Predictors of Scientific Understanding of Middle School Students: Socioeconomic Status

Joshua Matthew Strate; David Devraj Kumar; John D. Morris

doi:10.12973/eurasia.2013.926a

Full Text (PDF)

Joshua Matthew Strate ¹ ^* , David Devraj Kumar ², John D. Morris ²

More Detail

¹ Florida Virtual Global School, USA² Florida Atlantic University, USA^* Corresponding Author

Abstract

The purpose of this study was to determine if middle school student scientific understanding could be predicted by the variables: standardized 5th grade score in science, standardized 5th grade score in mathematics, standardized 5th grade score in reading, student attitude towards science, socioeconomic status, gender, and ethnicity. An Attitude towards Science Survey (SATS) and a Survey of Scientific Understandings were administered to 116 middle school 8th grade students during the 2010-2011 school year. SES was a significant predictor of scientific understanding of middle school students since correlation results showed that only SES was a significant contributor to predicting scientific understanding of these students. Low SES students performed lower on the scientific understanding survey, on average, than high SES students. This study can be a source of information for teachers in low-income schools by recognizing potential areas of concern for low-income students in their science classrooms.

Keywords

socioeconomic status
gender
ethnicity
science
mathematics
reading

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, 2013, Volume 9, Issue 2, 155-165

