نوع مقاله : مقاله علمی - پژوهشی

نویسندگان

1 گروه آموزشی مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه اراک، ایران

2 دانشجوی دکتری مهندسی صنایع، دانشگاه فردوسی مشهد، مشهد، ایران

چکیده

: ارزیابی کیفیت عملکرد مراکز آموزش عالی به‌واسطۀ نقش مهم و تأثیرگذاری که دارند، از اهمیت بسیار زیادی برخوردار است. ارزیابی عملکرد مراکز آموزش عالی سبب می‌‌شود تا نقاط قوت‌‌ و ضعف‌‌ و نیز فرصت‌‌ها و عوامل بازدارنده‌‌ توسعه مشخص شده و امکان برنامه‌‌ریزی‌‌ مناسب جهت بهبود و ارتقای وضعیت آن‌ها فراهم شود. برای اصلاح و بهبود مستمر نظام دانشگاهی، استقرار یک سازوکار مناسب ارزیابی عملکرد که به واسطۀ آن بتوان ضمن بهبود و ارتقای کیفیت علمی، بهبود کل نظام دانشگاهی را مدنظر قرار داد و در عین ‌حال از متن نظام دانشگاهی و ضرورت‌‌های آن برخاسته و منطبق بر خصوصیات این نظام باشد، ضروری به نظر می‌رسد. در این پژوهش با در نظر گرفتن محیط تصمیم‌‌گیری غیرقطعی، یک مدل ارزیابی جدید به‌‌ نام مدل پروفایل کارایی ورودی بهبودیافته فازی- وزن‌‌های مشترک مبتنی بر بهترین حل (FIIEP - CWBOS) پیشنهاد شده است که عملکرد مدل پروفایل کارایی ورودی کلاسیک را بهبود می‌‌دهد. نتایج این پژوهش نشان داد که مدل پیشنهادی از اعتبار لازم و عملکردی مطلوب نسبت به سایر مدل‌هایی که تا کنون ارائه شده برخوردار است.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluating the performance of higher education centers based on fuzzy improved input efficiency profile model

نویسندگان [English]

  • Ehsan Mansouri 1
  • leyla fazli 2

1 Department of Industrial Engineering, Engineering Faculty, Arak, Iran

2 PhD student, Department of Industrial Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.

چکیده [English]

Evaluating the quality of the performances of higher education centers is very important due to their important and effective role. Evaluating the quality of the performance of higher education centers leads to identifying strengths and weaknesses, as well as opportunities and factors hindering development, and the possibility of an appropriate planning for improving the situation is also provided. In order to continuously modify and improve the university system, it seems necessary to establish an appropriate performance evaluation mechanism through which the improvement of the entire university system can be considered in addition to improving and enhancing the scientific quality, and at the same time, which has arisen from the text of the university system and conforms to the characteristics of this system.In this research, considering the uncertain decision-making environment, a new evaluation model called Fuzzy Improved Input Efficiency Profiling – Common Weights Based on the Optimal Solution (FIIEP – CWBOS) has been proposed, which improves the performance of the classic input efficiency profile model. The results of this study showed that the proposed model has the necessary validity and optimal performance compared to other models presented so far.

کلیدواژه‌ها [English]

