Exploring Pre-Service Elementary Educator Anxiety for Facilitating Science Teaching Contexts Integrating 3D Modeling

Authors

  • Stuart White Purdue University
  • Timothy Newby

DOI:

https://doi.org/10.51355/j-stem.2024.163

Keywords:

Integrated STEM, Science Education, 3D Modeling, Anxiety, Cross-Curricular Learning

Abstract

Much of the research within K-12 STEM teacher education and integrated STEM instructional design (ID) involves illuminating how STEM subjects can be integrated to bridge gaps between methodological and pedagogical practices. ID involving the engineering design process within K-12 classrooms generally guides students through prototyping mechanical devices using everyday objects and/or 3D printing. One universal engineering process involved in STEM educational curriculum is modeling using computer aided design (CAD) software such as Tinkercad (a popular software in K-12 settings). This study focuses on the application of 3D modeling as a learning activity within an undergraduate biology course designed to prepare pre-service teachers to facilitate life science learning activities in their future classrooms. A mix methods approach was taken to explain the impact of Tinkercad modeling on anxiety for facilitating integrated STEM activities as well as pre-service teacher self-efficacy, confidence in, and competency for teaching integrated STEM. Analysis of student responses to survey questions, field notes, and informal interviews suggest that utilization of modeling software divorced of 3D printing, though conducive to reducing integrated STEM facilitation anxiety, has a limited effect on improving pre-service teacher self-efficacy, confidence in, and competency regarding leading integrated STEM learning activities targeted towards engaging learners in science exploration. However, participant comments on 3D modeling software usability, application within K-12 science learning environments, and perceived K-6 classroom strengths provide important commentary on likelihood of STEM resources such as Tinkercad being adopted into future classrooms.

References

Afzal, A., Khan, S., Daud, S., Ahmad, Z., & Butt, A. (2023). Addressing the digital divide: Access and use of technology in education. Journal of Social Sciences Review, 3(2), 883–895. https://doi.org/10.54183/jssr.v3i2.326

Ahmad, S., Sultana, N., & Jamil, S. (2020). Behaviorism vs constructivism: A paradigm shift from traditional to alternative assessment techniques. Journal of Applied Linguistics and Language Research, 7(2), 19-33.

Ashton, J. (2014). Barriers to implementing STEM in K-12 virtual programs. Distance Learning, 11(1), 51–57.

Ausubel, D. G. (1963). Cognitive structure and the facilitation of meaningful verbal learning1. Journal of Teacher Education, 14(2), 217–222. https://doi.org/10.1177/002248716301400220

Avraamidou, L. (2014). Studying science teacher identity: Current insights and future research directions. Studies in Science Education, 50(2), 145–179. https://doi.org/10.1080/03057267.2014.937171

Ball, C., Huang, K.-T., Cotten, S. R., & Rikard, R. V. (2017). Pressurizing the STEM pipeline: An expectancy-value theory analysis of youths’ STEM attitudes. Journal of Science Education and Technology, 26(4), 372–382. https://doi.org/10.1007/s10956-017-9685-1

Bevan, B. (2017). The promise and the promises of Making in science education. Studies in Science Education, 53(1), 75–103. https://doi.org/10.1080/03057267.2016.1275380

Bevan, B., Gutwill, J. P., Petrich, M., & Wilkinson, K. (2015). Learning through STEM-rich tinkering: Findings from a jointly negotiated research project taken up in practice. Science Education, 99(1), 98–120. https://doi.org/10.1002/sce.21151

Bilim, I. (2014). Pre-service elementary teachers’ motivations to become a teacher and its relationship with teaching self-efficacy. Procedia-Social and Behavioral Sciences, 152, 653–661. https://doi.org/10.1016/j.sbspro.2014.09.258

Breiner, J. M., Harkness, S. S., Johnson, C. C., & Koehler, C. M. (2012). What is STEM? A discussion about conceptions of STEM in education and partnerships. School Science and Mathematics, 112(1), 3–11. https://doi.org/10.1111/j.1949-8594.2011.00109.x

Brown, A., L. (1975). The development of memory: Knowing, knowing about knowing, and knowing how to know. Advances in Child Development and Behavior, 10, 103–152. https://doi.org/10.1016/S0065-2407(08)60009-9

Caprara, G. V., Barbaranelli, C., Steca, P., & Malone, P. S. (2006). Teachers’ self-efficacy beliefs as determinants of job satisfaction and students’ academic achievement: A study at the school level. Journal of School Psychology, 44(6), 473–490. https://doi.org/10.1016/j.jsp.2006.09.001

Clements, D. H., & Sarama, J. (2023). Rethinking STEM in the elementary grades. American Educator, 17, 16-21.

