The Effects of Instructional Strategies, College Division, and Gender on Students’ Performance in College Algebra at a University in South Texas

Authors

  • Andres Padilla Oviedo South Texas College Marie-Anne Mundy Texas A&M University Kingsville Lori Kupczynski Texas A&M University Kingsville
  • Marie Anne Mundy South Texas College Marie-Anne Mundy Texas A&M University Kingsville Lori Kupczynski Texas A&M University Kingsville
  • Lori Kupczynski South Texas College Marie-Anne Mundy Texas A&M University Kingsville Lori Kupczynski Texas A&M University Kingsville

DOI:

https://doi.org/10.53555/nnms.v3i7.550

Keywords:

gatekeepers, toward, universities, graduation

Abstract

College Algebra courses have often served as gatekeepers to advancement toward a bachelor’s degree for many undergraduate students in colleges and universities all over the United States. As College Algebra is a core requirement for graduation, it is very important that fail and drop rates for this course be minimized. The ability to pass this class has a direct influence on 4-year/6-year graduation rates as well as retention rates for undergraduate students. Research has been carried out throughout the United States in attempts to tackle this issue. The present study was designed to explore the differences in final grades in College Algebra courses regarding different instructional strategies, college division and gender. Test findings indicated that the performance of college students as measured by final grade in College Algebra courses was significantly different among diverse instructional strategies. In addition, the performance of college students as measured by final grade in College Algebra courses was not significantly different in each college division or gender for different instructional strategies. Findings provided useful information for increasing student retention in college mathematics courses. Students will be more likely to learn and retain mathematical knowledge when diverse approaches for teaching and learning mathematics are applied. 

References

Assessment and LEarning in Knowledge Spaces (ALEKS). (2014). Overview of ALEKS. Retrieved from http://www.aleks.com/about_aleks/overview

Albert, D., & Hockemeyer, C. (1997). Adaptive and dynamic hypertext tutoring systems based on knowledge. In B. D. Boulay, & R. Mizoguchi, Artificial intelligence in education: Knowdege and media in learning systems (pp. 553-555). Amsterdam, NL: IOS Press

Albert, D., & Lukas, J. (Eds). (1999). Knowledge spaces: Theories, empirical research, and applications. Mahwah, NJ: Erlbaum

Allen, J. (2007). Perkins Reports. Retrieved from: http://faculty.ccri.edu/joallen/Research/ Reports_main.htm

Andersen, M. H. (2011). The world is my school: Welcome to the era of personalized learning. Futurist, 45(1),12-17

Archer, K. J., Lemeshow, S., & Hosmer, D. W. (2007). Goodness-of-fit tests for logistic regression models when data are collected using a complex sampling design. Computational Statistics & Data Analysis, 51(9), 4450-4464

Artzt, A., & Newman, C. (1990). How to use cooperative learning in the mathematics class. Reston, VA: National Council of Teachers of Mathematics

Barefoot, B. O. (2004). Higher education’s revolving door: Confronting the problem of student drop out in U.S. colleges and universities. Open Learning, 19(1), 9-17

Bartlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Organizational research: Determining appropriate sample size in survey research. Information Technology, Learning, and Performance Journal, 19(1), 43-50. Retrieved from http://www.ncde.appstate.edu/reserve_reading/what_works.htm

Boylan, H. R., & Saxon, D. P. (2002). What works in remediation: Lessons from 30 years of research. Paper prepared for The League for Innovation in the Community College. Retrieved from. http://www.hawaii.edu/offices/cc/docs/goal_a/What _Works_in_Remediation.pdf

Booth, C., & Brecher, D. (2014). Ok, library. College & Research Libraries News, 7(5), 234-239

Brookhart, S. M. (1999). The art and science of classroom assessment: The missing part of pedagogy. ASHEERIC Higher Education Report, 27(1). ERIC Clearinghouse on Higher Education

Bulut, M., & Bulut, N. (2011). Pre service teachers’ usage of dynamic mathematics software. The Turkish Online Journal of Educational Technology, 10(4), 294-299

Burns, R., & Burns, R. (2008). Business research methods and statistics using SPSS. London, UK: Sage Publications Ltd

Burrill, G. F. (1998). A “snapshot” of our country's mathematics education. Momentum, 29(3), 60-62

Casazza, M. E. (1998). Strengthening practice with theory. Journal of Developmental Education, 22(2), 1-11

Chin, S. (2014). Mobile technology and gamification: The future is now! Proceedings of the Fourth Annual Conference: Digital Information, and communication technology and Its Applications, May 2004. Piscataway, NJ: Institute of Electrical and Electronics Engineers

