Quantitative Literacy and Reasoning of Freshman Students with Different Senior High School Academic Background Pursuing STEM-Related Programs
This paper investigates the quantitative literacy and reasoning (QLR) of freshmen students pursuing a Science, Technology, Engineering, and Mathematic.
- Pub. date: January 15, 2021
- Pages: 231-242
- 1321 Downloads
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- 2 Citations
This paper investigates the quantitative literacy and reasoning (QLR) of freshmen students pursuing a Science, Technology, Engineering, and Mathematics (STEM)–related degree but do not necessarily have a Senior High School (SHS) STEM background. QLR is described as a multi-faceted skill focused on the application of Mathematics and Statistics rather than just a mere mastery of the content domains of these fields. This article compares the QLR performance between STEM and non-STEM SHS graduates. Further, this quantitative-correlational study involves 255 freshman students, of which 115 have non-STEM academic background from the SHS. Results reveal that students with a SHS STEM background had significantly higher QLR performance. Nevertheless, this difference does not cloud the fact that their overall QLR performance marks the lowest when compared to results of similar studies. This paper also shows whether achievement in SHS courses such as General Mathematics, and Statistics and Probability are significant predictors of QLR. Multivariate regression analysis discloses that achievement in the latter significantly relates to QLR. However, the low coefficient of determination (10.30%) suggests that achievement in these courses alone does not account to the students’ QLR. As supported by a deeper investigation of the students’ answers, it is concluded that QLR indeed involves complex processes and is more than just being proficient in Mathematics and Statistics.
Keywords: Numeracy, quantitative literacy, quantitative reasoning, STEM.
References
Adelman, C., Ewell, P., Gaston, P., & Schneider, C. G. (2014). The degree qualifications profile 2.0: Defining U.S. degrees through demonstration and documentation of college learning. Lumina Foundation.
Alexopoulos, E. C. (2010). Introduction to multivariate regression analysis. Hippokratia, 14(1), 23-28. https://hippokratia.gr/images/PDF/14Sup1/734.pdf
Association of American Colleges and Universities. (2007). College learning for the new global century: A report from the national leadership council for liberal education and America’s promise. https://bit.ly/3vJA6oL
Ben-Zvi, D., & Garfield, J. (Eds.). (2004). The challenge of developing statistical literacy, reasoning and thinking. Springer Science & Business Media. https://doi.org/10.1007/1-4020-2278-6
Briggs, W. (2021). Looking back at quantitative reasoning. Numeracy, 14(2), 2-3. https://doi.org/10.5038/1936-4660.14.2.1392
Capraro, R. M., Roe, M. F., Caskey, M. M., Strahan, D., Bishop, P., Weiss, C., & Swanson, K. W. (2012). Research summary: Assessment. Association for Middle Level Education, 1-6. http://archives.pdx.edu/ds/psu/9268
Clinkenbeard, J. E. (2021). Course design and academic outcomes in quantitative literacy after eliminating required remediation. Numeracy, 14(1), 9-15. https://doi.org/10.5038/1936-4660.14.1.1373
Cobb, G. W., & Moore, D. S. (1997). Mathematics, statistics, and teaching. The American Mathematical Monthly, 104(9), 801-823. https://doi.org/10.2307/2975286
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Routledge Academic.
Creative Research Systems. (2018). The survey system: Sample size calculator. https://bit.ly/30sWwzp
Cumming, G. (2011). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. Routledge. https://doi.org/10.4324/9780203807002
Elrod, E., & Park, J. Y. (2020). A comparison of students’ quantitative reasoning skills in STEM and non-STEM math pathways. Numeracy, 13(2), 9-11. https://doi.org/10.5038/1936-4660.13.2.1309
Follette, K. B., Mccarthy, D. W., Dokter, E., Buxner, S., & Pra-Ther, E. (2015). The quantitative reasoning for college science assessment: Development and validation. Numeracy, 8(2), 30-32. https://doi.org/10.5038/1936-4660.8.2.2
Gal, I., & Garfield, J. B. (1997). The assessment challenge in statistics education. IOS Press and the International Statistical Institute.
