Skip to main content

Advertisement

SpringerLink
Log in

Evaluating educational robotics as a maker learning tool for pre-service teacher computer science instruction

  • Research Article
  • Published:
Educational technology research and development Aims and scope Submit manuscript

Abstract

Computer science teaching standards for grades K-8 have been implemented in nearly all U.S. states, and the core subject area teachers (e.g., math, science, English, social studies) have been asked to integrate these standards into their instruction. Thus, it is important that K-8 pre-service teachers of all subjects are both prepared and motivated to teach computer science concepts—such as programming—upon entering the field. However, little is known about how pre-service teachers learn and retain programming knowledge or obtain and sustain their motivation related to programming. Maker-focused educational robotics activities have the potential to both reduce abstract cognitive load and work as motivational tools for STEM learning. The purpose of this study was to examine pre-service teachers’ motivational persistence and retention of programming concepts after learning with educational robotics through maker-focused computer science activities. Hands-on maker robotics programming activities were used to teach and motivate pre-service teachers. This quantitative study utilized repeated measures through pre-, post-, and 6-month follow-up surveys and tests. The findings indicated the pre-service teachers’ programming comprehension gains exhibited on the posttest deteriorated substantially to near-baseline levels within 6 months of instruction. Conversely, pre-service teachers’ motivation related to programming continued to rise after the instruction had concluded. Both the retention of comprehension of programming concepts and motivational persistence findings imply that educator preparation providers should integrate programming instruction throughout their pre-service teacher curricula and support curricular initiatives that call for the integration of computer science instruction across pre-service teacher methods courses to reinforce computer science learning.

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
Fig. 2

Similar content being viewed by others

References

  • Advocacy Coalition. (2021). 2021 state of computer science education: Accelerating action through advocacy. https://advocacy.code.org/stateofcs

  • Ahmadzadeh, M., Elliman, D., & Higgins, C. (2007). The impact of improving debugging skill on programming ability. Innovation in Teaching and Learning in Information and Computer Sciences, 6(4), 72–87. https://doi.org/10.11120/ital.2007.06040072

    Article  Google Scholar 

  • Ala-Mutka, K. (2004). Problems in learning and teaching programming. Codewitz Needs Analysis, 20, 1–13.

    Google Scholar 

  • Alkaria, A., & Alhassan, R. (2017). The effect of in-service training of computer science teachers on Scratch programming language skills using an electronic learning platform on programming skills and the attitudes towards teaching programming. Journal of Education and Training Studies, 5(11), 1–12. https://doi.org/10.11114/jets.v5i11.2608

    Article  Google Scholar 

  • Altin, H., & Pedaste, M. (2013). Learning approaches to applying robotics in science education. Journal of Baltic Science Education, 12(3), 365–377.

    Article  Google Scholar 

  • Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.

    Google Scholar 

  • Bandura, A. (1997). Self-efficacy. Harvard Mental Health Letter, 13(9), 4–5.

    Google Scholar 

  • Bean, N., Weese, J. Feldhausen R., & Bell R. S. (2015). Starting from scratch: Developing a pre-service teacher training program in computational thinking. In 2015 IEEE Frontiers in Education Conference (FIE), (pp. 1–8) https://doi.org/10.1109/FIE.2015.7344237

  • Black, A. E., & Deci, E. L. (2000). The effects of instructors’ autonomy support and students’ autonomous motivation on learning organic chemistry: A self-determination theory perspective. Science Education, 84(6), 740–756.

    Article  ADS  Google Scholar 

  • Bland, J. M., & Altman, D. G. (1995). Multiple significance tests: The Bonferroni method. BMJ, 310(6973), 170.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Burke, Q., Schep, M., & Dalton, T. (2016). CS for SC: A landmark report on K-12 computer science in South Carolina. National Science Foundation.

    Google Scholar 

  • Cenberci, S., & Beyhan, A. (2016). Research for motivational persistence levels of prospective teachers in terms of age, gender and teaching branch variables. Journal of Education and Training Studies, 4(12), 21–26. https://doi.org/10.11114/jets.v4i12.1882

    Article  Google Scholar 

  • Chambers, J. M., & Carbonaro, M. (2003). Designing, developing, and implementing a course on LEGO robotics for technology teacher education. Journal of Technology and Teacher Education, 11(2), 209–242.

