Would a STEM School ‘by any Other Name Smell as Sweet’?

Abstract views: 37 / PDF downloads: 33


  • Bilgin Navruz
  • Niyazi Erdogan
  • Ali Bicer
  • Robert M Capraro
  • Mary Margaret Capraro


STEM, Inclusive STEM schools, TAKS, T-STEM academies


The purpose was to understand how students’ math scores change on Texas Assessment of Knowledge and Skills (TAKS) after their schools changed into specialized, inclusive STEM high schools. In the present study, the sample was selected from five schools in the state of Texas and included 142 students who could be tracked from 7th to 11th grade (2007-2011). The longitudinal data were obtained from the database at the Texas Education Agency (TEA). Paired t-tests using Mplus 7 were computed, and the 95% CIs were interpreted to determine how students’ math scores on Texas Assessment of Knowledge and Skills (TAKS) changed. Results showed students’ achievement during their STEM school experiences had a statistically significant increase (p<0.05; d=0.64) from 10th to 11th grade. When considering longitudinal change, there was a statistically significant difference in the growth rates favoring STEM school participation (p<0.05, d=0.34), and both genders experienced practically important change (Male, d=0.30; Female, d=0.44). The changes that occurred as schools earned STEM designation seemed to have a positive impact longitudinally. However, it is important to monitor schools to determine if the improvements are durable.

Author Biographies

Bilgin Navruz

Corresponding Author: Bilgin Navruz, bilgin@tamu.edu, Texas A&M University.

Niyazi Erdogan

Texas A&M University

Ali Bicer

Texas A&M University

Robert M Capraro

Texas A&M University

Mary Margaret Capraro

Texas A&M University


America COMPETES Act, House of Representatives, 110th Cong. 1 (2007).

Ashlock, B. R. (2005). Error patterns in computation: Using error patterns to improve instruction. Upper

Saddle River, NJ: Prentice Hall.

Avery, S., Chambliss, D., Pruiett, R., & Stotts, J. L. (2010). T-STEM academy design blueprint, rubric, and

glossary. Retrieved from http://www.edtx.org/uploads/general/pdf-downloads/miscPDFs/2011_TSTEMDesignBlueprint.pdf

Capraro, R. M. (2004). Statistical significance, effect size reporting, and confidence intervals: Best reporting strategies. Journal for Research in Mathematics Education, 35, 57-62.

Capraro, M. M., & Capraro, R. M. (2003). Exploring the APA 5th Edition Publication Manual’s impact on the analytic preferences of journal editorial board members. Educational and Psychological Measurement,

, 554-565.

Corlu, M. S., Capraro, R. M., & Capraro, M. M. (2014). Introducing STEM education: Implications for

educating our teachers in the age of innovation. Education and Science, 39(171), 74-85.

Cumming, G., & Finch, S. (2005). Inference by eye: Confidence intervals, and how to read pictures of data.

American Psychologist, 60, 170-180.

Educate America Act, 20 U.S.C. § 5801 (1994).

Elementary and Secondary Education Act, 20 U.S.C. § 239 (1965).

Engle, R. F. (1984). Wald, likelihood ratio, and Lagrange multiplier tests in econometrics. Handbook of

econometrics, 2, 775-826.

Erdogan, N., Corlu, M. S., Capraro, R. M. (2013). Defining innovation literacy: Do robotics programs help

students develop innovation literacy skills? International Online Journal of Educational Sciences, 5(1),


Gourgey, H., Asiabanpour, B., Crawford, R., Grasso, A., & Herbert, K. (2009). Case study of manor new tech high school: promising practices for comprehensive high schools. Retrieved from


Lunenburg, F. C., & Ornstein, A. C. (1996). Educational administration: Concepts and practices (2nd ed.). Belmont, CA: Wadsworth Publishing Company.

Lynch, S. J., Behrend, T., Burton, E. P., & Means, B. (2013, April). Inclusive STEM-focused high schools:

STEM education policy and opportunity structures. Paper presented at the annual conference of National Association for Research in Science Teaching (NARST), Rio Grande, Puerto Rico.

Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén &


Myers, R. E., & Fouts, J. T. (1992). A cluster analysis of high school science classroom environments and

attitude toward science. Journal of Research in Science Teaching, 29(9), 929-937.

National Academy of Sciences. (2005). Rising above the gathering storm: Energizing and employing America for a brighter economic future. Washington, DC: The National Academies Press.

National Defense Education Act, 72 Stat. § 13247 (1958).

National Research Council. (2011). Successful K-12 STEM education: Identifying effective approaches in

science, technology, engineering, and mathematics. Committee on Highly Successful Science Programs for K-12 Science Education. Board on Science Education and Board on Testing and Assessment, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

Navruz B., & Delen E. (2014). Understanding confidence intervals with visual representations. Abant İzzet

Baysal Üniversitesi Eğitim Fakültesi Dergisi, 14(1), 346-360.

No Child Left Behind Act, 20 U.S.C. § 6301 (2001).

Oakes, J. (1990). Multiplying inequalities: The effects of race, social Class, and tracking on opportunities to learn mathematics and science. Santa Monica, CA: The RAND Corporation.

Parker, F. (1993). Turning points: Books and reports that reflected and shaped U.S. education, 1749-1990s.

Cullowhee, NC: Western Carolina University. (ERIC Document Reproduction Service No. 369695).

Ravitch, D. (1995). National standards in American education: A citizen's guide. Washington, DC: Brookings Institute Press.

Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: John Willey & Sons.

Seymour, E., & Hewitt, N. (1997). Talking about leaving: Why undergraduates leave the sciences. Boulder,

CO: Westview Press.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for

generalized causal inference. Boston: Houghton-Mifflin.

Stotts, J. L. (2011). The STEM initiative-a multiple case study of mission-driven leadership in two schools

implementing STEM in Texas: Successes, obstacles, and lessons learned (Doctoral dissertation). Retrieved from ProQuest. (UMI: 3454109).

Texas Education Agency. (2013). Texas science, technology, engineering, and mathematics initiative (TSTEM). Retrieved from http://www.tea.state.tx.us/index2.aspx?id=4470&menu_id=814

Texas Education Agency. (2014). TAKS resources. Retrieved from


Thomas, J. & Williams, C. (2009). The history of specialized STEM schools and the formation and role of

NCSSSMST. Roeper Review, 32(1), 17-24.

Thompson, B. (2006). Foundations of behavioral statistics: An insight-based approach. New York: Guilford.

Thompson, B. (2007). Effect sizes, confidence intervals, and confidence intervals for effect sizes. Psychology in Schools, 44, 423-432.

Young, M. V., House, A., Wang, H., Singleton, C, SRI International, & Klopfestein, K. (2011, May). Inclusive STEM schools: Early promise in Texas and unanswered questions. Paper prepared for the National Academies Board on Science Education and Board on Testing and Assessment for “Highly Successful STEM Schools or Programs for K-12 STEM Education: A Workshop”, Washington, DC.

Wilkinson, L., & APA Task Force on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594-604.




How to Cite

Navruz, B., Erdogan, N., Bicer, A., Capraro, R. M., & Capraro, M. M. (2022). Would a STEM School ‘by any Other Name Smell as Sweet’?. International Journal of Contemporary Educational Research, 1(2), 67–75. Retrieved from https://ijcer.net/index.php/pub/article/view/17