Principal Component Analysis of Students’ Academic Performance in Mathematics and Statistics

Author(s)

E. A Aboagye , C. A Mensah ,

Download Full PDF Pages: 01-10 | Views: 1074 | Downloads: 161 | DOI: 10.5281/zenodo.3441775

Volume 5 - October 2016 (10)

Abstract

This study seeks to identify a basis of assessments of students’ performance in the Department of Mathematics and Statistics of University of Cape Coast. Data on level 300 students in the Department of Mathematics and Statistics of the 2013/2014 academic year was obtained. These covered ten courses, four of which are mathematics courses and the remaining six (6), statistics. The ten subjects served as variables to be studied each with several observations that are the grades of students in the various courses.  Principal Component Analysis was used to analyse the data. This technique is used since the Principal components generated could serve as indices of measuring the students’ performance. From the analysis, three principal components were retained as rules or indices for classification of students’ performance. The first principal component was used to classify overall performance of students as good, average, below average or excellent. The second principal component was used to classify students on semester basis. That is whether or not the students perform well in either of the semester or both. It was observed that the third principal component can be used to classify the performance of students on subjects’ basis. This can either be mathematics or statistics inclined. Thus using the three principal components it was further observed that majority of students exhibited uniform but just average performance in both subjects and in both semesters. A few showed specific strengths in either of the two subjects. This study confirms the notion that only a few students could major in only one subject in the department with greater success

Keywords

Principal Component Analysis,Academic Performance, Mathematics and Statistics

References

          i.        Anderson, G., and D. Benjamin. (1994). The determinants of success in university introductory economics courses. Journal of Economic Education 25(2): 99-119.

ii.      Beron, K. (1990). Joint determination of current classroom performance and additional economics classes: A Binary/continuous model. Journal of Economic Education 21(3): 255-264.

iii.    Cohn, E., S. Cohn, and J. Bradley. 1995. Note taking, working memory, and learning in principles of economics. Journal of Economic Education 26(4): 291-308.

iv.     Devadoss, S., and J. Foltz. (1996). Evaluation of factors influencing students attendance and performance. American Journal of Agricultural Economics August: 499-507.

v.       Hair,J.F et al.(2006). Multivariate Data Analysis. 6th ed. Upper Saddle River, New Jersey, USA Prentices –Hall

vi.     Kennedy, P., and Tay, R. (1994). Students' performance in economics: Does the norm hold across cultural and institutional settings? Journal of Economic Education 25(4): 291-301.

vii.   Pearson, J. “ Concert Inform” www.Pearsondigital.com/pres/pres/2003/pr-1106

viii. Romer, D. (1993). Do Students go to class? Should they? Journal of Economic Perspectives 7 (3): 167-74.

ix.     Sharma S(1996). Applied Multivariate Techniques; John Wiley & Sons, Inc. pp 58-70, 84-87

x.       Siegfried, J., and R. Fels. (1979). Research on teaching college economics: A Survey. Journal of Economic Literature: 17(3): 923-69.

xi.     Sullivan, W.G, Daghestani, S.F.(1996). Multivariate Analysis of Large Engineering Economy Classes, proceedings of the ASEE Annual Conference-succeed.ufl.edu.

xii.   Tabachnick, B.G and Fidell, L.S. (1989). Using Multivariate Statistics, 3rd ed. New York: Harper and Row.

xiii. Williams, M., L. Waldauer, C. Duggal, and G. Vijaya. 1992. Gender differences in economic knowledge: An extension of the analysis. Journal of Economic Education 23(3): 219- 231

xiv. Zimmer, J., and D. Fuller. 1996. Factors affecting undergraduate performance in statistics: A Review of Literature. Paper presented at the Annual Meeting of the Mid- South Educational Research Association (Tuscaloosa, AL, November 1996). The ERIC Database 1992-2003.ED406424

xv.   Zwick, W.R, and Veliicer, W.F: Comparison of five rules for determining the number of components to retain, Psychological Bulletin, 99(3), pp 432-442

Cite this Article: