Abstract:

Active methodologies involve the need of interaction between students to analyze different tasks to achieve learning outcomes. The possibility of using online tools implies the possibility of increasing the number of participants in these interactions, which implies a new learning situation, in which the amount of information generated is higher and therefore, it can increase learning. However, it can also induce a risk to manage this amount of data generated in this process. The aim of this work is conceptually analyze the possibilities that massive online interaction environments can offer and to provide data from an experience developed with students of the Teaching Degree in Primary Education at the University of Zaragoza using an online interaction platform called Thinkub and designed from collective intelligence models.

Collective intelligence models (Woolley et al., 2010) propose generating interaction contexts that allow get a quality solution for a problem, a solution that is assumed to be better than individual solutions. On the other hand, the studies called “crowd intelligence” analyze as a wide group of people face a relatively difficult problem or situation trying to find new alternatives (Bernstein et al., 2018). Some studies support the possibility that these results emerge, but some difficulties are also detected (Toyokawa et al., 2019).

Researchers from the University Institute for Biocomputing and Complex Systems Physics (BIFI) of the University of Zaragoza and the company Kampal Data Solutions (https://www.kampal.com/), have created the Thinkub tool (https://ic.kampal.com/projects). Its aim is to generate an interaction model that allows the emergence of high-quality solutions to the problems proposed. So, it tries to solve some of the problems of wide group interactions such as the absence of responses from some participants, the appearance of extreme responses, the copy of answers according to the prestige of the participants or the proliferation of multiple answers.

Thus, this work presents the assessment of 261 students who have used the platform to solve as a learning activity an examen from previous courses. From them, some conclusions and suggestions for its use are offered.

References:
[1] Bernstein, E., Shore, J., & Lazer, D. (2018). How intermittent breaks in interaction improve collective intelligence. Proceedings of the National Academy of Sciences of the United States, 35, 8734-8739. https://doi.org/10.1073/pnas.1802407115
[2] Toyokawa, W., Whalen, A., & Laland, K. N. (2019). Social learning strategies regulate the wisdom and madness of interactive crowds. Nature Human Behaviour, 3 (2),183-193. doi.org/10.1038/s41562-018-0518-x
[3] Woolley, A.C., Chabris, C.F., Pentland, A., Hashmi, N. & Malone, T.W. ( 2010). Evidence for a Collective Intelligence Factor in the Performance of Human Groups, Science, 330, 686-688.

Keywords:

Collective inteligence, learning, active methodologies, wide groups interactiion, Teaching Degree in Primary Education.