DOI : https://doi.org/10.18517/ijaseit.10.4.12766

Does Inquiry-based Education Using Robots Have an Effect on Learners’ Inquiry Skills, Subject Knowledge and Skills, and Motivation?

Margus Pedaste (1), Heilo Altin (2)
(1) Institute of Education, University of Tartu, Salme 1a, Tartu, 50103, Estonia
(2) Institute of Computer Science, University of Tartu, Liivi 2, Tartu, 50409, Estonia
Fulltext View | Download
How to cite (IJASEIT) :
[1]
M. Pedaste and H. Altin, “Does Inquiry-based Education Using Robots Have an Effect on Learners’ Inquiry Skills, Subject Knowledge and Skills, and Motivation?”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 4, pp. 1403–1409, Aug. 2020.
Citation Format :
Robots have been applied in science education for a long time. Inquiry-based learning, as a student-centred method to discover different relations, has been considered as an effective learning approach in science education and robots are often used to apply student-guided inquiry. It is, however, not clear what the effect of inquiry-based scenarios is in learning science when students’ motivation and novelty effect are taken into account. In our study, we tested seven inquiry-based scenarios in secondary school physics with a sample of 47 students in the experiment classes and 41 in the control classes. Results revealed that the inquire-based scenarios improved students’ inquiry skills and subject knowledge and skills in the case of the experiment classes and also in the case of the control classes. Study motivation did not improve in the study, explained by the fact that the schools have used robots previously in learning and the novelty effect has faded out. Based on our discussion, the use of robots in education needs to focus more on supporting students’ thinking activities and on increasing their awareness about their own skills and learning process. Further studies are needed to understand in-depth how teachers’ activities in the classroom might have an effect on the usability of robots in education and how students’ thinking and awareness of the learning process could be improved in order to have a stronger effect on learning outcomes as well.

H. Altin and M. Pedaste, “Learning approaches to applying robotics in science education,” Journal of Baltic Science Education, vol. 12, pp. 365-377, 2013.

C. L. van Straten, J. Peter, and R. Kuhne, “Child-Robot Relationship Formation: A Narrative Review of Empirical Research,” International Journal of Social Robotics, vol. 12, pp. 325-344, 2020.

S. Serholt, L. Pareto, S. Ekstrom, and S. Ljungblad, “Trouble and Repair in Child-Robot Interaction: A Study of Complex Interactions With a Robot Tutee in a Primary School Classroom,” Frontiers in Robotics and AI, vol. 7, 2020.

M. Hernandez-de-Menendez, C. E Diaz, and R. Morales-Menendez, “Technologies for the future of learning: state of the art,” International Journal of Interactive Design and Manufacturing, vol. 12, pp. 683-695, 2020.

S. Papert, Mindstorms - Children, Computers, and Powerful Ideas, New York: Basic Books, 1980.

R. S. Lindberg, T. H. Laine, and L. Haaranen, “Gamifying Programming Education in K-12: A Review of Programming Curricula in Seven Countries and Programming Games,” British Journal of Educational Technology, vol. 50, pp. 1979-1995, 2018.

D. Alimisis, “Robotics in Education & Education in Robotics: Shifting Focus from,” in Proceedings of the 3rd International Conference on Robotics in Education, 2012, pp. 7-14.

M. Pedaste and í„. Leijen, “How Can Advanced Technologies Support the Contemporary Learning Approach?” in Proceedings of the 18th IEEE International Conference on Advanced Learning Technologies, 2018, pp. 21-23.

M. Pedaste, T. Palts, K. Kori, M. Síµrmus, and í„. Leijen, “Complex Problem Solving as a Construct of Inquiry, Computational Thinking and Mathematical Problem Solving,” in Proceedings of the 19th IEEE International Conference on Advanced Learning Technologies, 2019, pp. 227-231.

M. Pedaste, M. Mí¤eots, L. A. Siiman, T. de Jong, S. A. van Riesen, E. T. Kamp, C. C. Manoli, Z. C. Zacharia, and E. Tsourlidaki, “Phases of inquiry-based learning: Definitions and the inquiry cycle,” Educational Research Review, vol. 14, pp. 47-61, 2015.

T. Palts and M. Pedaste, “A Model for Developing Computational Thinking Skills,” Informatics in Education, vol. 19, pp. 113-128, 2020.

