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Benchmarking higher education institutes using data envelopment analysis: capturing perceptions of prospective engineering students

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Abstract

In today’s exceptionally demanding education domain, there is a critical need for higher education institutes to continuously improve their credibility and skill offerings for prospective students. More specifically, institutes need to know how they are evaluated and perceived by the prospective students. Though there are plethora of rating agencies that perform an independent evaluation, their assessment is primarily focused on internal institute practices rather than how students’ needs and perceptions be captured. The objective of this study is to evaluate how students rank a higher education institute for taking admission that is the most crucial decision while starting their long-term career. Academic experts and prospective engineering students are surveyed using interviews and convenience sampling method. As a result, the problem evolves as a multi-criteria decision-making problem that considers numerous criteria simultaneously. Considering the relevant criteria as inputs and outputs, the Data Envelopment Analysis is employed to evaluate the weights corresponding to each criterion/factor. The model is then deployed as a linear programming formulation that is tackled using professional linear programming solver. The engineering stream, fees, location, and perceptions of employment opportunities are found as the top parameters that drive the decision of a prospective engineering student. The identified important factors, interests, and capabilities of the student are amalgamated for the selection of the appropriate engineering stream and institute, optimally. The insights are important for engineering institutes to strategize and align their offerings and marketing approach as per the needs and perceptions of prospective students. The study will also be helpful for international universities looking ahead for collaborative and individual opportunities in the Indian education sector.

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References

  1. Adali, E.A., Işik, A.T., Kundakci, N.: Todim method for the selection of the elective courses. Eur. Sci. J. 12(10), 314–324 (2016)

    Google Scholar 

  2. Ahmad, S.Z., Buchanan, F.R.: Motivation factors in students decision to study at international branch campuses in Malaysia. Stud. Higher Educ. 42(4), 651–668 (2017)

    Google Scholar 

  3. Ahmad, S.Z., Buchanan, F.R., Ahmad, N.: Examination of students’ selection criteria for international education. Int. J. Educ. Manag. 30(6), 1088–1103 (2016)

    Article  Google Scholar 

  4. Alam, T.: Benchmarking of Academic Departments using Data Envelopment Analysis (DEA). https://hdl.handle.net/11244/321120(2019)

  5. Alecke, B., Burgard, C., Mitze, T.: The Effect of Tuition Fees on Student Enrollment and Location Choice-Interregional Migration, p. 404. Ruhr Economic Paper, Border Effects and Gender Differences (2013)

    Google Scholar 

  6. Asamoah, E. K.: Measuring the efficiency of basic student’s performance using Data Envelopment Analysis (DEA) (Doctoral dissertation) (2017).

  7. Balasubramanyam, S., Usharani, D. P., Reddy, A. H. V., Swetha, D., Kumar, G. N. S., Anusha, K., Ahammad, S. H.: Selecting a college academic branch-a design decision taking system for student career selection. Int. J. Eng. Technol. 7(4.19): 323–328 (2018).

  8. Bardia, S.: A study on career preferences of final-year undergraduate management students in Kolkata. Our Heritage 68(8), 151–174 (2020)

    Google Scholar 

  9. Bedir N., Özder E.H., Eren T.: ‘Course selection with AHP & PROMETHEE methods for post graduate students: an application in Kirikkale University Graduate School of Natural and Applied Sciences’ The 3rd International Conference on Industrial Engineering and Applications (ICIEA 2016) in Hong Kong, 68, 1–7, 20004 (2016).

  10. Bennett, R.: Determinants of undergraduate student dropout rates in a university business studies department. J. Further Higher Educ. 27(2), 123–141 (2003)

    Article  Google Scholar 

  11. Bhargava, R. N.: Present engineering education in India—an emerging economy—and a glimpse of the scenario in the 21 st century. In: Educating the Engineer for the 21st Century (pp. 77–80). Springer, Dordrecht (2001).