    Google Scholar 

  • Code.org. (2017). Universities aren’t preparing enough computer science teachers. Medium. https://codeorg.medium.com/universities-arent-preparing-enough-computer-science-teachers-dd5bc34a79aa#:~:text=We%20found%20that%20in%202016,physics%2C%20and%20earth%20science).

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Erlbaum.

    Google Scholar 

  • Computer Science Teachers Association (2020). Standards for Computer Science Teachers. https://csteachers.org/teacherstandards.

  • Constantin, T., Holman, A., & Hojbotă, M. A. (2011). Development and validation of a motivational persistence scale. Psihologija, 45(2), 99–120.

    Article  Google Scholar 

  • Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed method approaches. Sage Publications.

    Google Scholar 

  • DeVellis, R. F. (2003). Scale development: Theory and applications. Sage Publications.

    Google Scholar 

  • Dong, W., Li, Y., Sun, L., & Liu, Y. (2023). Developing pre-service teachers’ computational thinking: A systematic literature review. International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-023-09811-3

    Article  PubMed  PubMed Central  Google Scholar 

  • Eguchi, A. (2013). Educational robotics for promoting 21st century skills. Journal of Automation, Mobile Robotics & Intelligent Systems, 8(1), 1–42. https://doi.org/10.14313/JAMRIS

    Article  Google Scholar 

  • Faisal, A., Kapila, V., & Iskander, M. G. (2012, June). Using robotics to promote learning in elementary grades. In 2012 ASEE Annual Conference & Exposition (pp. 25–1439).

  • 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 and Development, 70(6), 2211–2234. https://doi.org/10.1007/s11423-022-10174-0

    Article  Google Scholar 

  • Fegely, A., Winslow, J., Lee, C., & Rubbo, L. J. (2021). The effects of robotics professional development on science and mathematics teaching performance and student achievement in underserved middle schools. Contemporary Issues in Technology and Teacher Education, 21(4), 655–679.

    Google Scholar 

  • Francis, K., & Davis, B. (2018). Coding robots as a source of instantiations for arithmetic. Digital Experiences in Mathematics Education, 4(2), 71–86.

    Article  Google Scholar 

  • Franklin, D., Hill, C., Dwyer, H., Iveland, A., Killian, A., & Harlow, D. (2015). Getting started in teaching and researching computer science in the elementary classroom. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education (pp. 552–557).

  • 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.

    Article  Google Scholar 

  • Glynn, S. M., Brickman, P., Armstrong, N., & Taasoobshirazi, G. (2011). Science motivation questionnaire II: validation with science majors and nonscience majors. Journal of Research in Science Teaching, 48(10), 1159–1176. https://doi.org/10.1002/tea.20442

    Article  ADS  Google Scholar 

  • Glynn, S. M., Taasoobshirazi, G., & Brickman, P. (2009). Science motivation questionnaire: construct validation with nonscience majors. Journal of Research in Science Teaching, 46(2), 127–146. https://doi.org/10.1002/tea.20267

    Article  ADS  Google Scholar 

  • Google Inc., & Gallup Inc. (2016). Trends in the state of computer science in U.S. K-12 schools. http://goo.gl/j291E0

  • Grover, S., & Pea, R. (2018). Computational thinking: A competency whose time has come. Computer Science Education: Perspectives on Teaching and Learning in School, 19(1), 19–38.

    Google Scholar 

  • Han, I. (2013). Embodiment: A new perspective for evaluating physicality in learning. Journal of Educational Computing Research, 49(1), 41–59.

    Article  Google Scholar 

  • Haynie, W. J. (1994). Effects of multiple-choice and short answer tests on delayed retention learning. Journal of Technology Education, 6(1), 32–44.

    Article  Google Scholar 

  • Henry, A. (2020). Possible selves and personal goals: What can we learn from episodic future thinking? Eurasian Journal of Applied Linguistics, 6(3), 481–500.

    Google Scholar 

  • Jaipal-Jamani, K., & Angeli, C. (2017). Effect of robotics on elementary preservice teachers’ self-efficacy, science learning, and computational thinking. Journal of Science Education and Technology, 26(2), 175–192. https://doi.org/10.1007/s10956-016-9663-z

    Article  ADS  Google Scholar 

  • Kay, J. S., Moss, J. G., Engelman, S., & McKlin, T. (2014). Sneaking in through the back door: Introducing K-12 teachers to robot programming. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (pp. 499–504).