  • Performance quality assessment
  • fuzzy uncertainty
  • input efficiency profile model
  • higher education centers
Abzari, M., Baloei Jamkhaneh, H., Khazaei Pool, J., & Pour Mostafa Khoshkroudi, M. (2013). Performance evaluation of public university departments using DEA and SWOT models and structural equations and strategic strategies presentation to improve efficiency, Journal of Operational Research and Its Applications, 10 (1), 19 - 41. [In Persian].
Agasisti, T., Egorov, A., Zinchenko, D., & Leshukov, O. (2021). Efficiency of regional higher education systems and regional economic short - run growth: empirical evidence from Russia, Industry and Innovation, 28 (4), 507 - 534.
Alem Tabriz, A., Saiedy, H., & Deilami Moezi, S. (2011). Using composed approach of DEA and AHP for efficiency evaluation faculties of Shahid Beheshti University, Journal of Future Studies Management, 22 (89), 25 - 36. [In Persian].
Aleskerov, F., & Petrushchenko, S. (2013). DEA by sequential exclusion of alternatives. Higher School of Economics Working Paper Series, WP7/2013/02.Andersson, C., Antelius, J., Månsson, J., & Sund, K. (2017). Technical efficiency and productivity for higher education institutions in Sweden. Scandinavian Journal of Educational Research, 61 (2), 205 - 223.
Arab Mazar, F. (2011). DEA versus other performance measurement techniques, 3rd Conference on Data Envelopment Analysis, Firuzkuh Islamic Azad University, Tehran, Iran. [In Persian]
Ardila, A. (2001). Predictors of university academic performance in Colombia. International Journal of Educational Research, 35(4), 411-417.
Azar, A., & Motameni, A. R. (2003). Designing a productivity dynamics model with data envelopment analysis approach, Management Research in Iran, 7 (3), 1 - 22. [In Persian].
Azar, A., & Torkashvand, A. R. (2006). Assessing the teaching researching performance with the help of data envelopment analysis model: teaching groups of humanity sciences faculty, Tarbiat Modares University. Management Research in Iran, 10 (1), 1 - 23. [In Persian].
Bazargan, A. (1996). Internal evaluation of the university and its application in the continuous improvement of the quality of higher education, Research and planning in higher education, 3(4), 46-49. [In Persian].
Beasley, J. E. (1995). Determining teaching and research efficiencies. Journal of the Operational Research Society, 46(4), 441-452.
Brennan, J., & Shah, T. (2000). Managing quality in higher education: An international perspective on institutional assessment and change. Open University Press.
Chames, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
Cinar, Y. (2016). Research and teaching efficiencies of Turkish Universities with heterogeneity considerations: application of multi-activity DEA and DEA by sequential exclusion of alternatives methods. ArXiv Preprint ArXiv: 1701.07318.
De La Torre, E. M., Agasisti, T., & Perez-Esparrells, C. (2017). The relevance of knowledge transfer for universities’
efficiency scores: an empirical approximation on the Spanish public higher education system. Research Evaluation, 26(3), 211-229.
Do, Q. H., & Chen, J.F. (2014). A hybrid fuzzy AHP-DEA approach for assessing university performance. WSEAS Transactions on Business and Economics, 11(1), 386-397.
Dressel, P. L. (1961). Evaluation in higher education. Boston: Houghton Mifflin Co.USA.
Fitzpatrick, J. L., Sanders, J. R., & Worthen, B. R. (2017). Program evaluationalternative approaches and practical guidelines (4th Editio, Issue 379.154097 F5). Pearson. https://www.amazon.com/Program-Evaluation-Alternative-Approaches-Guidelines-ebook/dp/B0716S3JSB
Ghimire, S., Hassanzadeh Amin, S., & Wardley, L. J. (2021). Developing new data envelopment analysis models to evaluate the efficiency in Ontario Universities, Journal of Informetrics, 15 (3), 101172.
Gnewuch, M., & Wohlrabe, K. (2018). Super-efficiency of education institutions: an application to economics departments. Education Economics, 26(6), 610-623.
Gromov, A. (2017). The efficiency of Russian higher education institutions and its determinants (Vol. 40). Higher School of Economics Research Paper.
Harman, G. S. (1996). Quality assurance for higher education: developing and managing quality assurance for higher education systems and institutions in Asia and the Pacific. Asia-Pacific Centre of Educational Innovation for Development (ACEID).
Hashemi, S., & Pouraminzad, S. (2012). Analysis of accreditation model and internal evaluation for evaluation and quality assurance in the university system. The fifth conference on quality evaluation in the university system. 
[In Persian].
Ismail, I., Ramalingam, S., Azahan, A. H., & Khezrimotlagh, D. (2014). Relative efficiency of public universities in Malaysia. Scholars Journal of Economics, Business and Management, 1(11), 606-612.
Jimenez, M., Arenas, M., Bilbao, A., & Rodriguez, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 177 (3), 1599 - 1609.Johnes, G. (2013). Efficiency in English higher education institutions revisited: a network approach. Economics Bulletin, 33(4), 2698-2706.
Katharaki, M., & Katharakis, G. (2010). A comparative assessment of Greek universities’ efficiency using quantitative analysis. International Journal of Educational Research, 49(4-5), 115-128.
Khodabakhshi, M., & Kheirollahi, H. (2013). Performance evaluation of Iran universities with stochastic data envelopment analysis (SDEA). International Journal of Data Envelopment Analysis, 1(1), 7-13.
