Skip to main content
SpringerLink
Log in

Epistemic Agency in Preservice Teachers’ Science Lessons with Robots

  • Published:
Journal of Science Education and Technology Aims and scope Submit manuscript

Abstract

Science teachers have been urged to use emerging technologies, such as robots, in ways that empower K-12 students as active participants responsible for their learning and knowledge development within the scientific domain. And yet, little is known about whether the use of robots effectively supports students’ epistemic agency in science learning. The purpose of this qualitative case study was to investigate to what extent elementary preservice teachers use educational robots in ways that promote epistemic agency in science lessons. Seven data sources were gathered for this study: individual reflections about lesson planning and lesson design, team reflection about teaching with robots, robotics-enhanced science lessons, posters, video-recorded presentations about designed lessons, and participant interview. A framework of epistemic practices for science inquiry was adopted to analyze the data followed by qualitative thematic analysis. Results indicate that the use of robots in science lessons promotes content assimilation rather than self-driven inquiry, robot movement rather than evidence drives science explanations, science activities with robots are situated in a social vacuum, and robot assembly and programming are underutilized in the lessons. Implications for preservice science teacher education and future research are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Availability of Data and Materials

Not applicable.

References

  • Altin, H., & Pedaste, M. (2013). Learning approaches to applying robotics in science education. Journal of Baltic Science Education, 12(3), 365–377. https://doi.org/10.33225/jbse/13.12.365

  • Anwar, S., Bascou, N. A., Menekse, M., & Kardgar, A. (2019). A systematic review of studies on educational robotics. Journal of Pre-College Engineering Education Research (J-PEER), 9(2). https://doi.org/10.7771/2157-9288.1223

  • Ayres, L., Kavanaugh, K., & Knafl, K. A. (2003). Within-case and across-case approaches to qualitative data analysis. Qualitative Health Research, 13(6), Article 6. https://doi.org/10.1177/1049732303013006008

  • Bächtold, M. (2013). What do students “construct” according to constructivism in science education? Research in Science Education, 43, 2477–2496. https://doi.org/10.1007/s11165-013-9369-7

    Article  Google Scholar 

  • Balaton, M., Cavadas, J., Simeão Carvalho, P., & Lima, J. J. G. (2021). Programming ozobots for teaching astronomy. Physics Education, 56(4), 045018. https://doi.org/10.1088/1361-6552/abfb44

    Article  Google Scholar 

  • Berland, L. K., Schwarz, C. V., Krist, C., Kenyon, L., Lo, A. S., & Reiser, B. J. (2016). Epistemologies in practice: Making scientific practices meaningful for students. Journal of Research in Science Teaching, 53(7), Article 7. https://doi.org/10.1002/tea.21257

  • Bravo, F. A., Hurtado, J. A., & González, E. (2021). Using robots with storytelling and drama activities in science education. Education Sciences, 11(7), 329. https://doi.org/10.3390/educsci11070329

    Article  Google Scholar 

  • Castellano, G., De Carolis, B., D’Errico, F., Macchiarulo, N., & Rossano, V. (2021). PeppeRecycle: Improving children’s attitude toward recycling by playing with a social robot. International Journal of Social Robotics, 13(1), 97–111. https://doi.org/10.1007/s12369-021-00754-0

    Article  Google Scholar 

  • Clarke, V., & Braun, V. (2013). Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist, 26(2), Article 2.

  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104

    Article  Google Scholar 

  • Damşa, C. I. (2014). Shared epistemic agency and agency of individuals, collaborative groups, and research communities. ICLS 2014 Proceedings, 440–447.

