Potential for Blocking Advancement: Teaching Surveys for Student Evaluation of Lecturers

Year 2019, , 401 - 406, 15.08.2019

Eyal Eckhaus Nitza Davidovitch

https://doi.org/10.12973/ijem.5.3.401

Abstract

In the current study we examined the relationships between student evaluations of lecturers (teaching surveys) and faculty members' perceptions of these surveys as capable of blocking and limiting their professional advancement. Faculty members are judged and evaluated by academic authorities for their academic performance in research and teaching. 178 questionnaires were collected from the faculty of several academic institutions. We employ a mix method analysis, and form a model that reflects the factors perceived by faculty members as having the potential to block their professional advancement in academia. The research findings show that lecturers are of the opinion that teaching load has a detrimental effect on students' evaluations in the surveys. Lecturers at the beginning of their academic life, those in lower ranks: senior teacher and senior lecturer, address the negative aspects of the surveys more than others. The research findings indicate that although more hours are taught in colleges than at universities, it is harder to receive positive survey ratings at colleges. Moreover, since in Israeli academia research is still the main criterion for promotion – faculty members born in Israel were found to teaching less than those born elsewhere. Hence, faculty members think that student surveys are destructive and entail risks for their professional advancement. Assuming that students' voice and opinions on teaching are important – how can a balance be achieved between the research achievements of faculty members and student satisfaction?

Keywords

Blocking advancement, teaching surveys, student evaluation

References

• Baldwin, T., & Blattner, N. (2003). Guarding against potential bias in student evaluations. College Teaching, 51(1), 27-33.
• Barnes, B. (1985). About science. Oxford, UK: Basil Blackwell.
• Chen, Y., & Hoshower, L. B. (1998). Assessing student motivations to participate in teaching evaluations: an application of expectancy theory, Issues in Accounting Education, 13(3), 531–549.
• Centra, J. (2003). Will teachers receive higher student evaluations by giving higher grades and less course work? Research in Higher Education, 44(5), 495-518.
• Chandler, T. (1978). The questionable status of student evaluations of teaching. Teaching of Psychology, 5(3), 150-152.
• Davidovitch, N., & Eckhaus, E. (2018a). Effect of faculty on research cooperation and publication: Employing natural language processing. Economics and Sociology, 11(4), 173-180. http://dx.doi.org/10.14254/2071-789X.2018/11-4/11
• Davidovitch, N., & Eckhaus, E. (2018b). The influence of birth country on selection of conference destination-employing natural language processing. Higher Education Studies, 8(2), 92-96.
• De Vries, E., Schoonvelde, M., & Schumacher, G. (2018). No longer lost in translation: Evidence that Google Translate works for comparative bag-of-words text applications. Political Analysis, 26(4), 417-430.
• Eckhaus, E., & Ben-Hador, B. (2018). To gossip or not to gossip: Reactions to a perceived request to gossip – a qualitative study. Trames: A Journal of the Humanities and Social Sciences, 22(3), 273-288. https://doi.org/10.3176/tr.2018.3.04
• Eckhaus, E., & Ben-Hador, B. (2019). Gossip and gender differences: a content analysis approach. Journal of Gender Studies, 28(1), 97-108. https://doi.org/10.1080/09589236.2017.1411789
• Eckhaus, E., & Davidovitch, N. (2018a). Impact of gender and conference size on conference preferences – employing natural language processing. International Journal of Educational Methodology, 4(1), 45-52. https://doi.org/10.12973/ijem.4.1.45
• Eckhaus, E., & Davidovitch, N. (2018b). Improving academic conferences – criticism and suggestions utilizing natural language processing. European Journal of Educational Research, 7(3), 445-450.
• Eckhaus, E., & Davidovitch, N. (2019). How do academic faculty members perceive the effect of teaching surveys completed by students on appointment and promotion processes at academic institutions? A case study. International Journal of Higher Education, 8(1), 171-180.
• Eckhaus, E., & Sheaffer, Z. (2018a). Factors affecting willingness to contribute goods and services on social media. The Social Science Journal. https://doi.org/10.1016/j.soscij.2018.08.001
• Eckhaus, E., & Sheaffer, Z. (2018b). Happiness enrichment and sustainable happiness. Applied Research in Quality of Life. https://doi.org/10.1007/s11482-018-9641-0
• Eckhaus, E., & Sheaffer, Z. (2018c). Managerial hubris detection: the case of Enron. Risk Management, 20(4), 304-325. https://doi.org/10.1057/s41283-018-0037-0
• Eckhaus, E., Taussig, R., & Ben-Hador, B. (2018). The effect of top management team's tacit persuasion on the stock market. e - Journal of Social & Behavioural Research in Business, 9(2), 9-22.
• Ehie, I. & Karathanos, D. (1994). Business faculty performance evaluation based on the new AACSB accreditation standards. Journal of Education for Business, 69(5), 257- 262. doi: 10.1080/08832323.1994.10117695
• Feldman, K. A. (1983). The seniority and instructional experience of college teachers as related to the evaluations they receive from their students. Research in Higher Education, 18, 3–124.
• Feldman, K.A. (1978) Course characteristic s and college students' ratings of their teachers: what we know and what we don’t. Research in Higher Education, 9(2), 199–242.
• 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.
• Harrison, P., Douglas, D., & Burdsal, C. (2004). The relative merits of different types of overall evaluations of teaching effectiveness. Research in Higher Education, 45(3), 311-323.
• Hativa, N. (2008). Myths and facts about evaluation surveys by students. Al Hagova, 7, 13-14. [In Hebrew]
• Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
• Kulik, J. A. (2001). Student ratings: validity, utility, and controversy. New Directions for Institutional Research, 2001(109), 9–25.
• Marsh, H. W. (1987). Students’ evaluations of university teaching: Research findings, methodological issues, and directions for future research. International Journal of Educational Research, 11(3), 253–288.
• Marsh, H. W. & Roche, L. A. (1994). The use of students' evaluations of university teaching to improve teaching effectiveness. Final project report for the Evaluations and Investigations Program of the Department of Employment and Education. Canberra, Australia: Australian Government Printing Service.
• Mehta, A., Parekh, Y., & Karamchandani, S. (2018). Performance evaluation of machine learning and deep learning techniques for sentiment analysis. In V. Bhateja, B. L. Nguyen, N. G. Nguyen, S. C. Satapathy, & D.-N. Le (Eds.), Information systems design and intelligent applications (pp. 463-471). Singapore: Springer.
• Mueller, R. O., & Hancock, G. R. (2018). Structural equation modeling The reviewer’s guide to quantitative methods in the social sciences (pp. 457-468). Abingdon, UK: Routledge.
• National survey seeks to improve retention, graduation rates. (2002). Black Issues in Higher Education, 19(14), 18.
• Nascimento, G. G., Baelum, V., Dahlen, G., & Lopez, R. (2018). Methodological issues in assessing the association between periodontitis and caries among adolescents. Community Dentistry and Oral Epidemiology, 46(3), 303-309. https://doi.org/10.1111/cdoe.12367
• Renaud, R. D., & Murray, H. G. (1996). Aging, personality, and teaching effectiveness in academic psychologists. Research in Higher Education, 37(3), 323–340.
• Ryans, D. G. (1960). Prediction of teacher effectiveness. In C. W. Harris (Ed.), Encyclopedia of educational research (pp. 1486–1491). New York, NY: Macmillan.
• Smith, K., & Pollak , M. W. (2008). What can they say about my teaching? Teacher educators' attitudes to standardised student evaluation of teaching. European Journal of Teacher Education, 31(2), 203-214.
• Wachtel, H. K. (1998). Student evaluation of college teaching effectiveness: a brief review, Assessment and Evaluation in Higher Education, 23(2), 191–211.
• Worthington, A. (2002). The impact of student perceptions and characteristics on teaching evaluations: a case study in finance education. Assessment & Evaluation in Higher Education, 27(1), 49-64.
• Wu, K., Chen, S., & Yuan, Y. (2018). Research on the customer loyalty of bicycle-sharing company based on PLS-SEM model. In Proceedings of the 2018 2nd International Conference on Management Engineering, Software Engineering and Service Sciences (pp. 68-72). Newyork, NY: The Association for Computing Machinery.