https://doi.org/10.12973/eurasia.2013.926a

Publication date: 14 Dec 2013

Article Views: 1686

Article Downloads: 966

Open Access References How to cite this article

References

Allen, J. & Le, H. (2008).An additional measure of overall effect size for logistic regression models. Journal of Educational and Behavioral Statistics, 33(4), 416–441.
Altschuld, R. A. (2003). U.S. science education: The view from a practicing scientist. Review of Policy Research, 20(4), 635-645.
Altschuld, J.W. & Kumar, D. (2010).Needs assessment: An overview. Los Angeles, CA: Sage.
Anderson, B.T., Brown, C.L. & Lopez-Ferrao, J. (2003). Systemic reform: Good educational practice with positive impacts and unresolved problems and issues. Review of Policy Research, 20(4), 617-627.
Aschbacher, P.R., Li, E., &Roth, E.J. (2010). Is science me? High school students’ identities, participation and aspirations in science, engineering, and medicine. Journal of Research in Science Teaching, 47(5), 564–582.
Battle, J., & Lewis, M. (2002). The increasing significance of class: The relative effects of race and socioeconomic status on academic achievement. Journal of Poverty, 6(2), 21-35.
Brotman, J.S., & Moore, F.M. (2008). Girls and science: A review of four themes in the science education literature. Journal of Research in Science Teaching, 45(9), 971- 1002.
Burns, T. W., O’Connor, D.J, & Stocklmayer, S.M. (2003). Science communication: a contemporary definition. Public Understanding of Science, 12(2), 183-202.
Bybee, R.W. (2003). The teaching of science: Content, coherence, and congruence. Journal of Science Education and Technology, 12(4), 343-358.
Bybee, R. W. (2007). Do we need another Sputnik? American Biology Teacher, 69(8), 454-457.
Caldas, S. J., & Bankston, C. (1997).Effect of school population socioeconomic status on individual academic achievement. Journal of Educational Research, 90(5), 269-277.
Carbonaro, W. (2005).Tracking student’s effort, and academic achievement. Sociology of Education, 78(1), 27-49.
Carter, L. (2005). Globalization and science education: Rethinking science education reforms. Journal of Research in Science Teaching, 42(5), 561–580.
Chen, C.H. & Howard, B. (2010).Effect of live simulation on middle school students' attitudes and learning toward science. Educational Technology and Society, 13(1), 133-139.
Considine, G. & Zappala, G. (2002).The influence of social and economic disadvantage in the academic performance of school students in Australia. Journal of Sociology, 38(2), 129-148.
Daniels, E., & Arapostathis, M. (2005). What do they really want? Student voices and motivation research. Urban Education, 40(1), 34-59.
Davis, E. A., Petish, D., & Smithey, J. (2006). Challenges new science teachers’ face. Review of Educational Research, 76(4), 607-651.
Debacker, T. K., & Nelson, R.M. (2000). Motivation to learn science: Differences related to gender, class type, and ability. The Journal of Educational Research, 93(4), 245-264.
DeBoer, G.E. (2000). Scientific literacy: Another look at its historical and contemporary meanings and its relationship to science education reform. Journal of Research in Science Teaching, 37(6), 582-601.
De Jong, O. (2007). Trends in western science curricula and science education research: A bird’s eye view. Journal of Baltic Science Education, 6(1), 15-22.
Dittmer, J. (2010). Immersive virtual worlds in university-level human geography courses. International Research in Geographical and Environmental Education,19 (2), 139-154.
Eamon, M.K. (2005). Socio-demographic, school, neighborhood, and parenting influences on academic achievement of Latino young adolescents. Journal of Youth and Adolescence, 34(2), 163-175.
Easton-Brooks, D., & Davis, A. (2007).Wealth, traditional socioeconomic indicators, and the achievement debt. Journal of Negro Education, 76(4), 530-541.
The Education Trust (2003).Need of improvement: Ten ways the U.S. Department of Education has failed to live up to its teacher quality commitments. Washington, DC: The Education Trust.
Eisenhart, M., Finkel, E., & Marion, S. F. (1996). Creating the conditions for scientific literacy: A re-examination. American Educational Research Journal, 33(2), 261-295.
ESANT. (1999). Economic and social aspects of nanotechnology. First draft report of Working Group 6 of the Euroconferences on Nanoscience for Nanotechnology. Europe, Institute of Nanotechnology. Retrieved from http://www.nano.org.uk/ESANT99.htm.
Farland-Smith, D. (2009). Exploring middle school girl's science identities: Examining attitudes and perceptions of scientists when working “side-by-side” with scientists. School Science and Mathematics, 109(7), 415-428.
Florida Department of Education. (2002). Class size reduction amendment. Retrieved from http://www.fldoe.org/classsize/
Florida Department of Education. (2004). Assessment and accountability briefing book. Retrieved from http://fcat.fldoe.org/pdf/fcataabb.pdf
George, R. (2003). Growth in students’ attitude about the utility of science over the middle and high school years: Evidence from the longitudinal study of American youth. Journal of Science Education and Technology, 12(4), 439-448.
Gunkel, D.J. (2003). Second thoughts: toward a critique of the digital divide. New Media and Society, 5(4), 499-522.
Harms, N. (1980). Project synthesis: An interpretative consolidation of research identifying needs in natural science education. Boulder, CO: University of Colorado.
Hartshorne, R. (2005). Effects of integrating hypermedia into elementary science professional development on science content knowledge. Journal of Science Education and Technology, 14(4), 415-424.
Heath, P. A. (1992). Organizing for STS teaching and learning: The doing of STS. Theory into Practice, 31(1), 52- 58.
Heimer, K.(1997). Socioeconomic status, subcultural definitions, and violent delinquency. Social Forces, 75(3), 795–833.
Interstate New Teacher Assessment and Support Consortium, Science Standards Drafting Committee. (2002). Model standards in science for beginning teacher licensing and development: A resource for state dialogue. Washington, DC: Council of Chief State School Officers.
Jalil, P.A., Sbeih, Z.A., Boutjettif, M., &Barakat, R. (2009). Autonomy in science education: A practical approach in attitude shifting towards science learning. Journal of Science Education and Technology, 18(6), 476-487.
Johnson, K.A. (2002). The downside to small class policies. Educational Leadership, 59(5), 27-29.
Kane, T., Rockoff, J., &Staiger, D. (2006, April).What does certification tell us about teacher effectiveness? Evidence from New York City. National Bureau of Economic Research, Working Paper 12155. Cambridge, MA: NBER. Retrieved May 25, 2011, from http://rsss.anu.edu.au/ themes/TQConf_Rockoff.pdf
Kesidou, S., & Roseman, J.E. (2002). How well do middle school science programs measure up? Findings from Project 2061’s curriculum review. Journal of Research in Science Teaching, 39(6), 522-549.
Khourey-Bowers, C., & Simonis, D.G. (2004). Longitudinal study of middle grades chemistry professional development: Enhancement of personal science teaching self-efficacy and outcome expectancy. Journal of Science Teacher Education, 15(3), 175-195.
Klapper, M. H., DeLucia, S., &Trent, J. (1993). A survey of scientific understandings: Comparing elementary and middle school teachers with college students. Columbus, OH: The National Center for Science Teaching and Learning, Ohio State University.
Koch, P.D. (2001). Underprivileged urban mothers’ perspective on science. Journal of Research in Science Teaching, 38(6), 688-711.