  • Damşa, C. I., Kirschner, P. A., Andriessen, J. E. B., Erkens, G., & Sins, P. H. M. (2010). Shared epistemic agency: An empirical study of an emergent construct. Journal of the Learning Sciences, 19(2), Article 2. https://doi.org/10.1080/10508401003708381

  • Darmawansah, D., Hwang, G., Chen, M. A., & Liang, J. (2023). Trends and research foci of robotics-based STEM education: A systematic review from diverse angles based on the technology-based learning model. International Journal of STEM Education, 10(1). https://doi.org/10.1186/s40594-023-00400-3

  • Edwards, N. (2020). Exploring pre-service science teachers’ epistemic agency to develop their pedagogy for science teaching. In C. America, N. Edwards, & M. Robinson (Eds.), Teacher education for transformative agency: Critical perspectives on design, content and pedagogy (1st ed.). (pp. 145–165). African Sun Media. https://doi.org/10.18820/9781928480938/08

  • Eguchi, A. (2014). Educational robotics for promoting 21st century skills. Journal of Automation Mobile Robotics and Intelligent Systems, 8(1), 5–11. https://doi.org/10.14313/JAMRIS_1-2014/1

  • Eguchi, A. (2016). Computational thinking with educational robotics. Proceedings of the Society for Information Technology & Teacher Education International Conference (AACE), 79–84.

  • Engel, P. (2013). Is epistemic agency possible? Philosophical Issues, 23(1), Article 1. https://doi.org/10.1111/phis.12008

  • Erkunt, H. (2010). Emergence of epistemic agency in college level educational technology course for preservice teachers engaged in CSC. The Turkish Online Journal of Educational Technology, 9(3), 38–51.

    Google Scholar 

  • Fegely, A., & Tang, H. (2022). Learning programming through robots: The effects of educational robotics on pre-service teachers’ programming comprehension and motivation. Educational Technology Research & Development, 70, 2211–2234. https://doi.org/10.1007/s11423-022-10174-0

    Article  Google Scholar 

  • Gleasman, C., & Kim, C. (2020). Pre-service teacher’s use of block-based programming and computational thinking to teach elementary mathematics. Digital Experiences in Mathematics Education, 6, 52–90. https://doi.org/10.1007/s40751-019-00056-1

    Article  Google Scholar 

  • Gouvea, J., & Passmore, C. (2017). ‘Models of’ versus ‘models for’: Toward an agent-based conception of modeling in the science classroom. Science & Education, 26(1), Article 1–2. https://doi.org/10.1007/s11191-017-9884-4

  • Han, I. (2013). Embodiment: A new perspective for evaluating physicality in learning. Journal of Educational Computing Research, 49(1), 41–59. https://doi.org/10.2190/EC.49.1.b

    Article  Google Scholar 

  • Harel, I., & Papert, S. (1991). Constructionism: Research reports and essays, 1985–1990. Ablex Publishing.

    Google Scholar 

  • Heljakka, K., Ihamaki, P., Tuomi, P., & Saarikoski, P. (2019). Gamified coding: Toy robots and playful learning in early education. International Conference on Computational Science and Computational Intelligence (CSCI), 2019, 800–805. https://doi.org/10.1109/CSCI49370.2019.00152

    Article  Google Scholar 

  • Jiménez-Aleixandre, M. P., & Crujeiras, B. (2017). Epistemic practices and scientific practices in science education. In K. S. Taber & B. Akpan (Eds.), Science Education (pp. 69–80). Sense Publishers. https://doi.org/10.1007/978-94-6300-749-8_5

  • Kelly, G. J. (2008). Inquiry, activity and epistemic practice. In R. A. Duschl & R. E. Grandy (Eds.), Teaching scientific inquiry: Recommendations for research and implementation (pp. 99–117). Sense Publishers.

    Chapter  Google Scholar 

  • Kelly, G. J., & Licona, P. (2018). Epistemic practices and science education. In M. R. Matthews (Ed.), History, philosophy, and science of teaching: New perspectives (pp. 139–165). Springer.

    Chapter  Google Scholar 

  • Kim, C., Belland, B. R., & Gleasman, C. (2020). Playful coding and playful learning among future early childhood educators. Proceedings of the 2020 Meeting of the International Conference of the Learning Sciences, 4, 2411–2412.