  12. Branham, D.: The wise man builds his house upon the rock: the effects of inadequate school building infrastructure on student attendance. Soc. Sci. Quart. 85(5), 1112–1128 (2004)

    Article  Google Scholar 

  13. Çalik, A., Pehlivan, N. Y., Pekgör, A.: Fuzzy AHP/DEA approach for relative efficiency of state university in turkey. In: Uncertainty Modeling in Knowledge Engineering and Decision Making, pp. 1064–1069 (2012).

  14. Charnes, A., Cooper, W.W., Rhodes, E.: ‘Measuring the efficiency of decision-making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)

    Article  Google Scholar 

  15. Cook, W.D., Tone, K., Zhu, J.: Data envelopment analysis: Prior to choosing a model. Omega 44, 1–4 (2014)

    Article  Google Scholar 

  16. Doshi, M.: Correlation based feature selection (CFS) technique to predict student performance. Int. J. Comput. Netw. Commun. 6(3), 197 (2014)

    Article  Google Scholar 

  17. Fatimah, S., Mahmudah, U.: Two-stage data envelopment analysis (DEA) for measuring the efficiency of elementary schools in Indonesia. Int. J. Environ. Sci. Educ. 12(8), 1971–1987 (2017)

    Google Scholar 

  18. Fisher, K.: Building better outcomes: the impact of school infrastructure on student outcomes and behaviour. In: Schooling Issues Digest (2001).

  19. Gupta, E.V., Mogale, D.G., Tiwari, M.K.: Optimal control of production and maintenance operations in smart custom manufacturing systems with multiple machines. IFAC Pap. OnLine 52(13), 241–246 (2019)

    Article  Google Scholar 

  20. Higuerey, A., Viñan-Merecí, C., Malo-Montoya, Z., Martínez-Fernández, V.A.: Data envelopment analysis (DEA) for measuring the efficiency of the hotel industry in ecuador. Sustainability 12(4), 1590 (2020)

    Article  Google Scholar 

  21. Hill, G.W., Woodworth, D.: Automatic Box-Jenkins forecasting. J. Oper. Res. Soc. 31(5), 413–422 (1980)

    Article  Google Scholar 

  22. https://www.iitsystem.ac.in/sites/default/files/parliamentaryquestion/5/PQ2016.pdf

  23. https://www.indiatoday.in/education-today/news/story/over-80-indian-engineers-are-unemployable-lack-new-age-technology-skills-report-1483222-2019-03-21.

  24. Janes, R.W.: The student-faculty ratio in graduate programs of selected departments of sociology. Am. Sociol. 4(2), 123–127 (1969)

    Google Scholar 

  25. Jenkins, A., Blackman, T., Lindsay, R., Paton-Saltzberg, R.: Teaching and research: Student perspectives and policy implications. Studies in Higher education 23(2), 127–141 (1998)

    Article  Google Scholar 

  26. Kohl, S., Schoenfelder, J., Fügener, A., Brunner, J.O.: The use of data envelopment analysis (DEA) in healthcare with a focus on hospitals. Health Care Manag. Sci. 22(2), 245–286 (2019)

    Article  Google Scholar 

  27. Kuah, C.T., Wong, K.Y.: Efficiency assessment of universities through data envelopment analysis. Proc. Comput. Sci. 3, 499–506 (2011)

    Article  Google Scholar 

  28. Kumar, A., Thakur, R. R.: Objectivity in performance ranking of higher education institutions using dynamic data envelopment analysis. In: International Journal of Productivity and Performance Management (2019)

  29. Kumar, M., Tiwari, M. K., Wong, K. Y., Govindan, K., Kuah, C. T.: Evaluating reverse supply chain efficiency: manufacturer’s perspective. In: Mathematical Problems in Engineering (2014).

  30. LeFevre, J.A., Kulak, A.G., Heymans, S.L.: Factors influencing the selection of university majors varying in mathematical content. Can. J. Behav. Sci. 24(3), 276 (1992)

    Article  Google Scholar 

  31. Leonard, D., Metcalfe, J., Becker, R., Evans, J.: Review of Literature on the Impact of Working Context and Support on the Postgraduate Research Student Learning Experience. The Higher Education Academy, New York (2006)

    Google Scholar 

  32. Leppel, K., Williams, M.L., Waldauer, C.: The impact of parental occupation and socioeconomic status on choice of college major. J. Fam. Econ. Issues 22(4), 373–394 (2001)

    Article  Google Scholar 

  33. Li, F.: Factors influencing Chinese students’ choice of international branch campuses. In: Journal of Studies in International Education, 1028315319835539 (2019).

