Skip to main content

Advertisement

Log in

Educational institutions selection using Analytic Hierarchy Process based on National Institutional Ranking Framework (NIRF) criteria

  • Published:
Interchange Aims and scope Submit manuscript

Abstract

Nowadays, with increasing competitiveness in every field, securing a good job may be difficult. In this connection, students aiming to get into the best educational institution (EI) would give them their best chance of quality education and good job opportunities. Institutional evaluation and selection are complex tasks that must simultaneously include different aspects and evaluation criteria. This work addresses the EI selection dilemma by formulating a multi-criteria decision-making computational model. This work utilizes the National Institutional Ranking Framework approved by the Ministry of Human Resource Development India to rank higher education institutions in India. The problem was converted into a multi-criteria decision-making (MCDM) model based on the accumulated criteria. This MCDM problem was further solved with the help of the Analytic Hierarchy Process by formulating a multi-criteria decision-making model for EI/university selection. Further, a new technique based on separated criteria benefits and recommendations (SCBR) has been incorporated with AHP, resulting in the advancement of the basic AHP method. The proposed technique allows comprehensibility of the qualitative method while maintaining the precision of the quantitative methodology for institutional selection of undergraduate and postgraduate students. This work is beneficial not only for the students but also for the academic job aspirants for choosing the appropriate institution. The proposed work is also applicable as a tool for assessing the effectiveness of higher education institutions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig.1
Fig.2
Fig.3
Fig.4
Fig.5

Similar content being viewed by others

Abbreviations

AI:

Academic institution

AHP:

Analytic Hierarchy Process

ANP:

Analytic network process

PHSE:

Combined % for Placement, Higher Studies, and Entrepreneurship

PU:

Combined metric for Publications

QP:

Combined metric for Quality of Publications

COMP:

Competitiveness

CI:

Consistence index

ESCS:

Economically and Socially Challenged Students

EI:

Educational institution

ELECTRE:

Elimination and choice translating reality

PCS:

Facilities for Physically Challenged Students

FQE:

Faculty with PhD (or equivalent) and Experience

FSR:

Faculty-student ratio

F:

Fee

FMCDM:

Fuzzy logic based multi-criteria decision-making

GP:

Goal programming

GO:

Graduation Outcome

H:

High

IPR:

Intellectual property rights

IPRP:

IPR and Patents

L:

Low

MS:

Median Salary

M:

Medium

EIE:

Metric for EI Examinations

GSATOP:

Metric for Graduating Students Admitted into Top Universities

PHDG:

Metric for Number of Ph.D. Students Graduated

MHRD:

Ministry of Human Resource Development

MAUT:

Multi-attribute utility theory

MCDM:

Multi-criteria decision-making

NIRF:

National Institutional Ranking Framework

Cn:

Nth Criteria

Sn:

Nth Sub-Criteria

OI:

Outreach and Inclusivity

PPA:

Peer Perception: Academics

PPERI:

Peer Perception: Employers and Research Investors

PSOS:

Percent Students from other states/countries

PW:

Percentage of Women

P:

Perception

PR:

Perception

PROMETHEE:

Projects and Professional Practice and Executive Development

PPPED:

Projects and Professional Practice and Executive Development

PPER:

Public Perception

RI:

Random inconsistency

RD:

Region Diversity

RP:

Research and Professional Practice

RPPCF:

Research Professional Practice and Collaborative Performance

S:

Score matrix

SCBR:

Separated criteria benefits and recommendations

SAW:

Simple Additive Weighting

SS:

Student Strength

TLR:

Teaching, Learning and Resources

TOPSIS:

Technique for order preference by similarity to ideal solutions

TBU:

Total Budget and Its Utilization:

WSM:

Weighted sum method

References

  • Agha, S. R., Jarbo, M. H., & Matr, S. J. (2013). A multi-criteria multi-stakeholder industrial projects prioritization in Gaza Strip. Arabian Journal for Science and Engineering, 38(5), 1217–1227.

    Article  Google Scholar 

  • All India Survey on Higher Education (2018–2019). Government of India. Ministry of Human Resource Development. Department of Higher Education, New Delhi. Retrieved from www.mhrd.gov.in

  • Ancheh, K. S. B., Krishnan, A., & Nurtjahja, O. (2007). Evaluative criteria for selection of private universities and colleges in Malaysia. Journal of International Management Studies, 2(1), 1–11.

    Google Scholar 

  • Ardeshir, A., Mohseni, N., Behzadian, K., & Errington, M. (2014). Selection of a bridge construction site using fuzzy analytical hierarchy process in geographic information system. Arabian Journal for Science and Engineering, 39(6), 4405–4420.

    Article  Google Scholar 

  • Begičević, N., Divjak, B., & Hunjak, T. (2010). Decision-making on prioritization of projects in higher education institutions using the analytic network process approach. Central European Journal of Operations Research, 18(3), 341–364.

    Article  Google Scholar 

  • Biswas, T. K., Chaki, S., & Das, M. C. (2019). MCDM technique application to the selection of an Indian institute of technology. Operational Research in Engineering Sciences: Theory and Applications, 2(3), 65–76.

    Google Scholar 

  • Budiyanto D (2017, November) AHP-TOPSIS on selection of new university students and the prediction of future employment. Paper presented at 2017 1st International Conference on Informatics and Computational Sciences (ICICoS) pp. 125–130). IEEE

  • Clayton, K. E., Blumberg, F. C., & Anthony, J. A. (2018). Linkages between course status, perceived course value, and students’ preference for traditional versus non-traditional learning environments. Computers & Education, 125, 175–181.

