Skip to main content

Advertisement

SpringerLink
Log in

Effect of Robotics on Elementary Preservice Teachers’ Self-Efficacy, Science Learning, and Computational Thinking

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

Abstract

The current impetus for increasing STEM in K-12 education calls for an examination of how preservice teachers are being prepared to teach STEM. This paper reports on a study that examined elementary preservice teachers’ (n = 21) self-efficacy, understanding of science concepts, and computational thinking as they engaged with robotics in a science methods course. Data collection methods included pretests and posttests on science content, prequestionnaires and postquestionnaires for interest and self-efficacy, and four programming assignments. Statistical results showed that preservice teachers’ interest and self-efficacy with robotics increased. There was a statistically significant difference between preknowledge and postknowledge scores, and preservice teachers did show gains in learning how to write algorithms and debug programs over repeated programming tasks. The findings suggest that the robotics activity was an effective instructional strategy to enhance interest in robotics, increase self-efficacy to teach with robotics, develop understandings of science concepts, and promote the development of computational thinking skills. Study findings contribute quantitative evidence to the STEM literature on how robotics develops preservice teachers’ self-efficacy, science knowledge, and computational thinking skills in higher education science classroom contexts.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Adedokun OA, Bessenbacher AB, Parker LC, Kirkham LL, Burgess WD (2013) Research skills and STEM undergraduate research students’ aspirations for research careers: mediating effects of research self-efficacy. J Res Sci Teach 50(8):940–951

    Article  Google Scholar 

  • Aho AV (2012) Computation and computational thinking. Comput J 55:832–835

    Article  Google Scholar 

  • Altin H, Pedaste M (2013) Learning approaches to applying robotics in science education. J Balt Sci Educ 12(3):365–377

    Google Scholar 

  • Bandura A (1977) Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev 84(2):191

    Article  Google Scholar 

  • Bandura, A. (1994). Self-efficacy. In: Ramachaudran V. S. (ed.), Encyclopedia of human behavior (Vol. 4). Academic Press, New York, pp. 71–81. (Reprinted in H. Friedman [Ed.], Encyclopedia of mental health. San Diego: Academic Press, 1998)

  • Bandura A (2006) Guide for constructing self-efficacy scales. Self-efficacy Beliefs of Adolescents 5:307–337

    Google Scholar 

  • Barker BS, Ansorge J (2007) Robotics as means to increase achievement scores in an informal learning environment. J Res Technol Educ 39(3):229–243

    Article  Google Scholar 

  • Barker BS, Nugent G, Grandgenett NF (2014) Examining fidelity of program implementation in a STEM-oriented out-of-school setting. Int J Technol Des Educ 24(1):39–52

    Article  Google Scholar 

  • Benitti FBV (2012) Exploring the educational potential of robotics in schools: a systematic review. Comput Educ 58(3):978–988

    Article  Google Scholar 

  • Bernard HR (2012) Social research methods: qualitative and quantitative approaches. Sage, Thousand Oaks, London

    Google Scholar 

  • Bers MU, Portsmore M (2005) Teaching partnerships: early childhood and engineering students teaching math and science through robotics. J Sci Educ Technol 14(1):59–73

    Article  Google Scholar 

  • Brown SD, Lent RW (2006) Preparing adolescents to make career decisions: a social cognitive perspective. In: Perjares F, Urdan T (eds), Self-efficacy beliefs of adolescents. Connecticut: Information Age, Greenwich, pp 201–223

  • Bybee R (2010) Advancing STEM education: a 2020 vision. Technology and Engineering Teacher 70(1):30–35

    Google Scholar 

  • Chung CC, Cartwright C, Cole M (2014) Assessing the impact of an autonomous robotics competition for STEM education. Journal of STEM Education: Innovations and Research 15(2):24

    Google Scholar 

  • Denis B, Hubert S (2001) Collaborative learning in an educational robotics environment. Comput Hum Behav 17(5):465–480

    Article  Google Scholar 

  • Dijkstra EW (1972) The humble programmer. Commun ACM 15(10):859-866

  • Epstein D, Miller RT (2011) Slow off the mark: elementary school teachers and the crisis in science, technology, engineering, and math education. Center for American Progress, Washington, DC

    Google Scholar 

  • Grover S, Pea R (2013) Computational thinking in K–12: a review of the state of the field. Educ Res 42(1):38–43

    Article  Google Scholar 

  • Karp T, Maloney P (2013) Exciting young students in grades K-8 about STEM through an afterschool robotics challenge. Am J Eng Educ 4(1):39–54