  • Kim, C., Kim, D., Yuan, J., Hill, R. B., Doshi, P., & Thai, C. N. (2015). Robotics to promote elementary education pre-service teachers’ STEM engagement, learning, and teaching. Computers & Education, 91, 14–31. https://doi.org/10.1016/j.compedu.2015.08.005

    Article  Google Scholar 

  • Kim, C., Vasconcelos, L., Belland, B. R., Umutlu, D., & Gleasman, C. (2022). Debugging behaviors of early childhood teacher candidates with or without scaffolding. International Journal of Educational Technology in Higher Education, 19, 1–26. https://doi.org/10.1186/s41239-022-00319-9

    Article  Google Scholar 

  • Kim, C., Yuan, J., Vasconcelos, L., Shin, M., & Hill, R. B. (2018). Debugging during block-based programming. Instructional Science, 46(5), Article 5. https://doi.org/10.1007/s11251-018-9453-5

  • Kim, C., Vasconcelos, L., Belland, B. R., Umutlu, D., & Gleasman, C. (2022). Debugging behaviors of early childhood teacher candidates with and without scaffolding. International Journal of Educational Technology in Higher Education, 19, 26. https://doi.org/10.1186/s41239-022-00319-9

  • Ko, M. M., & Krist, C. (2019). Opening up curricula to redistribute epistemic agency: A framework for supporting science teaching. Science Education, 103(4), 979–1010. https://doi.org/10.1002/sce.21511

    Article  Google Scholar 

  • Koray, A., & Duman, F. G. (2022). Subject-oriented educational robotics applications with Arduino in science teaching: Digital dynamometer activity in accordance with 5E instractional model. Science Activities, 13. https://doi.org/10.1080/00368121.2022.2093824

  • Krishnamoorthy, S. P., & Kapila, V. (2016). Using a visual programming environment and custom robots to learn c programming and K–12 stem concepts. Proceedings of the 6th Annual Conference on Creativity and Fabrication in Education, 41–48. https://doi.org/10.1145/3003397.3003403

  • Lai, K., & Campbell, M. (2018). Developing secondary students’ epistemic agency in a knowledge-building community. Technology, Pedagogy and Education, 27(1), 69–83. https://doi.org/10.1080/1475939X.2017.1369150

    Article  Google Scholar 

  • Lipponen, L., & Kumpulainen, K. (2011). Acting as accountable authors: Creating interactional spaces for agency work in teacher education. Teaching and Teacher Education, 27(5), 812–819. https://doi.org/10.1016/j.tate.2011.01.001

    Article  Google Scholar 

  • Manz, E., & Suárez, E. (2018). Supporting teachers to negotiate uncertainty for science, students, and teaching. Science Education, 102(4), 771–795. https://doi.org/10.1002/sce.21343

    Article  Google Scholar 

  • Mclellan, E. (2017). Shaping agency through theorizing and practising teaching in teacher education. In D. J. Clandinin & J. Husu (Eds.), SAGE Handbook of Research in Teacher Education (Vols. 1–2) (pp. 253–268). SAGE Publications.

  • Miller, E., Manz, E., Russ, R., Stroupe, D., & Berland, L. (2018). Addressing the epistemic elephant in the room: Epistemic agency and the next generation science standards. Journal of Research in Science Teaching, 55(7), 1053–1075. https://doi.org/10.1002/tea.21459

    Article  Google Scholar 

  • National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. The National Academies Press.

  • Nemiro, J., Larriva, C., & Jawaharlal, M. (2017). Developing creative behavior in elementary school students with robotics. The Journal of Creative Behavior, 51(1), 70–90. https://doi.org/10.1002/jocb.87

    Article  Google Scholar 

  • NGSS Lead States. (2013). Next generation science standards: For states, by states. National Academies Press.

  • Osborne, J. (2014). Teaching scientific practices: Meeting the challenge of change. Journal of Science Teacher Education, 25(2), Article 2. https://doi.org/10.1007/s10972-014-9384-1

  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.

