Exploring the Relationship between Interest in Higher Education and 12th Grade Mathematics NAEP Scores
This study presented a secondary analysis of the National Assessment of Educational Progress (NAEP) dataset. The paper examined if a gap exists between the mathematics scores of 12th-grade public school students who have different levels of interest in higher education. - This study used a quantitative descriptive research design to analyze data from the 2013, 2015, and 2019 NAEP data sets. The findings include (1) the average mathematics scale score of students who complete college entrance exams, ACT/SAT, is higher than those who do not complete these exams. (2) The average mathematics scale score of students who complete the FAFSA is higher than those who do not complete the FAFSA. (3) Students who applied to four-year colleges performed significantly higher on the 12th-grade mathematics NAEP than those who did not. (4) Students who applied to two-year colleges performed significantly lower on the 12th-grade mathematics NAEP than those who did not. (5) Students who perceived a future benefit to mathematics scored higher on the mathematics NAEP. These findings indicate that students who are interested in higher education, particularly four-year education, do have higher 12th NAEP mathematics scores. These findings may provide insight into college preparation and guidance at the high school level.
Alon, S. (2011). Who benefits most from financial aid? The heterogeneous effect of need-based grants on students’ college persistence. Social Science Quarterly, 92(3), 807-829.
Avery, C., & Kane, T. J. (2004). Student perceptions of college opportunities: The Boston COACH program. In C. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 355-394). University of Chicago Press.
Becker, L. (March 20, 2000). Effect size calculators: effect size. University of Colorado. https://lbecker.uccs.edu/effect-size.
Bettinger, E. P., & Evans, B. J. (2019). College guidance for all: A randomized experiment in pre‐college advising. Journal of Policy Analysis and Management, 38(3), 579-599.
Bettinger, E. P., Long, B. T., Oreopoulos, P., & Sanbonmatsu, L. (2012). The role of application assistance and information in college decisions: Results from the H&R Block FAFSA experiment. The Quarterly Journal of Economics, 127(3), 1205-1242.
Bohrnstedt, G.W., Zhang, J., Park, B. J., Ikoma, S., Broer, M., & Ogut, B. (2020). Mathematics Identity, self-efficacy, and interest and their relationships to mathematics achievement: A longitudinal analysis. In: Serpe R., Stryker R., & Powell B. (eds), Identity and Symbolic Interaction. Springer, Cham. https://doi.org/10.1007/978-3-030-41231-9_7.
Bond, J., & Zhang, M. (2017). The impact of conversations on fourth grade reading performance - What NAEP data explorer tells? European Journal of Educational Research, 6(4), 407-417. doi: 10.12973/eu-jer.6.4.407.
Bruce, M., & Bridgeland, J. (2014). The mentoring effect: Young people's perspectives on the outcomes and availability of mentoring. A Report for Mentor: The National Mentoring Partnership. Civic Enterprises. Retrieved from: https://files.eric.ed.gov/fulltext/ED558065.pdf.
Bryan, J., Moore-Thomas, C., Day-Vines, N., & Holcomb-McCoy, C. (2010). School counselors as social capital: The effects of high school college counseling on college application rates. Journal of Counseling & Development, 89, 190-199.
Bureau of Labor Statistics (2020). College enrollment and work activity of recent high school and college graduates: 2019. News release: U.S. Department of Labor. Retrieved from: https://www.bls.gov/news.release/pdf/hsgec.pdf.
Carpena, F., Cole, S. A., Shapiro, J., & Zia, B. (2011). Unpacking the causal chain of financial literacy. World Bank Policy Research Working Paper No. 5798. Retrieved from: https://ssrn.com/abstract=1930818.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ:Lawrence Earlbaum Associates.
Erickson, L. D., McDonald, S., & Elder Jr, G. H. (2009). Informal mentors and education: Complementary or compensatory resources?. Sociology of education, 82(4), 344-367.
Feeny, M., & Heroff, J. (2013). Barriers to need-based financial aid: Predictors of timely FAFSA completion among low-income students. Journal of Student Financial Aid, 43(2), 65-85.
Hannula M.S., Di Martino, P., Pantziara, M., Zhang, Q., Morselli, F., Heyd-Metzuyanim, E., et al. (2016). Attitudes, beliefs, motivation, and identity in mathematics education. Springer Nature. https://doi-org.cmich.idm.oclc.org/10.1007/978-3-319-32811-9_1.
Jaschik, S. (2019, April 1). New push for test options. Inside Higher Ed. Retrieved from: https://www.insidehighered.com/admissions/article/2019/04/01/more-colleges-go-test-optional-admissions.
Joshi, P., Beck, K., & Nsiah, C. (2009). Student characteristics affecting the decision to enroll in a community college: Economic rationale and empirical evidence. Community College Journal of Research and Practice, 33(10), 805-822.
Kafka, A. (2019, December 2). Why more colleges are teaching financial wellness. Retrieved from: https://www.chronicle.com/article/Why-More-Colleges-Are-Teaching/247640.