  • Kaya, E., Newley, A., Deniz, H., Yesilyurt, E., & Newley, P. (2015). Introducing engineering design to a science teaching methods course through educational robotics and exploring changes in views of preservice elementary teachers. Journal of College Science Teaching, 47(2), 66–75.

    Article  Google Scholar 

  • 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 and 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(26), 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), 767–787. https://doi.org/10.1007/s11251-018-9453-5

    Article  Google Scholar 

  • Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60–70.

    Google Scholar 

  • Kopcha, T. J. (2012). Teachers’ perceptions of the barriers to technology integration and practices with technology under situated professional development. Computers & Education, 59(4), 1109–1121.

    Article  Google Scholar 

  • Kucuk, S., & Sisman, B. (2018). Pre-service teachers’ experiences in learning robotics design and programming. Informatics in Education, 17(2), 301–320. https://doi.org/10.15388/infedu.2018.16

    Article  Google Scholar 

  • Law, K., Lee, V., & Yu, Y. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers and Education, 55(1), 218–228. https://doi.org/10.1016/j.compedu.2010.01.007

    Article  Google Scholar 

  • Liang, S., & Wang, J. (2019). Advanced remote sensing: Terrestrial information extraction and applications. Academic Press.

    Google Scholar 

  • Majherová, J., & Králík, V. (2017). Innovative methods in teaching programming for future informatics teachers. European Journal of Contemporary Education, 6(3), 390–401. https://doi.org/10.13187/ejced.2017.3.390

    Article  Google Scholar 

  • Malan, D. J., & Leitner, H. H. (2007). Scratch for budding computer scientists. ACM Sigcse Bulletin, 39(1), 223–227. https://doi.org/10.1145/1227504.1227388

    Article  Google Scholar 

  • Margulieux, L., Ketenci, T. A., & Decker, A. (2019). Review of measurements used in computing education research and suggestions for increasing standardization. Computer Science Education, 29(1), 49–78.

    Article  ADS  Google Scholar 

  • Martin, A. J. (2007). Examining a multidimensional model of student motivation and engagement using a construct validation approach. British Journal of Educational Psychology, 77(2), 413–440. https://doi.org/10.1348/000709906X118036

    Article  PubMed  Google Scholar 

  • Martin, F. G., Mikhak, B., Resnick, M., Silverman, B., & Berg, R. (2000). To mindstorms and beyond: Evolution of a construction kit for magical machines. In A. Druin, J. A. Hendler, & J. Hendler (Eds.), Robots for kids: Exploring new technologies for learning (pp. 9–33). Morgan Kaufmann.

    Google Scholar 

  • Marzano, R. J. (2007). The art and science of teaching. Association for Supervision and Curriculum Development.

  • Mikropoulos, T., & Bellou, I. (2013). Educational robotics as mindtools. Themes in Science & Technology Education, 6(1), 5–14.

    Google Scholar 

  • Nugent, G., Barker, B., Grandgenett, N., & Adamchuk, V. (2010). Impact of robotics and geospatial technology interventions on youth STEM learning and attitudes. Journal of Research on Technology in Education, 42(4), 391–408.

    Article  Google Scholar 

  • Núñez-Antón, V., Pérez-Salamero González, J. M., Regúlez-Castillo, M., & Vidal-Meliá, C. (2020). Improving the representativeness of a simple random sample: An optimization model and its application to the continuous sample of working lives. Mathematics, 8(1225), 1–26.

    Google Scholar 

  • Ortiz, A., Bos, B., & Smith, S. (2015). The power of educational robotics as an integrated STEM learning experience in teacher preparation programs. Journal of College Science Teaching, 44(5), 42–47. https://doi.org/10.2505/4/jcst15_044_05_42

    Article  Google Scholar 

  • Pallant, J. (2007). SPSS survival manual: A step by step guide to data analysis using SPSS. McGraw-Hill Companies.

    Google Scholar 

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

    Google Scholar 

  • Papert, S. (1993). The children’s machine. Basic Books.

    Google Scholar 

  • Papert, S. (1999). Constructionism: Research reports and essays. Ablex.