    Google Scholar 

  • Passmore, C., Schwarz, C. V., & Mankowski, J. (2016). Developing and using models. In C. V. Schwarz, C. Passmore, & B. J. Reiser (Eds.), Helping students make sense of the world using next generation science and engineering practices (pp. 109–134). NSTA Press. https://doi.org/10.2505/9781938946042

  • Poor, J.*, & Vasconcelos, L. (2023). Impact of virtual field trips on elementary students’ interest in science and STEM. In: C. Martin, D. Polly, B. T. Miller (Eds.) Technology integration and transformation in STEM classrooms (pp. 198-222). IGI Global. https://doi.org/10.4018/978-1-6684-5920-1.ch011

  • Scardamalia, M. (2002). Collective cognitive responsibility for the advancement of knowledge. In B. Smith (Ed.), Liberal Education in a Knowledge Society (pp. 67–98). Open Court.

    Google Scholar 

  • Scardamalia, M., & Bereiter, C. (1991). Higher levels of agency for children in knowledge building: A challenge for the design of new knowledge media. Journal of the Learning Sciences, 1(1), 37–68.

    Article  Google Scholar 

  • Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (pp. 97–118). Cambridge University Press.

    Google Scholar 

  • Scardamalia, M., Bereiter, C., & Lamon, M. (1994). The CSILE project: Trying to bring the classroom into World 3. In K. McGilley (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 201–228). MIT Press.

    Google Scholar 

  • Short, E., Swift-Spong, K., Greczek, J., Ramachandran, A., Litoiu, A., Grigore, E. C., Feil-Seifer, D., Shuster, S., Lee, J. J., Huang, S., Levonisova, S., Litz, S., Li, J., Ragusa, G., Spruijt-Metz, D., Mataric, M., & Scassellati, B. (2014). How to train your DragonBot: Socially assistive robots for teaching children about nutrition through play. The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 924–929. https://doi.org/10.1109/ROMAN.2014.6926371

  • Stake, R. E. (1995). The art of case study research. Sage Publications.

  • Stroupe, D. (2014). Examining classroom science practice communities: How teachers and students negotiate epistemic agency and learn science-as-practice. Science Education, 98(3), Article 3. https://doi.org/10.1002/sce.21112

  • Stroupe, D., Caballero, M. D., & White, P. (2018). Fostering students’ epistemic agency through the co-configuration of moth research. Science Education, 102(6), 1176–1200.

    Article  Google Scholar 

  • Subramaniam, K. (2022). Pre-service elementary teachers’ images of scientific practices: A social, epistemic, conceptual, and material dimension perspective. Research in Science Education. https://doi.org/10.1007/s11165-022-10074-6

    Article  Google Scholar 

  • Sullivan, A., Strawhacker, A., & Bers, M. U. (2017). Dancing, drawing, and dramatic robots: Integrating robotics and the arts to teach foundational STEAM concepts to young children. In M. Khine (Ed.), Robotics in STEM Education (pp. 231–260). Springer, Cham. https://doi.org/10.1007/978-3-319-57786-9_10

  • Toh, L. P. E., Causo, A., Tzuo, P., Chen, I., & Yeo, S. H. (2016). A review on the use of robots in education and young children. Journal of Educational Technology & Society, 19(2), 148–163.

    Google Scholar 

  • Unfried, A., Faber, M., Stanhope, D. S., & Wiebe, E. (2015). The development and validation of a measure of student attitudes toward science, technology, engineering, and math (S-STEM). Journal of Psychoeducational Assessment, 33(7), 622–639. https://doi.org/10.1177/0734282915571160

    Article  Google Scholar 

  • Vasconcelos, L. (2023). Scaffolding hypothesis formation and testing during simulation coding. In: C. Martin, D. Polly, B. T. Miller (Eds.) Technology integration and transformation in STEM classrooms (pp. 19-39). IGI Global. https://doi.org/10.4018/978-1-6684-5920-1.ch002

  • Vasconcelos, L., & Kim, C. (2020a). Coding in scientific modeling lessons (CS-ModeL). Educational Technology Research and Development, 68(3), 1247–1273. https://doi.org/10.1007/s11423-019-09724-w

  • Vasconcelos, L., & Kim, C. (2020b). Preparing preservice teachers to use block-based coding in scientific modeling lessons. Instructional Science, 48, 765–797. https://doi.org/10.1007/s11251-020-09527-0

  • Vasconcelos, L., & Kim, C. (2022). Preservice science teachers coding science simulations: Epistemological understanding, coding skills, and lesson design. Educational Technology Research and Development, 70, 1517–1549. https://doi.org/10.1007/s11423-022-10119-7

  • Vygotsky, L. S. (1986). Thought and language. MT Press.