Lee, B. L. (2011). Efficiency of research performance of Australian Universities: a reappraisal using a bootstrap truncated regression approach. Economic Analysis & Policy, 41(3).
Lopes, A. L. M., & Lanzer, E. A. (2002). Data envelopment analysis-DEA and fuzzy sets to assess the performance of academic departments: a case study at Federal University of Santa Catarina-UFSC. Pesquisa Operacional, 22(2), 217-230.
Mansouri, E., & Fazli, L. (2020). Providing a model for evaluating the performance quality of higher education centers. Journal of Operational Research in its Applications, 17 (3), 23 - 43. [In Persian].
Mansouri, E., & Fazli, L. (2021). Providing a model based on input efficiency profile model to evaluate the performance quality of higher education centers, Journal of Modern Research in Decision Making, 23, 189 - 213. 
[In Persian].
Momeni Rad, A., & Aliabadi, K. (2010). Quality assurance of e-learning by using electronic learning standards. Iranian Journal of Educational Strategies, 3(3), 87-92. [In Persian].
Monfared, M. A. S., & Safi, M. (2013). Network DEA: an application to analysis of academic performance. Journal of Industrial Engineering International, 9(1), 15.
Navas, L. P., Montes, F., Abolghasem, S., Salas, R. J., Toloo, M., & Zarama, R. (2020). Colombian higher education institutions evaluation, Socio - Economic Planning Sciences, 71 (C), 100801.
Nojavan, M., Heidari, A., & Mohammaditabar, D. (2021). A fuzzy service quality based approach for performance evaluation of educational units, Socio - Economic Planning Sciences, 73, 100816.
Payan, A., & Rahmani Parchicolaie, B. Performance evaluation of universities as groups of decision making units, International Journal of Mathematical, Computational, Natural and Physical Engineering, 8 (4), 2014, 659 - 665.
Ramezanzadeh, S. (2016). Desining a model for performance evaluation based on common weights in DEA and PCA approaches with fuzzy data (Case study: Amin Police University). Dissertation for the degree of Ph.D., Department of Management, School of Graduate Studies, Allameh Tabatabai University, Tehran. [In Persian].
Ruiz, J. L., Segura, J. V, & Sirvent, I. (2015). Benchmarking and target setting with expert preferences: An application to the evaluation of educational performance of Spanish universities. European Journal of Operational Research, 242(2), 594-605.
Safari, S., Ebrahimi Shaghaghi, M., Sheikh, M. J. (2011). Managing the credit risk of the bank’s clients in commercial banks DEA Approach (Credit Rating), Management Research in Iran, 14 (4), 137 - 164. [In Persian].
Sahney, S., & Thakkar, J. (2016). A comparative assessment of the performance of select higher education institutes in India. Quality Assurance in Education, 24(2), 278-302.
Shafia, M., A. (2002). Appropriate indicators to evaluate the quality of Iranian higher education performance. Higher Education Research and Planning Institute, Research Project. [In Persian].
Shields, P. M. (1999). Zen and the art of higher education maintenance: bridging classic and romantic notions of quality. Journal of Higher Education Policy and Management, 21(2), 165-172.
Shoja, N., Fallah Jelodar, M., & Darvish Motavli, M. H. (2011). Efficiency determination of units in district 12 of Islamic Azad University using a multi - component model in data envelopment analysis, Journal of Operational Research in its Applications, 8 (2), 11 - 28. [In Persian].
Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136 (1), 31-64.
Sun, J., Wu, J., & Guo, D. (2013). Performance ranking of units considering ideal and anti-ideal DMU with common weights. Applied Mathematical Modelling, 37, 6301 - 6310.Taleb, M., Ramli, R., & Khalid, R. (2019). Measuring the efficiency of community colleges using super efficiency approach for the case of non-discretionary factors in data envelopment analysis with sensitivity analysis. International Journal of Process Management and Benchmarking, 9(2), 149-172.
Tofallis, C. (1997). Input efficiency profiling: an application to airlines. Computers & Operations Research, 24(3), 253-258.
Tovar, E. (2001). A practical case for the self-evaluation management of European computer science school. 31st Annual frontiers in education conference. Impact on Engineering and Science Education. Conference Proceedings (Cat. No. 01CH37193), 3, F4A-7.
Villano, R. A., & Tran, C. D. T. T. (2018). Performance of private higher education institutions in Vietnam: evidence using DEA - based bootstrap directional distance approach with quasi - fixed inputs, Applied Economics, 50 (55), 5966 - 5978.
Vlasceanu, L., Grunberg, L., & Parlea, D. (2004). Quality assurance and accreditation: a glossary of basic terms and definitions. Unesco-Cepes Bucharest.
Wojcik, V., Dyckhoff, H., & Clermont, M. (2019). Is data envelopment analysis a suitable tool for performance measurement and benchmarking in non-production contexts? Business Research, 12 (2), 559 - 595.
Zeinabadi, H., Kiamanesh, A., & Valiolah, F. (2006). Internal evaluation of the quality of the counseling and guidance group of Tarbiat Moallem University of Tehran in order to propose a model for improving the quality and moving towards the accreditation of counseling and guidance groups in the country, Consulting research, 4(15), 69-92. [In Persian].
Zhang, L., & Luo, Y. (2016). Evaluation of input output efficiency in higher education based on data envelope analysis, International Journal of Database Theory and Application, 9 (5), 221 - 230.