 student teams (24 using MIDST) that shows that teams that used MIDST did experience the intended system affordances to support their work, did seem to coordinate at least in part stigmergically and performed better on an assigned project.

References

[1]
[n.d.]. The R Project for Statistical Computing. https://www.r-project.org/
[2]
Francesco Bolici, James Howison, and Kevin Crowston. 2016. Stigmergic coordination in FLOSS development teams: Integrating explicit and implicit mechanisms. Cognitive Systems Research, Vol. 38 (2016), 14--22. https://doi.org/10.1016/j.cogsys.2015.12.003 Special Issue of Cognitive Systems Research -- Human-Human Stigmergy.
[3]
Lars Rune Christensen. 2007. Practices of stigmergy in architectural work. In Proceedings of the 2007 International ACM Conference on Supporting Group Work (Sanibel Island, Florida, USA) (GROUP '07). ACM, New York, NY, USA, 11--20. https://doi.org/10.1145/1316624.1316627
[4]
Lars Rune Christensen. 2013. Stigmergy in human practice: Coordination in construction work. Cognitive Systems Research, Vol. 21 (2013), 40--51.
[5]
Lars Rune Christensen. 2014. Practices of stigmergy in the building process. Computer Supported Cooperative Work (CSCW), Vol. 23, 1 (2014), 1--19. https://doi.org/10.1007/s10606-012--9181--3
[6]
Kevin Crowston, Jeffery S. Saltz, Amira Rezgui, Yatish Hegde, and Sangseok You. 2019 a. MIDST: A System to Support Stigmergic Coordination in Data-Science Teams. In Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing (Austin, TX, USA) (CSCW '19). Association for Computing Machinery, New York, NY, USA, 5--8. https://doi.org/10.1145/3311957.3359509
[7]
Kevin Crowston, Jeff S. Saltz, Amira Rezgui, Yatish Hegde, and Sangseok You. 2019 b. Socio-Technical Affordances for Stigmergic Coordination Implemented in MIDST, a Tool for Data-Science Teams. Proc. ACM Hum.-Comput. Interact., Vol. 3, CSCW, Article 117 (Nov. 2019), bibinfonumpages25 pages. https://doi.org/10.1145/3359219
[8]
M. Das, R. Cui, D. R. Campbell, G. Agrawal, and R. Ramnath. 2015. Towards methods for systematic research on big data. In 2015 IEEE International Conference on Big Data (Big Data). 2072--2081. https://doi.org/10.1109/BigData.2015.7363989
[9]
Fred D Davis. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly (1989), 319--340.
[10]
Mark Elliot. 2006. Stigmergic collaboration: The evolution of group work. m/c journal, Vol. 9, 2 (2006). http://journal.media-culture.org.au/0605/03-elliott.php
[11]
J. A. Espinosa and F. Armour. 2016. The Big Data analytics gold rush: A research framework for coordination and governance. In Proceedings of the Hawaii International Conference on System Sciences (HICSS). 1112--1121. https://doi.org/10.1109/HICSS.2016.141
[12]
Pierre-Paul Grassé. 1959. La reconstrution du nid et les coordinations inter-individuelles chez Bellicositermes natalensis et Cubitermes sp. La théorie de la stigmergie: Essai d'interprétation du comportament de termites constructeurs. Insectes Sociaux, Vol. 6, 1 (1959), 41--80. https://doi.org/10.1007/BF02223791
[13]
James Howison and Kevin Crowston. 2014. Collaboration through superposition: How the IT artifact as an object of collaboration affords technical interdependence without organizational interdependence. MIS Quarterly, Vol. 38 (3/2104 2014), 29--50. https://doi.org/10.25300/MISQ/2014/38.1.02
[14]
Eirini Kalliamvakou, Daniela Damian, Leif Singer, and Daniel M German. 2014. The code-centric collaboration perspective: Evidence from GitHub. Report. Technical Report DCS-352-IR, University of Victoria. http://thesegalgroup.org/wp-content/uploads/2014/04/code-centric.pdf
[15]
Mary Beth Kery, Bonnie E John, Patrick O'Flaherty, Amber Horvath, and Brad A Myers. 2019. Towards effective foraging by data scientists to find past analysis choices. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1--13.
[16]
B. A. Kitchenham, S. L. Pfleeger, L. M. Pickard, P. W. Jones, D. C. Hoaglin, K. El Emam, and J. Rosenberg. 2002. Preliminary guidelines for empirical research in software engineering. IEEE Transactions on Software Engineering, Vol. 28, 8 (2002), 721--734. https://doi.org/10.1109/TSE.2002.1027796
[17]
Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian E Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica B Hamrick, Jason Grout, Sylvain Corlay, et almbox. [n.d.]. Jupyter Notebooks: A publishing format for reproducible computational workflows. In Positioning and Power in Academic Publishing: Players, Agents and Agendas (20th International Conference on Electronic Publishing).
[18]
Andrew J. Ko, Thomas D. LaToza, and Margaret M. Burnett. 2015. A practical guide to controlled experiments of software engineering tools with human participants. Empirical Software Engineering, Vol. 20, 1 (01 Feb 2015), 110--141. https://doi.org/10.1007/s10664-013-9279-3
[19]
Ayushi Malviya, Amit Udhani, and Suryakant Soni. 2016. R-tool: Data analytic framework for big data. In 2016 Symposium on Colossal Data Analysis and Networking (CDAN). IEEE, 1--5.
[20]
Gary M Olson and Judith S Olson. 2000. Distance matters. Human-computer interaction, Vol. 15, 2--3 (2000), 139--178.
[21]
Wanda J. Orlikowski and JoAnne Yates. 1994. Genre repertoire: The structuring of communicative practices in organizations. Administrative Science Quarterly, Vol. 33 (1994), 541--574.
[22]
H. V. Parunak. 2006. A survey of environments and mechanisms for human-human stigmergy. In Environments for Multi-Agent Systems II, D. Weyns, H. V. D. Parunak, and F. Michel (Eds.). Lecture Notes in Artificial Intelligence, Vol. 3830. 163--186. https://doi.org/10.1007/11678809_10
[23]
Adam Rule, Ian Drosos, Aurélien Tabard, and James Hollan. 2018. Aiding collaborative reuse of computational notebooks with annotated cell folding. Proceedings of the ACM on Human-Computer Interaction, Vol. 2(CSCW), 150.
[24]
I. Salman, A. T. Misirli, and N. Juristo. 2015. Are students representatives of professionals in software engineering experiments?. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, Vol. 1. 666--676. https://doi.org/10.1109/ICSE.2015.82
[25]
Jeff Saltz, Robert Heckman, Kevin Crowston, Sangseok You, and Yatish Hegde. 2019. Helping data science students develop task modularity. In Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS-52). http://hdl.handle.net/10125/59549
[26]
J. S. Saltz and I. Shamshurin. 2016. Big data team process methodologies: A literature review and the identification of key factors for a project's success. In 2016 IEEE International Conference on Big Data (Big Data). 2872--2879. https://doi.org/10.1109/BigData.2016.7840936
[27]
Kjeld Schmidt and Carla Simonee. 1996. Coordination mechanisms: Towards a conceptual foundation of CSCW systems design. Computer Supported Cooperative Work: The Journal of Collaborative Computing, Vol. 5 (1996), 155--200.
[28]
D. I. K. Sjoeberg, J. E. Hannay, O. Hansen, V. B. Kampenes, A. Karahasanovic, N. Liborg, and A. C. Rekdal. 2005. A survey of controlled experiments in software engineering. IEEE Transactions on Software Engineering, Vol. 31, 9 (Sep. 2005), 733--753. https://doi.org/10.1109/TSE.2005.97
[29]
Z. Soh, Z. Sharafi, B. Van den Plas, G. C. Porras, Y. Guéhéneuc, and G. Antoniol. 2012. Professional status and expertise for UML class diagram comprehension: An empirical study. In 20th IEEE International Conference on Program Comprehension (ICPC). 163--172. https://doi.org/10.1109/ICPC.2012.6240484

