Document Open Access Logo

Towards a Multidimensional Interaction Framework for Promoting Public Engagement in Citizen Science Projects

Authors Maryam Lotfian , Jens Ingensand , Christophe Claramunt

PDF
Thumbnail PDF

File

LIPIcs.GIScience.2023.8.pdf
  • Filesize: 1.79 MB
  • 16 pages

Document Identifiers

Author Details

Maryam Lotfian
  • Institute INSIT, School of Business and Engineering Vaud, University of Applied Sciences and Arts Western Switzerland, Yverdon-les-Bains, Switzerland
Jens Ingensand
  • Institute INSIT, School of Business and Engineering Vaud, University of Applied Sciences and Arts Western Switzerland, Yverdon-les-Bains, Switzerland
Christophe Claramunt
  • Naval Academy Research Institute, Brest Naval, Lanveoc-Poulmic, BP 600, 29240 Brest Naval, France

Acknowledgements

We would like to express our appreciation to the two reviewers for their invaluable and constructive feedback, which assisted us in improving the manuscript.

Cite AsGet BibTex

Maryam Lotfian, Jens Ingensand, and Christophe Claramunt. Towards a Multidimensional Interaction Framework for Promoting Public Engagement in Citizen Science Projects. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 8:1-8:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.8

Abstract

Citizen science (CS) projects are expanding into various fields and the number of CS applications is expanding. Despite this growth, engaging the public and sustaining their participation remains a challenge. Some studies have proposed that interacting with participants is an effective way to sustain their participation. This paper introduces a framework that outlines complementary levels of interaction including basic, incentivized, user-centered and action-oriented interactions. The interaction levels range from basic acknowledgments to instructions for taking action. The integration of these interactions within the spatial, temporal, and thematic dimensions is also discussed. The proposed framework is applied to a biodiversity CS project that involves different types of real-time feedback to participants based on the location, time, and image of the species observations. Location-based feedback is based on the species distribution models, and provides information on the probability of observing a certain species in a given location, as well as suggestions on the species to be observed in the participant’s vicinity. Overall, the multi-dimensional interaction framework provides CS practitioners with insights into the various ways they can maintain communication with participants, whether through real-time machine-generated interactions or interactions between the project team and participants.

Subject Classification

ACM Subject Classification
  • Human-centered computing → Collaborative content creation
Keywords
  • Citizen Science
  • Multidimensional Interaction
  • Participation
  • User-centered Feedback
  • Machine Learning
  • Biodiversity