    Article  Google Scholar 

  • Dawes, P. L., & Brown, J. (2002). Determinants of awareness, consideration, and choice set size in University choice. Journal of Marketing for Higher Education, 12(1), 49–75.

    Article  Google Scholar 

  • Gabrielsen E (1992) The role of self-monitoring, conformity, and social intelligence in selection of a college major. Annual Conference of the Eastern Psychological Association.

  • Goraya, M. S., & Singh, D. (2021). A comparative analysis of prominently used MCDM methods in cloud environment. The Journal of Supercomputing, 77(4), 3422–3449.

    Article  Google Scholar 

  • Job, J., & Sriraman, B. (2013). A framework for quality assurance in globalization of higher education: A view toward the future. Interchange, 43(2), 75–93.

    Article  Google Scholar 

  • Kaynama, S. A., & Smith, L. W. (1996). Using consumer behavior and decision models to aid students in choosing a major. Journal of Marketing for Higher Education, 7(2), 57–73.

    Article  Google Scholar 

  • Kiani, M., Bagheri, M., Ebrahimi, A., & Alimohammadlou, M. (2019). A model for prioritizing outsourceable activities in universities through an integrated fuzzy-MCDM method. International Journal of Construction Management. https://doi.org/10.1080/15623599.2019.1645264

    Article  Google Scholar 

  • Kramulová, J., & Jablonský, J. (2016). AHP model for competitiveness analysis of selected countries. Central European Journal of Operations Research, 24(2), 335–351.

    Article  Google Scholar 

  • Kumar, P., & Tandon, P. (2017). Improvised concept development process in design through product ingredients. International Conference on Research into Design (pp. 453–463). Singapore: Springer.

    Google Scholar 

  • Kumar, P., & Tandon, P. (2019). A paradigm for customer-driven product design approach using extended axiomatic design. Journal of Intelligent Manufacturing, 30(2), 589–603.

    Article  Google Scholar 

  • Kumar, P., & Tandon, P. (2021). Design Decision in the Manufacturing Environment Using an Improved Multiple-Criteria Performance Evaluation Method. Arabian Journal for Science and Engineering, 1–12.

  • Kumar, P., & Tiwari, A. (2021). MCDM-Based Decision Support System for Product Design and Development. Design for Tomorrow—Volume 2 (pp. 575–584). Singapore: Springer.

    Chapter  Google Scholar 

  • Lapan, R. T. (1996). Efficacy expectations and vocational interests as mediators between sex and choice of math/ science college majors: A longitudinal study. Journal of Vocational Behavior, 49(3), 277–291.

    Article  Google Scholar 

  • Madeshia, P. K., & Verma, S. (2020). Review on higher education in India. Journal of Critical Reviews, 7(10), 1161–1164.

    Google Scholar 

  • Mardani, A., & Jusoh, A. (2015). Zavadskas EK (2015) Fuzzy multiple criteria decision-making techniques and applications–Two decades’ review from 1994 to 2014. Expert Systems with Applications, 42(8), 4126–4148.

    Article  Google Scholar 

  • Mousavi, S. M., Tavakkoli-Moghaddam, R., Heydar, M., & Ebrahimnejad, S. (2013). Multi-criteria decision making for plant location selection: An integrated Delphi–AHP–PROMETHEE methodology. Arabian Journal for Science and Engineering, 38(5), 1255–1268.

    Article  Google Scholar 

  • Proboyo, A., & Soedarsono, R. (2015). Influential factors in choosing higher education institution: A case study of a private EI in Surabaya. Jurnal Manajemen Pemasaran, 9(1), 1–7.

    Article  Google Scholar 

  • Saaty, R. W. (1987). The analytic hierarchy process—What it is and how it is used. Mathematical Modelling, 9(3–5), 161–176.

    Article  Google Scholar 

  • Salimi, N., & Rezaei, J. (2015). Multi-criteria university selection: Formulation and implementation using a fuzzy AHP. Journal of Systems Science and Systems Engineering, 24(3), 293–315.

    Article  Google Scholar 

  • Shayganmehr, M., & Montazer, G. A. (2020). An extended model for assessing E-services of Iranian Universities websites using Mixed MCDM method. Education and Information Technologies, 25(5), 3723–3757.

    Article  Google Scholar 

  • Smutny, P., & Schreiberova, P. (2020). Chatbots for learning: A review of educational chatbots for the Facebook Messenger. Computers & Education, 151, 103862.

    Article  Google Scholar 

  • Soutar, G. N., & Turner, J. P. (2002). Students’ preferences for university: A conjoint analysis. International Journal of Educational Management, 16(1), 40–45.

    Google Scholar 

  • Vikaspedia, Accessed 14 May 2021 https://vikaspedia.in/education/education-best-practices/national-institutional-ranking-framework

  • Winchester, I. (1992). Elite and ordinary: The essential tension in the university. Interchange, 23(1), 91–95.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prabhat Kumar.

Ethics declarations

Conflict of interest

None.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahirwal, M.K., Kumar, P. Educational institutions selection using Analytic Hierarchy Process based on National Institutional Ranking Framework (NIRF) criteria. Interchange 54, 203–227 (2023). https://doi.org/10.1007/s10780-023-09488-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10780-023-09488-6

Keywords

Navigation