    Google Scholar 

  • Kazakoff ER, Sullivan A, Bers MU (2013) The effect of a classroom-based intensive robotics and programming workshop on sequencing ability in early childhood. Early Childhood Educ J 41(4):245–255

    Article  Google Scholar 

  • Kim C, Kim D, Yuan J, Hill RB, Doshi P, Thai CN (2015) Robotics to promote elementary education preservice teachers’ STEM engagement, learning, and teaching. Comput Educ 91:14–31

    Article  Google Scholar 

  • Lindh J, Holgersson T (2007) Does lego training stimulate pupils’ ability to solve logical problems? Comput Educ 49(4):1097–1111

    Article  Google Scholar 

  • Mioduser D, Levy ST, Talis V (2009) Episodes to scripts to rules: concrete-abstractions in kindergarten children’s explanations of a robot’s behavior. Int J Technol Des Educ 19(1):15–36

    Article  Google Scholar 

  • Nadelson LS, Callahan J, Pyke P, Hay A, Dance M, Pfiester J (2013) Teacher STEM perception and preparation: inquiry-based STEM professional development for elementary teachers. J Educ Res 106(2):157–168

    Article  Google Scholar 

  • National Science and Technology Council (2013) Federal science, technology, engineering, and mathematics (stem) education 5-year strategic plan. Committee on STEM Education National Science and Technology Council, Washington, DC

    Google Scholar 

  • NGSS (2013) Next generation science standards. Retrieved from http://www.nextgenscience.org/

  • Nugent G, Barker B, Grandgenett N, Adamchuk VI (2010) Impact of robotics and geospatial technology interventions on youth STEM learning and attitudes. J Res Technol Educ 42(4):391–408

    Article  Google Scholar 

  • Nugent G, Barker B, Welch G, Grandgenett N, Wu C, Nelson C (2015) A model of factors contributing to STEM learning and career orientation. Int J Sci Educ 37(7):1067–1088

    Article  Google Scholar 

  • Ortiz AM, Bos B, Smith S (2015) The power of educational robotics as an integrated STEM learning experience in teacher preparation programs. J Coll Sci Teach 44(5):42–47

    Article  Google Scholar 

  • Papanikolaou K, Frangou S, Alimisis D (2008) Teachers as designers of robotics-enhanced projects: the TERECoP course in Greece. In: Proceedings of the SIMPAR 2008 conference/workshop: teaching with robotics: didactic approaches and experiences. University of Padova, Padua, Italy, pp. 100–111

    Google Scholar 

  • Park J (2015) Effect of robotics-enhanced inquiry-based learning in elementary science education. J Comput Math Sci Teach 34(1):71–95

  • Perrenet J, Kaasenbrood E (2006) Levels of abstraction in students’ understanding of the concept of algorithm: the qualitative perspective. In ACM SIGCSE Bulletin 38(3), 270–274

  • Perrenet J, Groote JF, Kaasenbrood E (2005) Exploring students’ understanding of the concept of the algorithm: levels of abstraction. In ACM SIGCSE Bulletin (Vol. 37, No. 3, pp. 64–68). ACM

  • Renninger KA, Hidi S (2011) Revisiting the conceptualization, measurement, and generation of interest. Educ Psychol 46(3):168–184

    Article  Google Scholar 

  • Robinson M (2005) Robotics-driven activities: can they improve middle school science learning. Bull Sci Technol Soc 25(1):73–84

    Article  Google Scholar 

  • Rogers C, Portsmore MD (2004) Bringing engineering to elementary school. Journal of STEM Education 5(3 &4):17–28

    Google Scholar 

  • Rohaan EJ, Taconis R, Jochems WM (2012) Analysing teacher knowledge for technology education in primary schools. Int J Technol Des Educ 22(3):271–280

    Article  Google Scholar 

  • Sanders ME (2008) STEM, STEM education, STEMmania. Technol Teach 68(4):20–26

  • Schoon KJ, Boone WJ (1998) Self-efficacy and alternative conceptions of science of preservice elementary teachers. Sci Educ 82(5):553–568

    Article  Google Scholar 

  • Sinay E, Jaipal-Jamani K, Nahornick A, Douglin M (2016) STEM teaching and learning in the Toronto District School Board: towards a strong theoretical foundation and scaling up from initial implementation of the K-12 STEM strategy. Research Series I. (Research Report No. 15/16-16) 200pp. Toronto, Ontario, Canada: Toronto District School Board

  • Slangen L, van Keulen H, Gravemeijer K (2011) What pupils can learn from working with robotic direct manipulation environments. Int J Technol Des Educ 21(4):449–469

    Article  Google Scholar 

  • Soodak LC, Wodell DM (1996) Teacher efficacy: toward the understanding of a multi-faceted construct. Teach Teach Educ 12(4):401–411

