Factors Influencing the Design of Educational Robotics Tasks Supporting Collaborative Problem-Solving

Authors

  • Raoul Kamga Université du Québec à Montréal
  • Sylvie Barma Université Laval
  • Frédéric Fournier Université du Québec à Montréal
  • Pierre Lachance Réseau Éducation Collaboration Innovation Technologie
  • Joelle Bérubé-Daigneault Université du Québec à Montréal
  • Sarah Cool-Charest Université de Montréal

DOI:

https://doi.org/10.21432/cjlt28754

Keywords:

activity theory, collaborative problem-solving, educational robotics, task design, teacher education

Abstract

A fundamental skill for primary school students is the ability to solve problems collaboratively. Most research has focused on the analysis and assessment of this skill in primary school students. However, little attention has been paid to the process of designing tasks to foster the development of collaborative problem-solving in these students. Furthermore, collaborative problem-solving can only emerge in a meaningful way if the tasks are designed in such a way as to encourage students to collaborate. This research focuses specifically on the process of designing tasks related to educational robotics, using the theoretical framework of Engeström’s activity theory. Participants, made up of primary school teachers and educational consultants, completed a questionnaire about their task design process and took part in two group interviews. The results highlight that the design of educational robotics tasks, aimed at developing collaborative problem-solving in students, is dependent on the technological and educational robotics task design skills of the designer. The rules governing the design of educational robotics tasks include the time needed to set them up and teamwork.

Author Biographies

Raoul Kamga, Université du Québec à Montréal

Raoul Kamga est professeur au département de didactique de l’Université du Québec à Montréal, Canada. Il détient un doctorat en technologie éducative et est coresponsable du groupe de recherche sur le numérique en éducation (ENUMED). Ses domaines d’expertise incluent l’intelligence artificielle en éducation, la résolution collaborative de problèmes et la robotique pédagogique.

Sylvie Barma, Université Laval

Sylvie Barma, professeure titulaire à l’Université Laval, Canada, se focalise sur les enseignants cherchant à innover dans leur pratique. Elle a dirigé le Centre de recherche sur la réussite scolaire et a été professeure invitée dans diverses universités mondiales. Elle a mené ses recherches en innovation éducative dans une perspective socioculturelle.

Frédéric Fournier, Université du Québec à Montréal

Frédéric Fournier est professeur au département de didactique de l’Université du Québec à Montréal, Canada. Il a enseigné des cours sur le design d’activités numériques innovantes et sur l’apprentissage en laboratoire de sciences et technologie. Ses recherches portent sur la didactique des sciences, en s’appuyant notamment sur les laboratoires créatifs et la robotique pédagogique.

Pierre Lachance, Réseau Éducation Collaboration Innovation Technologie

Pierre Lachance a été enseignant de sciences au secondaire. Il est désormais une personne-ressource au service national du Réseau Éducation Collaboration Innovation et Technologie, où il accompagne les enseignants de sciences dans l’intégration pédagogique du numérique, en leur proposant des formations en présence ou à distance.

Joelle Bérubé-Daigneault, Université du Québec à Montréal

Joelle Bérubé-Daigneault, titulaire d’une maîtrise en éducation et d’un baccalauréat en études littéraires, enseigne le français au secondaire. Dynamique et créative, elle transmet sa passion de l’apprentissage à ses élèves. Curieuse de voyages et d’éducation, elle a enseigné en Afrique du Sud et explore l'usage du numérique en éducation.

Sarah Cool-Charest, Université de Montréal

Sarah Cool-Charest a fait une maîtrise en éducation, option éducation préscolaire et enseignement primaire, à l’Université de Montréal, Canada. Auparavant, elle a suivi des études supérieures en études littéraires ainsi qu’en enseignement du français, langue première, au secondaire, à l’UQAM.

References

Atman Uslu, N., Yavuz, G. Ö., & Koçak Usluel, Y. (2022). A systematic review study on educational robotics and robots. Interactive Learning Environments, 1–25. https://doi.org/10.1080/10494820.2021.2023890

Avry, S., Chanel, G., Betrancourt, M., & Molinari, G. (2018). Effet des antécédents émotionnels de contrôle et de valeur sur la résolution de problème dans un jeu vidéo collaboratif. Revue des sciences et techniques de l’information et de la communication pour l’éducation et la formation, 25(1). https://doi.org/10.23709/STICEF.25.1.3

Barma, S. (2008). Un contexte de renouvellement de pratiques en éducation aux sciences et aux technologies : une étude de cas réalisée sous l’angle de la théorie de l’activité [thèse]. Université Laval. http://hdl.handle.net/20.500.11794/20215

