The Effects of Instructional Strategies, College Division, and Gender on Students’ Performance in College Algebra at a University in South Texas
DOI:
https://doi.org/10.53555/nnms.v3i7.550Keywords:
gatekeepers, toward, universities, graduationAbstract
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
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