Kormondy, E.J. (1985). Science education: The challenge of the ‘80s. The American Biology Teacher, 47(7), 402-409.
Kose, S., Sahin, A., Ergun, A., & Gezer, K. (2010).The effects of cooperative learning experience on 8th grade students' achievement and attitude toward science. Education, 131(1), 169-181.
Kulm, G. (2007). Learning from the history of mathematics and science education. School Science and Mathematics, 107(1), 368.
Kumar, D.D. (1997a).Public education, money, and social policies. Policy Studies Journal, 25(3), 489-491.
Kumar, D.D. (1997b). Public school choice and science education: A survey of preservice elementary teachers. Contemporary Education, 68(3), 170-173.
Kumar, D.D. (2003). Trends in post-secondary science in the United States. The Annals of American Academy of Political and Social Science, 585(1), 124-133.
Kumar, D.D., &Altschuld, J. (1999). Evaluation of interactive media in science education. Journal of Science Education and Technology, 8(1), 55-65.
Kumar, .D.D. & Altschuld, J.W. (Eds.) (2003). Science education policy: A symposium. Review of Policy Research, 20(4), 561-645.
Kumar, D.D. & Altschuld, J.W. (2008). University science and education faculty partnership in teacher preparation: Role of a technology innovation. Science and Society, 6(2), 197-202.
Kumar, D. D. & Chubin, D. E. (2000). STS: Adding value to research and practice. Journal of Science Education and Technology, 9 (2), 135-139.
Kumar, D.D. & Helgeson, S.L. (1996). Effect of computer interfaces on chemistry problem solving among various ethnic groups: A comparison of Pen-Point and Powerbook computers. Journal of Science Education and Technology, 5(2), 121-130.
Kumar, D. D. & Maslin-Ostrowski, P. (2008a). The digital frontier: Policy issues and recommendations for laptop computers in science learning. Journal for Computing Teachers (Spring Edition), Retrieved from http://www.iste.org.
Kumar, D. D. & Maslin-Ostrowski, P. (2008b).Policy considerations for nanoscience education. Journal of Materials Education, 30(5-6), 385-388.
Kumar, D.D. & Morris, J.D. (2005).Predicting scientific understanding of prospective elementary teachers: Role of gender, education level, courses in science, and attitudes toward science and mathematics.Journal of Science Education and Technology, 14(4), 387-391.
Kumar, D.D. & Scuderi, P. (2000). Opportunites for teachers as policymakers. Kappa Delta Pi Record, 36(2), 61-64.
Lacey, H. (1999).Scientific understanding and the control of nature. Science and Education, 8(1), 13-35.
Leach, J. (2002). Teachers’ views on the future of the secondary science curriculum. School Science Review, 83(204), 43-50.
Lee, V.E., & Burkam, D. T. (1996). Gender differences in middle grade science achievement: Subject domain, ability test, and course emphasis. Science Education, 80(6), 613-650.
Lee, S.Y. & Songer, N.B. (2001).Promoting scientific understanding through electronic discourse. Asia Pacific Education Review, 2(1), 32–43.
Lewis, D. (2004). The FCAT is out of the bag: Prominent concerns regarding Florida's comprehensive assessment test. University of Florida Journal of Law and Public Policy, 15(2), 313-338.
Lopez, O. (2007). Classroom diversification: A strategic view of educational productivity. Review of Educational Research, 77(1), 28-80.
Lumpe, A.T. (2005). The emperor has no clothes: The tension between the standards-testing movement and a viable curriculum. Journal of Science Teacher Education, 16(4), 259-261.
Ma, X. (2000). Socioeconomic gaps in academic achievement within schools: Are they consistent across subject areas? Educational Research and Evaluation: An International Journal on Theory and Practice, 6(4), 337-355.
Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370-396.
National Commission on Excellence in Education. (1983). A nation at risk: the imperative for educational reform. Washington, DC: U.S. Department of Education. Retrieved from http://www2.ed.gov/pubs /NatAtRisk/risk.html
National Research Council. (1996). The National Science Education Standards. National Academy of Sciences. Washington, DC: National Academy Press.
National Science Teachers Association. (1982). Sciencetechnology-society: Science education for the 1980s. Washington, DC: Author.
Owen, S. V., Toepperwein, M. A., Marshall, C. E., Lichtenstein, M. J., Blalock, C. L., Liu, Y., Pruski, L. A.,& Grimes, K. (2008). Finding pearls: Psychometric reevaluation of the Simpson–Troost Attitude Questionnaire (STAQ). Science Education, 92(6), 1076- 1095.
Parker, V. & Gerber, B. (2000). Effects of a science intervention program on middle-grade student achievement and attitudes. School Science and Mathematics, 100(5), 236-242.
Peters, S. & Oliver, L.A. (2009).Achieving quality and equity through inclusive education in an era of high-stakes testing. Prospects, 39(3), 265-279.
Rodgers, J.L. & Nicewander, W.A. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1), 59-66.
Rolin, K. (2008). Gender and physics: Feminist philosophy and science education. Science and Education, 17(10), 1111-1125.
Rutherford, F.J. (2005). The 2005 Paul F. Brandwein lecture: Is our past our future? Thoughts on the next 50 years of science education reform in the light of judgments on the past 50 years. Journal of Science Education and Technology, 14(4), 367-386.
Ryder, J. & Banner, I. (2010). Multiple aims in the development of a major reform of the national curriculum for science in England. International Journal of Science Education, 33(5), 709-725.
Sadler, T.D., & Zeidler, D.L. (2009). Scientific literacy, PISA, and socioscientific discourse: Assessment for progressive aims of science education. Journal of Research in Science Teaching, 46(8), 909-921.
Sandoval, W.A. (2005). Understanding students’ practical epistemologies and their influence on learning through inquiry. Science Education, 89(4), 634-656.
Schmidt, W.H., McKnight, C., Cogan, L.S., Jakwerth, P.M., & Houang, R.T. (1999).Facing the consequences: Using TIMMS for a closer look at U.S. mathematics and science education. Dordrecht, The Netherlands: Kluwer Academic Publishers.
Schurz, G., & Lambert, K. (1994). Outline of a theory of scientific understanding. Synthese, 101(1), 65-120.
Simpson, R.D., & Oliver, J.S. (1990).A summary of major influences on attitude toward and achievement in science among adolescent students. Science Education, 74(1), 1-18.
Sirin, S.R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417-453.
Spelke, E.S. (2005). Sex differences in intrinsic aptitude for mathematics and science? A critical review. American Psychologist, 60, 950-958.
Stefanou, C., & Parkes, J. (2003). Effects of classroom assessment on student motivation in 5th grade science. The Journal of Educational Research, 96(3), 152-163.
Sumida, M. (2004).The reproduction of scientific understanding about pendulum motion in the public. Science and Education, 13(4), 473-492.
Tate, W.F., &Malancharuvil-Berkes, E. (2006). A contract for excellence in science education. May I have your signature please? Journal of Teacher Education, 57(3), 278- 285.
Wang, J.,& Staver, J.R. (2001).Examining relationships between factors of science education and student career aspiration. The Journal of Educational Research, 94(5), 312- 319.
Watt, H.G. (2010). Gender and occupational choice. Handbook of Gender Research in Psychology, 5(2), 379-400.
Yager, S. O., Yager, R.E., &Lim, G. (2006). The advantages of an STS approach over a typical textbook dominated approach in middle school science. School Science and Mathematics,106(5), 248-260.
Yokoo, Y. (2008). Report on the annual AAAS Forum on science and technology policy. Quarterly Review, 29(6), 83-92.