J. Fagerlund, P. Hakkinen, M. Vesisenaho, and J. Viiri, “Computational thinking in programming with scratch in primary schools: A systematic review,” Computer Applications in Engineering Education, 2020.

J. Jerrim, M. Oliver, and S. Sims, “The Relationship Between Inquiry-based Teaching and Students’ Achievement. New Evidence from a Longitudinal PISA Study in England,” Learning and Instruction, vol. 61, pp. 35-44, 2019.

F. L. Luccio, “Learning Distributed Algorithms by Programming Robots,” Journal of E-Learning and Knowledge Society, vol. 15, pp. 89-100, 2019.

E. Senft, S. Lemaignan, P. E. Baxter, M. Bartlett, and T. Belpaeme, “Teaching robots social autonomy from in situ human guidance,” Science Robotics, vol. 4, 2019.

R. Ryan and E. Deci, “Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being,” The American Psychologist, vol. 55, pp. 68-78, 2000.

J. Vermunt and Y. Vermetten, “Patterns in Student Learning: Relationships Between Learning Strategies, Conceptions of Learning, and Learning Orientations,” Educational Psychology Review, vol. 16, pp. 359-384, 2004.

E. Mcauley, T. Duncan, and V. Tammen, “Psychometric Properties of the Intrinsic Motivation Inventory in a Competitive Sport Setting: A Confirmatory Factor Analysis,” Research Quarterly for Exercise and Sport, vol. 60, pp. 48-58, 1989.

N. Randall, “A Survey of Robot-Assisted Language Learning (RALL),” ACM Transactions on Human-Robot Interaction, vol. 9, 2020.

S. M. S. Khaksar, B. Slade, J. Wallace, and K. Gurinder, “Critical success factors for application of social robots in special developmental schools Development, adoption and implementation,” International Journal of Educational Management, vol. 34, pp. 677-696, 2010.

P. Ponce, A. Molina, E. O. L. Caudana, G. B. Reyes, and N. M. Parra, “Improving education in developing countries using robotic platforms,” International Journal of Interactive Design and Manufacturing, vol. 13, pp. 1401-1422, 2019.

A. Jackson, N. Mentzer, and R. Kramer-Bottiglio, “Pilot analysis of the impacts of soft robotics design on high-school student engineering perceptions,” International Journal of Technology and Design Education, vol. 29, pp. 1083-1104, 2019.

A. Vorholzer and C. Aufschnaiter, “Guidance in Inquiry-based Instruction - An Attempt to Disentangle a Manifold Construct,” International Journal of Science Education, vol. 41, pp. 1-16, 2019.

M. Dobber, R. Zwart, M. Tanis, and B. Oers, “Literature Review: The role of the Teacher in Inquiry-based Education,” Educational Research Review, vol. 22, pp. 194-214, 2017.

P. Newman and M. DeCaro, “Learning by Exploring: How Much Guidance is Optimal?” Learning and Instruction, vol. 62, pp. 49-63, 2019.

M. Wang and R. Wegerif, “From Active-in-Behaviour to Active-in-Thinking in Learning with Technology,” British Journal of Educational Technology, vol. 50, pp. 2067-2777, 2019.

D. Hooshyar, K. Kori, M. Pedaste, and E. Bardone, “The Potential of Open Learner Models to Promote Active Thinking by Enhancing Self-regulated Learning in Online Higher Education Learning Environments,” British Journal of Educational Technology, vol. 50, pp. 2365-2386, 2019.

D. Hooshyar, M. Pedaste, K. Saks, í„. Leijen, E. Bardone, and M. Wang, “Open learner models in supporting self-regulated learning in higher education: A systematic literature review,” Computers & Education, 2020.

L. Al-Shanfari, C. D. Epp, C. Baber, and M. Nazir, “Visualising alignment to support students' judgment of confidence in open learner models,” User Modeling and User-Adapted Interaction, vol. 30, pp. 150-194, 2020.

T. van Woezik, R. , Rob, and J. Koksma, “Exploring Open Space: A self-directed learning approach for higher education,” Cogent Education, vol. 6, 2019.

M. Tissenbaum, “I see what you did there! Divergent collaboration and learner transitions from unproductive to productive states in open-ended inquiry,” Computers & Education, vol. 145, 2020.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. 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.
    2. 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.
    3. 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).