Bergner, Y., Andrews, J. J., Zhu, M., & Gonzales, J. E. (2016). Agent-based modeling of collaborative problem solving : Agent-based modeling of collaborative problem solving. ETS Research Report Series, 2016(2), 1–14. https://doi.org/10.1002/ets2.12113

Care, E., Griffin, P., Scoular, C., Awwal, N., & Zoanetti, N. (2015). Collaborative problem solving tasks. Dans P. Griffin et E. Care (dir.), Assessment and teaching of 21st century skills (pp. 85–104). Springer Netherlands. https://doi.org/10.1007/978-94-017-9395-7_4

Cevikbas, M., & Kaiser, G. (2021). A systematic review on task design in dynamic and interactive mathematics learning environments (DIMLEs). Mathematics, 9(4), 399. https://doi.org/10.3390/math9040399

Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162–175. https://doi.org/10.1016/j.compedu.2017.03.001

De Hei, M. S. A., Sjoer, E., Admiraal, W., & Strijbos, J.-W. (2016). Teacher educators’ design and implementation of group learning activities. Educational Studies, 42(4), 394–409. https://doi.org/10.1080/03055698.2016.1206461

DeVane, B., & Squire, K. D. (2012). Activity theory in the learning technologies. Dans Theoretical foundations of learning environments (2e éd., pp. 242–267). Routledge. https://www.taylorfrancis.com/chapters/edit/10.4324/9780203813799-12/activity-theory-learning-technologies-benjamin-devane-kurt-squire

Dindar, M., Järvelä, S., Nguyen, A., Haataja, E., & Çini, A. (2022). Detecting shared physiological arousal events in collaborative problem solving. Contemporary Educational Psychology, 69, 102050. https://doi.org/10.1016/j.cedpsych.2022.102050

Engeström, Y. (1987). Learning by expanding: An activity-theoretical approach to developmental research. https://lchc.ucsd.edu/mca/Paper/Engestrom/Learning-by-Expanding.pdf

Engeström, Y. (1999). Activity theory and individual and social transformation. Dans Y. Engeström, R. Miettinen et R.-L. Punamäki-Gitai (dir.), Perspectives on activity theory (pp. 19–38). Cambridge University Press.

Engeström, Y., & Pyörälä, E. (2021). Using activity theory to transform medical work and learning. Medical Teacher, 43(1), 7–13. https://doi.org/10.1080/0142159X.2020.1795105

Graesser, A., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W., & Hesse, F. W. (2018). Advancing the science of collaborative problem solving. Psychological Science in the Public Interest, 19(2), 59–92. https://doi.org/10.1177/1529100618808244

Graesser, A., Kuo, B.-C., & Liao, C.-H. (2017). Complex problem solving in assessments of collaborative problem solving. Journal of Intelligence, 5(2), 10. https://doi.org/10.3390/jintelligence5020010

Hall, B. M. (2014). Designing collaborative activities to promote understanding and problem-solving. International Journal of e-Collaboration, 10(2), 55–71.

Hesse, F., Care, E., Buder, J., Sassenberg, K., & Griffin, P. (2015). A framework for teachable collaborative problem solving skills. Dans P. Griffin et E. Care (dir.), Assessment and teaching of 21st century skills (pp. 37–56). Springer Netherlands. https://doi.org/10.1007/978-94-017-9395-7_2

Kamga, R. (2019). Analyse de la compétence de résolution collaborative de problèmes des futur(e)s enseignant(e)s de l’enseignement primaire. Université Laval. https://corpus.ulaval.ca/jspui/handle/20.500.11794/36794

Kamga, R., Romero, M., Komis, V., & Mirsili, A. (2017). Design requirements for educational robotics activities for sustaining collaborative problem solving. Dans D. Alimisis, M. Moro et E. Menegatti (dir.), Educational robotics in the makers era (vol. 560, pp. 225–228). Springer International Publishing. https://doi.org/10.1007/978-3-319-55553-9_18

Kolfschoten, G., French, S., & Brazier, F. (2014). A discussion of the cognitive load in collaborative problem-solving: The decision-making phase. EURO Journal on Decision Processes, 2(3–4), 257–280. https://doi.org/10.1007/s40070-014-0034-9

L’Écuyer, R. (1990). Méthodologie de l’analyse développementale de contenu. Méthode GPS et concept de soi. Presses de l’Université du Québec. http://www.deslibris.ca/ID/422680

Leroy, A., & Romero, M. (2022). Creative intention and persistence in educational robotic. Educational Technology Research and Development, 70. https://doi.org/10.1007/s11423-022-10128-6