  34. Li, H.L., Ma, L.C.: Ranking decision alternatives by integrated DEA, AHP and gower plot techniques. Int. J. Inf. Technol. Decis. Making 7(02), 241–258 (2008)

    Article  Google Scholar 

  35. Lokare, V. T., & Jadhav, P. M.: Using the AHP and TOPSIS methods for decision making in the best course selection after HSC. In: 2016 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–6. IEEE (2016, January).

  36. Mogale, D. G., Kumar, S. K., & Tiwari, M. K.: Green food supply chain design considering risk and post-harvest losses: a case study. In: Annals of Operations Research, pp. 1–28 (2020).

  37. Mogale, D.G., Lahoti, G., Jha, S.B., Shukla, M., Kamath, N., Tiwari, M.K.: Dual market facility network design under bounded rationality. Algorithms 11(4), 54 (2018)

    Article  Google Scholar 

  38. Mokher, C.G., Rosenbaum, J.E., Gable, A., Ahearn, C., Jacobson, L.: Ready for what? Confusion around college and career readiness. Phi Delta Kappan 100(4), 40–43 (2018)

    Article  Google Scholar 

  39. Noble, J.H., Jr.: Cherchez l’argent: A contribution to the debate about class size, student–faculty ratios, and use of adjunct faculty. J. Soc. Work Educ. 36(1), 89–102 (2000)

    Article  Google Scholar 

  40. Saha, O., Chakraborty, A., & Banerjee, J.S.: A decision framework of IT-based stream selection using analytical hierarchy process (AHP) for admission in technical institutions. 4th International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, 2017, pp. 1–6 (2017).

  41. Salas-Velasco, M.: The technical efficiency performance of the higher education systems based on data envelopment analysis with an illustration for the Spanish case. Educ. Res. Policy Pract. 19(2), 159–180 (2020)

    Article  Google Scholar 

  42. Shi, Y.: Assessment of agricultural vulnerability to floods in Shanghai by the DEA method. Chin. J. Urban Environ. Stud. 6(01), 1850003 (2018)

    Article  Google Scholar 

  43. Tanna, M.: Decision support system for admission in engineering colleges based on entrance exam marks. Int. J. Comput. Appl. 52, 11 (2012)

    Google Scholar 

  44. Naess, T.: Master’s degree graduates in Norway: field of study and labour market outcomes. J. Educ. Work 33(1), 1–18 (2020). https://doi.org/10.1080/13639080.2019.1708870

    Article  Google Scholar 

  45. Verma, P., Sood, S.K., Kalra, S.: Student career path recommendation in engineering stream based on three-dimensional model. Comput. Appl. Eng. Educ. 25(4), 578–593 (2017)

    Article  Google Scholar 

  46. Wu, C.-Y., Irazusta, F., Lancaster, J.T.: A decision support system for college selection. Comput. Ind. Eng. 23(1–4), 397–400 (1992). https://doi.org/10.1016/0360-8352(92)90145-A

    Article  Google Scholar 

  47. Yang, W., Li, L.: Efficiency evaluation of industrial waste gas control in China: a study based on data envelopment analysis (DEA) model. J. Clean. Prod. 179, 1–11 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank the Editor-in-Chief and esteemed reviewers for their valuable comments, that helped us to further improve the paper.

Funding

The authors acknowledge the funding given by Indian Council of Social Science Research, New Delhi, India (Grant number: IMPRESS P2479) for this study.

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Correspondence to Yash Daultani.

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Daultani, Y., Dwivedi, A. & Pratap, S. Benchmarking higher education institutes using data envelopment analysis: capturing perceptions of prospective engineering students. OPSEARCH 58, 773–789 (2021). https://doi.org/10.1007/s12597-020-00501-5

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