nach oben

TechTrends

Erschienen in:

02.04.2019 | Original Paper

Elementary School Student Development of STEM Attitudes and Perceived Learning in a STEM Integrated Robotics Curriculum

verfasst von: Yu-Hui Ching, Dazhi Yang, Sasha Wang, Youngkyun Baek, Steve Swanson, Bhaskar Chittoori

Erschienen in: TechTrends | Ausgabe 5/2019

Einloggen, um Zugang zu erhalten

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Robotics has been advocated as an emerging approach to engaging K-12 students in learning science, technology, engineering, and mathematics (STEM). This study examined the impacts of a project-based STEM integrated robotics curriculum on elementary school students’ attitudes toward STEM and perceived learning in an afterschool setting. Three elementary school teachers and 18 fourth to sixth graders participated in an eight-week-long program. Quantitative and qualitative data were collected and analyzed, and showed students’ attitudes toward math improved significantly at the end of the robotics curriculum. Three specific areas of perceived learning were identified, including STEM content learning and connection, engagement and perseverance, and development and challenge in teamwork. The findings also identified the opportunities and challenges in designing a STEM integrated robotics afterschool curriculum for upper elementary school students. Implications for future research studies and curriculum design are discussed.

Vorheriger Artikel Faculty Perceptions of the Usefulness of Integrating Graduate Student-Created Resources into Teacher Preparation Coursework

Nächster Artikel Trade-Offs in Using Mobile Tools to Promote Scientific Action with Socioscientific Issues

Altin, H., & Pedaste, M. (2013). Learning approaches to applying robotics in science education. Journal of Baltic Science Education, 12(3), 365–378.

Bandura, A. (1986). Social foundations of thoughts and action: A social cognitive theory. Englewood Cliffs: Prentice Hall.

Barr, D., Harrison, J., & Conery, L. (2011). Computational thinking: a digital age skill for everyone. Learning & Leading with Technology, 38(6), 20–23.

Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: a systematic review. Computers & Education, 58(3), 978–988. https://doi.org/10.1016/j.compedu.2011.10.006.CrossRef

Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: exploration of an early childhood robotics curriculum. Computers & Education, 72, 145–157. https://doi.org/10.1016/j.compedu.2013.10.020.CrossRef

Binns, I. C., Polly, D., Conrad, J., & Algozzine, B. (2016). Student perceptions of a summer ventures in science and mathematics camp experience. School Science and Mathematics, 116(8), 420–429. https://doi.org/10.1111/ssm.12196.CrossRef

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa.CrossRef

Buck Institute of Education (BIE) (2017). Why project based learning? Retrieved March 20, 2019 from http://bie.org/.

Ching, Y.-H., Hsu, Y.-C., & Baldwin, S. (2018a). Developing computational thinking with educational technologies for young learners. TechTrends, 62(6), 563–573. https://doi.org/10.1007/s11528-018-0292-7.CrossRef

Ching, Y.-H., Yang, D., Wang, S., Baek, Y., Swanson, S., & Chittoori, B. (2018b). Improving student attitudes inSTEM through a project-based robotics program. In American Educational Research Association (AERA) Annual Meeting and Exhibition, New York, NY, USA, April 13–17, 2018.

Conrad, J., Polly, D., Binns, I., & Algozzine, B. (2018). Student perceptions of a summer robotics camp experience. The Clearing House: A Journal of Educational Strategies, Issues and Ideals, 91(3), 131–139. https://doi.org/10.1080/00098655.2018.1436819.CrossRef

Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132.CrossRef

Eguchi, A. (2014). Robotics as a learning tool for educational transformation. Paper presented at the 4^th International Workshop Teaching Robotics, Teaching with Robotics & 5^th International Conference Robotics in Education, Padova, Italy. https://doi.org/10.4018/978-1-4666-8363-1.ch002

Elkin, M., Sullivan, A., & Bers, M. U. (2014). Implementing a robotics curriculum in an early childhood Montessori classroom [electronic version]. Journal of Information Technology Education: Innovations in Practice, 13, 153–169. Retrieved September 17, 2018, from http://www.jite.org/documents/Vol13/JITEv13IIPvp153-169Elkin882.pdf.

Friday Institute for Educational Innovation. (2012). Elementary school STEM - student survey. Raleigh: Author.

Kafai, Y. B., & Burke, Q. (2015). Constructionist gaming: understanding the benefits of making games for learning. Educational Psychologist, 50(4), 313–334. https://doi.org/10.1080/00461520.2015.1124022.CrossRef

Kandlhofer, M., & Steinbauer, G. (2015). Evaluating the impact of educational robotics on pupils’ technical- and social-skills and science related attitudes. Robotics and Autonomous Systems, 75, 679–685. https://doi.org/10.1016/j.robot.2015.09.007.CrossRef

Khanlari, A. (2016). Teachers’ perceptions of the benefits and the challenges of integrating educational robots into primary/elementary curricula. European Journal of Engineering Education, 41(3), 320–330.CrossRef

Kopcha, T. J., McGregor, J., Shin, S., Qian, Y., Choi, J., Hill, R., et al. (2017). Developing an integrative STEM curriculum for robotics education through educational design research. Journal of Formative Design in Learning, 1(1), 31–44. https://doi.org/10.1007/s41686-017-0005-1.CrossRef

Leonard, J., Buss, A., Gamboa, R., Mitchell, M., Fashola, O. S., Hubert, T., & Almughyirah, S. (2016). Using robotics and game design to enhance children’s self-efficacy, STEM attitudes, and computational thinking skills. Journal of Science Education and Technology, 25(6), 860–876. https://doi.org/10.1007/s10956-016-9628-2.CrossRef

