Abstract
Assuring high-quality teaching and learning continues to be a priority for institutions of higher learning. Higher education institutions use a variety of tools to ensure the accomplishment of their initiatives, which focus primarily on student feedback. The purpose of this study is to ascertain the overall effectiveness of the teaching and learning processes across various business school courses by utilizing the most effective tool available, namely Data Envelopment Analysis (DEA). The data was collected from seventy sections of a wide range of courses at a business school in Saudi Arabia. Almost 1470 students’ opinions in the form of a course evaluation survey (CES) were used to calculate the efficiency score of decision-making units using DEA. The study examined four inputs and six outputs. On the Charnes Cooper and Rhodes (CCR) scale, twenty-five decision-making units (DMUs) are found to be efficient, accounting for 35.71% of the total population studied. On the Banker Charnes and Cooper (BCC) scale, this number increases to 34, nearly half of the total number of DMUs analysed. The current study establishes a model for internal benchmarking by using a generic model. Where the constantly efficient DMU could be probed to collect best practices and implement them for internal benchmarking and continuous improvement in the teaching and learning process. It proposes a model for optimizing the CES results through internal benchmarking. Following that, it discusses how these examples can be used to develop internal benchmarking systems.
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References
Abbott, M., Doucouliagos, C.: The efficiency of Australian universities: a data envelopment analysis. Econ. Educ. Rev. 22(1), 89–97 (2003). https://doi.org/10.1016/S0272-7757(01)00068-1
Agasisti, T., Pérez-Esparrells, C.: Comparing efficiency in a cross-country perspective: the case of Italian and Spanish state universities. High. Educ. 59(1), 85–103 (2010). https://doi.org/10.1007/s10734-009-9235-8
Arthur, L.: From performativity to professionalism: lecturers’ responses to student feedback. Teach. High. Educ. 14(4), 441–454 (2009). https://doi.org/10.1080/13562510903050228
Banker, R.D., Charnes, A., Cooper, W.W.: Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage. Sci. 30(9), 1078–1092 (1984)
Banker, R.D., Conrad, R.F., Strauss, R.P.: A comparative application of data envelopment analysis and translog methods: an illustrative study of hospital production. Manage. Sci. 32(1), 30–44 (1986)
Benton, S., Cashin, W.: Student ratings of teaching: A summary of research and literature (IDEA Paper no. 50). Manhattan, KS: The IDEA Center (2012)
Bessent, A.M., Bessent, E.W.: Determining the comparative efficiency of schools through data envelopment analysis. Educ. Adm. Q. 16(2), 57–75 (1980)
Cashin, W.E.: Student ratings of teaching: a summary of the research. Instructor 69, 34 (1988)
Charnes, A., Cooper, W.W., Lewin, A.Y., Seiford, L.M.: Data Envelopment Analysis: Theory, Methodology, and Applications. Springer Science & Business Media (2013)
Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6), 429–444 (1978). https://doi.org/10.1016/0377-2217(78)90138-8
Colbert, A., Levary, R.R., Shaner, M.C.: Determining the relative efficiency of MBA programs using DEA. Eur. J. Oper. Res. 125(3), 656–669 (2000)
Cook, W.D., Tone, K., Zhu, J.: Data envelopment analysis: prior to choosing a model. Omega 44, 1–4 (2014). https://doi.org/10.1016/j.omega.2013.09.004
Cooper, W.W., Seiford, L.M., Zhu, J.: Data envelopment analysis. In: Handbook on Data Envelopment Analysis, pp. 1–39. Springer, Boston (2004)
De Witte, K., Rogge, N.: Accounting for exogenous influences in a benevolent performance evaluation of teachers (2009)
Feldman, K.A.: Identifying Exemplary Teachers and Teaching: Evidence from Student Ratings1, pp. 93–143. Springer, Dordrecht (2007)
Flegg, A.T., Allen, D.O., Field, K., Thurlow, T.W.: Measuring the efficiency of British universities: a multi-period data envelopment analysis. Educ. Econ. 12(3), 231–249 (2004). https://doi.org/10.1080/0904529042000258590
