The Effects of Instructional Approach and Social Support on College Algebra Students’ Motivation and Achievement: Classroom Climate Matters
- 1 Downloads
College algebra has been noted as a critical course in post-secondary institutions because it serves as a gateway for major selection and college completion. Combinatorial topics like repeatable permutations are often overlooked in K-12 and undergraduate curricula. Likewise, students’ achievement and motivation are affected by the type of classroom climate created in the undergraduate mathematics classroom. Inquiry-based mathematical education (IBME) is a viable instructional approach because of its focus on community meaning-making of the mathematical content. However, lecture-style approaches still dominate post-secondary mathematics classrooms even though they have been criticized for their focus on procedural knowledge and disinviting environment. Therefore, the purpose of this quasi-experimental study was to test the effects of instructional approach (i.e., lecture-style vs. IBME) and social support (i.e., absence or presence) on undergraduate student motivation and achievement of combinatorial mathematics. Findings indicated that intentional social support-building – regardless of pedagogical method – had the strongest effects on students’ perceived autonomy-support, competence and achievement. Although no differing pedagogical effects were discovered (most likely due to the one-time implementation of the lesson formats), the findings provide evidence for the necessity of community-building efforts -- an aspect of education that is often overlooked in the undergraduate mathematics classroom.
KeywordsAutonomy-support College algebra Social support Self-determination theory
This research was supported in part by a Faculty-Undergraduate Student Engagement (FUSE) grant from Western Kentucky University.
Compliance with Ethical Standards
Conflict of Interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
- Adams, G. L., & Engelmann, S. (1996). Research on direct instruction: 25 years beyond DISTAR. Seattle: Educational Achievement Systems.Google Scholar
- Aelterman, N., Vansteenkiste, M., Haerens, L. Boenens, B., Fontaine, J.R.J., & Reeve, J. (2018). Toward an integrative and fine-grained insight in motivation and demotivating teaching styles: The merits of a circumplex approach. Journal of Educational Psychology. Advance online publication. https://doi.org/10.1037/edu0000293.
- Black, A. E., & Deci, E. L. (2000). The effects of instructors' autonomy support and students' autonomous motivation on learning organic chemistry: A self-determination theory perspective. Science Education, 84, 740–756. https://doi.org/10.1002/1098-237X(200011)84:6<740::AID-SCE4>3.0.CO;2-3.CrossRefGoogle Scholar
- Bressoud, D. (2015). Insights from the MAA National Study of college Calculus. Mathematics Teacher, 109, 179–185.Google Scholar
- Bybee, R. W. (2015). The BSCS 5E instructional model: Creating teachable moments. Arlington: NSTA Press.Google Scholar
- Bybee, R. W., Taylor, J. A., Gardner, A., Van Scotter, P., Powell, J. C., Westbrook, A., & Landes, N. (2006). The BSCS 5E instructional model: Origins and effectiveness. Colorado Springs: BSCS.Google Scholar
- Chen, B., Vansteenkiste, M., Beyers, W., Boone, L., Deci, E.L., Van der Kapp-Deeder, J … Verstuyf, J. (2015). Basic psychological need satisfaction, need frustration, and need strength across four cultures. Motivation and Emotion, 39, 216–236. https://doi.org/10.1007/s11031-014-9450-1.
- Eagan, K. (2016). Becoming more student-centered? An examination of faculty teaching practices across STEM and non-STEM disciplines between 2004 and 2014: A report prepared for the Alfred P. Sloan Foundation.Google Scholar
- European Schoolnet (2018). Science, Technology, Engineering and Mathematics Education Policies in Europe. Scientix Observatory report. October 2018, European Schoolnet, Brussels.Google Scholar
- Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York: Routledge.Google Scholar
- Hattie, J., Fisher, D., & Frey, N. (2017). Visible learning for mathematics: What works best to optimize student learning. Thousand Oaks: Corwin.Google Scholar
- Johnson, E., Keller, R., & Fukawa-Connelly, T. (2018). Results from a survey of abstract algebra instructors across the United States: Understanding the choice to (not) lecture. International Journal of Research in Undergraduate Mathematics, 4, 254–285. https://doi.org/10.1007/s40753-017-0058-1.CrossRefGoogle Scholar
- Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41, 75–86. https://doi.org/10.1080/00461520701263426.CrossRefGoogle Scholar
- Kozioff, M. A., LaNunziata, L., Cowardin, J., & Bessellieu, F. B. (2001). Direct instruction: Its contributions to high school achievement. The High School Journal, 84(2), 54–71.Google Scholar
- Lahdenperä, J., Postareff, L., & Rämö, J. (2018). Supporting quality of learning in university mathematics: A comparison of two instructional designs. International Journal of Research in Undergraduate Mathematics. https://doi.org/10.1007/s40753-018-0080-y.
