The Effect Of T-STEM Designation On Charter Schools: A Longitudinal Examination Of Students’ Mathematics Achievement

Abstract

STEM interested students and graduates shape the future of a country. However, in the U.S., the number of STEM graduates was not sufficient; therefore, to increase this number, STEM school designation started. The number of STEM schools has been increasing and Texas was one of the states showing growth over time. STEM schools in Texas (T-STEM) were converted from different schools by specific procedures. The highest number of T-STEM conversion was from charter schools. The effectiveness of T-STEM charter schools compared to regular charter schools (non-T-STEM charter) was worth to examine because the number of T-STEM schools converted from charter schools was noteworthy. Moreover, the most important goal of T-STEM schools was to improve students’ STEM achievement. In this study, to investigate the effectiveness of T-STEM charter schools, students’ mathematics achievement over three years (through high school) was examined. There were 1481 participants in the study. To have comparable two groups, propensity score matching was used. After matching, hierarchical linear modeling was used to analyze students’ mathematics achievement longitudinally considering student variables. The findings showed that T-STEM charter schools were effective to increase one minority group’s (i.e. Hispanic students) mathematics achievement over time.

Keywords

• STEM,Texas STEM schools; charter schools; mathematics achievement.

Longitudinal Examination of Students’ Mathematics Achievement

Öz

FeTeMM’e ilgi duyan öğrenciler ve FeTeMM (Fen, Teknoloji, Mühendislik, Matematik) mezunları bir ülkenin geleceğini şekillendirir. Fakat Amerika Birleşik Devletleri’nde, FeTeMM mezunlarının sayısı yetersizdi ve bundan dolayı bu sayıyı artırmak için FeTeMM okul tayini başlamıştır. FeTeMM okullarının sayısı gün geçtikçe artmaktadır. Teksas’ta zamanla FeTeMM okulları sayısında artış görülen eyaletlerden birisidir. Teksas’ta yer alan FeTeMM okulları (T-FeTeMM) belirli prosedürler doğrultusunda farklı türlerde okulların FeTeMM okullarına dönüşümü ile oluşmuştur ve TFeTeMM okullarına en yüksek sayıda dönüşüm, sözleşmeli okullar tarafından yapılmıştır. TFeTeMM sözleşmeli okullarının etkililiğinin diğer sözleşmeli okullarla karşılaştırılarak incelenmesi önemlidir çünkü T-FeTeMM sözleşmeli okullarına dönüşen sözleşmeli okulların sayısı kayda değer bir sayıdır. Ayrıca T-FeTeMM okullarının en önemli amacı öğrencilerin FeTeMM başarısını artırmaktır. Bu çalışmada T-FeTeMM sözleşmeli okullarının etkililiğini araştırmak amacıyla öğrencilerin üç yıllık matematik başarısı incelenmiştir. Çalışmada 1481 katılımcı bulunmaktadır. Karşılaştırılabilir iki grubun oluşturulması için eğilim değerleri eşleştirme yöntemi kullanılmıştır. Eşleştirmeden sonra öğrenci değişkenleri de dikkate alınarak öğrencilerin boylamsal matematik başarılarını incelemek amacıyla hiyerarşik lineer modelleme yöntemi kullanılmıştır. Sonuçlar T-FeTeMM sözleşmeli okullarının, bir azınlık grubu olan Hispanik öğrencilerin matematik başarılarının artmasında zamanla etkili olduğunu göstermiştir

Anahtar Kelimeler

• FeTeMM, Teksas FeTeMM okulları, sözleşmeli okullar, matematik başarısı

References

1. Avery, S. (2010). T-STEM: Texas science, technology, engineering, and math. Retrieved from http://www.sedl.org/blueprint/files/avery_blueprintforum_stem_pres.pdf
2. Avery, S., Chambliss, D., Pruiett, R., & Stotts, J. L. (2010). Texas science, technology, engineering, and math-ematics academies design blueprint, rubric and glossary. Report of Texas High School Project T-STEM Initiative. Retrieved from http://www.edtx.org/uploads/general/pdf-downloads/misc-PDFs/2011_TSTEM DesignBlueprint.pdf
3. Barr, J. M., Sadovnik, A. R., & Visconti, L. (2006). Charter schools and urban education improvement: A comparison of Newark’s district and charter schools. The Urban Review, 38(4), 291-311.
4. Bicer, A., Navruz, B., Capraro, R. M., Capraro, M.M., Öner, A. T., & Boedeker, P. (2015). STEM schools vs. non-STEM schools: Comparing students' mathematics growth rate on high-stakes test per-formance. International Journal of New Trends in Education and Their Implications, 6(1), 138-150.
5. Bryk, A. S. & Raudenbush, S. W. (1988). Toward a more appropriate conceptualization of research on school effects: A three-level hierarchical linear model. American Journal of Education, 97(1), 65-108.
6. Capraro, R. M., Capraro, M. M., & Morgan, J. (Eds.) (2013). STEM project-based learning: An integrated science, technology, engineering, and mathematics (STEM) approach (2nd ed.). Rotterdam, The Net-herland: Sense.
7. Capraro, R. M., Capraro, M. M., Morgan, J., Scheurich, J., Jones, M., Huggins, K., … & Younes, R. (2015, In Press). The impact of sustained professional development in STEM in a diverse urban dis-trict. Journal of Educational Research.
8. Choi, S. (2012). A study on charter school effects on student achievement and on segregation in Florida public schools (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database (UMI No. 3519299).

