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Systematic Influences on Teaching Evaluations : The Case for Caution

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Listed:
  • M. Davies
  • J. Hirschberg
  • J. Lye
  • C. Johnston
  • I. McDonald

Abstract

The evaluation of teaching and learning has become an important activity in tertiary education institutions. Student surveys provide information about student perceptions and judgments of a particular subject. However, as is widely recognised, the appropriate interpretation of this data is problematic. There is a large literature, mainly for the US, on the use and usefulness of student subject evaluations. This literature has highlighted a number of ‘mitigating factors’ such as subject difficulty, discipline area, etc., that should be taken into account in interpreting the results of these questionnaires. In this paper we examine 8 years of QOT responses from an Economics Department in an Australian University which accounted for more than 79,000 student subject enrolments in 565 subjects. The purpose of this analysis is to establish how the information contained in these data can be used to interpret the responses. In particular, we determine to what extent other factors besides the instructor in charge of the subject have an impact on the raw average student evaluation scores. We find that the following characteristics of the students in these classes had an influence on the average QOT score: year level, enrolment size, the quantitative nature of the subject, the country of origin of the students, the proportion that are female, Honours status of the student, the differential in their mark from previous marks, quality of workbook, quality of textbook and the relative QOT score versus other subjects taught at the same time. However, a number of other factors proposed in the literature to be important influences were found not to be. These include the student’s fee paying status, whether they attended a public, private or catholic secondary school, which other faculty within the University they came from, and if the subject was taught in multiple sessions.

Suggested Citation

  • M. Davies & J. Hirschberg & J. Lye & C. Johnston & I. McDonald, 2005. "Systematic Influences on Teaching Evaluations : The Case for Caution," Department of Economics - Working Papers Series 953, The University of Melbourne.
  • Handle: RePEc:mlb:wpaper:953
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    File URL: http://www.economics.unimelb.edu.au/downloads/wpapers-05/953.pdf
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    References listed on IDEAS

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    1. Omer Gokcekus, 2000. "How do university students value economics courses? A hedonic approach," Applied Economics Letters, Taylor & Francis Journals, vol. 7(8), pages 493-496.
    2. L. F. Jameson Boex, 2000. "Attributes of Effective Economics Instructors: An Analysis of Student Evaluations," The Journal of Economic Education, Taylor & Francis Journals, vol. 31(3), pages 211-227, September.
    3. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    4. William Bosshardt & Michael Watts, 2001. "Comparing Student and Instructor Evaluations of Teaching," The Journal of Economic Education, Taylor & Francis Journals, vol. 32(1), pages 3-17, January.
    5. Mason, Paul M. & Steagall, Jeffrey W. & Fabritius, Michael M., 1995. "Student evaluations of faculty: A new procedure for using aggregate measures of performance," Economics of Education Review, Elsevier, vol. 14(4), pages 403-416, December.
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    Cited by:

    1. Silvia Ferrini & Marco P. Tucci, 2011. "Evaluating Research Activity:Impact Factor vs. Research Factor," Department of Economics University of Siena 614, Department of Economics, University of Siena.
    2. Bredtmann, Julia & Crede, Carsten J. & Otten, Sebastian, 2013. "Methods for evaluating educational programs: Does Writing Center Participation affect student achievement?," Evaluation and Program Planning, Elsevier, vol. 36(1), pages 115-123.
    3. De Witte, K. & Rogge, N., 2009. "Accounting for exogenous influences in a benevolent performance evaluation of teachers," Working Papers 15, Top Institute for Evidence Based Education Research.
    4. Cho, Donghun & Baek, Wonyoung & Cho, Joonmo, 2015. "Why do good performing students highly rate their instructors? Evidence from a natural experiment," Economics of Education Review, Elsevier, vol. 49(C), pages 172-179.
    5. Donghun Cho & Joonmo Cho, 2017. "Does More Accurate Knowledge of Course Grade Impact Teaching Evaluation?," Education Finance and Policy, MIT Press, vol. 12(2), pages 224-240, Spring.
    6. Benjamin Artz & David M. Welsch, 2013. "The Effect of Student Evaluations on Academic Success," Education Finance and Policy, MIT Press, vol. 8(1), pages 100-119, January.
    7. Joe Hirschberg & Jenny Lye, 2014. "The influence of student experiences on post-graduation surveys," Department of Economics - Working Papers Series 1187, The University of Melbourne.
    8. Wagner, N. & Rieger, M. & Voorvelt, K.J., 2016. "Gender, ethnicity and teaching evaluations : Evidence from mixed teaching teams," ISS Working Papers - General Series 617, International Institute of Social Studies of Erasmus University Rotterdam (ISS), The Hague.
    9. Rieger, Matthias & Voorvelt, Katherine, 2016. "Gender, ethnicity and teaching evaluations: Evidence from mixed teaching teamsAuthor-Name: Wagner, Natascha," Economics of Education Review, Elsevier, vol. 54(C), pages 79-94.
    10. Joe Hirschberg & Jenny Lye & Martin Davies & Carol Johnston, 2011. "Measuring Student Experience: Relationships between Teaching Quality Instruments (TQI) and Course Experience Questionnaire (CEQ)," Department of Economics - Working Papers Series 1134, The University of Melbourne.
    11. Yoav Gal & Adiv Gal, 2014. "Knowledge Bias: Is There a Link Between Students’ Feedback and the Grades They Expect to Get from the Lecturers They Have Evaluated? A Case Study of Israeli Colleges," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 5(3), pages 597-615, September.
    12. repec:zbw:rwirep:0275 is not listed on IDEAS
    13. De Witte, Kristof & Rogge, Nicky, 2011. "Accounting for exogenous influences in performance evaluations of teachers," Economics of Education Review, Elsevier, vol. 30(4), pages 641-653, August.
    14. Yoav Gal & Adiv Gal, 2016. "Knowledge Bias by Utilizing the Wording on Feedback Questionnaires: A Case Study of an Israeli College," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 7(3), pages 753-770, September.
    15. Marco p. Tucci & Sandra Fontani & Silvia Ferrini, 2008. "L’ “R-Factor”: un nuovo modo di valutare la ricerca scientifica," Department of Economics University of Siena 527, Department of Economics, University of Siena.

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