Cited By

View all
  • (2023)NBGuru: Generating Explorable Data Science Flowcharts to Facilitate Asynchronous Communication in Interdisciplinary Data Science TeamsCompanion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3584931.3607020(6-11)Online publication date: 14-Oct-2023
  • (2023)Bridging the Gap in AI-Driven Workflows: The Case for Domain-Specific Generative Bots2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386894(2421-2430)Online publication date: 15-Dec-2023
  • (2022)Current approaches for executing big data science projects—a systematic literature reviewPeerJ Computer Science10.7717/peerj-cs.8628(e862)Online publication date: 21-Feb-2022

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Human-Computer Interaction
Proceedings of the ACM on Human-Computer Interaction  Volume 5, Issue CSCW1
CSCW
April 2021
5016 pages
EISSN:2573-0142
DOI:10.1145/3460939
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 April 2021
Published in PACMHCI Volume 5, Issue CSCW1

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. awareness
  2. data-science teams
  3. stigmergic coordination
  4. translucency

Qualifiers

  • Research-article

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)78
  • Downloads (Last 6 weeks)17
Reflects downloads up to 18 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2023)NBGuru: Generating Explorable Data Science Flowcharts to Facilitate Asynchronous Communication in Interdisciplinary Data Science TeamsCompanion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3584931.3607020(6-11)Online publication date: 14-Oct-2023
  • (2023)Bridging the Gap in AI-Driven Workflows: The Case for Domain-Specific Generative Bots2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386894(2421-2430)Online publication date: 15-Dec-2023
  • (2022)Current approaches for executing big data science projects—a systematic literature reviewPeerJ Computer Science10.7717/peerj-cs.8628(e862)Online publication date: 21-Feb-2022

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Full Access

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media