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How Do Students Evaluate Instructors’ Performance? Implication of Teaching Abilities, Physical Attractiveness and Psychological Factors

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Abstract

One instrument regularly seen as a basic resource in assessing pedagogical knowledge and vivid learning in different circumstances is through the method of conducting student assessment appraisal of their instructors. Nevertheless, deciding the nature of instructional abilities requires as rationale and unbiased judgments. The concern is that there are no formal techniques or formulas that would prompt accurate responses from the students. In spite of the contention surrounding students’ rating on instructors, this study aims to investigate how university students in Malaysia would evaluate instructors based on non-instructional factors, such as physical attractiveness and psychological factors, which in turn may affect students’ perceptions towards instructors’ performance. PLS-SEM was appropriated to perform the path modeling analysis. Practical implication is discussed.

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References

  • Altbach, P. G., Reisberg, L., & Rumbley, L. E. (2009). Trends in global higher education: Tracking an academic revolution. Paris: UNESCO Publishing.

    Google Scholar 

  • Bakar, A. R., Mohamed, S., & Zakaria, N. S. (2013). They are train to teach, but how confience thay are? A study of student teachers’ sense of efficiency. Journal of Social Sciences,8(4), 497–504.

    Google Scholar 

  • Barrie, S., & Ginns, P. (2007). The linking of national teaching performance indicators to improvements in teaching and learning in classrooms. Quality in Higher Education,13(3), 275–286.

    Google Scholar 

  • Bassi, F., Clerci, R., & Aquario, D. (2017). Students’ evaluation of teaching at a large Italian university: Measurement scale validation. Electronic Journal of Applied Statistical Analysis,10(1), 93–117.

    Google Scholar 

  • Bénabou, R., & Tirole, J. (2002). Self confidence and personal motivation. The Quarterly Journal of Economics,117(3), 871–915.

    Google Scholar 

  • Benton, S. L., & Cashin, W. E. (2014). Student ratings of instruction in college and university courses. In M. B. Paulsen (Ed.), Higher education: Handbook of theory and research (pp. 279–326). Dordrecht: Springer.

    Google Scholar 

  • Byrne, M., & Flood, B. (2005). A study of accounting students’ motives, expectations and preparedness for higher education. Journal of Further and Higher Education,29(2), 111–124.

    Google Scholar 

  • Chan, B. Y.-F., Yeoh, S. F., & Ho, J. S.-Y. (2012). Student evaluation of lecturer performance among private university students. Canadian Social Science,8(4), 238–243.

    Google Scholar 

  • Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information System Research,14(2), 189–217.

    Google Scholar 

  • Ciavolino, E. (2012). General distress as second order latent variable estimated through PLS-PM approach. Electronic Journal of Applied Statistical Analysis,5(3), 458–464.

    Google Scholar 

  • Ciavolino, E., & Calcagnì, A. (2015). Generalized cross entropy method for analysing the SERVQUAL model. Journal of Applied Statistics,42(3), 520–534.

    Google Scholar 

  • Ciavolino, E., & Carpita, M. (2015). The GME estimator for the regression model with a composite indicator as explanatory variable. Quality & Quantity,49(3), 955–965.

    Google Scholar 

  • Ciavolino, E., Carpita, M., & Nitti, M. (2015). High-order pls path model with qualitative external information. Quality & Quantity,49(4), 1609–1620.

    Google Scholar 

  • Ciavolino, E., & Nitti, M. (2013). Using the hybrid two-step estimation approach for the identification of second-order latent variable models. Journal of Applied Statistics,40(3), 508–526.

    Google Scholar 

  • Cipriani, G. P., & Zago, A. (2011). Productivity or discrimination? Beauty and the exams. Oxford Bulletin of Economics and Statistics,73(3), 428–447.

    Google Scholar 

  • Coates, H. (2005). The value of student engagement for higher education quality assurance. Quality in Higher Education,11(1), 25–36.

    Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Copland, F., Ma, G., & Mann, S. J. (2009). Reflecting in and on post-observation feedback in initial teacher training on certificate courses. English Language Teacher Education and Development,12, 14–23.

