Harnessing collective intelligence for the future of learning – a co-constructed research and development agenda
DOI:
https://doi.org/10.15346/hc.v10i1.141Abstract
Learning, defined as the process of constructing meaning and developing competencies to act on it, is instrumental in helping individuals, communities, and organizations tackle challenges. When these challenges increase in complexity and require domain knowledge from diverse areas of expertise, it becomes difficult for single individuals to address them. In this context, collective intelligence, a capacity of groups of people to act together and solve problems using their collective knowledge, becomes of great importance. Technologies are instrumental both to support and understand learning and collective intelligence, hence the need for innovations in the area of technologies that can support user needs to learn and tackle collective challenges. Use-inspired research is a fitting paradigm that spans applied solutions and scientific explanations of the processes of learning and collective intelligence, and that can improve the technologies that may support them. Although some conceptual and theoretical work explaining and linking learning with collective intelligence is emerging, technological infrastructures as well as methodologies that employ and evidence that support them are nascent. We convened a group of experts to create a middleground and engage with the priorities for use-inspired research. Here we detail directions and methods they put forward as most promising for advancing a scientific agenda around learning and collective intelligence.Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Dusan Misevic, Ignacio Atal, Denis Bedard, Eric Cherel, José Escamilla, Linda Evans, Valerie Hannon, Caroline Huron, Olivier Irrmann, Rene Kizilcec, Emmanuel Lazega, Kerri Lemoie, Ariel Lindner, Mariana Macedo, Gaell Mainguy, Richard Mann, Camille Masselot, Pietro Michelucci, Iryna Nikolayeva, Amy Ogan, Mar Pérez-Sanagustín, Niccolo Pescetelli, Sasha Poquet, Janet Rafner, Dominic Regester, Marc Santolini, Jean-Marc Sevin, Dafna Shahaf, Jacob Sherson, Jacksón Smith, Mattias Söllner, Françoise Soulié, François Taddei, Liubov Tupikina, Sander Van Der Leeuw
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).