Cohen, L., Manion, L., & Morrison, K. (2000). Research methods in education (5th ed.). London: Routledge Falmer

Cole, M., John-Steiner, V., Scribner, S., & Souberman, E. (Eds.). (1978). L.S. Vygotsky, mind in society: The development of higher processes. Cambridge, MA: Harvard University Press

Conlan, O., O'Keeffe, I., Hampson, C., & Heller, J. (2006). Using knowledge space theory to support learner modeling and personalization. World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2006, (1), 1912-1919

Corbishley, J. B., & Truxman, M. P. (2010). Mathematical readiness of entering college freshmen. School of Science and Mathematics, 110(2), 71-85

Creswell, J. W. (2009). Research design qualitative, quantitative and mixed methods approaches (3rd ed.). Thousand Oaks, CA: Sage Publication Inc

Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco, CA: Jossey-Bass

Davidson, N. (1990). Cooperative learning in mathematics: A handbook for teachers. Location: AddisonWesley Publishing Company, Inc

Davies, R. S., Dean, D. L., & Ball, N. (2013). Flipping the classroom and instructional technology integration in a college-level information systems spreadsheet course. Educational Technology Research and Development, 61(4), 563-580

Davis, J. (2011). Student as institutional mirror: What campuses can learn from nontraditional populations. About Campus, 16(4), 2-10

Demski, J. (2012). This time it's personal. T.H.E. Journal, 39(1), 32-36

Dietz, E. J. (1993). A cooperative learning activity on methods of selecting a sample, The American Statistician, 47, 104-108

Doignon, J. P., & Falmagne, J. C. (1985). Spaces for the assessment of knowledge. International Journal of Man-Machine Studies, 23(2), 175-196

Doignon, J., & Falmagne, J. (1999). Knowledge spaces. New York, NY: Springer

Duncan, A. (2010). Learning, engage and empower: National educational technology plan. Retrieved from http://www.ed.gov/technology/netp-2010/learning-engage-and-empower

Ertmer, P. A., & Newby, T. J. (1993). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 6(4), 50-70

Falmagne, J. C., Cosyn, E., Doignon, J. P., & Thiéry, N. (2006). The assessment of knowledge, in theory and in practice. In B. Garter and Likwida (Eds.) Formal concept analysis (pp. 61-79). Irvin, CA: University of California

Ferreira, J. (2013). Heavy duty infrastructure for the adaptive world. Retrieved from http://www.knewton.com/platform/

Gano, L. R. (2011). Fitting technology to the mathematics pedagogy: Its effect on students' academic achievement. Journal of College Teaching & Learning, 8(11), 29-36

Garcia, M. (2010). When Hispanic students attempt to succeed in college, but do not. Community College Journal of Research and Practice, 34, 839-847

Garfield, J. (1993). An authentic assessment of students' statistical knowledge. National Council of Teachers of Mathematics, 187-196

Garfield, J., Le, L., Zieffler, A., & Ben-Zvi, D. March, (2014). Developing students’ reasoning about samples and sampling variability as a path to expert statistical thinking. Educational Studies in Mathematics, 1-16

Garfinkel, S. (2014). Glass, darkly. Technology Review, 117(2), 70-77

Gay, L. R., Mills, G. E., & Airasian, P. (2006). Educational research: Competencies for analysis and applications (8th ed.). Upper Saddle River, NJ: Pearson

Gillard, J., Robathan, K., & Wilson, R. (2011). Measuring the effectiveness of a mathematics support service: An e-mail survey. Teaching Mathematics and Its Applications, 30, 43-52

Giovanni, P., Pirrone, R., & Rizzo, R. (2008). A KST-based system for student tutoring. Applied Artificial Intelligence, 22(4), 283-308

Glass, R. D., & Nygreen, K. (2011). Class, race and the discourse of “college for all:” A response to “schooling for democracy.” Democracy & Education, 19(1), 1-8

Gradel, K., & Edson, A. J. (2011). Cooperative learning: Smart pedagogy and tools for online and hybrid courses. Journal of Educational Technology Systems, 39(2), 193-212

Goodsell, A., Maher, M., & Tinto, V. (1992). Collaborative learning: A sourcebook for higher education. University Park, PA: National Center on Postsecondary Teaching, Learning and Assessment

Goodwin, B., & Miller, K. (2013). Evidence on flipped classrooms is still coming in. Educational Leadership, 70(6), 78-80

Gribbons, B., & Herman, J. (1997). True and quasi-experimental designs. Practical Assessment, Research & Evaluation, 5(14)

Gury, N. (2011). Dropping out of higher education in France: A micro-economic approach using survival analysis. Education Economics, 19(1), 51-64