    Google Scholar 

  • De Paola, M., & Scoppa, V. (2015). Gender discrimination and evaluators’ gender: Evidence from Italian academia. Economica,82(325), 162–188.

    Google Scholar 

  • Dion, K., Berscheid, E., & Walster, E. (1972). What is beautiful is good. Journal of Personality and Social Psychology,24(3), 285.

    Google Scholar 

  • Eagly, A. H., Ashmore, R. D., Makhijani, M. G., & Longo, L. C. (1991). What is beautiful is good, but…: A meta-analytic review of research on the physical attractiveness stereotype. Psychological Bulletin,110(1), 109.

    Google Scholar 

  • Eison, J. (1990). Confidence in the classroom: Ten maxims for new teachers. College Teaching,38(1), 21–25.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research,18(1), 39–50.

    Google Scholar 

  • Fulmer, S. M., & Turner, J. C. (2014). The perception and implementation of challenging instruction by middle school teachers. The Elementary School Journal,114(3), 303–326.

    Google Scholar 

  • Gurung, R. A., & Vespia, K. (2007). Looking good, teaching well? Linking liking, looks, and learning. Teaching of Psychology,34(1), 5–10.

    Google Scholar 

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles, CA: Sage Publications Inc.

    Google Scholar 

  • Hamermesh, D. S., & Parker, A. (2005). Beauty in the classroom: Instructors’ pulchritude and putative pedagogical productivity. Economics of Education Review,24(4), 369–376.

    Google Scholar 

  • Hatfield, E., & Rapson, R. L. (2000). Physical attractiveness. The Corsini Encyclopedia of Psychology and Behavioral Science,3, 1203–1205.

    Google Scholar 

  • Ingusci, E., Palma, F., De Giuseppe, M., & Iacca, C. (2016). Social and scholar integration and students satisfaction: The mediating role of career adaptability. Electronic Journal of Applied Statistical Analysis,9(4), 704–715.

    Google Scholar 

  • Johnson, S. M. (2012). Having it both ways: Building the capacity of individual teachers and their schools. Harvard Educational Review,82(1), 107–122.

    Google Scholar 

  • Klassen, R. M., & Chiu, M. M. (2010). Effects on teachers’ self-efficacy and job satisfaction: Teacher gender, years of experience, and job stress. Journal of Educational Psychology,102(3), 741–756.

    Google Scholar 

  • Kogan, L. R., Schoenfeld-Tacher, R., & Hellyer, P. W. (2010). Student evaluations of teaching: Perceptions of faculty based on gender, position, and rank. Teaching in Higher Education,15(6), 623–636.

    Google Scholar 

  • Kurtulmuş, B. E., Warner, B., & Özarı, Ç. (2016). Research or teaching oriented? Game theory models for the strategic decision-making process of universities with the external environment held neutral. Electronic Journal of Applied Statistical Analysis,9(3), 469–490.

    Google Scholar 

  • Liaw, S. H., & Goh, K. L. (2003). Evidence and control of biases in student evaluations of teaching. International Journal of Educational Management,17(1), 37–43.

    Google Scholar 

  • MacNell, L., Driscoll, A., & Hunt, A. N. (2015). What’s in a name: Exposing gender bias in student ratings of teaching. Innovative Higher Education,40(4), 291–303.

    Google Scholar 

  • 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. 319–383). Dordrecht: Springer.

    Google Scholar 

  • McGrath, B., Brennan, M., Dolan, P., & Barnett, R. (2009). Adolescent well-being Electronic Journal of Applied Statistical Analysis 715 and supporting contexts: A comparison of adolescents in Ireland and Florida. Journal of Community & Applied Social Psychology,19(4), 299–320.

    Google Scholar 

  • Mobius, M. M., & Rosenblat, T. S. (2006). Why beauty matters. American Economic Review,96(1), 222–235.

