Useful assessment of general education courses is one impotant tool of a successful approach to advising. This article describes two methods for evaluating the effect general education math and English courses are having on students. One procedure involves examining the grade distribution for students enrolled in math and English general education courses who have successfully passed prior general education courses. The second method describes how a logistic regression procedure is used to determine what factors influence the successful completion of math and English general education courses. Both of these methods produce results of interest to advisors.
Get full access to this article
View all access options for this article.
References
1.
AlexitchL. (1997, July-August). Students' educational orientation and preferences for advising from university professors. Journal of College Student Development, 38(4), 333–343.
2.
AthanassopoulosA. D., & ShaleE. (1997). Assessing the comparative efficiency of higher education institutions in the UK by means of data envelopment analysis. Education Economics, 5(2), 117.
3.
BonarJ. R. (1976, September). Review of operating program model using para-professionals. Journal of College Student Personnel, 5, 400–404.
4.
BressoudD. M. (2001). What's been happening to undergraduate mathematics. Journal of Chemical Education, 78(5), 578–581.
5.
CrockettD. S. (Ed.). (1988). Advising skills, techniques and resources. Iowa City: American College Testing Program.
6.
CrookstonB. B. (1972). A developmental view of academic advising as teaching. Journal of College Student Personnel, 13, 12–17.
7.
ForrestA. (1982). Increasing student competence and persistence—The best case for general education. Iowa City: American College Testing Program.
8.
GrobL., & KuehlJ. R. (1997, Winter). Coherence & assessment in a general education program. Liberal Education, 83(1), 34–39.
9.
HosmerD. W.Jr., & LemeshowS. (1989). Applied logistic regression. New York: John Wiley & Sons, Inc.
10.
JohnsonD. E. (1998). Applied multivariate methods for data analysis. Pacific Grove, CA: Duxbury Press.
11.
LopezC. (1998, Summer). How campuses are assessing general education. Liberal Education, 84(3), 36.
12.
McCullaghP., & NelderJ. A. (1989). Generalized linear models (2nd ed.). New York: Chapman & Hall.
13.
PryorB., & DallamJ. W. (1999, Summer). Student assessment of general education at the University of Iowa. College and University Summer, 75(1), 15.
14.
RechJ.HartzellJ., & StephensL. (1993). Comparisons of mathematical competencies and attitudes of elementary education majors with established norms of a general college population. School Science and Mathematics, 93(3), 141–144.
15.
SAS Institute Inc. (1995). SAS/IML software: Changes and enhancements through release 6.11. Cary, NC: SAS Institute Inc.
16.
UttsJ. M. (2003). What educated citizens should know about statistics and probability. The American Statistician, 57(2), 74–79.
17.
Van VughtF. A., & WesterheijdenD. F. (1994). Towards a general model of quality assessment in higher education. Higher Education, 28, 355–371.
18.
WalvoordB. E., & AndersonV. J. (1998). Effective Grading. San Francisco: Jossey-Bass.