9. Educate Texas. (2013). T-STEM academy design blueprint. Retrieved from http://www.edtx.org/uploads/general/pdf-downloads/misc PDFs/EDTX_TSTEM_ Academy-blueprint.pdf
10. Erdogan, N. (2014). Modeling successful inclusive STEM high schools: An analysis of students’ college entry indicators in Texas (Unpublished doctoral dissertation). Texas A&M University, College Station, TX.
11. Erdogan, N. & Stuessy, C. L. (2015). Modeling successful STEM high schools in the United States: An ecology framework. International Journal of Education in Mathematics, Science and Technology, 3(1), 77-92.
12. Gutierrez, M. H. (2012). Charter schools in greater Los Angeles: An evaluative comparison of charter schools vis-à-vis traditional public schools (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database (UMI No. 3549282).
13. Hinojosa, M. (2009). A comparison of open enrollment charter schools and traditional public schools in a Texas region (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database (UMI No. 3400333).
14. Hox, J. J. (2002). Multilevel analysis: Techniques and applications. Mahwah, NJ: Erlbaum.
15. Marshall, S. P. (2010). Re-imagining specialized STEM academies: Igniting and nurturing decidedly different minds, by design. Roeper Review, 32(1), 48-60.
16. Means, B. Confrey, J., House, A., & Bhanot, R. (2008). STEM high schools: Specialized science technology engineering and mathematics secondary schools in the U.S. (Bill and Melinda Gates Foundation Re-port). Retrieved from http://www.sri.com/work/projects
17. Means, B., House, A., Young, V., Wang, H., & Lynch, S. (2013). Expanding access to STEM-focused educa-tion: What are the effects [White paper]? Washington, DC: SRI International.
18. Means, B., Wang, H., Young, V., House, A., & Lynch, S. (2014, August). Effects of attending an inclusive STEM high school. Presentation for the DRK-12 PI meeting, Washington, DC.
19. Nathan, J. (1996). Charter schools: Creating hope and opportunity for American education. San Francisco, CA: Jossey Bass.
20. Öner, A. T. (2015). Longitudinal examination of Texas science, technology, engineering, and mathematics (STEM) academies (Unpublished doctoral dissertation). Texas A&M University, College Station, TX.
21. Öner, A. T. (In press). STEM-FeTeMM okulları. In E. Çallı & S. M. Çorlu (Eds.), STEM - fen, teknoloji, mühendislik, ve matematik eğitimi: Kuram ve uygulamaları (pp. xx-xx). İstanbul.
22. Öner, A. T., & Capraro, R. M. (2016). Is STEM academy designation synonymous with higher student achievement? Education & Science, 41 (185), 1-17. Doi: 10.15390/EB.2016.3397
23. Osborne, J. W. (2000). Advantages of hierarchical linear modeling. Practical Assessment, Research, & Evaluation, 7(1). Retrieved from http://pareonline.net/getvn.asp?v=7&n=1
24. Pardo, A. (2013). The relationship between student achievement and charter high schools in Washington, DC (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database (UMI No. 3591495).
25. Peters-Burton, E. E., Lynch, S. J., Behrend, T. S., & Means, B. B. (2014). Inclusive STEM high school de-sign: 10 critical components. Theory Into Practice, 53(1), 64-71.
26. Philips, R. L. (2013). A comparison of college readiness among students enrolled in Texas science, technology, engineering, and mathematics academies and traditional comprehensive high schools (Doctoral disserta-tion). Retrieved from ProQuest Dissertations and Theses database (UMI No. 3567905).
27. President’s Council of Advisors on Science and Technology. (2010). Prepare and inspire: K-12 education in science, technology, engineering, and math (STEM) for America’s future. Washington, DC: Author.
28. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Thousand Oaks, CA: Sage.
29. Rose, C. P. (2013). The academic impacts of attending a KIPP charter school in Arkansas (Doctoral disserta-tion). Retrieved from ProQuest Dissertations and Theses database (UMI No. 3589161).
30. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational stud-ies for causal effects. Biometrika, 70(1), 41-55.
31. Sahin, A., Willson, V. L., & Capraro, R. M. (2013, April). Can Charter Schools Be Silver Bullets to the Ameri-can Educational System? Paper presented at the annual meeting of American Educational Re-search Association.
32. Scott, C. (2012). An investigation of science, technology, engineering and mathematics (STEM) focused high schools in the U.S. Journal of STEM Education, 13(5), 30-39.
33. Shadish, W. R., & Steiner, P. M. (2010). A primer on propensity score analysis. Newborn and Infant Nurs-ing Reviews, 10(1), 19-26.
34. Shrout, B. F. (2009). Comparative assessment of academic growth between retained freshmen at traditional Texas public schools and those at charter schools (Doctoral dissertation). Retrieved from ProQuest Disser-tations and Theses database (UMI No. 3377853).
35. Snijders, T., & Bosker, R. (1999). Multilevel analysis: An introduction to basic and advanced multilevel model-ing. London: Sage.
36. Subotnik, R. F., Tai, H. R., Rickoff, R., & Almarode, J. (2010). Specialized public high schools of science, mathematics, and technology and the STEM pipeline: What do we know now and what will we know in 5 years? Roeper Review, 32(1), 7-16.
37. Taylor, L. L., Alford, B. L., Rollikns, K. B., Brown, D. B., Stilisano, J. R., Waxman, H. C. (2011) Evaluation of Texas charter schools 2009-10. College Station, TX: Education Research Center at Texas A & M University.
38. Texas Education Agency. (2014). Charter schools. Retrieved from: http://tea.texas.gov/Texas_Schools/Charter_Schools/
39. Texas Education Agency, & Pearson. (2011). Technical digest for the academic year 2010-2011. Retrieved from http://tea.texas.gov
40. Texas Education Agency, & Pearson. (2013). Technical digest for the academic year 2011-2012. Retrieved from http://tea.texas.gov
41. Texas Education Agency, & Pearson. (2014). Technical digest for the academic year 2012-2013. Retrieved from http://tea.texas.gov
42. Thoemmes, F. J., & Kim, E. S. (2011). A systematic review of propensity score methods in social scienc-es. Multivariate Behavioral Research, 46, 90-118.
43. Turner, J. E. (2013). Charter school education in Texas: Student achievement on the exit level assessment in math and science (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses database (UMI No. 3595362).
44. Tuttle, C. C., Gill, B., Gleason, P., Knechtel, V., Nichols-Barrer, I., & Resch, A. (2013). KIPP middle schools: Impacts on achievement and other outcomes, final report. Washington, DC: Mathematica Policy Re-search.
45. Tuttle, C. C., Teh, B., Nichols-Barrer, I., Gill, B. P., & Gleason, P. (June 2010). Student characteristics and achievement in 22 KIPP middle schools. Washington, DC: Mathematica Policy Research.
46. Tuttle, C. C., Teh, B., Nichols-Barrer, I., Gill, B. P., & Gleason, P. (July 2010). Supplemental analytical sam-ple equivalence tables for student characteristics and achievement in 22 KIPP middle schools: A report from the National Evaluation of KIPP Middle Schools. Washington, DC: Mathematica Policy Re-search.
47. U.S. Department of Commerce Economics and Statistics Administration. (2011, July) STEM: Good jobs now and for the future (Issue Brief No. 03-11). Washington, DC: Langdon, McKittrick, Beede, Khan, & Doms.
48. Wiswall, M., Stiefel, L., Schwartz, A. E., & Boccardo, J. (2014). Does attending a STEM high school im-prove student performance? Evidence from New York City. Economics of Education Review, 40, 93-105.
49. Woodworth, K. R., David, J. L., Guha, R., Wang, H., & Lopez-Torkos, A. (2008). San Francisco Bay Area KIPP schools: A study of early implementation and achievement. Final report. Menlo Park, CA: SRI In-ternational.
50. Young, M. V., House, A., Wang, H., Singleton, C., & Klopfenstein, K. (2011, May). Inclusive STEM schools: Early promise in Texas and unanswered questions. Paper prepared for the National Acade-mies 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.