    Google Scholar 

  • Moss-Racusin, C. A., Dovidio, J. F., Brescoll, V. L., Graham, M. J., & Handelsman, J. (2012). Science faculty’s subtle gender biases favor male students. Proceedings of the National Academy of Sciences,109(41), 16474–16479.

    Google Scholar 

  • Mousavi, A., Mares, C., & Stonham, T. J. (2015). Continuous feedback loop for adaptive teaching and learning process using student surveys. International Journal of Mechanical Engineering Education,43(4), 247–264.

    Google Scholar 

  • Muda, N., Samsudin, H. B., Majid, N., Ali, K. A. M., & Ismail, W. R. (2012). Students perspective on lecturer characteristic for effective teaching. Procedia-Social and Behavioral Sciences,59, 535–540.

    Google Scholar 

  • Nitti, M., & Ciavolino, E. (2014). A deflated indicators approach for estimating second-order reflective models through PLS-PM: An empirical illustration. Journal of Applied Statistics,41(10), 2222–2239.

    Google Scholar 

  • Olson, I. R., & Marshuetz, C. (2005). Facial attractiveness is appraised in a glance. Emotion,5(4), 498–502.

    Google Scholar 

  • Ottoboni, K., Boring, A., & Stark, P. (2016). Student evaluations of teaching (mostly) do not measure teaching effectiveness. Science Open Research. https://doi.org/10.14293/s2199-1006.1.sor-edu.aetbzc.v1.

    Article  Google Scholar 

  • Ponzo, M., & Scoppa, V. (2013). Professors’ beauty, ability, and teaching evaluations in Italy. The B.E. Journal of Economic Analysis & Policy,13(2), 811–835.

    Google Scholar 

  • Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.

    Google Scholar 

  • Putman, S. M. (2012). Investigating teacher efficacy: Comparing pre-service and in service teachers with different levels of experience. Action in Teacher Education,34(1), 26–40.

    Google Scholar 

  • Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0: An updated and practical guide to statistical analysis (2nd ed.). Singapore: Pearson.

    Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology,25(1), 54–67.

    Google Scholar 

  • Sekaran, U., & Bougie, R. (2010). Research method for business: A skill building approach. Chichester, West Sussex: Wiley.

    Google Scholar 

  • Simonetti, B., Sarnacchiaro, P., & Rosario Gonzalez Rodrıguez, M. (2017). Goodness of fit measures for logistic regression model: An application for students’ evaluations of university teaching. Quality & Quantity,51(6), 2545–2554.

    Google Scholar 

  • Smimou, K., & Dahl, D. W. (2012). On the relationship between students’ perceptions of teaching quality, methods of assessment, and satisfaction. Journal of Education for Business,87(1), 22–35.

    Google Scholar 

  • Spooren, P., Brockx, B., & Mortelmans, D. (2013). On the validity of student evaluation of teaching the state of the art. Review of Educational Research,83(4), 598–642.

    Google Scholar 

  • Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education,17(7), 783–805.

    Google Scholar 

  • Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (2010). Handbook of partial least squares: Concepts, methods and applications. New York, NY: Springer.

    Google Scholar 

  • Wong, K. K. K. (2013). Partial Least Squares Structural Equation Modeling (PLS-SEM) techniques using Smart PLS. Marketing Bulletin,24, 1–32.

    Google Scholar 

  • Worthington, A. C. (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.

    Google Scholar 

  • Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). Business research methods. Cengage Learning.

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Acknowledgements

The authors would like to thank the two anonymous referees and the editor for their helpful comments and suggestions on an earlier drafts. The authors gratefully acknowledges financial support from Universiti Malaysia Sarawak (UNIMAS) Geran Penyelidikan Khas (Top Down) 03(TD04)/1054/2013(02). As usual, the responsibility of errors and omissions rests with the authors.

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Correspondence to Evan Lau.

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Tan, S., Lau, E., Ting, H. et al. How Do Students Evaluate Instructors’ Performance? Implication of Teaching Abilities, Physical Attractiveness and Psychological Factors. Soc Indic Res 146, 61–76 (2019). https://doi.org/10.1007/s11205-019-02071-6

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