Nieminen, J. H., Chan, M. C. E., & Clarke, D. (2022). What affordances do open-ended real-life tasks offer for sharing student agency in collaborative problem-solving? Educational Studies in Mathematics, 109(1), 115–136. https://doi.org/10.1007/s10649-021-10074-9

OCDE. (2013). Pisa 2015: draft collaborative problem solving framework. Organization for Economic Cooperation & Development. https://www.oecd.org/pisa/pisaproducts/Draft%20PISA%202015%20Collaborative%20Problem%20Solving%20Framework%20.pdf

Oliver, M., & Higgins, S. (2023). Exploring task design to promote discipline-specific reasoning in primary English. Thinking Skills and Creativity, 47, 101230. https://doi.org/10.1016/j.tsc.2022.101230

Papadakis, S., & Kalogiannakis, M. (2022). Learning computational thinking development in young children with bee-bot educational robotics. Dans I. R. Management Association (dir.), Research anthology on computational thinking, programming, and robotics in the classroom (pp. 926–947). IGI Global. https://doi.org/10.4018/978-1-6684-2411-7.ch040

Papert, S. (1980). Turtle geometry: A mathematics made for learning. Dans Mindstorms. Children, computers and powerful ideas (pp. 55–94). Basic Books. http://worrydream.com/refs/Papert%20-%20Mindstorms%201st%20ed.pdf

Rojas, M., Nussbaum, M., Chiuminatto, P., Guerrero, O., Greiff, S., Krieger, F., & Van Der Westhuizen, L. (2021). Assessing collaborative problem-solving skills among elementary school students. Computers & Education, 175, 104313. https://doi.org/10.1016/j.compedu.2021.104313

Romero, M., & DeBlois, L. (2022). Analyse du processus de construction de connaissances dans des activités de programmation à l’école. Canadian Journal of Science, Mathematics and Technology Education, 22(2), 405–421. https://doi.org/10.1007/s42330-022-00210-9

Sannino, A. (2015). The emergence of transformative agency and double stimulation: Activity-based studies in the Vygotskian tradition. Learning, Culture and Social Interaction, 4, 1–3. https://doi.org/10.1016/j.lcsi.2014.07.001

Siddiq, F., & Scherer, R. (2017). Revealing the processes of students’ interaction with a novel collaborative problem solving task: An in-depth analysis of think-aloud protocols. Computers in Human Behavior, 76, 509–525. https://doi.org/10.1016/j.chb.2017.08.007

Socratous, C., & Ioannou, A. (2022). Evaluating the impact of the curriculum structure on group metacognition during collaborative problem-solving using educational robotics. TechTrends, 66. https://doi.org/10.1007/s11528-022-00738-5

Song, Y. (2018). Improving primary students’ collaborative problem solving competency in project-based science learning with productive failure instructional design in a seamless learning environment. Educational Technology Research and Development, 66(4), 979–1008. https://doi.org/10.1007/s11423-018-9600-3

Sullivan, P., Knott, L., & Yang, Y. (2015). The relationships between task design, anticipated pedagogies, and student learning. Task design in mathematics education. An ICMI Study 22, (pp. 8–114). Springer Nature.

Taylor, K., & Baek, Y. (2018). Collaborative robotics, more than just working in groups. Journal of Educational Computing Research, 56(7), 979–1004. https://doi.org/10.1177/0735633117731382

Tissenbaum, M., Lui, M., & Slotta, J. (2012). Co-designing collaborative smart classroom curriculum for secondary school science. Journal of Universal Computer Science, 18(3), 327–352.

Unal, E., & Cakir, H. (2021). The effect of technology-supported collaborative problem solving method on students’ achievement and engagement. Education and Information Technologies, 26(4), 4127–4150. https://doi.org/10.1007/s10639-021-10463-w

Warneken, F., Steinwender, J., Hamann, K., & Tomasello, M. (2014). Young children’s planning in a collaborative problem-solving task. Cognitive Development, 31, 48–58. https://doi.org/10.1016/j.cogdev.2014.02.003

Watson, A. & Ohtani, M. (2015). Task design in mathematics education. An ICMI Study 22. Springer Nature.

Yin, K. Y., Abdullah, A. G. K., & Alazidiyeen, N. J. (2011). Collaborative problem solving methods towards critical thinking. International Education Studies, 4(2), 58–62. https://doi.org/10.5539/ies.v4n2p58

Zhang, S., Gao, Q., Sun, M., Cai, Z., Li, H., Tang, Y., & Liu, Q. (2022). Understanding student teachers’ collaborative problem solving: Insights from an epistemic network analysis (ENA). Computers & Education, 183, 104485. https://doi.org/10.1016/j.compedu.2022.104485

Published

2025-04-04

Issue

Section

Articles