Abstract
Students’ evaluations of teaching are increasingly used by universities to evaluate teaching performance. However, these evaluations are controversial mainly due to fact that students value various aspects of excellent teaching differently. Therefore, in this paper we propose a new approach to students’ evaluations of university teaching based on data from conjoint analysis. Conjoint analysis is a multivariate technique used to analyze the structure of individuals’ preference. In particular, our approach accounts for different importance students attach to various aspects of teaching. Moreover, it accounts explicitly for heterogeneity arising from students’ preferences, and incorporates it to form comprehensive teaching evaluation score. We have conducted survey and confirmed applicability and efficiency of the proposed approach.
This is a preview of subscription content, access via your institution.



References
Abrami, P. C., & d’Apollonia, S. (1991). Multidimensional students’ evaluations of teaching effectiveness; The generalizability of “N = 1” research: comment on Marsh (1991). Journal of Educational Psychology, 83, 411–415.
Arias, J. T. G. (1996). Conjoint-based preferential segmentation in the design of a new financial service. International Journal of Bank Marketing, 14(3), 30–32.
Berk, R. A. (2005). Survey of 12 strategies to measure teaching effectiveness. International Journal of Teaching and Learning in Higher Education, 17(1), 48–62.
Biesma, R. G., Pavlova, M., van Merode, G. G., & Groot, W. (2007). Using conjoint analysis to estimate employers’ preferences for key competencies of master level Dutch graduates entering the public health field. Economics of Education Review, 26(3), 375–386.
Cashin, W. E., & Downey, R. G. (1992). Using global student rating items for summative evaluation. Journal of Educational Psychology, 84, 563–572.
Choi, Y. R., & Shepherd, D. A. (2004). Entrepreneurs’ decision to exploit opportunities. Journal of Management, 30(3), 377–395.
Davies, M., Hirschberg, J. G., Lye, J. N., Johnston, C., & McDonald, I. M. (2007). Systematic influences on teaching evaluations: The case for caution. Australian Economic Papers, 46(1), 18–38.
Ellis, L., Burke, D. M., Lomire, P., & McCormack, D. R. (2003). Student grades and average ratings of instructional quality: The need for adjustment. The Journal of Educational Research, 97(1), 35–40.
Feldman, K. A. (1997). Identifying exemplary teachers and teaching: Evidence from student ratings. In R. P. Perry & J. C. Smart (Eds.), Effective teaching in higher education: Research and practice (pp. 368–395). New York: Agathon Press.
Gray, M., & Bergmann, B. R. (2003). Student teaching evaluations: Inaccurate, demeaning, misused. Academe, 89(5), 44–46.
Green, P. E., & Rao, V. R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8, 355–363.
Gursoy, D., & Umbreit, W. T. (2005). Exploring student’s evaluations of teaching effectiveness: What factors are important? Journal of Hospitality and Tourism Research, 29(1), 91–109.
Haddad, Y., Haddad, J., Olabi, A., Shuayto, N., Haddad, T., & Toufeili, I. (2007). Mapping determinants of purchase intent of concentrated yogurt (Labneh) by conjoint analysis. Food Quality and Preference, 18, 795–802.
Hair, J. F., Anderson, R. E., Tatham, R. L. & Black W.C. (1998). Multivariate data analysis, 5th Ed. Upper Saddle River, New Jersey. Prentice-Hall International.
Hensher, D. (2001). The valuation of commuter travel time savings for car drivers: Evaluating alternative model specifications. Transportation, 28, 101–118.
Hur, J. S., & Pak, R. J. (2007). Conjoint analysis for the preferred subjects of elementary school computer education. Journal of the Korean Data and Information Science Society, 18(2), 357–364.
Kim, C., Choe, S., Choi, C., & Park, Y. (2008). A systematic approach to new mobile service creation. Expert Systems with Applications, 35, 762–771.
Kim, A., Son, Y. D., & Sohn, S. Y. (2009). Conjoint analysis of enhanced English Medium instruction for college students. Expert Systems with Applications, 36, 10197–10203.
Kulik, J. A. (2001). Student ratings: validity, utility, and controversy. New Directions for Institutional Research, 109, 9–25.
Kuzmanovic, M., & Martic, M. (2012a). An approach to competitive product line design using conjoint data. Expert Systems with Application, 39(8), 7262–7269. doi:10.1016/j.eswa.2012.01.097.
Kuzmanovic, M., & Martic, M. (2012b). Using conjoint analysis to create superior value to customers. Metalurgia International, 17(2), 93–99.
Kuzmanovic, M., Panic, B., & Martic, M. (2011). Identification of key positioning factors in the retail sector: A conjoint analysis approach. African Journal of Business Management, 5(26), 10376–10386. doi:10.5897/AJBM11.535.