    Google Scholar 

  • Paul, A. M. (2016). The coding revolution. Scientific American, 35(1), 42–49.

    Article  Google Scholar 

  • Pintrich, P. R., & Schunk, D. H. (1996). Motivation in education: Theory, research, and applications. Merrill/Prentice-Hall.

    Google Scholar 

  • Polly, D., Mims, C., Shepherd, C. E., & Inan, F. (2010). Evidence of impact: Transforming teacher education with preparing tomorrow’s teachers to teach with technology (PT3) grants. Teaching and Teacher Education, 26(4), 863–870.

    Article  Google Scholar 

  • Ramalingam, V., & Wiedenbeck, S. (1997). An empirical study of novice program comprehension in the imperative and object-oriented styles [Paper presentation]. Seventh Workshop on Empirical Studies of Programmer, Alexandria, VA, USA. https://doi.org/10.1145/266399.266411

    Article  Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.

    Article  CAS  PubMed  Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, 1–11. https://doi.org/10.1016/j.cedpsych.2020.101860

    Article  Google Scholar 

  • Serrano, R., & Muñoz, C. (2007). Same hours, different time distribution: Any difference in EFL? System, 35(3), 305–321.

    Article  Google Scholar 

  • Sisman, B., & Kucuk, S. (2019). An educational robotics course: Examination of educational potentials and preservice teachers’ experiences. International Journal of Research in Education and Science, 5(1), 510–531.

    Google Scholar 

  • Stehlik-Barry, K., & Babinec, A. J. (2017). Data analysis with IBM SPSS statistics. Packt Publishing Ltd.

    Google Scholar 

  • Timotheou, S., & Ioannou, A. (2019). On a making- & -tinkering STEAM approach to learning Mathematics: Knowledge gains, attitudes, and 21st century skills. In: K. Lund, G. P. Niccolai, E. Lavoué, C. Hmelo-Silver, G. Gweon, and M. Baker (eds). A wide lens: Combining embodied, enactive, extended, and embedded learning in collaborative settings. In 13th International Conference on Computer Supported Collaborative Learning (CSCL), International Society of the Learning Sciences, Lyon

  • Ushioda, E. (2014). Context and complex Dynamic Systems Theory. In Z. Dörnyei, P. D. MacIntrye, & A. Henry (Eds.), Motivational dynamics in language learning. Multilingual Matters.

    Google Scholar 

  • Warner, J. R., Torbey, R., Fletcher, C. L., & Garbrecht, L. S. (2019). Increasing capacity for computer science education in rural areas through a large-scale collective impact model. In SIGCSE 2019 – Proceedings in the 50th ACM Technology Symposium on Computer Science Education, (pp. 1157–1163). https://doi.org/10.1145/3287324.3287418

  • Weintrop, D., & Wilensky, U. (2017). Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education, 18(1), 1–25. https://doi.org/10.1145/3089799

    Article  Google Scholar 

  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.

    Article  Google Scholar 

  • Yadav, A., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education, 14(1), 1–16. https://doi.org/10.1145/2576872

    Article  Google Scholar 

  • Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S., & Korb, J. T. (2011). Introducing computational thinking in education courses. In Proceedings of the 42nd ACM technical symposium on computer science education

  • Zhang, M., & Wan, Y. (2020). Improving learning experiences using LEGO Mindstorms EV3 robots in control systems course. International Journal of Electrical Engineering. https://doi.org/10.1177/0020720920965873

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instructional Technology, Department of Educational Studies, Spadoni College of Education & Social Sciences, Coastal Carolina University, P.O. Box 261954, Conway, SC, 29528, USA

    Alex Fegely & Tammi Kolski

  2. Department of Curriculum & Instruction, College of Education, Tennessee Tech University, Cookeville, TN, 38501, USA

    Cory Gleasman

Authors
  1. Alex Fegely
    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. Tammi Kolski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alex Fegely.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Research involving human and animal rights

Human participants only.

Informed consent

Yes.

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

Fegely, A., Gleasman, C. & Kolski, T. Evaluating educational robotics as a maker learning tool for pre-service teacher computer science instruction. Education Tech Research Dev 72, 133–154 (2024). https://doi.org/10.1007/s11423-023-10273-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11423-023-10273-6

Keywords

Search

Navigation