    Article  Google Scholar 

  • Sullivan FR (2008) Robotics and science literacy: thinking skills, science process skills and systems understanding. J Res Sci Teach 45(3):373–394

    Article  Google Scholar 

  • Sullivan FR, Heffernan J (2016) Robotic construction kits as computational manipulatives for learning in the STEM disciplines. J Res Technol Educ 48(2):105–128

    Article  Google Scholar 

  • Tai RH, Liu CQ, Maltese AV, Fan X (2006) Planning early for careers in science. Life Sci 1:0–2

    Google Scholar 

  • The Royal Society Science Policy Centre (2014). Vision for science and mathematics education. The Royal Society, London, UK

  • Tschannen-Moran M, Hoy AW (2007) The differential antecedents of self-efficacy beliefs of novice and experienced teachers. Teach Teach Educ 23(6):944–956

    Article  Google Scholar 

  • Tytler R, Osborne J, Williams G, Tytler K, Cripps Clark J (2008) Opening up pathways: engagement in STEM across the primary-secondary school transition. Australian Department of Education, Employment and Workplace Relations, Canberra

    Google Scholar 

  • van Merriënboer JJ (1997) Training complex cognitive skills: a four-component instructional design model for technical training. Educational Technology Publications, New Jersey

  • Vedder-Weiss D, Fortus D (2012) Adolescents’ declining motivation to learn science: a follow-up study. J Res Sci Teach 49(9):1057–1095

    Article  Google Scholar 

  • Velthuis C, Visser P, Pieters J (2014) Teacher training and preservice primary teachers’ self-efficacy for science teaching. J Sci Teach Educ 25(4):445–464

    Article  Google Scholar 

  • Wang X (2013) Why students choose STEM majors motivation, high school learning, and postsecondary context of support. Am Educ Res J 50(5):1081–1121

    Article  Google Scholar 

  • Watters JJ, Ginns IS (2000) Developing motivation to teach elementary science: effect of collaborative and authentic learning practices in preservice education. J Sci Teach Educ 11(4):301–321

    Article  Google Scholar 

  • Weintrop D, Beheshti E, Horn M, Orton K, Jona K, Trouille L, Wilensky U (2016) Defining computational thinking for mathematics and science classrooms. J Sci Educ Technol 25(1):127–147

    Article  Google Scholar 

  • Wigfield A, Cambria J (2010) Students’ achievement values, goal orientations, and interest: definitions, development, and relations to achievement outcomes. Dev Rev 30:1–35

    Article  Google Scholar 

  • Williams D, Ma Y, Prejean L, Ford MJ (2007) Acquisition of physics content knowledge and scientific inquiry skills in a robotics summer camp. J Res Technol Educ 40:201–216

    Article  Google Scholar 

  • Wing JM (2006) Computational thinking. Commun ACM 49(3):33–35

    Article  Google Scholar 

  • Wing JM, (2010) Computational thinking: what and why? Retrieved from http://www.cs.cmu.edu/~CompThink/resources/TheLinkWing.pdf

  • Yadav A, Mayfield C, Zhou N, Hambrusch S, Korb JT (2014) Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education (TOCE), 14(1), article 5. 1–16. doi:10.1145/25

  • Yilmaz-Tuzun O, Topcu MS (2008) Relationships among preservice science teachers’ epistemological beliefs, epistemological world views, and self-efficacy beliefs. Int J Sci Educ 30(1):65–85

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Teacher Education, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON, L2S 3A1, Canada

    Kamini Jaipal-Jamani

  2. Department of Education, University of Cyprus, Nicosia, Cyprus

    Charoula Angeli

Authors
  1. Kamini Jaipal-Jamani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charoula Angeli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kamini Jaipal-Jamani.

Ethics declarations

Ethics Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and national ethics research council and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Appendices

Appendix 1

Pre/Postassessment of Science Concepts

figure a

Appendix 2

Interest Questionnaire

Table 1 We are interested in learning about your attitude toward programming, robotics and using robotics in teaching. Read the statements below and check the box that describes your opinion
Full size table

Appendix 3

Self-efficacy for Teaching Robotics Questionnaire

figure b

Appendix 4

Sample Programming Worksheet for Robot 1

figure cfigure c

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jaipal-Jamani, K., Angeli, C. Effect of Robotics on Elementary Preservice Teachers’ Self-Efficacy, Science Learning, and Computational Thinking. J Sci Educ Technol 26, 175–192 (2017). https://doi.org/10.1007/s10956-016-9663-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10956-016-9663-z

Keywords

Search

Navigation