Kuzmanovic, M., Vujosevic, M., & Martic, M. (2012). Using conjoint analysis to elicit patients’ preferences for public primary care service in serbia. HealthMED, 6(2), 496–504.
Levy, D. S. (1995). Modern marketing research techniques and the property professional. Property Management, 13, 33–40.
Liaw, S.-H., & Goh, K.-L. (2003). Evidence and control of biases in student evaluations of teaching. The International Journal of Educational Management, 17(1), 37–43.
Lin, Y., McKeachie, W. J., & Tucker, D. G. (1984). The use of student ratings in promotion decisions. Journal of Higher Education, 55, 583–589.
Luce, R. D., & Tukey, J. W. (1964). Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology, 1, 1–27.
Makila, M. (2004). Retaining students in retail banking through price bundling: Evidence from the Swedish Market. European Journal of Operational Research, 155, 299–316.
Marsh, H. W. (1987). Students’ evaluations of university teaching: Research findings, methodological issues, and directions for future research. International Journal of Educational Research, 11, 253–388.
Marsh, H. W. (2007). Students’ evaluations of university teaching: Dimensionality, reliability, validity, potential biases and usefulness. In R. P. Perry & J. C. Smart (Eds.), The scholarship of teaching and learning in higher education: An evidence-based perspective (pp. 319–384). New York: Springer.
Marsh, H. W., Ginns, P., Morin, A. J. S., Nagengast, B., & Martin, A. J. (2011). Use of student ratings to benchmark universities: Multilevel modeling of responses to the australian course experience questionnaire (CEQ). Journal of Educational Psychology, 103(3), 733–748.
Marsh, H. W., & Roche, L. A. (1997). Making students’ evaluations of teaching effectiveness effective. American Psychologist, 52, 1187–1197. doi:10.1037/0003-066X.52.11.1187.
Marsh, H. W., & Roche, L. A. (2000). Effects of grading leniency and low workloads on students’ evaluations of teaching: Popular myth, bias, validity, or innocent bystanders? Journal of Educational Psychology, 92, 202–228. doi:10.1037/0022-0663.92.1.202.
Martínez-Gómez, M., Sierra, J., Jabaloyes, J., & Zarzo, M. (2011). A multivariate method for analyzing and improving the use of student evaluation of teaching questionnaires: A case study. Quality & Quantity, 45, 1415–1427. doi:10.1007/s11135-010-9345-5.
McCallum, L. W. (1984). A meta-analysis of course evaluation data and its use in the tenure decision. Research in Higher Education, 21, 150–158.
McKeachie, W. J. (1997). Student ratings: the validity of use. American Psychologist, 52, 1218–1225. doi:10.1037/0003-066X.52.11.1218.
Onwuegbuzie, A. J., Daniel, L. G., & Collins, K. M. T. (2009). A meta-validation model for assessing the score validity of student teacher evaluations. Quality & Quantity, 43, 197–209.
Orme, B. (2006). sample size issues for conjoint analysis (Chapter 7). Getting started with conjoint analysis: strategies for product design and pricing research. Reprinted from Orme B (2006). Wis: Research Publishers LLC, Madison.
Popović, M., Kuzmanović M., & Martić, M. (2012). Using conjoint analysis to elicit employers’ preferences toward key competencies for a business manager position. Management—Journal for Theory and Practice Management, 17(63), 17–26. doi: 10.7595/management.fon.2012.0011.
Ryan, M., & Farrar, S. (2000). Using conjoint analysis to elicit preferences for health care. British Medical Journal, 320(7248), 1530–1533.
Sohn, S. Y., & Ju, Y. H. (2010). Conjoint analysis for recruiting high quality students for college education. Expert Systems with Applications, 37, 3777–3783.
Soutar, G. N., & Turner, J. P. (2002). Students’ preferences for college: A conjoint analysis. International Journal of Educational Management, 16(1), 40–45.
Spencer, K. J., & Schmelkin, L. P. (2002). Students’ perspectives on teaching and its evaluation. Assessment & Evaluation in Higher Education, 27, 397–408.
Sproule, R. (2000). Student evaluation of teaching: A methodological critique of conventional practices. Education Policy Analysis Archives, 8(50), 125–142.
Witte, K. D., & Rogge, N. (2011). Accounting for exogenous influences in performance evaluations of teachers. Economics of Education Review, 30(4), 641–653. doi:10.1016/j.econedurev.2011.02.002.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kuzmanovic, M., Savic, G., Popovic, M. et al. A new approach to evaluation of university teaching considering heterogeneity of students’ preferences. High Educ 66, 153–171 (2013). https://doi.org/10.1007/s10734-012-9596-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10734-012-9596-2
Keywords
- Students’ evaluation of teaching
- Conjoint analysis
- Students’ preferences
- Criteria importance
- Heterogeneity
- Overall rating