Gaze, E. C., Montgomery, A., Kilic-Bahi, S., Leoni, D., Misener, L., & Taylor, C. (2014). Towards developing a quantitative literacy/reasoning assessment instrument. Numeracy, 7(2), 7-17. https://doi.org/10.5038/1936-4660.7.2.4
Glen, S. (2020). Kuder-Richardson 20 (KR-20) & 21 (KR-21). Statistics How To. https://www.statisticshowto.com/kuder-richardson/
Grawe, N. D. (2011). Beyond math skills: Measuring quantitative reasoning in context. New Directions for Institutional Research, 2011(149), 41-52. https://doi.org/10.1002/ir.379
Grawe, N. D., & O’Connel, K. (2018). Using the quantitative literacy and reasoning assessment (QLRA) for early detection of students in need of academic support in introductory courses in a quantitative discipline: A case study. Numeracy, 11(1), 7-8. https://doi.org/10.5038/1936-4660.11.1.5
Hughes-Hallett, D. (2001). Achieving numeracy: The challenge of implementation. In L. A. Steen (Ed.), Mathematics and democracy: The case for quantitative literacy (pp. 93 – 98). The National Council on Education and the Disciplines.
Jordan, J., & Haines, B. (2006). The role of statistics educators in the quantitative literacy movement. Journal of Statistics Education, 14(2), 5-9 https://doi.org/10.1080/10691898.2006.11910583
Karaali, G., Villafane, E. H., & Taylor, J. A. (2016). What's in a name? A critical review of definitions of quantitative literacy, numeracy, and quantitative reasoning. Numeracy 9(1), 15-16. https://doi.org/10.5038/1936-4660.9.1.2
Manaster, A. B. (2001). Mathematics and numeracy: Mutual reinforcement. In L. A. Steen (Ed.), Mathematics and democracy: The case for quantitative literacy (pp. 67–72). The National Council on Education and the Disciplines.
Mathematical Reasoning Assessment Committee. (n.d.). Counting up: The assessment of mathematical reasoning within general education in Chico State, 2014-2015. https://bit.ly/3jCjCtA
Moore, D. (1992). Teaching statistics as a respectable subject. In F. & S. Gordon (Eds.), Statistics for the 21st century (pp. 14–25). Mathematical Association of America.
Organisation for Economic Co-operation and Development. (2012b). The survey of adult skills: Reader's companion. OECD Publishing.
Organisation for Economic Co-operation and Development. (2016). The survey of adult skills: Reader’s companion (2nd ed.). OECD Publishing.
Piercey, V. I. (2017). A quantitative reasoning approach to algebra using inquiry-based learning. Numeracy, 10(2), 34-35. http://doi.org/10.5038/1936-4660.10.2.4
Republic of the Philippines Department of Education. (2016). K to 12 curriculum guide: Mathematics. https://bit.ly/3nuAIe4
Rhodes, T. L. (2010). Assessing outcomes and improving achievement: Tips and tools for using rubrics. Association of American Colleges and Universities.
Rocconi, L., Dumford, A., McCormick, A., & Sarraf, S. (2013). Making college count: An examination of quantitative reasoning activities in higher education. Numeracy, 6(2), 10-13. https://doi.org/10.5038/1936-4660.6.2.10
Roohr, K., Graf, E., & Liu, O. (2014). Assessing quantitative literacy in higher education: An overview of existing research and assessments with recommendations for next-generation assessment. ETS Research Report Series, 2014(2), 1-26. https://doi.org/10.1002/ets2.12024
Sarstedt, M., & Mooi, E. (2014). A concise guide to market research. Springer Texts in Business and Economics. https://doi.org/10.1007/978-3-642-53965-7
Science Education Institute-Department of Science and Technology & Philippine Council of Mathematics Teacher Education Inc. (2011). Mathematics framework for Philippine basic education. https://bit.ly/3GurDuG
Sons, L. R. (1996). Quantitative reasoning for college graduates: A complement to the standards. Mathematical Association of America.
Steen, L. A. (2001). Mathematics and democracy: The case for quantitative literacy. The National Council on Education and the Disciplines.
Stith, J. H. (2001). Connecting theory and practice. In L. A. Steen (Ed.), Mathematics and democracy: The case for quantitative literacy (pp. 73–78). The National Council on Education and the Disciplines.
Vacher, H. (2014). Looking at the multiple meanings of numeracy, quantitative literacy, and quantitative reasoning. Numeracy, 7(2), 19-12 https://doi.org/10.5038/1936-4660.7.2.1
Wang, F., & Wilder, E. I. (2015). Numeracy infusion course for higher education (NICHE), 1: Teaching faculty how to improve students' quantitative reasoning skills through cognitive illusions. Numeracy, 8(2), 12-14. https://doi.org/10.5038/1936-4660.8.2.6
Williams, M. N., Grajales, C. A., & Kurkiewicz, D. (2013). Assumptions of multiple regression: Correcting two misconceptions. Practical Assessment, Research, and Evaluation, 18, 3-4. https://doi.org/10.7275/55hn-wk47