LaDell-Thomas, J. & Zhang, M. (2017). Computer use and reading achievement: Evidence from the 2015 NAEP fourth grade reading scores. International Journal of Current Research, 9(11), 814-826.
Lopez, F. & Lent, R. (1992). Sources of mathematics self-efficacy in highschool students. The Career Development Quarterly, 41, 3-12.
Mangu, D., Lee, A., Middleton, J. A., & Nelson, J. K. (2015). Motivational Factors Predicting STEM and Engineering Career Intentions for High School Students. 2015 IEEE frontiers in education conference proceedings (pp. 2285–2291). IEEE: El Paso, TX.
McKinney, L., & Novak, H. (2012). The relationship between FAFSA filing and persistence among first-year community college students. Community College Review, 41(1), 63-85.
McKinney, L., & Novak, H. (2015). FAFSA filing among first-year college students: Who files on time, who doesn’t, and why does it matter?. Research in Higher Education, 56(1), 1-28.
Morgan, T. L., Zakem, D., & Cooper, W. L. (2018). From high school access to postsecondary success: An exploratory study of the impact of high-rigor coursework. Education Sciences, 8(191), 1-20. https://doi.org/10.3390/educsci8040191.
National Center for Educational Statistics. (2020a). What does the NAEP Mathematics assessment measure? https://nces.ed.gov/nationsreportcard/mathematics/whatmeasure.aspx.
National Center for Educational Statistics. (2020b). Select the participants. https://nces.ed.gov/nationsreportcard/assessment_process/selection.aspx.
National Center for Educational Statistics. (2020c). Frequently asked questions. https://nces.ed.gov/nationsreportcard/about/faqs.aspx#:~:text=For%20assessments%20to%20report%20national,schools%20in%20each%20participating%20jurisdiction.
National Center for Educational Statistics. (2018a). An overview of the NAEP. https://nces.ed.gov/nationsreportcard/subject/about/pdf/naep_overview_brochure_2018.pdf
National Center for Educational Statistics. (2018b). Select the participants. Retrieved from https://nces.ed.gov/nationsreportcard/assessment_process/selection.aspx.
National Center for Educational Statistics. (2017). Grade 12 participation and engagement in NAEP. Department of Education. Retrieved from: https://www.nationsreportcard.gov/focus_on_naep/files/g12_companion.pdf.
National Center for Educational Statistics. (2008). NAEP technical documentation: NAEP data explorer.https://nces.ed.gov/nationsreportcard/tdw/database/data_tool.asp#:~:text=The%20NAEP%20Data%20Explorer%20is,.gov%2Fnationsreportcard%2Fnde.
National Collegiate Attainment Network. (2021, February 16). National FAFSA completion rates for high school seniors and graduates. National Collegiate Attainment Network. Retrieved from:https://www.ncan.org/page/NationalFAFSACompletionRatesforHighSchoolSeniorsandGraduates#:~:text=Using%20the%20most%20recent%20data,school%20graduates%20is%2061%20percent.
Novak, H., & McKinney, L. (2011). The consequences of leaving money on the table: Examining persistence among students who do not file a FAFSA. Journal of Student Financial Aid, 41(3), 5-23.
Owen, L., & Westlund, E. (2016). Increasing college opportunity: school counselors and fafsa completion. Journal of College Access, 2(1), 3.
Schneider, B., Broda, M., Judy, J., & Burkander, K. (2013). Pathways to college and STEM careers: Enhancing the high school experience. New directions for youth development, 2013(140), 9-29.
Schneider, M., Kitmitto, S., Muhisani, H., & Zhu, B. (2015). Using the National Assessment of Educational Progress as an indicator for college and career preparedness. American Institute for Research. Retrieved from: https://www.air.org/sites/default/files/downloads/report/Using-NAEP-as-an-Indicator-College-Career-Preparedness-Oct-2015.pdf.
Scott, L. A., Ingels, S. J., & Owings, J. A. (2007). Interpreting 12-graders; NAEP-scaled mathematics performance using high school predictors and postsecondary outcomes from the National Education Longitudinal Study of 1988 (NELS:88): Statistical analysis report. National Center for Education Statistics, Institute of Education Sciences. Retrieved from: https://nces.ed.gov/pubs2007/2007328.pdf.
Smith-Barrow, D. (2018, July 20). Are too few college students asking for federal aid. The Hechinger Report. Retrieved from: https://hechingerreport.org/are-too-few-college-students-asking-for-zederal-aid/.
Stokes, T. & Somers, P. (2009). Who enrolls in two-year colleges? A national study of price response. Journal of Student Financial Aid, 39(1), 4-18.
Walton, G. M., & Cohen, G. L. (2007). A question of belonging: race, social fit, and achievement. Journal of personality and social psychology, 92(1), 82-96.
Zhang, M. & Li, X. (2009). Exploring the relationship between non-school factors and NAEP reading scores. STETS Language and Communication Review, 8(1), 1-8.