National Research Council. (2000). How people learn: Brain, mind, experience, and school: expanded edition. The National Academies Press. https://doi.org/10.17226/9853.

National Academy of Engineering and National Research Council. (2014). STEM integration in K-12 education: Status, prospects, and an agenda for research. The National Academies Press. https://doi.org/10.17226/18612.

Dare, E. A., Ellis, J. A., & Roehrig, G. H. (2018). Understanding science teachers’ implementations of integrated STEM curricular units through a phenomenological multiple case study. International Journal of STEM Education, 5(1), 4. https://doi.org/10.1186/s40594-018-0101-z

Deci, E. L., Eghrari, H., Patrick, B. C., & Leone, D. R. (1994). Facilitating internalization: The self-determination theory perspective. Journal of Personality, 62(1), 119–142. https://doi.org/10.1111/j.1467-6494.1994.tb00797.x

Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of Research in Personality, 19(2), 109–134. https://doi.org/10.1016/0092-6566(85)90023-6

Dewey, J. (1910). Science as subject-matter and as method. Science, 31(787), 121–127.

Domingo, M. G., & Garganté, A. B. (2016). Exploring the use of educational technology in primary education: Teachers’ perception of mobile technology learning impacts and applications’ use in the classroom. Computers in Human Behavior, 56, 21–28. https://doi.org/10.1016/j.chb.2015.11.023

Douglass, H., & Verma, G. (2022). Examining STEM teaching at the intersection of informal and formal spaces: Exploring science pre-service elementary teacher preparation. Journal of Science Teacher Education, 33(3), 247–261. https://doi.org/10.1080/1046560X.2021.1911456

Ejiwale, J. A. (2013). Barriers to successful implementation of STEM education. Journal of Education and Learning (EduLearn), 7(2), Article 2. https://doi.org/10.11591/edulearn.v7i2.220

English, L. D. (2016). STEM education K-12: Perspectives on integration. International Journal of STEM Education, 3(1), 3. https://doi.org/10.1186/s40594-016-0036-1

Enochs, L. G., & Riggs, I. M. (1990). Further development of an elementary science teaching efficacy belief instrument: A preservice elementary scale. National Association for Research in Science Teaching https://eric.ed.gov/?id=ED319601

Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43–71. https://doi.org/10.1002/piq.21143

Godhe, A.-L., Lilja, P., & Selwyn, N. (2019). Making sense of making: Critical issues in the integration of maker education into schools. Technology, Pedagogy and Education, 28(3), 317–328. https://doi.org/10.1080/1475939X.2019.1610040

Hallström, J., & Schönborn, K. J. (2019). Models and modelling for authentic STEM education: Reinforcing the argument. International Journal of STEM Education, 6(1), 22. https://doi.org/10.1186/s40594-019-0178-z

Han, J., Kelley, T., & Knowles, J. G. (2023). Building a sustainable model of integrated stem education: Investigating secondary school STEM classes after an integrated STEM project. International Journal of Technology and Design Education, 33(4), 1499–1523. https://doi.org/10.1007/s10798-022-09777-8

Holincheck, N., & Galanti, T. (2022). Are you a STEM teacher?: Exploring K-12 teachers’ conceptions of STEM education. Journal of STEM Education: Innovations and Research, 23(2), 23-28.

Huitt, W., & Hummel, J. (2003). Piaget’s theory of cognitive development. Educational Psychology Interactive, 3(2).

