Student Ability Best Predicts Final Grade in a College Algebra Course

Kyle Anthony O'Connell
Elijah Wostl
Matt Crosslin
T. Lisa Berry
James P. Grover


Historical student data can help elucidate the factors that promote student success in mathematics courses. Herein we use both multiple regression and principal component analyses to explore ten years of historical data from over 20,000 students in an introductory college-level Algebra course in an urban American research university with a diverse student population in order to understand the relationship between course success and student performance in previous courses, student demographic background, and time spent on coursework. We find that indicators of students’ past performance and experience, including grade-point-average and the number of accumulated credit hours, best predict student success in this course. We also find that overall final grades are representative of the entire course and are not unduly weighted by any one topic. Furthermore, the amount of time spent working on assignments led to improved grade outcomes. With these baseline data, our team plans to design targeted interventions that can increase rates of student success in future courses.

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Ali, A., & Smith, D. (2014). Comparing students performance in online versus face-to-face courses in computer literacy courses. In Competition Forum (Vol. 12, No. 2, p. 118). American Society for Competitiveness.

Ali, A., & Smith, D. T. (2015). Comparing social isolation effects on students attrition in online versus face-to-face courses in computer literacy. Issues in Informing Science and Information Technology, 12.

American College Testing, Inc. (2012). National collegiate retention and persistence to degree rates. Iowa City, IA: Author. Retrieved from

Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge, 267-270.

Bahr, P. R. (2010). Preparing the underprepared: An analysis of racial disparities in postsecondary mathematics remediation. The Journal of Higher Education, 81, 209-237.

Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In Learning analytics, 61-75. Springer New York.

Bakharia, A., Corrin, L., de Barba, P., Kennedy, G., Gašević, D., Mulder, R., ... & Lockyer, L. (2016, April). A conceptual framework linking learning design with learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, 329–338.

Berger, J. B., & Malaney, G. D. (2003). Assessing the transition of transfer students from community colleges to a university. NASPA journal, 40, 1-23.

Berk, R. A. (2013). Face-to-face versus online course evaluations: A" consumer's guide" to seven strategies. Journal of Online Learning and Teaching, 9, 140.

Biel, R., & Brame, C. J. (2016). Traditional Versus Online Biology Courses: Connecting Course Design and Student Learning in an Online Setting. Journal of microbiology & biology education, 17, 417.

Bigelow, C. A. (2009). Comparing Student Performance in an Online versus a Face to Face Introductory Turfgrass Science Course A-Case Study. NACTA Journal, 2¬–7.

Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19, 205–220.

Callister, R. R., & Love, M. S. (2016). A Comparison of Learning Outcomes in Skills‐Based Courses: Online Versus Face‐To‐Face Formats. Decision Sciences Journal of Innovative Education, 14, 243–256.

Cavanaugh, J., & Jacquemin, S. J. (2015). A large sample comparison of grade based student learning outcomes in online vs. face-to-face courses. Online Learning, 19.

Cheema, J. R., & Sheridan, K. (2015). Time spent on homework, mathematics anxiety and mathematics achievement: Evidence from a US sample. Issues in Educational Research, 25, 246–259.

Davidson, J. C. (2015). Precollege factors and leading indicators: Increasing transfer and degree completion in a community and technical college system. Community College Journal of Research and Practice, 39, 1007–1021.

Diaz, D. (2002). Online drop rates revisited. The Technology Source. Available online at

Driscoll, A., Jicha, K., Hunt, A. N., Tichavsky, L., & Thompson, G. (2012). Can online courses deliver in-class results? A comparison of student performance and satisfaction in an online versus a face-to-face introductory sociology course. Teaching Sociology, 40, 312–331.

Faridhan, Y. E., Loch, B., & Walker, L. (2013). Improving retention in first-year mathematics using learning analytics. In ASCILITE-Australian Society for Computers in Learning in Tertiary Education Annual Conference. AustralAsian American Society for Computers in Learning in Tertiary Education, 278¬–282.

