Advertisement

Research in Higher Education

, Volume 49, Issue 4, pp 293–316 | Cite as

Antecedents and Correlates of Course Cancellation in a University “Drop and Add” Period

  • Elisha BabadEmail author
  • Tamar Icekson
  • Yaacov Yelinek
Article

Abstract

Most institutions of higher education allow students to drop or add courses in the first 2–3 weeks of each term (D&A). Arguing that course cancellation is not merely an administrative issue involving enrollment trends but represents complex decision making processes taken by students, this study investigated antecedents and correlates of course cancellation during a D&A period in 109 elective courses. Student ratings of the teachers (SRT) and characteristics of the syllabi distributed in the first class session were investigated as predictors of course cancellation. Rates of cancellation were significantly predicted from SRT and from syllabus workload difficulty—lower quality teachers (SRT-based) and more difficult courses (syllabus-based) being cancelled more frequently. Analysis of a sub-sample of truly elective, high-priority courses revealed that these correlations were intensified in teacher-centered lecture courses, but nullified in student-centered seminar courses, in which students write independent research papers. The importance of students’ course selection and course cancellation as decision making processes, the methodology based on institutional data rather than students’ self-reports, and the unique effects of course difficulty on students’ decisions were discussed.

Keywords

Course selection Course cancellation Course difficulty Students' ratings of teachers (SRT) Syllabus analysis Institutional data 

