How do Student Evaluations of Courses and of Instructors Relate?

Tamara Sliusarenko, Line H. Clemmensen, Bjarne Kjær Ersbøll

2014

Abstract

Course evaluations are widely used by educational institutions to assess the quality of teaching. At the course evaluations, students are usually asked to rate different aspects of the course and of the teaching. We propose to apply canonical correlation analysis (CCA) in order to investigate the degree of association between how students evaluate the course and how students evaluate the teacher. Additionally it is possible to reveal the structure of this association. Student evaluations data is characterized by high correlations between the variables within each set of variables, therefore two modifications of the CCA method; regularized CCA and sparse CCA, together with classical CCA were applied to find the most interpretable model. Both methods give results with increased interpretability over traditional CCA on the present student evaluation data. The method shows robustness when evaluations over several years are examined.

References

  1. Abrami, P. C., d'Apollonia, S., and Rosenfield, S. (1997). The dimensionality of student ratings of instruction: what we know and what we do not. Perry, R.P., Smart J.C., editors: effective teaching in higher education: research and practice. New York: Agathon Press.
  2. Cohen, P. A. (1981). Student rating of instruction and student achievement. Review of Educational Research, 51(3):281-309.
  3. Déjean, S. and González, I. (2009). Package ”CCA: Canonical correlation analysis”. CRAN.
  4. Feldman, K. A. (1989). The association between student ratings of specific instructional dimensions and student achievement: Refining and extending the synthesis of data from multisection validity studies. Research in Higher education, 30(6).
  5. González, I., Déjean, S., Martin, P. G. P., and Baccini, A. (2009). Highlighting relationships between heterogeneous biological data through graphical displays based on regularized canonical correlation analysis. Journal of Biological Systems, 17(2):173-199.
  6. Hardoon, D. R. and Shawe-Taylor, J. (2011). Sparse canonical correlation analysis. Machine Learning, 83(3):331-353.
  7. Hoerl, A. and R.W., K. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12:55-67.
  8. Hotelling, H. (1935). The most predictable criterion. Journal of Educational Psychology, 26:139-142.
  9. Le Cao, K.-A., Martin, P. G. P., Christele, R., and Besse, P. (2009). Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinformatics, 10:Article 34.
  10. Leurgans, S., Moyeed, R., and Silverman, B. (1993). Canonical correlation analysis when the data are curves. Journal of the Royal Statistical Society B, 55(3):725-740.
  11. Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140:155.
  12. 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 - 388.
  13. Marsh, H. W. (2007). Students evaluations of university teaching: Dimensionality, reliability, validity, potential biases and usefulness. R.P. Perry and J.C. Smart (eds.), The Scholarship of Teaching and Learning in Higher Education: An Evidence-Based Perspective, pages 319 -383.
  14. Marsh, H. W. and Roche, L. A. (1997). Making students' evaluations of teaching effectiveness effective: The critical issues of validity, bias, and utility. American Psychologist, 52(11):1187.
  15. Parkhomenko, E., Tritchler, D., and Beyene, J. (2007). Genome-wide sparse canonical correlation of gene expression with genotypes. BMC Proceedings, 1.
  16. Vinod, H. D. (1976). Canonical ridge and econometrics of joint production. Journal of Econometrics, 4(2):147- 166.
  17. Waaijenborg, S. and Zwinderman, A. (2007). Penalized canonical correlation analysis to quantify the association between gene expression and dna markers. BMC Proceedings, 1(Suppl 1):S122.
  18. Witten, D. M., Tibshirani, R., and Gross, S. (2013). Package ”PMA”: Penalized multivariate analysis.
  19. Witten, D. M., Tibshirani, R., and Hastie, T. (2009). A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostat, 10(3):515-534.
  20. Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B, 67:301320.
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Paper Citation


in Harvard Style

Sliusarenko T., H. Clemmensen L. and Kjær Ersbøll B. (2014). How do Student Evaluations of Courses and of Instructors Relate? . In Proceedings of the 6th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-021-5, pages 280-287. DOI: 10.5220/0004945902800287


in Bibtex Style

@conference{csedu14,
author={Tamara Sliusarenko and Line H. Clemmensen and Bjarne Kjær Ersbøll},
title={How do Student Evaluations of Courses and of Instructors Relate?},
booktitle={Proceedings of the 6th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2014},
pages={280-287},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004945902800287},
isbn={978-989-758-021-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - How do Student Evaluations of Courses and of Instructors Relate?
SN - 978-989-758-021-5
AU - Sliusarenko T.
AU - H. Clemmensen L.
AU - Kjær Ersbøll B.
PY - 2014
SP - 280
EP - 287
DO - 10.5220/0004945902800287