The effect of the math emporium instructional method on students’ performance in college algebra

Kathy Cousins-Cooper 1 * , Katrina N. Staley 1, Seongtae Kim 1, Nicholas S. Luke 1
More Detail
1 Department of Mathematics, North Carolina A&T State University
* Corresponding Author
EUR J SCI MATH ED, Volume 5, Issue 1, pp. 1-13. https://doi.org/10.30935/scimath/9493
OPEN ACCESS   1460 Views   950 Downloads
Download Full Text (PDF)

ABSTRACT

This study aims to investigate the effectiveness of the Emporium instructional method in a course of college algebra and trigonometry by comparing to the traditional lecture method.The math emporium method is a nontraditional, instructional method of learning math that has been implemented at several universities with much success and has been shown to improve the performance of students taking college algebra and trigonometry courses. In the math emporium method, students spend time working on the problems using interactive software. The students are able to get immediate feedback on their progress by soliciting help from the instructors. This method encourages students to be actively involved in the acquiring of their knowledge. In this study, we compare the performance of students using the math emporium method to that of students using the traditional, lecture method. Performance was measured using posttest scores and the final course grades. We found that the math emporium method shows promise to improving students’ performance in college algebra and trigonometry classes.
Keywords: achievement, algebra, computer-assisted learning, cooperative learning, math emporium

CITATION

Cousins-Cooper, K., Staley, K. N., Kim, S., & Luke, N. S. (2017). The effect of the math emporium instructional method on students’ performance in college algebra. European Journal of Science and Mathematics Education, 5(1), 1-13. https://doi.org/10.30935/scimath/9493

