Attitudes regarding Perceived Values and Usefulness of Mathematics in a Community College Mathematical Literacy Course

Öz

As the nation seeks for new and efficient ways to address the high failing rate in developmental mathematics, many community colleges have begun experimenting with alternative design for remedial mathematics sequences. One approach was to implement mathematical literacy in their program, focusing on quantitative reasoning. The mathematics literacy course involved in this study is MAT 41, “an innovative remedial mathematics course designed to prepare and move students to college-level work more quickly” (Carnegie Foundation, n.d.). The drive of this study was to observe and evaluate learner attitudes regarding perceived values and usefulness mathematics in a community college mathematical literacy course using a mixed method approach. Two groups of students who were in need of basic skills in mathematics at the level of remediation were surveyed, one group taking mathematical literacy courses and another group taking algebra courses. To measure students’ growth in the perceived value and usefulness of mathematics, quantitative data were collected with a pre- and post-mathematics attitudes survey from the mathematical literacy course and the elementary algebra course. In addition, qualitative data were gathered with an open-ended question administered to participants in the mathematical literacy sections during the last week of the semester. Findings from the quantitative data revealed statistically significant effects for participants in the mathematical literacy course compared to their counterparts in the elementary algebra course in attitudes regarding the perceived value and usefulness of mathematics. Qualitative data were aligned with the findings from the quantitative data and indicated participants’ positive views on the usefulness of the mathematical literacy course, and improvement of their attitudes regarding mathematics.

Kaynakça

1. Allen, K. (2011). Mathematics as thinking: A response to “Democracy and math.” Democracy and Education, 19(2), 1-7.
2. Bailey, T. (2009). Challenge and opportunity: Rethinking the role and function of developmental education in community college. New Directions for Community Colleges, 145, 11-30.
3. Bailey, T., Jeong, D. W., & Cho, S.-W. (2010). Referral, enrollment, and completion in developmental education sequences in community college. Economics of Education Review, 29, 255–270. doi: 10.1016/j.econedurev.2009.09.002
4. Carnegie Foundation. (n.d.). Quantway. Retrieved on July 31, 2012 from http://www.carnegiefoundation.org/quantway.
5. Creswell, J. (2018). Research design: Qualitative, quantitative and mixed methods approaches (5th ed.). Thousand Oaks, CA: Sage.
6. CUNY, Office of Institutional Research and Assessment. (2015). Pass rates of selected math courses: Fall 2014. Unpublished data report.
7. George, M. (2010). Ethics and motivation in remedial mathematics education. Community College Review, 38, 82–92. doi: 10.1177/0091552110373385
8. Howell, J. S. (2011). What influences students’ need for remediation in college? Evidence from California. Journal of Higher Education, 82, 292–318. doi: 10.1353/jhe.2011.0014

9. Jordan, J., & Haines, B. (2003). Fostering quantitative literacy: Clarifying goals, assessing student progress. Peer Review, 5(4), 16-19.
10. Koller, O., Baumert, J., & Schnabel, K. (2001). Does interest matter? The relationship between academic interest and achievement in mathematics. Journal for Research in Mathematics Education, 32(5), 448-470.
11. Malcom, S. (1997). Making mathematics the great equalizer. In L. A. Steen (Ed.), Why numbers count: Quantitative literacy for tomorrow’s America (pp. 30-35). New York, NY: College Board.
12. Mejia, M. C., Rodriguez, O., & Johnson, H. (2016). Preparing students for success in California community colleges (Public Policy Institute of California). Retrieved from http://www.ppic.org/content/pubs/report/ R_1116MMR.pdf
13. Middleton, J. A., & Spanias, P. A. (1999). Motivation for achievement in mathematics: Findings, generalizations, and criticisms of the research. Journal for Research in Mathematics Education, 30(1), 65–88.
14. Nunan, D. (1999). Research methods in language learning (8th printing). Cambridge: CUP
15. Schiefele, U., & Csikszentmihalyi, M. (1995). Motivation and ability as factors in mathematics experience and achievement. Journal for Research in Mathematics Education, 26(2), 163-181.
16. Steen, L. A. (2001c). Mathematics and numeracy: Two literacies, one language. Mathematics Educator, 6(1), 10-16.
17. Stith, J. H. (2001). Connecting theory and practice. In L. A. Steen (Ed.), Mathematics and democracy: The case for quantitative literacy (pp. 73-78). Washington, DC: National Council on Education and the Disciplines.
18. Tapia, M. (1996). The attitudes toward mathematics instrument. Retrieved from http://search.proquest.com.proxy.lib.wayne.edu/docview/62613882?accountid 14925
19. Van Peursem, D., Keller, C., Pietrzak, Wagner, C., & Bennett, C. (2012). A comparison of performance and attitudes between students enrolled in college algebra vs. quantitative literacy. Mathematics and Computer Education, 46(2), 107-118.