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Supplementary Materials

References

  1. Oludare Isaac Abiodun, Aman Jantan, Abiodun Esther Omolara, Kemi Victoria Dada, Nachaat AbdElatif Mohamed, and Humaira Arshad. State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11):e00938, 2018. URL: https://doi.org/10.1016/j.heliyon.2018.e00938.
  2. Zahra Putri Agusta et al. Modified balanced random forest for improving imbalanced data prediction. International Journal of Advances in Intelligent Informatics, 5(1):58-65, 2019. Google Scholar
  3. Rick Bonney, Heidi Ballard, Rebecca Jordan, Ellen McCallie, Tina Phillips, Jennifer Shirk, and Candie C Wilderman. Public participation in scientific research: Defining the field and assessing its potential for informal science education. a caise inquiry group report. Washington D.C.: Center for Advancement of Informal Science Education (CAISE), 2009. Google Scholar
  4. Leo Breiman. Random forests. Machine learning, 45:5-32, 2001. Google Scholar
  5. Francesco Cappa, Jeffrey Laut, Maurizio Porfiri, and Luca Giustiniano. Bring them aboard: Rewarding participation in technology-mediated citizen science projects. Computers in Human Behavior, 89:246-257, 2018. Google Scholar
  6. Francesco Cappa, Federica Rosso, and Darren Hayes. Monetary and social rewards for crowdsourcing. Sustainability, 11(10), 2019. URL: https://doi.org/10.3390/su11102834.
  7. Sarah Composto, Jens Ingensand, Marion Nappez, Olivier Ertz, Daniel Rappo, Rémi Bovard, Ivo Widmer, and Stéphane Joost. How to recruit and motivate users to utilize vgi-systems? In Proceedings of 19th AGILE International Conference on Geographic Information Science, 14-17th June 2016, Helsinki, Finland, 2016. Google Scholar
  8. Vickie Curtis. Online citizen science projects: an exploration of motivation, contribution and participation. Open University (United Kingdom), 2015. Google Scholar
  9. Caroline Gottschalk Druschke and Carrie E. Seltzer. Failures of engagement: Lessons learned from a citizen science pilot study. Applied Environmental Education & Communication, 11(3-4):178-188, 2012. URL: https://doi.org/10.1080/1533015X.2012.777224.
  10. Jane Elith and John R. Leathwick. Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40:677-697, December 2009. URL: https://doi.org/10.1146/annurev.ecolsys.110308.120159.
  11. Glyn Everett and Hilary Geoghegan. Initiating and continuing participation in citizen science for natural history. BMC ecology, 16(1):15-22, 2016. Google Scholar
  12. Hilary Geoghegan, Alison Dyke, Rachel Pateman, Sarah West, and Glyn Everett. Understanding motivations for citizen science. Final report on behalf of UKEOF, University of Reading, Stockholm Environment Institute (University of York) and University of the West of England, 2016. Google Scholar
  13. Muki Haklay. Citizen science and volunteered geographic information: Overview and typology of participation. Crowdsourcing geographic knowledge, pages 105-122, 2013. Google Scholar
  14. Muki Haklay et al. Participatory citizen science. Citizen science: Innovation in open science, society and policy, pages 52-62, 2018. Google Scholar
  15. Susanne Hecker and Monika Taddicken. Deconstructing citizen science: a framework on communication and interaction using the concept of roles. Journal of Science Communication, 21(1):A07, 2022. Google Scholar
  16. Jens Ingensand, Maryam Lotfian, Olivier Ertz, David Piot, Sarah Composto, Mathias Oberson, Simon Oulevay, and Mélanie Da Cunha. Augmented reality technologies for biodiversity education. In Proceedings of the 21st Conference on Geo-information science, AGILE, Lund, Sweden. 12-15 June, 2018. Google Scholar
  17. Jens Ingensand, Marion Nappez, Stéphane Joost, Ivo Widmer, Olivier Ertz, and Daniel Rappo. The urbangene project: Experience from a crowdsourced mapping campaign. In 2015 1st International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM), pages 1-7, 2015. Google Scholar
  18. Anne Land-Zandstra, Gaia Agnello, and Yaşar Selman Gültekin. Participants in citizen science. The science of citizen science, 243, 2021. Google Scholar
  19. Anne M. Land-Zandstra, Jeroen L. A. Devilee, Frans Snik, Franka Buurmeijer, and Jos M. van den Broek. Citizen science on a smartphone: Participants’ motivations and learning. Public Understanding of Science, 25(1):45-60, 2016. URL: https://doi.org/10.1177/0963662515602406.
  20. Rob Lemmens, Vyron Antoniou, Philipp Hummer, and Chryssy Potsiou. Citizen Science in the Digital World of Apps, pages 461-474. Springer International Publishing, Cham, 2021. URL: https://doi.org/10.1007/978-3-030-58278-4_23.
  21. Maryam Lotfian, Jens Ingensand, and Maria Antonia Brovelli. A framework for classifying participant motivation that considers the typology of citizen science projects. ISPRS International Journal of Geo-Information, 9(12):704, 2020. Google Scholar
  22. Maryam Lotfian, Jens Ingensand, Olivier Ertz, Simon Oulevay, and Thibaud Chassin. Auto-filtering validation in citizen science biodiversity monitoring. In Proceedings of the ICA; Proceedings of 29th International Cartographic Conference, Tokyo, Japan. 15-20 July, 2019. Google Scholar
  23. Michael Meder, Till Plumbaum, Aleksander Raczkowski, Brijnesh Jain, and Sahin Albayrak. Gamification in e-commerce: Tangible vs. intangible rewards. In Proceedings of the 22nd International Academic Mindtrek Conference, Mindtrek '18, pages 11-19, New York, NY, USA, 2018. Association for Computing Machinery. URL: https://doi.org/10.1145/3275116.3275126.
  24. Abraham Miller-Rushing, Richard Primack, and Rick Bonney. The history of public participation in ecological research. Frontiers in Ecology and the Environment, 10(6):285-290, August 2012. URL: https://doi.org/10.1890/110278.
  25. Greg Newman, Andrea Wiggins, Alycia Crall, Eric Graham, Sarah Newman, and Kevin Crowston. The future of citizen science: emerging technologies and shifting paradigms. Frontiers in Ecology and the Environment, 10(6):298-304, 2012. Google Scholar
  26. Rafael Núñez and Kensy Cooperrider. The tangle of space and time in human cognition. Trends in Cognitive Sciences, 17(5):220-229, May 2013. URL: https://doi.org/10.1016/j.tics.2013.03.008.
  27. Simone Rüfenacht, Tim Woods, Gaia Agnello, Margaret Gold, Philipp Hummer, Anne Land-Zandstra, and Andrea Sieber. Communication and dissemination in citizen science. The Science of Citizen Science, 475:520, 2021. Google Scholar
  28. Brian L Sullivan, Christopher L Wood, Marshall J Iliff, Rick E Bonney, Daniel Fink, and Steve Kelling. ebird: A citizen-based bird observation network in the biological sciences. Biological conservation, 142(10):2282-2292, 2009. Google Scholar
  29. René van der Wal, Nirwan Sharma, Chris Mellish, Annie Robinson, and Advaith Siddharthan. The role of automated feedback in training and retaining biological recorders for citizen science. Conservation Biology, 30(3):550-561, April 2016. URL: https://doi.org/10.1111/cobi.12705.
  30. Geoffrey I Webb, Eamonn Keogh, and Risto Miikkulainen. Naïve bayes. Encyclopedia of machine learning, 15:713-714, 2010. Google Scholar
  31. Sarah Elizabeth West and Rachel Mary Pateman. Recruiting and retaining participants in citizen science: what can be learned from the volunteering literature? Citizen Science: Theory and Practice, 2016. Google Scholar
  32. Wanmin Wu, Ahsan Arefin, Raoul Rivas, Klara Nahrstedt, Renata Sheppard, and Zhenyu Yang. Quality of experience in distributed interactive multimedia environments: toward a theoretical framework. In Proceedings of the 17th ACM international conference on Multimedia, pages 481-490, 2009. Google Scholar
  33. Walter W Wymer Jr. Differentiating literacy volunteers: A segmentation analysis for target marketing. International Journal of Nonprofit and Voluntary Sector Marketing, 8(3):267-285, 2003. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact