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The Impact of Design-Based STEM Integration Curricula on Student Achievement in Engineering, Science, and Mathematics

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Abstract

The new science education reform documents call for integration of engineering into K-12 science classes. Engineering design and practices are new to most science teachers, meaning that implementing effective engineering instruction is likely to be challenging. This quasi-experimental study explored the influence of teacher-developed, engineering design-based science curriculum units on learning and achievement among grade 4–8 students of different races, gender, special education status, and limited English proficiency (LEP) status. Treatment and control students (n = 4450) completed pretest and posttest assessments in science, engineering, and mathematics as well as a state-mandated mathematics test. Single-level regression results for science outcomes favored the treatment for one science assessment (physical science, heat transfer), but multilevel analyses showed no significant treatment effect. We also found that engineering integration had different effects across race and gender and that teacher gender can reduce or exacerbate the gap in engineering achievement for student subgroups depending on the outcome. Other teacher factors such as the quality of engineering-focused science units and engineering instruction were predictive of student achievement in engineering. Implications for practice are discussed.

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References

  • American Educational Research Association, American Psychological Association, & National Council on Measurement in Education (1999) Standards for educational and psychological testing. American Educational Research Association, Washington, DC

    Google Scholar 

  • Apedoe XS, Reynolds B, Ellefson MR, Schunn CD (2008) Bringing engineering design into high school science classrooms: the heating/cooling unit. J Sci Educ Technol 17(5):454–465

    Article  Google Scholar 

  • Azevedo FS, Martalock PL, Keser T (2015) The discourse of design-based science classroom activities. Cult Stud Sci Educ 10(2):285–315

    Article  Google Scholar 

  • Berland, L., Steingut, R., & Ko, P. (2014). High school student perceptions of the utility of the engineering design process: creating opportunities to engage in engineering practices and apply math and science content. Journal of Science Education and Technology, 705–720.

  • Brophy S, Klein S, Portsmore M, Rogers C (2008) Advancing engineering education in K-12 classrooms. J Eng Educ:369–387

  • Brown JS, Collins A, Duguid P (1989) Situated cognition and the culture of learning. Educ Res 18(1):32–42

    Article  Google Scholar 

  • Bryk AS, Raudenbush SW, Congdon R (2011) Hierarchical linear and nonlinear modeling [computer software manual]. Scientific Software International, Lincolnwood, IL

    Google Scholar 

  • Cobb P, Bowers J (1999) Cognitive and situated learning perspective in theory and practice. Educ Res 28(2):4–15

    Article  Google Scholar 

  • Cantrell P, Pekcan G, Itani A, Velasquez-Bryant N (2006) The effects of engineering modules on student learning in middle school science classrooms. J Eng Educ 95:301–309

    Article  Google Scholar 

  • Carlson LE, Sullivan JF (2004) Exploiting design to inspire interest in engineering across K-16 curriculum. Int J Eng Educ 20(3):372–380

    Google Scholar 

  • Dare E, Ellis J, Roehrig GH (2014) Driven by beliefs: understanding challenges physical science teachers face when integrating engineering and physics. J Pre-College Eng Educ Res 4(2)

  • Dehejia RH, Wahba S (2002) Propensity score-matching methods for nonexperimental causal studies. Revi Econ Stat 84(1):151–161

    Article  Google Scholar 

  • Desimone LM (2011) A primer on effective professional development. Phi Delta Kappan 92(6):68–71

    Article  Google Scholar 

  • Desimone LM, Smith TM, Phillips KJR (2013) Linking student achievement growth to professional development participation and changes in instruction: a longitudinal study of elementary students and teachers in Title I schools. Teach Coll Rec 115:1–46

    Google Scholar 

  • Desimone L, Garet M (2015) Best practices in teachers’ professional development in the United States. Psychol Soc Educ 7(3):252–263

    Google Scholar 

  • Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, Cambridge, U.K.

    Google Scholar 

  • Garet MS, Porter AC, Desimone L, Birman BF, Yoon KS (2001) What makes professional development effective? Results from a national sample of teachers. Am Educ Res J 38(4):915–945

    Article  Google Scholar 

  • Guo XS, Rosenbaum PR (1993) Comparison of multivariate matching methods: structures, distances, and algorithms. J Comput Graph Stat 2:405–420

    Google Scholar 

  • Guzey SS, Moore T, Harwell M (2014) Development of an instrument to measure students’ attitudes toward STEM. Sch Sci Math 114(6):271–279

  • Guzey SS, Moore T, Harwell M (2016) Building up STEM: an analysis of teacher-developed engineering design-based STEM integration curricular materials. Journal of Pre-College Engineering Education Research (JPEER) 6(1). doi:10.7771/2157-9288.1129

  • Harwell M, Philips A, Mareno M, Guzey SS, Moore T (2015) A study of STEM assessments in Engineering, Science, and Mathematics Assessments for elementary and middle school students. Sch Sci Math 115(2):66–74

  • Hedges LV, Hedberg EC (2007) Intraclass correlation values for planning group-randomized trials in education. Educ Eval Policy Anal 29:60–87

    Article  Google Scholar 

  • Kolen MJ, Brennan RL (2004) Test equating, scaling, and linking. Springer, New York, NY

    Book  Google Scholar 

  • Lachapelle C, Cunningham C (2014) Engineering in elementary schools. In: Purzer S, Strobel J, Cardella M (eds) Engineering in pre-college settings: synthesizing research, policy, and practices. Purdue University Press, West Lafayette: IN, pp. 61–88

    Google Scholar 

  • Lachapelle CP, Cunningham CM, Jocz J, Kay AE, Phadnis P, Wertheimer J, Arteaga R (2011) Engineering is elementary: an evaluation of years 4 through 6 field testing. Museum of Science, Boston, MA

    Google Scholar 

  • Little RJA, Rubin DB (2002) Statistical analysis with missing data (2nd Ed). Wiley, New York, NY

    Google Scholar 

  • Ludlow LH, Haley SM (1995) Rasch model logits: interpretation, use, and transformation. Educ Psychol Meas 55:967–975

    Article  Google Scholar 

  • Mehalik MM, Doppelt Y, Schunn CD (2008) Middle-school science through design-based learning versus scripted inquiry: better overall science concept learning and equity gap reduction. J Eng Educ 97(71–85)

  • Moore TJ, Stohlmann MS, Wang H, Tank KM, Glancy AW, Roehrig GH (2014) Implementation and integration of engineering in K-12 STEM education. In: Purzer S, Strobel J, Cardella M (eds) Engineering in pre-college settings: Research into practice. Purdue University Press, West Lafayette, pp 35–60

  • 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, Washington, DC

    Google Scholar 

  • National Research Council (2009) Engineering in K-12 education: understanding the status and improving the prospects. The National Academies Press, Washington, DC

    Google Scholar 

  • National Research Council (2010) Standards for K-12 engineering education? The National Academies Press, Washington, DC

    Google Scholar 

  • National Research Council (2011) Successful K-12 STEM education: identifying effective approaches in science, technology, engineering, and mathematics. National Academies Press, Washington, DC

    Google Scholar 

  • National Research Council (2012) A framework for K–12 science education. Retrieved from www.nap.edu/catalog.php?record_id=13165

  • Nelson T, Lesseig K, Slavit D, Kennedy C, Seidel R (2015) Supporting middle school teachers implementation of STEM design challenges. Paper presented at NARST conference. IL, Chicago

    Google Scholar 

  • Neter J, Kutner MH, Nachtsheim CJ, Wasserman W (1996) Applied linear statistical models (4th ed.). Irwin, Chicago, IL

    Google Scholar 

  • NGSS Lead States (2013) Next generation science standards: for states, by states. The National Academic Press, Washington, DC

    Google Scholar 

  • Oh Y, Lachapelle C, Shams M, Hertel J, Cunnigham C (2016) Evaluating the efficacy of engineering is elementary for student learning of engineering and science concepts. Poster presented at the American Educatonal Research Association Annual Meeting. Washington, DC Retrieved from http://www.eie.org/sites/default/files/research_article/research_file/aera_oh_evaluating_the_efficacy_poster.pdf

    Google Scholar 

  • Raudenbush SW, Bryk AS (2002) Hierarchical linear models: applications and data analysis methods (2nd Ed). Sage, Newbury Park, CA

    Google Scholar 

  • Riskowski JL, Todd CD, Wee B, Dark M, Harbor J (2009) Exploring the effectiveness of an interdisciplinary water resources engineering module in an eighth grade science course. Int J Eng Educ 25(1):181–195

    Google Scholar 

  • Sawada D, Piburn MD, Judson E, Turley J, Falconer K, Benford R, Bloom I (2002) Measuring reform practices in science and mathematics classrooms: the reformed teaching observation protocol. Sch Sci Math 102(6):245–253

    Article  Google Scholar 

  • Schnittka CG, Bell RL (2011) Engineering design and conceptual change in the middle school science classroom. Int J Sci Educ 33:1861–1887

    Article  Google Scholar 

  • Sekhon JS (2011) Multivariate and propensity score matching software with automated balance optimization: the matching package for R. J Stat Softw 42(7):1–52

    Article  Google Scholar 

  • Shadish WR, Cook TD, Campbell DT (2002) Experimental and quasi-experimental designs for generalized causal inference. Houghton-Mifflin, Boston

    Google Scholar 

  • Spybrook, L., Bloom, H., Congdon, R., Hill, C., Martinez, A., & Raudenbush, S. (2011). Optimal design plus empirical evidence: documentation for the “Optimal Design” software (version 3.0) [computer software]. Retrieved from http://sitemaker.umich.edu/group-based

  • Tran NA, Nathan MJ (2010) Pre-college engineering studies: an investigation of the relationship between pre-college engineering studies and student achievement in science and mathematics. J Eng Educ 99(2):143–157

    Article  Google Scholar 

  • U. S. Department of Education. (2010). Digest of education statistics. Retrieved from http://nces.ed.gov/programs/digest/d09/tables/dt09_066.asp

  • U. S. Department of Education. (2014a). Digest of education statistics. Retrieved from http://nces.ed.gov/programs/digest/d13/tables/dt13_221.40.asp

  • U. S. Department of Education. (2014b). Digest of education statistics. Retrieved from http://nces.ed.gov/programs/digest/d13/tables/dt13_222.50.asp

  • U. S. Department of Education. (2014c). Digest of education statistics. Retrieved from http://nces.ed.gov/programs/digest/d13/tables/dt13_102.40.asp

  • U. S. Department of Education. (2014d). Public high school four-year on-time graduation rates and event dropout rates: school years 2010–11 and 2011–12. Retrieved from http://nces.ed.gov/pubs2014/2014391.pdf

  • U. S. Bureau of the Census. (2014). Public education finances 2012. Retrieved from http://www2.census.gov/govs/school/12f33pub.pdf

  • Valtorta CG, Berland LK (2015) Math, science, and engineering integration in a high school engineering course: a qualitative study. J Pre-College Eng Educ 5(1):15–29

    Google Scholar 

  • Wang HH, Moore T, Roehrig G, Park MS (2011) STEM integration: teacher perceptions and practice. Journal of Pre-College Engineering Education Research (J-PEER) 1(2). doi:10.5703/1288284314636

  • Wendell K, Rogers C (2013) Engineering design-based science, science content performance, and science attitudes in elementary school. J Eng Educ 102(4):513–540

    Article  Google Scholar 

  • What Works Clearinghouse (2014). Procedures and standards handbook. Retrieved from http://ies.ed.gov/ncee/wwc/

  • Wilson SM (2013) Professional development for science teachers. Science 340:310–313

    Article  Google Scholar 

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Correspondence to S. Selcen Guzey.

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Selcen Guzey, S., Harwell, M., Moreno, M. et al. The Impact of Design-Based STEM Integration Curricula on Student Achievement in Engineering, Science, and Mathematics. J Sci Educ Technol 26, 207–222 (2017). https://doi.org/10.1007/s10956-016-9673-x

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