Fuentes, R., Fuster, B., Lillo-Bañuls, A.: A three-stage DEA model to evaluate learning-teaching technical efficiency: key performance indicators and contextual variables. Expert Syst. Appl. 48, 89–99 (2016). https://doi.org/10.1016/j.eswa.2015.11.022
Gillen, D., Lall, A.: Developing measures of airport productivity and performance: an application of data envelopment analysis. Transp. Res. Part E Logistics Transp. Rev. 33(4), 261–273 (1997)
Lutovac, S., Kaasila, R., Komulainen, J., Maikkola, M.: University lecturers’ emotional responses to and coping with student feedback: a finnish case study. Eur. J. Psychol. Educ. 32(2), 235–250 (2016). https://doi.org/10.1007/s10212-016-0301-1
Marsh, H.W.: Students’ Evaluations of University Teaching: Dimensionality, Reliability, Validity, Potential Biases and Usefulness, pp. 319–383. Springer, Dordrecht (2007)
Montoneri, B.: Teaching improvement model designed with DEA method and management matrix. IAFOR J. Educ. 2(1), 125–155 (2014)
Montoneri, B., Lee, C.-C., Lin, T.T., Huang, S.-L.: A learning performance evaluation with benchmarking concept for English writing courses. Expert Syst. Appl. 38(12), 14542–14549 (2011). https://doi.org/10.1016/j.eswa.2011.05.029
Munoz, D.A.: Assessing the research efficiency of higher education institutions in Chile: a data envelopment analysis approach. Int. J. Educ. Manage. 30(6), 809–825 (2016). https://doi.org/10.1108/IJEM-03-2015-0022
Nasser, F., Fresko, B.: Predicting student ratings: the relationship between actual student ratings and instructors’ predictions. Assess. Eval. High. Educ. 31(1), 1–18 (2006). https://doi.org/10.1080/02602930500262338
Ory, J.C.: Faculty thoughts and concerns about student ratings. New Dir. Teach. Learn. 2001(87), 3–15 (2001). https://doi.org/10.1002/tl.23
Peterson, R.L., Berenson, M.L., Misra, R.B., Radosevich, D.J.: An Evaluation of factors regarding students’ assessment of faculty in a business school. Decis. Sci. J. Innov. Educ. 6(2), 375–402 (2008). https://doi.org/10.1111/j.1540-4609.2008.00182.x
Sarkis, J., Seol, I.: Course evaluation validation using data envelopment analysis. Account. Educ. J. 20(1) (2010)
Spooren, P., Brockx, B., Mortelmans, D.: On the validity of student evaluation of teaching: the state of the art. Rev. Educ. Res. 83(4), 598–642 (2013). https://doi.org/10.3102/0034654313496870
Theall, M., Franklin, J.: Creating responsive student ratings systems to improve evaluation practice. New Dir. Teach. Learn. 2000(83), 95–107 (2000)
Tomkins, C., Green, R.: An experiment in the use of data envelopment analysis for evaluating the efficiency of UK university departments of accounting. Financ. Account. Manage. 4(2), 147–164 (1988). https://doi.org/10.1111/j.1468-0408.1988.tb00066.x
Tucker, B.: Student evaluation surveys: anonymous comments that offend or are unprofessional. High. Educ. 68(3), 347–358 (2014). https://doi.org/10.1007/s10734-014-9716-2
Worthington, A.C.: The Impact of student perceptions and characteristics on teaching evaluations: a case study in finance education. Assess. Eval. High. Educ. 27(1), 49–64 (2002). https://doi.org/10.1080/02602930120105054
Yeh, Q.-J.: The application of data envelopment analysis in conjunction with financial ratios for bank performance evaluation. J. Oper. Res. Soc. 47(8), 980–988 (1996)
Zabaleta, F.: The use and misuse of student evaluations of teaching. Teach. High. Educ. 12(1), 55–76 (2007). https://doi.org/10.1080/13562510601102131
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Naushad, M., Syed, A.M. (2023). An Investigation of Teaching and Learning Process Efficiency in a Business School Using DEA. In: Alareeni, B., Hamdan, A. (eds) Explore Business, Technology Opportunities and Challenges After the Covid-19 Pandemic. ICBT 2022. Lecture Notes in Networks and Systems, vol 495. Springer, Cham. https://doi.org/10.1007/978-3-031-08954-1_16
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