Hagerty, G., Smith, S., & Goodwin, D. (2010). Redesigning college algebra: Combining educational theory and web-based learning to improve student attitudes and performance. Philadelphia, PA: LTD. Retrieved from http://www.tandfonline.com/loi/upri20

Halinski, R. S., & Feldt, L. S. (1970). The selection of variables in multiple regression analyses. Journal of Educational Measurement, 7(3), 151-158

Hampikian, J., Gardner, J., Moll, A., Pyke, P., & Schrader, C. (2006, June). Integrated pre-freshman engineering and precalculus mathematics. Proceedings of the 2006 Annual Conference of the American Society for Engineering Education, Chicago, IL

Handal, B., & Herringhton, A. (2003). Re-examining categories of computer-based learning and mathematics education. Contemporary Issues in Technology and Teacher Education, 3(3)

Hanna, R. E., & Carpenter, J. (2006, June). Predicting student preparedness in calculus. Proceedings of the 2006 American Society for Engineering Education Annual Conference. Chicago, IL

Hardy, M. E. (2004). Use and evaluation of the ALEKS interactive tutoring system. Journal of Computing Sciences in Colleges, 19(4), 342-347

Hasselbring, T. S. (1988). Developing math automaticity in learning handicapped children: The role of computerized drill and practice. Focus on Exceptional Children, 20(6), 1-7

Hurley, D. J., McBain, L., Harnisch, T. L., Parker, E., & Russell, A. (2012). Top 10 higher education state policy issues for 2012. A Higher Education Policy Brief. 1, 1-6

Johnson, D., Johnson, R., & Smith, K. (1991). Active learning: Cooperation in the college classroom, Edina, MN: Interaction Brook Co

Johnson, D., Johnson, R., & Smith, K. (1991). Cooperative learning: Increasing college faculty instructional productivity (ASHE-ERIC Higher Education Report No. 4). Washington, DC: The George Washington University

Jones, L. (1991). Using cooperative learning to teach statistics. (Research Report Number 91-2). University of North Carolina: The L.L. Thurstone Psychometric Laboratory

Juan, A. A., Steegmann, C., Huertas, A., Martinez, J., & Simosa, J. (2011). Teaching mathematics online in the european area of higher education: An instructor's point of view. International Journal of Mathematical Education in Science and Technology, 42(2), 141-153

Kendricks, K. D. (2011). Creating a supportive environment to enhance computer based learning for underrepresented minorities in college algebra classrooms. Journal of the Scholarship of Teaching and Learning, 11(4), 12-25

Kezar, A. (2011). What is the best way to achieve broader reach of improved practices in higher education? Springer Science Business Media, 36, 235-247

Lesser, L. (2007). Using graphing calculators to do statistics: A pair of problematic pitfalls. Mathematics Teacher, 100, 375–378

Lewis, A. S. (2013). Dispelling myths: Personalized learning tools will not replace teachers. VentureBeat.Mobilebeat 2013. Retrieved from http://venturebeat.com/ 2013/04/26/dispelling-mythspersonalized-learning-tools-will-not-replace-teachers/

Litterman, R. B. (2014, May 13). Higher Education Price Index. Home. Retrieved from http://www.commonfund.org/CommonfundInstitute/HEPI /Pages/default.aspx

Li, Z., Cheng, Y., & Liu, C. (2013). A constructionism framework for designing game-like learnings systems: Its effect on different learners. British Journal of Educational Technology, 44(2), 208-224

Loredo, G. (2012). Agenda item VI-G. Retrieved from http://www.thea.state.tx.us/generalpubs/agenda/ag2012_04/VIG/VIGSR.pdf

Lucas, L., Postma, C., & Jay, C. (1974). A comparative study of cognitive retention using simulation-gaming as opposed to lecture-discussion technique. Peabody Journal of Education, 52(4), 261-266

McFadden, C. (2012). Are textbooks dead? Making sense of the digital transition. Publishing Research Quarterly, 28, 93-99

McKeachie, W., Pintrich, P., Yi-Guang, L., & Smith, D. (1986), Teaching and Learning in the College Classroom: A Review of the Research Literature. Ann Arbor, MI: Regents of the University of Michigan

Mergel, B. (1988). Instructional design & learning theory. Retrieved from http://www.U.S.sk.ca/education/coursework/802papers/mergel/brenda.htm

Miller, D. E., & Kunce, J. T. (1973). Prediction and statistical overkill revisited. Measurement and Evaluation in Guidance, 6(3), 157-163

Mireles, S. (2014). Developmental mathematics. Texas State University. Retrieved from http://www.math.txstate.edu/devmath/