How to cite this article

APA

Strate, J. M., Kumar, D. D., & Morris, J. D. (2013). Predictors of Scientific Understanding of Middle School Students: Socioeconomic Status. Eurasia Journal of Mathematics, Science and Technology Education, 9(2), 155-165. https://doi.org/10.12973/eurasia.2013.926a

Vancouver

Strate JM, Kumar DD, Morris JD. Predictors of Scientific Understanding of Middle School Students: Socioeconomic Status. EURASIA J Math Sci Tech Ed. 2013;9(2):155-65. https://doi.org/10.12973/eurasia.2013.926a

AMA

Strate JM, Kumar DD, Morris JD. Predictors of Scientific Understanding of Middle School Students: Socioeconomic Status. EURASIA J Math Sci Tech Ed. 2013;9(2), 155-165. https://doi.org/10.12973/eurasia.2013.926a

Chicago

Strate, Joshua Matthew, David Devraj Kumar, and John D. Morris. "Predictors of Scientific Understanding of Middle School Students: Socioeconomic Status". Eurasia Journal of Mathematics, Science and Technology Education 2013 9 no. 2 (2013): 155-165. https://doi.org/10.12973/eurasia.2013.926a

Harvard

Strate, J. M., Kumar, D. D., and Morris, J. D. (2013). Predictors of Scientific Understanding of Middle School Students: Socioeconomic Status. Eurasia Journal of Mathematics, Science and Technology Education, 9(2), pp. 155-165. https://doi.org/10.12973/eurasia.2013.926a

MLA

Strate, Joshua Matthew et al. "Predictors of Scientific Understanding of Middle School Students: Socioeconomic Status". Eurasia Journal of Mathematics, Science and Technology Education, vol. 9, no. 2, 2013, pp. 155-165. https://doi.org/10.12973/eurasia.2013.926a