A Multi-Criteria Mathematical Model for Evaluating NAAC Accreditation Readiness of Arts and Science Colleges

Abstract

The National Assessment and Accreditation Council (NAAC) evaluates Indian higher education institutions on seven weighted criteria that span curricular design, teaching quality, research output, infrastructure, student support, governance, and institutional values. For arts and science colleges, especially those functioning with constrained budgets and largely undergraduate teaching mandates, gauging readiness for this assessment before formally engaging with it is often done informally, through checklists and committee impressions rather than a structured quantitative process. This paper develops a multi-criteria mathematical model that converts NAAC\'s seven-criteria structure into a composite, numerically defensible Accreditation Readiness Index. The model combines the Analytic Hierarchy Process (AHP) for deriving criterion weights from expert pair wise judgments with a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) ranking procedure, and it incorporates a triangular fuzzy extension to absorb the subjectivity inherent in indicators such as governance quality or institutional values that resist crisp numerical description. A worked illustration involving five hypothetical arts and science colleges demonstrates how the model separates institutions that are genuinely accreditation-ready from those that merely appear so on isolated metrics. A sensitivity analysis further shows how the ranking of colleges shifts when criterion weights are perturbed, offering administrators a way to identify which improvement levers yield the largest gain in readiness per unit of institutional effort. The paper closes by discussing the practical value of the model for internal quality assurance cells, its current limitations, and directions for extending it with machine-learning-assisted weight elicitation.

Introduction

This study proposes a Multi-Criteria Decision-Making (MCDM) model to measure NAAC accreditation readiness of Indian Arts and Science colleges. It addresses the difficulty faced by Internal Quality Assurance Cells (IQACs) in determining whether an institution is ready to apply for accreditation, should postpone the application, or needs improvement in specific areas.

The study is based on the idea that institutional quality cannot be represented by a single factor. NAAC evaluates colleges through seven criteria:

1. Curricular Aspects (C1)
2. Teaching-Learning and Evaluation (C2)
3. Research, Innovations and Extension (C3)
4. Infrastructure and Learning Resources (C4)
5. Student Support and Progression (C5)
6. Governance, Leadership and Management (C6)
7. Institutional Values and Best Practices (C7)

The proposed model combines AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). AHP is used to determine the relative importance or weights of the seven criteria based on expert judgments, while TOPSIS combines the weighted and normalized scores to calculate a Composite Readiness Index (CRI) for each college. A fuzzy-number extension is also proposed to handle qualitative indicators where exact numerical scores are difficult to assign.

The methodology involves:

• Converting NAAC criteria and indicators into a structured decision matrix.
• Assigning criterion weights using AHP and checking consistency.
• Normalizing college performance scores.
• Applying weighted TOPSIS to determine each college's distance from the ideal and least-ideal institution.
• Calculating a CRI between 0 and 1.
• Classifying institutions into four readiness levels:
  • Not Ready: CRI < 0.35
  • Developing Readiness: 0.35–0.54
  • Application Ready: 0.55–0.74
  • Strongly Ready: ≥ 0.75

An illustrative example involving five hypothetical colleges demonstrates the model. College E achieved the highest readiness score (CRI = 0.888), followed by College C (0.831), College A (0.679), College B (0.481), and College D (0.132). Thus, E and C were classified as Strongly Ready, A as Application Ready, B as Developing Readiness, and D as Not Ready under the stated classification thresholds.

Key finding

The proposed MCDM framework provides a systematic, transparent, quantitative, and evidence-based method for assessing NAAC readiness. It can help IQACs identify weak criteria, prioritize resources, monitor improvement over time, and make better decisions about when to apply for accreditation.

In one sentence

The study develops an AHP-TOPSIS-based Composite Readiness Index, enhanced with fuzzy logic, to quantitatively assess and compare the NAAC accreditation readiness of Arts and Science colleges across the seven NAAC criteria.