National Council of Teachers of Mathematics (1989). Curriculum and evaluation standards for school mathematics. Reston, VA: National Council of Mathematics

Olszewski-Kubilius, P., & Lee, S. (2011). Gender and other group differences in performance on off-level test: Changes in the 21st century. Gifted Child Quartely, 55(1), 54-73

Pappas, C. (2014). Wearable technology in the classroom infographic - e-learning infographics. e Learning Infographics. Retrieved from http://elearninginfographics.com/ wearable-technology-in-the-classroominfographic

Peters, M. L. (2013). Examining the relationships among classroom climate, self-efficacy, and achievement in undergraduate mathematics: A multi-level analysis. International Journal of Science and Mathematics Education, 11(2), 459-480

Pitre, P. E. (2011). P-20 education policy: School to college transition policy in Washington state. Education Policy Analysis Archives, 19(5), 1-14

Powell, B. A., Gilleland, D. S., & Pearson, L. C. (2012). Expenditure, efficiency, and effectiveness in U.S. undergraduate higher education: A nation benchmark model. The Journal of Higher Education, 83(1), 102-127

Sáinz, M., & Eccles, J. (2012). Self-concept of computer and math ability: Gender implications across time and within ICT studies. Journal of Vocational Behavior, 80(2), 486-499

Salkind, N. J. (Ed.). (2010). Encyclopedia of research design (Vol. 1).Thousand Oaks, CA: Sage

Salnikov, N., & Burukhin, S. (2009). Current state and problems of higher education reform. Russian Education and Society, 51(11), 71-89

Scheaffer, R. L., Mendenhall III, W., & Ott, L. R. (2006). Elementary survey sampling (6th ed.). Belmont, CA: Thompson Brooks/Cole

Schreyer-Bennethum, L., & Albright, L. (2011). Evaluating the incorporation of technology and applications projects in higher education mathematics classrooms. International Journal of Mathematics Education in Science and Technology, 42(1), 53-63

Shaughnessy, J. M. (1977), Misconceptions of Probability: An experiment with a small-group activity-based model building approach to introductory probability at the college level. Educational Studies in Mathematics, 8, 285-316

Soares, L. (2011). The 'personalization' of higher education. Center for American Progress. Retrieved from http://www.americanprogress.org/issues/labor/report/ 2011/10/04/10484/the-personalization-ofhigher-education/

Stahl, C. (2011). Knowledge space theory: 2008-06-18. [Report No. 2009-12-22]. Retrieved from http://cran.itam.mx/web/packages/kst/vignettes/kst.pd

Starkweather, J., & Moske, A. K. (2011). Multinomial logistic regression. Retrieved from: http://www.unt.edu/rss/class/Jon/Benchmarks/MLR_JDS_Aug2011.pdf

Taylor, J. (2008). The effects of a computerized-algebra program on mathematics achievement of college and university freshmen enrolled in a developmental mathematics course. Journal of College Reading and Learning, 39(1), 35-50

Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of research. Review of Educational Research, 45(1). 89-125

Tinto, V. (1999). Taking retention seriously: Rethinking the first year of college. NACADA Journal, 19(2), 5-9

Tóth, Z. (2007). Mapping students’ knowledge structure in understanding density, mass, percent, molar mass, molar volume and their application in calculations by the use of the knowledge space theory. Chemistry Education Research and Practice, 8(4), 376-389

Traxler, J. (2007). Defining, discussing and evaluating mobile learning: The moving finger writes and having writ… International Review of Open and Distance Learning, 8(2)

Urban-Lurain, M. (2004). Intelligent tutoring systems: An historic review in the context of the development of artificial intelligence and educational psychology. Retrieved from http://www.cse.msu.edu/rgroups/cse101/ITS/its.htm

Varsavsky, C. (2010). Chances of success in and engagement with mathematics for students who enter university with a weak mathematics background. International Journal of Mathematics in Science and Technology, 41(8), 1037-1049

Vygotsky, L. (1987). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press

Walcott, C., & Walcott, A. (1976). Simple simulations: A guide to the design and use of simulation/games in teaching political science. Washington, D.C: American Political Science Association

Zakaria, E., Chin, L. C., & Daud, M. Y. (2010). The effects of cooperative learning on students' mathematics achievement and attitude towards mathematics. Journal of Social Sciences, 6(2), 272

Published

2016-07-31

How to Cite

Oviedo, A. P., Mundy, M. A., & Kupczynski, L. (2016). The Effects of Instructional Strategies, College Division, and Gender on Students’ Performance in College Algebra at a University in South Texas. Journal of Advance Research in Mathematics and Statistics (ISSN 2208-2409), 3(7), 11-19. https://doi.org/10.53555/nnms.v3i7.550