REFERENCES

  • Antil, L.R., Jenkins, J.R., and Wayne, S.K. (1998). Cooperative Learning: Prevalence, Conceptualizations, and the Relation Between Research and Practice, American Educational Research Journal, 35(3), 419-454.
  • Blimling, G. S., Whitt, E. J., & Associates (1999). Good practice in student affairs: Principles to foster student learning. San Francisco, CA: Jossey-Bass.
  • Burch, K. J., & Kuo, Y. (2010). TRADITIONAL VS. ONLINE HOMEWORK IN COLLEGE ALGEBRA. Mathematics and Computer Education, 44(1), 53-63.
  • Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practice in undergraduate education. AAHE Bulletin, 39(7), 3–7.
  • DePree, J. (1998). Small-Group Instruction: Impact on Basic Algebra Students. Journal Of Developmental Education, 22(1), 2-6.
  • Drijvers, P., Doorman, M., Kirschner, P., Hoogveld, B., & Boon, P. (2014). The effect of online tasks for algebra on student achievement in grade 8. Technology, Knowledge and Learning, 19(1-2), 1-18.
  • Eddy, S., and Hogan, K. (2014). Getting under the hood: how and for whom does increasing course structure work?. CBE-Life Sciences Education, 13(3), 453-468.
  • Furner, J. M. & Gonzalez-DeHass, A. (2011). How do students’ mastery and performance goals relate to math anxiety? Eurasia Journal of Mathematics, Science, & Technology Education, 7(4), 227-242.
  • Fullilove, R.E. and Treisman, P.U. (1990), Mathematics Achievement Among African American Undergraduates at the University of California, Berkeley: An Evaluation of the Mathematics Workshop Program. The Journal of Negro Education, 59 (3), 463-478.
  • Gillies, R.M. and Ashman, A.F. (2003). Co-Operative Learning: The Social and Intellectual Outcomes of Learning in Groups. New York: Routledge Falmer.
  • Gleason, J. (2012). Using Technology-Assisted Instruction and Assessment to Reduce the Effect of Class Size on Student Outcomes in Undergraduate Mathematics Courses. College Teaching, 60(3), 87-94.
  • Gordon, S. P. (2008). What's wrong with college algebra? Primus : Problems, Resources, and Issues in Mathematics Undergraduate Studies, 18(6), 516-541.
  • Hake, R.R. (1998), Interactive-engagement versus traditional methods: A six thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66(1), 64.
  • Hegeman, J. S. (2015). Using Instructor-Generated Video Lectures in Online Mathematics Courses Improves Student Learning. Online Learning, 19(3), 70-87.
  • Herriott, S. R., & Dunbar, S. R. (2009). Who takes college algebra? Primus : Problems, Resources, and Issues in Mathematics Undergraduate Studies, 19(1), 74-87.
  • Lipnevich, A. A., MacCann, C., Krumm, S., Burrus, J., & Roberts, R. D. (2011). Mathematics attitudes and mathematics outcomes of U.S. and Belarusian middle school students. Journal Of Educational Psychology, 103(1), 105-118. doi:10.1037/a0021949
  • Liu, F & Cavanaugh C. Factors influencing student academic performance in online high school algebra. Open Learning, 27(2), 149-167.
  • Mathai, E., & Olsen, D. (2013). Studying the effectiveness of online homework for different skill levels in a college algebra course. Primus : Problems, Resources, and Issues in Mathematics Undergraduate Studies, 23(8), 671-682.
  • McCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models. doi:10.1007/978-1-4899-3242-6.
  • National Mathematics Advisory Panel. (2008). Foundations for Success: The Final Report of the National Mathematics Advisory Panel. Washington, D.C.: U.S. Department of Education.
  • Rakes, C. R., Valentine, J. C., McGatha, M. B., & Ronau, R. N. (2010). Methods of instructional improvement in algebra: A systematic review and meta-analysis. Review of Educational Research, 80(3), 372. Retrieved from http://search.proquest.com/docview/756977989?accountid=12711, (accessed June 2014)
  • Robinson, D. R., Schofield, J. W., & Steers-wentzell1, K. L. (2005). Peer and Cross-Age Tutoring in Math:Outcomes and Their Design Implications. Educational Psychology Review, 17(4), 327-362. doi:10.1007/s10648-005-8137-2
  • Shechtman, N., Haertel, G., Roschelle, J., Knudsen, K., & Singleton, C. (2010). Design and development of the student and teacher mathematical assessments. Menlo Park, CA: SRI International.
  • Singh, K., Granville, M., & Dika, S. (2002). Mathematics and science achievement: Effects of motivation, interest, and academic engagement. Journal of Educational Research, 95, 323–332. doi:10.1080/ 00220670209596607
  • Slavin, R. (2010). Co-operative learning: what makes group-work work? In Dumont, H., Istance, D., & Benavides, F. (eds.), The Nature of Learning: Using Research to Inspire Practice. OECD Publishing.
  • Spradlin, K., & Ackerman, B. (2010). The Effectiveness of Computer-Assisted Instruction in Developmental Mathematics. Journal Of Developmental Education, 34(2), 12-14,16,18,42.
  • Stacey, K., Chick, H., & Kendal, M. (Eds.). (2004). The future of the teaching and learning of algebra: The 12th ICMI study. Dordrecht, Netherlands: Kluwer.
  • Stevenson, H. W., & Newman, R. S. (1986). Long-term prediction of achievement and attitudes in mathematics and reading. Child Develop- ment, 57, 646–659. doi:10.2307/1130343
  • Strayhorn, T.L. (2011). Bridging the Pipeline: Increasing Underrepresented Students’ Preparation for College Through a Summer Bridge Program. American Behavior Scientist, 55(2), 142-159.
  • Tienken, C. H., & Maher, J. A. (2008). The Influence of Computer-Assisted Instruction on Eighth Grade Mathematics Achievement. RMLE Online, 32(3), 1-13.
  • Twigg, Carol A. (2011). The Math Emporium: Higher Education's Silver Bullet. Change: The Magazine of Higher Learning, 43(3), 25-34.
  • Watson, A. (2009). Paper 6: Algebraic reasoning. In T. Nunes, P. Bryant, & A. Watson (Eds.), Key understandings in mathematics learning. London: Nuffield Foundation.
  • Zakaria, E., Solfitri, T., Daud, Y., & Abidin, Z. Z. (2013). Effect of Cooperative Learning on Secondary School Students’ Mathematics Achievement. Creative Education, 04(02), 98–100. doi:10.4236/ce.2013.42014.