Hunt, R. R. (2013). Precision in memory through distinctive processing. Current Directions in Psychological Science, 22(1), 10–15. https://doi.org/10.1177/0963721412463228

Justo López, A. C., López Morteo, G. A., Flores Ríos, B. L., & Castro García, L. (2019). Model for evaluating process capacity for interoperability between environments of learning objects. 2019 XIV Latin American Conference on Learning Technologies (LACLO), 69–74. https://doi.org/10.1109/LACLO49268.2019.00022

Karpicke, J. D. (2017). Retrieval-based learning: A decade of progress. In John H. Bryne (Ed.), Learning and Memory: A Comprehensive Reference (pp. 487-514). Academic Press https://doi.org/10.1016/B978-0-12-809324-5.21055-9

Kay, R. H., & Knaack, L. (2007). Evaluating the learning in learning objects. Open Learning: The Journal of Open, Distance and e-Learning, 22(1), 5–28. https://doi.org/10.1080/02680510601100135

Kelley, T. R., & Knowles, J. G. (2016). A conceptual framework for integrated STEM education. International Journal of STEM Education, 3(1), 11. https://doi.org/10.1186/s40594-016-0046-z

Lawrence, J. E., & Tar, U. A. (2018). Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educational Media International, 55(1), 79–105. https://doi.org/10.1080/09523987.2018.1439712

MacLeod, C. M., & Bodner, G. E. (2017). The production effect in memory. Current Directions in Psychological Science, 26(4), 390–395. https://doi.org/10.1177/0963721417691356

Martin, L. (2015). The promise of the maker movement for education. Journal of Pre-College Engineering Education Research (J-PEER), 5(1), Article 4. https://doi.org/10.7771/2157-9288.1099

McDaniel, M. A., & Einstein, G. O. (2005). Material appropriate difficulty: A framework for determining when difficulty is desirable for improving learning. In Experimental cognitive psychology and its applications (pp. 73–85). American Psychological Association. https://doi.org/10.1037/10895-006

Merrill, M. D. (2002). A pebble-in-the-pond model for instructional design. Performance Improvement, 41(7), 41–46. https://doi.org/10.1002/pfi.4140410709

Mojavezi, A., & Tamiz, M. P. (2012). The impact of teacher self-efficacy on the students’ motivation and achievement. Theory and Practice in Language Studies, 2(3), 483–491.

Moreno-Bote, R., Ramírez-Ruiz, J., Drugowitsch, J., & Hayden, B. Y. (2020). Heuristics and optimal solutions to the breadth–depth dilemma. Proceedings of the National Academy of Sciences, 117(33), 19799–19808. https://doi.org/10.1073/pnas.2004929117

Novak, E., Soyturk, I., & Navy, S. L. (2022). Development of the science teaching anxiety scale for preservice elementary teachers: A Rasch analysis. Science Education, 106(3), 739–764. https://doi.org/10.1002/sce.21707

Novak, E., & Wisdom, S. (2018). Effects of 3D printing project-based learning on preservice elementary teachers’ science attitudes, science content knowledge, and anxiety about teaching science. Journal of Science Education and Technology, 27(5), 412–432. https://doi.org/10.1007/s10956-018-9733-5

Ornek, F. (2008). Models in science education: Applications of models in learning and teaching science. International Journal of Environmental and Science Education, 3(2), 35–45.

Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., & Koole, M. (2021). Balancing technology, pedagogy and the new normal: Post-pandemic challenges for higher education. Postdigital Science and Education, 3(3), 715–742. https://doi.org/10.1007/s42438-021-00249-1

Raulston, C. G., & Alexiou-Ray, J. (2018). Preparing more technology-literate preservice teachers: A changing paradigm in higher education. Delta Kappa Gamma Bulletin, 84(5), 9–13.

Reich, J. (2019). Teaching our way to digital equity. Educational Leadership, 76(5), 30–35.

Samara, V., & Kotsis, K. T. (2023). Primary school teachers’ perceptions of using STEM in the classroom attitudes, obstacles, and suggestions: A literature review. Contemporary Mathematics and Science Education, 4(2), ep23018. https://doi.org/10.30935/conmaths/13298

Sandall, B., Sandall, D., & Walton, A. (2018). Educators’ perceptions of integrated STEM: A phenomenological study. Journal of STEM Teacher Education, 53(1), 27-47. https://doi.org/10.30707/JSTE53.1Sandall

Sanders, M. E. (2008). STEM, STEM Education, STEMmania. The Technology Teacher, 68, 20–26.