Maltese, A. V., & Tai, R. H. (2011). Pipeline persistence: examining the association of educational experiences with earned degrees in STEM among U.S. students. Science Education, 95(5), 877–907 https://doi.org/10.1002/sce.20441.CrossRef

Moore, T., Stohlmann, M., Wang, H., Tank, K., Glancy, A., & Roehrig, G. (2014). Implementation and integration of engineering in K-12 STEM education. In J. S. Purzer & M. Cardella (Eds.), Engineering in pre-college settings: Synthesizing research, policy, and practices (pp. 35–60). West Lafayette: Purdue University Press.CrossRef

National Governors Association Center for Best Practices, & Council of Chief State School Officers (2010). Common Core State Standards for mathematics: Grade 4 measurement & data. Retrieved March 20, 2019 from http://www.corestandards.org/Math/Content/4/MD/.

National Research council [NRC]. (2014). STEM integration in K-12 education: Status, prospects, and an agenda for research. Washington: National Academies Press.

Nugent, G., Barker, B., Grandgenett, N., & Adamchuk, V. I. (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. https://doi.org/10.1080/15391523.2010.10782557.CrossRef

Papert, S. (1991). Situating constructionism. In S. Papert & I. Harel (Eds.), Constructionism (pp. 1–11). Norwood: Ablex.

Pea, R. (1987). Cognitive technologies for mathematics education. In A. Schoenfled (Ed.), Cognitive science and mathematics education (pp. 89–122). Hillsdale: Erlbaum.

Petre, M., & Price, B. (2004). Using robotics to motivate “back door” learning. Education and Information Technologies, 9(2), 147–158. https://doi.org/10.1023/B:EAIT.0000027927.78380.60.CrossRef

Scaradozzi, D., Sorbi, L., Pedale, A., Valzano, M., & Vergine, C. (2015). Teaching robotics at the primary school: An innovative approach. Procedia - Social and Behavioral Sciences, 174, 3838–3846. https://doi.org/10.1016/j.sbspro.2015.01.1122.CrossRef

Schmidt, J., Kafkas, S., Maier, K., Shumow, L., & Kackar-Cam, H. (2018). Why are we learning this? Using mixed methods to understand teachers’ relevance statements and how they shape middle school students’ perceptions of science utility. Contemporary Educational Psychology. https://doi.org/10.1016/j.cedpsych.2018.08.005.

Sha, L., Schunn, C., & Bathgate, M. (2015). Measuring choice to participate in optional science learning experiences during early adolescence. Journal of Research in Science Teaching, 52(5), 686–709. https://doi.org/10.1002/tea.21210.CrossRef

Simpkins, S. D., Davis-Kean, P. E., & Eccles, J. S. (2006). Math and science motivation: a longitudinal examination of the links between choices and beliefs. Developmental Psychology, 42, 70–83. https://doi.org/10.1037/0012-1649.42.1.70.CrossRef

Slangen, L., Van Keulen, H., & Gravemeijer, K. (2011). What pupils can learn from working with robotic direct manipulation environments. International Journal of Technology and Design Education, 21(4), 449–469. https://doi.org/10.1007/s10798-010-9130-8.CrossRef

Stohlmann, M., Moore, T., & Roehrig, G. (2012). Considerations for teaching integrated STEM education. Journal of Pre-College Engineering Education Research, 2(1), 28–34. https://doi.org/10.5703/1288284314653.CrossRef

Taylor, K. (2016). Collaborative robotics, more than just working in groups: effects of student collaboration on learning motivation, collaborative problem solving, and science process skills in robotic activities. (Doctoral dissertation). Retrieved March 20, 2019 from https://scholarworks.boisestate.edu/cgi/viewcontent.cgi?article=2179&context=td.

Ucgul, M., & Cagiltay, K. (2014). Design and development issues for educational robotics training camps. International Journal of Technology and Design Education, 24(2), 203–222. https://doi.org/10.1007/s10798-013-9253-9.CrossRef

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.CrossRef

Wigfield, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–68.CrossRef

Williams, D. C., Ma, Y., Prejean, L., Ford, M. J., & Lai, G. (2007). Acquisition of physics content knowledge and scientific inquiry skills in a robotics summer camp. Journal of Research on Technology in Education, 40(2), 201–216. https://doi.org/10.1080/15391523.2007.10782505.CrossRef

Yin, R. K. (2012). Applications of case study research. Thousand Oaks: Sage.

Titel: Elementary School Student Development of STEM Attitudes and Perceived Learning in a STEM Integrated Robotics Curriculum
verfasst von: Yu-Hui Ching
Dazhi Yang
Sasha Wang
Youngkyun Baek
Steve Swanson
Bhaskar Chittoori
Publikationsdatum: 02.04.2019
Verlag: Springer US
Erschienen in: TechTrends / Ausgabe 5/2019
Print ISSN: 8756-3894
Elektronische ISSN: 1559-7075
DOI: https://doi.org/10.1007/s11528-019-00388-0

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Weitere Artikel der Ausgabe 5/2019

Faculty Perceptions of the Usefulness of Integrating Graduate Student-Created Resources into Teacher Preparation Coursework

Evaluating the Impact of a Quiz Question within an Educational Video

Navigating Paradigms in Educational Technology

Creating Animated Videos as an Innovative Instructional Alternative to Writing Essays for Presenting Research

Introducing the September 2019 Issue

What Do We Mean by Blended Learning?