Conclusion

This paper has developed a multi-criteria mathematical model for evaluating the NAAC accreditation readiness of arts and science colleges, combining AHP-derived criterion weights, a normalised weighted TOPSIS aggregation procedure, and a triangular fuzzy extension for qualitative indicators into a single Composite Readiness Index. The illustrative application demonstrated that the model does more than rank colleges; it exposes which specific criteria are responsible for a low readiness score, and the accompanying sensitivity analysis showed how robust, or fragile, a given ranking is to reasonable disagreement about criterion weights. Positioned against the existing literature, the model extends prior NAAC-focused MCDM work, which has concentrated on grade prediction after the fact (Dey Mondal et al., 2024) or on narrow sub-domains (Aithal & Aithal, 2021), toward a pre-assessment readiness tool explicitly calibrated to the seven-criterion structure and tailored to the resource profile of arts and science colleges. Future work could extend the model in at least three directions: validating the AHP weight vector against a larger, multi-institution expert panel; replacing the static AHP weights with a hybrid Entropy-AHP scheme that blends objective data-driven weighting with expert judgment, in the spirit of Wang et al. (2022); and integrating the model into a lightweight software tool that IQACs can update continuously as new institutional data becomes available, aligning readiness tracking with NAAC\'s own move toward maturity-based, data-verified assessment cycles (OpenEducat, 2026).

References

[1] Aithal, P. S., & Aithal, S. (2021). A comparative study on research performance of Indian universities with NAAC A++ grade accreditation. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3871987 [2] Alhat, S. S. (2020). Understanding revised NAAC grading pattern. Shanlax International Journal of Arts, Science and Humanities, 8(1), 57–60. https://doi.org/10.34293/sijash.v8i1.3081 [3] Benmoussa, N., Elyamami, A., Mansouri, K., Qbadou, M., & Illoussamen, E. (2019). A multi-criteria decision making approach for enhancing university accreditation process. Engineering, Technology & Applied Science Research, 9(1), 3726–3733. https://doi.org/10.48084/etasr.2352 [4] Dey Mondal, S., Ghosh, D. N., & Dey, P. K. (2024). Statistical-based multi-criteria decision making approach for prediction of the grade of an institution in NAAC. International Journal of Mathematics in Operational Research, 27(3), 317–327. https://doi.org/10.1504/IJMOR.2024.10061847 [5] Do, Q. H., Tran, V. T., & Tran, T. T. (2024). Evaluating lecturer performance in Vietnam: An application of fuzzy AHP and fuzzy TOPSIS methods. Heliyon, 10(11), Article e30772. https://doi.org/10.1016/j.heliyon.2024.e30772 [6] Edhitch. (2026, May 12). NAAC accreditation process 2026: Steps, criteria & new rules. https://www.edhitch.com/naac-accreditation-guide-steps-strategies-tips.html [7] James, N., Loganathan, S., Nathan, R. J., Victor, V., & Ng, P. K. (2023). Integrated fuzzy AHP and TOPSIS as innovative student selection methodology at institutions of higher learning. Human Systems Management, 42(6), 615–633. https://doi.org/10.3233/HSM-220046 [8] Kramah Software. (2026a, June 3). NAAC binary accreditation: New 10 criteria & AI assessment. https://www.kramah.com/blog/naac-binary-accreditation-colleges-guide/ [9] Kramah Software. (2026b, June 3). NBA vs NAAC accreditation 2025: Which should you prioritize? https://www.kramah.com/blog/nba-vs-naac-which-to-prioritize/ [10] Liu, S. (2021). Research on the teaching quality evaluation of physical education with intuitionistic fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems, 40(4), 6873–6883. https://doi.org/10.3233/JIFS-201672 [11] Mu, E., & Nicola, C. B. (2019). Managing university rank and tenure decisions using a multi-criteria decision-making approach. International Journal of Business and Systems Research, 13(3), 297–320. [12] National Assessment and Accreditation Council. (n.d.). Assessment & accreditation. Retrieved August 12, 2026, from http://naac.gov.in/index.php/en/assessment-accreditation [13] Nazari-Shirkouhi, S., Mousakhani, S., Tavakoli, M., Dalvand, M. R., Šaparauskis, J., & Antuchevi?ien?, J. (2020). Importance-performance analysis based balanced scorecard for performance evaluation in higher education institutions: An integrated fuzzy approach. Journal of Business Economics and Management, 21(3), 647–678. https://doi.org/10.3846/jbem.2020.11940 [14] OpenEducat. (2026, March 15). NAAC grading in India: What A++, A+, A, B++ mean for institutions. https://openeducat.org/articles/naac-accreditation-grading-india-institutions/ [15] Wang, T.-C., Nguyen, T. T. T., & Phan, B. N. (2022). Analyzing higher education performance by entropy-TOPSIS method: A case study in Viet Nam private universities. Measurement + Control, 55(5–6), 274–286. https://doi.org/10.1177/00202940221089504.

Copyright

Copyright © 2026 Dr. M. Vijayakumar, Dr. K. R. Ananth, Dr. T. Annadurai, Dr. V. C. Srinivasan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.