- Lockwood, E., Wasserman, N. H., & McGuffey, W. (2018). Classifying combinations: Investigating undergraduate students’ responses to different categories of combination problems. International Journal of Research in Undergraduate Mathematics, 4, 305–322. https://doi.org/10.1007/s40753-018-0073-x.CrossRefGoogle Scholar
- McDuffie, A. R., & Graeber, A. O. (2003). Institutional norms and policies that influence college mathematics professors in the process of changing to reform-based practices. School Science and Mathematics, 103, 331–344. https://doi.org/10.1111/j.1949-8594.2003.tb18210.x.CrossRefGoogle Scholar
- Niemiec, C. P., Lynch, M. F., Vansteenkiste, M., Bernstein, J., Deci, E. L., & Ryan, R. M. (2006). The antecedents and consequences of autonomous self-regulation for college: A self-determination theory perspective on socialization. Journal of Adolescence, 29, 761–775. https://doi.org/10.1016/j.adolescence.2005.11.009.CrossRefGoogle Scholar
- President’s Council of Advisors on Science and Technology (PCAST) (2012). Engage to excel: Producing one million additional college graduates with degrees in science, technology, engineering, and mathematics (Executive Report). Retrieved from The White House Office of Science and Technology Policy website: http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-executive-report-final_2-13-12.pdf
- Reeve, J., Ryan, R. M., Deci, E. L., & Jang, H. (2007). Understanding and promoting autonomous self-regulation: A self-determination theory perspective. In D. Schunk & B. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and application (pp. 223–244). Mahwah: Lawrence Erlbaum Associates Publishers.Google Scholar
- Rockswald, G. K. (2012). Essentials of college algebra with modeling and visualizations (4th ed.). New York: Pearson.Google Scholar
- Rodríguez-Meirinhos, A., Antolín-Suárez, L., Brenning, K., Vansteenkiste, M., & Olivia, A. (2019). A bright and dark path to adolescents’ functioning: The role of need satisfaction and need frustration across gender, age, and socioeconomic status. Journal of Happiness Studies. Advanced online publication. https://doi.org/10.1007/s10902-018-00072-9.
- Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. New York: The Guilford Press.Google Scholar
- Salazar, D.A. (2014). Salazar’s grouping method: Effects on student’s achievement in integral calculus. Journal of Education and Practice, 5, 119–126.Google Scholar
- Salazar, D. A. (2015). Razalas’ grouping method and mathematics achievement. Journal of Education and Practice, 6(8), 118–127.Google Scholar
- Stigler, J. W., Givvin, K. B., & Thompson, B. J. (2010). What community college developmental mathematics students understand about mathematics. MathAMATYC Educator, 1(3), 4–16.Google Scholar
- Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston: Pearson Education.Google Scholar
- Trusty, J., Thompson, B., & Petrocelli, J. V. (2004). Practical guide for reporting effect size in quantitative research in the “journal of counseling & development”. Journal of Counseling & Development, 82, 107–110. https://doi.org/10.1002/j.1556-6678.2004.tb00291.x.CrossRefGoogle Scholar
- Walker, J. T., Martin, T. M., Haynie, L., Norwood, A., White, J., & Grant, L. (2007). Preferences for teaching methods in a baccalaureate nuring program: How second-degree and traditional students differ. Nursing Education Perspectives, 28, 246–250.Google Scholar
- Wright, E. L., Sunal, D. W., & Day, J. B. (2004). Reform in undergraduate science classrooms. In D. W. Sunal, E. L. Wright, & J. B. Day (Eds.), Reform in undergraduate science teaching for the 21st century (pp. 137–152). Greenwich: Information Age Publishing.Google Scholar