Farkas, G. J., Mazurek, E., & Marone, J. R. (2016). Learning style versus time spent studying and career choice: Which is associated with success in a combined undergraduate anatomy and physiology course?. Anatomical sciences education, 9, 121–131.

Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84.

Hauck, W. E. (2006). Online versus traditional face-to-face learning in a large introductory course. Journal of Family and Consumer Sciences, 98, 27.

Haynes, A. F., Mullins, A. G., & Stein, B. S. (2004). Differential models for math anxiety in male and female college students. Sociological Spectrum, 24, 295–318.

Heublein, U. (2014). Student drop‐out from german higher education institutions. European Journal of Education, 49, 497-513.

Khoule, A., Pacht, M., Schwartz, J. W., & van Slyck, P. (2015). Enhancing faculty pedagogy and student outcomes in developmental math and English through an online community of practice. Research & Teaching in Developmental Education, 32, 39.

Kuh, G. D., Kinzie, J. L., Buckley, J. A., Bridges, B. K., & Hayek, J. C. (2006). What matters to student success: A review of the literature (Vol. 8). Washington, DC: National Postsecondary Education Cooperative.

Lattimore, T. N. (2012). Reading online: Comparing the student completion frequencies in an instructor-led face-to-face versus online developmental reading course (Doctoral dissertation, Capella University).

Lu, F., & Lemonde, M. (2013). A comparison of online versus face-to-face teaching deli in statistics instruction for undergraduate health science students. Advances in Health Sciences Education, 18, 963–973.

Mann, J. T., & Henneberry, S. R. (2014). Online versus face-to-face: Students' preferences for college course attributes. Journal of Agricultural and Applied Economics, 46, 1–19.

Morris, L. V., Wu, S. S., & Finnegan, C. L. (2005). Predicting retention in online general education courses. The American Journal of Distance Education, 19, 23–36.

Nfor, S. K. (2015). Online versus face-to-face nutrition courses at a community college: A comparative study of learning outcomes (Doctoral dissertation, University of the Incarnate Word).

Oliver, K., Kellogg, S., & Patel, R. (2010). An investigation into reported differences between online math instruction and other subject areas in a virtual school. Journal of Computers in Mathematics and Science Teaching, 29, 417–453.

Onah, D. F., Sinclair, J., & Boyatt, R. (2014). Dropout rates of massive open online courses: behavioural patterns. EDULEARN14 Proceedings, 5825-5834.

Richardson, J. T. (2008). The attainment of ethnic minority students in UK higher education. Studies in Higher Education, 33, 33–48.

Richardson, J. T. (2012a). The attainment of white and ethnic minority students in distance education. Assessment & Evaluation in Higher Education, 37, 393–408.

Richardson, J. T. (2012b). Face‐to‐face versus online tuition: Preference, performance and pass rates in white and ethnic minority students. British Journal of Educational Technology, 43, 17–27.

Ro, H. K., & Loya, K. I. (2015). The effect of gender and race intersectionality on student learning outcomes in engineering. The Review of Higher Education, 38, 359–396.

Siemens, G. (2012). Learning analytics: envisioning a research discipline and a domain of practice. In Proceedings of the 2nd international conference on learning analytics and knowledge, 4–8.

Smith, G. G., Sorensen, C., Gump, A., Heindel, A. J., Caris, M., & Martinez, C. D. (2011). Overcoming student resistance to group work: Online versus face-to-face. The Internet and Higher Education, 14, 121–128.

Snyder, V., Hackett, R. K., Stewart, M., & Smith, D. (2003). Predicting academic performance and retention of private university freshmen in need of developmental education. Research and Teaching in Developmental Education, 17–28.

Stinebrickner, R., & Stinebrickner, T. (2014). Academic performance and college dropout: Using longitudinal expectations data to estimate a learning model. Journal of Labor Economics, 32, 601-644.

Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning Analytics in a data-rich context. Computers in Human Behavior, 47, 157–167.

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



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