  • Yang, H. (2021). Epistemic agency, a double-stimulation, and video-based learning: A formative intervention study in language teacher education. System, 96(1). https://doi.org/10.1016/j.system.2020.102401

  • Yin, R. K. (2014). Case study research: Design and methods. Sage Publications.

  • Yuan, J., Kim, C., Vasconcelos, L., Shin, M. Y., Gleasman, C., & Umutlu, D. (2022). Preservice elementary teachers’ engineering design during a robotics project. Contemporary Issues in Technology and Teacher Education, 22(1), 74–104.

    Google Scholar 

  • Zha, S., Jin, Y., Wheeler, R., & Bosarge, E. (2022). A mixed-method cluster analysis of physical computing and robotics integration in middle-grade math lesson plans. Computers & Education, 190. https://doi.org/10.1016/j.compedu.2022.104623

  • Zhang, Y., Luo, R., Zhu, Y., & Yin, Y. (2021). Educational robots improve K-12 students’ computational thinking and STEM attitudes: Systematic review. Journal of Educational Computing Research, 59(7), 1450–1481. https://doi.org/10.1177/0735633121994070

    Article  Google Scholar 

  • Zhang, Y., & Zhu, Y. (2022). Effects of educational robotics on the creativity and problem-solving skills of K-12 students: A meta-analysis. Educational Studies. https://doi.org/10.1080/03055698.2022.2107873

    Article  Google Scholar 

Download references

Funding

This research is supported by Grants 1927595 (PI ChanMin Kim) and 1906059 (PI Brian Belland) from the National Science Foundation. Any opinions, findings, or conclusions are those of the authors and do not necessarily represent official positions of the National Science Foundation.  National Science Foundation,1927595,ChanMin Kim,1906059

Author information

Authors and Affiliations

  1. Department of Leadership, Learning Design, and Inquiry, University of South Carolina, 820 Main St, Columbia, SC, 29208, USA

    Lucas Vasconcelos

  2. Department of Curriculum and Instruction, Tennessee Tech University, 1 William L Jones Dr., Cookeville, TN, 38505, USA

    Cory Gleasman

  3. Department of Computer Education and Educational Technology, Boğaziçi University, Boğaziçi University North Campus, ETA B Block 34342, Istanbul, Turkey

    Duygu Umutlu

  4. Department of Learning and Performance Systems, Penn State University, 314D Keller Building, University Park, PA, 16802, USA

    ChanMin Kim

  5. Department of Educational Psychology, Counseling, and Special Education, Penn State University, 314D Keller Building, University Park, PA, 16802, USA

    ChanMin Kim

Authors
  1. Lucas Vasconcelos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cory Gleasman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Duygu Umutlu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. ChanMin Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.K. secured funding. L.V. and C.K. prepared materials and collected data. L.V. conceptualized the study and refined the study idea with all authors. L.V., C.G., and D.U. analyzed the data. L.V. wrote the first draft of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Lucas Vasconcelos.

Ethics declarations

Ethical Approval

This research received Institutional Review Board approval at the university where data was collected. No biological data was used in this study.

Consent to Participate

Informed consent was obtained from all individual participants included in the study. A consent form approved by the Institutional Review Board was used to obtain informed consent.

Consent for Publication

The participants have consented to the submission of the data from their cases to the journal.

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vasconcelos, L., Gleasman, C., Umutlu, D. et al. Epistemic Agency in Preservice Teachers’ Science Lessons with Robots. J Sci Educ Technol (2024). https://doi.org/10.1007/s10956-024-10092-1

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10956-024-10092-1

Keywords

Search

Navigation