Schmidt, M., & Huang, R. (2021). Defining learning experience design: Voices from the field of learning design & technology. TechTrends, 66, 141-158. https://doi.org/10.1007/s11528-021-00656-y

Shernoff, D. J., Sinha, S., Bressler, D. M., & Ginsburg, L. (2017). Assessing teacher education and professional development needs for the implementation of integrated approaches to STEM education. International Journal of STEM Education, 4(1), 13. https://doi.org/10.1186/s40594-017-0068-1

Sims, R. (2006). Beyond instructional design: Making learning design a reality. Journal of Learning Design, 1(2), 1–9.

Smith, S., Talley, K., Ortiz, A., & Sriraman, V. (2021). You want me to teach engineering? Impacts of recurring experiences on K-12 teachers’ engineering design self-efficacy, familiarity with engineering, and confidence to teach with design-based learning pedagogy. Journal of Pre-College Engineering Education Research (J-PEER), 11(1), 26-41. https://doi.org/10.7771/2157-9288.1241

Sprague, D. R., Williamson, J., & Foulger, T. S. (2022). Design Guidelines for Post-COVID Era Preparation Programs: Action Steps Toward Technology Infusion. Journal of Technology and Teacher Education, 30(2), 177–187.

Stewart, M. (2021). Understanding learning: Theories and critique. In L. Hunt & D. Chalmers (Eds.), University Teaching in Focus (pp. 1-18). Routledge.

Strimel, G. J., Grubbs, M. E., & Wells, J. G. (2017). Engineering education: A clear decision. Technology and Engineering Teacher, 76(4), 18-24.

Struyf, A., De Loof, H., Boeve-de Pauw, J., & Van Petegem, P. (2019). Students’ engagement in different STEM learning environments: Integrated STEM education as promising practice? International Journal of Science Education, 41(10), 1387–1407. https://doi.org/10.1080/09500693.2019.1607983

Stubbs, E. A., & Myers, B. E. (2016). Part of what we do: Teacher perceptions of STEM integration. Journal of Agricultural Education, 57(3), 87–100.

Thorndike, E. L. (1927). The law of effect. The American Journal of Psychology, 39(1/4), 212–222. https://doi.org/10.2307/1415413

Tondeur, J., van Braak, J., Sang, G., Voogt, J., Fisser, P., & Ottenbreit-Leftwich, A. (2012). Preparing pre-service teachers to integrate technology in education: A synthesis of qualitative evidence. Computers & Education, 59(1), 134–144. https://doi.org/10.1016/j.compedu.2011.10.009

Trust, T., Maloy, R. W., & Edwards, S. (2018). Learning through making: Emerging and expanding designs for college classes. TechTrends, 62(1), 19–28. https://doi.org/10.1007/s11528-017-0214-0

Turner, S. F., Bettis, R. A., & Burton, R. M. (2002). Exploring depth versus breadth in knowledge management strategies. Computational & Mathematical Organization Theory, 8(1), 49–73. https://doi.org/10.1023/A:1015180220717

Wang, H.-H., Moore, T., Roehrig, G., & Park, M. (2011). STEM integration: Teacher perceptions and practice. Journal of Pre-College Engineering Education Research (J-PEER), 1(2), 1-13. https://doi.org/10.5703/1288284314636

Westerback, M. E. (1984). Studies on anxiety about teaching science in preservice elementary teachers. Journal of Research in Science Teaching, 21(9), 937–950. https://doi.org/10.1002/tea.3660210908

Xie, Y., Fang, M., & Shauman, K. (2015). STEM education. Annual Review of Sociology, 41(1), 331–357. https://doi.org/10.1146/annurev-soc-071312-145659

Downloads

Published

2024-12-31

How to Cite

White, S., & Newby, T. (2024). Exploring Pre-Service Elementary Educator Anxiety for Facilitating Science Teaching Contexts Integrating 3D Modeling. Journal of Research in STEM Education, 10(1-2), 19–46. https://doi.org/10.51355/j-stem.2024.163

Issue

Section

Articles

Similar Articles

1 2 3 4 5 6 7 8 > >> 

You may also start an advanced similarity search for this article.