References

  1. Ambady, N., & Rosenthal, R. (1993). Half a minute: Predicting teacher evaluations from thin slices of behavior and physical attractiveness. Journal of Personality and Social Psychology, 64, 431–441.CrossRefGoogle Scholar
  2. Babad, E. (2001). Students’ course selection: Differential considerations for first and last course. Research in Higher Education, 42, 469–492.CrossRefGoogle Scholar
  3. Babad, E. (2007). Teachers’ nonverbal behavior and its effects on students. In R. Perry & J. Smart (Eds.) The scholarship of teaching and learning: An evidence-based perspective (pp. 201–261). Holland: Springer Publications. Also in: J. Smart (Ed.). Higher education: Handbook of theory and research, Vol. 22, (pp. 219–279). Holland: Springer Publications.Google Scholar
  4. Babad, E., Avni-Babad, D., & Rosenthal, R. (2003). Teachers’ brief nonverbal behaviors in defined instructional situations can predict students’ evaluations. Journal of Educational Psychology, 95, 553–562.CrossRefGoogle Scholar
  5. Babad, E., Avni-Babad, D., & Rosenthal, R. (2004) Prediction of student’s evaluations from professors’ nonverbal behavior in defined instructional situations. Social Psychology of Education, 7, 3–33.CrossRefGoogle Scholar
  6. Babad, E., Darley, J., & Kaplowitz, H. (1999a). Developmental aspects in students’ course selection. Journal of Educational Psychology, 91, 157–168.CrossRefGoogle Scholar
  7. Babad, E., Kaplowitz, H., & Darley, J. (1999b). A “classic” revisited: Students’ immediate and delayed evaluations of a warm/cold instructor. Social Psychology of Education, 3, 81–102.CrossRefGoogle Scholar
  8. Babad, E., & Tayeb, A. (2003). Experimental analysis of student’s course selection. British Journal of Educational Psychology, 73, 373–393.CrossRefGoogle Scholar
  9. Borgida, E. (1978). Scientific education—evidence is not necessarily informative: A reply to Wells and Harvey. Journal of Personality and Social Psychology, 36, 477–482.CrossRefGoogle Scholar
  10. Borgida, E., & Nisbett, R. (1977). The differential impact of abstract vs. concrete information on decisions. Journal of Applied Social Psychology, 7, 258–271.CrossRefGoogle Scholar
  11. Coleman, J., & McKeachie, W. (1981). Effects of instructor/course evaluations on student course selection. Journal of Educational Psychology, 73, 224–226.CrossRefGoogle Scholar
  12. Dellar, G. (1994). The school subject selection process: A case study. Journal of Career Development, 20, 185–204.Google Scholar
  13. Gati, I. (1996). Computer-assisted career counseling: Challenges and prospects. In M. Savickas & B. Walsh (Eds.), Handbook of career counseling theory and practice (pp. 169–190). Palo Alto: Davies-Black.Google Scholar
  14. Gati, I., & Asher, I. (2001). The PIC model for career decision making: Prescreening, in-depth exploration, and choice. In F. Leong & A. Barak (Eds.), Contemporary models in vocational psychology (pp. 7–54). Mahwah: Erlbaum.Google Scholar
  15. Greenwald, A., & Gillmore, S. (1997a). No pain, no gain? The importance of measuring course workload in student ratings of instruction. Journal of Educational Psychology, 89, 743–751.CrossRefGoogle Scholar
  16. Greenwald, A., & Gillmore, S. (1997b). Grading leniency is a removable containment of student ratings. American Psychologist, 52, 1209–1217.CrossRefGoogle Scholar
  17. Hendel, P. (1982, April). Evaluating the effects of a course evaluation system designed to assist students in electing courses. Paper presented at the Annual Meeting of the American Educational Research Association, New York.Google Scholar
  18. Kerin, R., Harvey, M., & Crandall, N. (1975). Student course selection in a non-requirement program: An exploratory study. Journal of Educational Research, 175–177.Google Scholar
  19. Leventhal, L., Abrami, P., & Perry, R. (1976). Do teacher rating forms reveal as much about students as about teachers? Journal of Educational Psychology, 441–445.Google Scholar
  20. Leventhal, L., Abrami, P., Perry, R., & Breen, L. (1975). Section selection in multi-section courses: Implications for the validation and use of teacher rating forms. Educational and Psychological Measurement, 35, 885–895.CrossRefGoogle Scholar
  21. Lorenz, G. (1982). Faculty and student views of information systems to improve course selection. Paper presented at the Annual Meeting of the American Educational Research Association, New York.Google Scholar
  22. Marsh, H. (1981). The use of path analysis to estimate teacher and course effects in student ratings of instructional effectiveness. Applied Psychological Measurement, 6, 47–60.CrossRefGoogle Scholar
  23. Marsh, H. (1984). Student evaluation of university teaching: Dimensionality, reliability, validity, potential biases, and utility. Journal of Educational Psychology, 76, 707–754.CrossRefGoogle Scholar
  24. Marsh, H. (1987). Students’ evaluation of university teaching: Research findings, methodological issues and directions for future research. Elmford, N.Y.: Pergamon.Google Scholar
  25. Marsh, H. (2007). Students’ evaluations of university teaching: Dimensionality, reliability, validity, potential biases and usefulness. In R. Perry & J. Smart (Eds.), The scholarship of teaching and learning: An evidence-based perspective (pp. 319–383). Holland: Springer Publications.CrossRefGoogle Scholar
  26. Marsh, H., & Hocevar, D. (1984). The factorial invariance of students’ evaluations of college teaching. Americam Educational Research Journal, 21, 341–366.Google Scholar
  27. Marsh, H., & Roche, L. (2000). Effects of grading leniency and low workload on students’ evaluations of teaching: Popular myth, bias, validity, or innocent bystanders? Journal of Educational Psychology, 92, 202–228.CrossRefGoogle Scholar
  28. McCroskey, P., & Richmond, J. (1992). Increasing teacher influence through immediacy. In V. Richmond & J. McCroskey (Eds.), Power in the classroom: Communication, control and concern (pp. 101–119). Hillsdale: Erlbaum.Google Scholar
  29. Pascarella, E., & Terenzini, P. (2005). How college affects students (Vol. 2): A third decade of research. San Francisco: Jossey-Bass.Google Scholar
  30. Porter, S. (2006). Institutional structure and student engagement. Research in Higher Education, 47, 521–538.CrossRefGoogle Scholar
  31. Sauermann, H. (2005). Vocational choice: A decision making perspective. Journal of Vocational Behavior, 66, 273–303.CrossRefGoogle Scholar
  32. Van Esboreck, R., Tibos, K., & Zaman, M. (2005). A dynamic model of career choice development. International Journal for Educational and Vocational Guidance, 5, 5–18.CrossRefGoogle Scholar
  33. Warton, P. (1997). Motivational goals, information sources and subject choice in adolescence. Paper presented in the Biennial Meeting of the Society for Research in Child Development, Washington DC.Google Scholar
  34. Warton, P., & Cooney, G. (1997). Information and choice of subjects in the senior school. British Journal of Guidance and Counseling, 25, 389–397.Google Scholar
  35. Webb, E., Campbell, D., Schwartz, R., & Sechrest, L. (2000). Unobtrusive measures (Revised Edition). Thousand Oaks: Sage Publications.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.The School of EducationHebrew University of JerusalemJerusalemIsrael

Personalised recommendations