AI-supported analysis of institutional self-assessment reports: A study on the example of Ankara University (2015-2024)

Gamze Yılmaz; İman Askerzade

doi:10.62189/ci.1961669

AI-supported analysis of institutional self-assessment reports: A study on the example of Ankara University (2015-2024)

Abstract

While Institutional Self-Assessment Reports (ISARs) are critical for higher education quality assurance, their manual evaluation is highly subjective and time-consuming. This methodological gap prevents university administrations from transforming retrospective text data into predictive governance tools. Addressing this gap, this study proposes an AI-supported analytical framework to evaluate the ten-year ISARs of Ankara University (2015-2024). The dataset combines unstructured qualitative narratives with specific quantitative Institutional Performance Indicators (IPIs), including staff size, student enrollment, and project budgets. Using Latent Dirichlet Allocation (LDA) for topic modeling and lexicon-based sentiment analysis, the research identifies key thematic shifts and the emotional tone of the reports. These text mining results were cross-referenced with quantitative IPIs using K-Means clustering, Isolation Forest for anomaly detection, and Long Short-Term Memory (LSTM) networks. The analysis reveals a distinct evolution in institutional priorities: an initial focus on administrative processes shifted toward research, followed by a recent emphasis on societal contribution and education. Notably, 2020 emerged as a statistical anomaly, capturing the systemic shock of the pandemic. Furthermore, LSTM projections forecast continued growth in selected IPIs for 2025. Ultimately, this framework provides a scalable, data-driven methodology that transitions ISAR analysis from static compliance auditing to proactive strategic management.

Keywords

References

Higher Education Quality Council (YÖKAK). Kurum iç değerlendirme raporu (KİDR) hazırlama kılavuzu (Sürüm 3.2.1) [Institutional self-evaluation report (ISAR) preparation guide (Version 3.2.1)]. Ankara: YÖKAK; 2025. Available from: https://www.yokak.gov.tr/documents/national-doc/KIDR_Hazırlama_Kılavuzu_3.2.1_yeni.pdf [Accessed 23 June 2026].
Verschueren N, Van Dessel J, Verslyppe A, Schoensetters Y, Baelmans M. A maturity matrix model to strengthen the quality cultures in higher education. Education Sciences. 2023;13(2):123. https://doi.org/10.3390/educsci13020123
Hsu Y-P. Developing quality culture in higher education in Taiwan: the changes within quality assurance mechanisms. Studies in Higher Education. 2023;48(11):1741-53. https://doi.org/10.1080/03075079.2023.2212270
Teshome S. Key performance indicators in higher education: A systematic review of the conceptualization, dimensions, frameworks, and challenges of KPIs. International Journal of Education, Management, and Technology. 2025;3(2):611-23. https://doi.org/10.58578/IJEMT.v3i2.6493
Williamson B, Bayne S, Shay S. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in higher education. 2020;25(4):351-365. https://doi.org/10.1080/13562517.2020.1748811
Atatekin E, Dulupçu MA. A new page in quality or a repetition of the old? Educational perspective of institutional self-evaluation reports of some state universities. Journal of University Research. 2018;1(1):14-24. https://doi.org/10.32329/uad.394020
Yetişen S, Ünlü HG. Quality perspective in higher education: a comparative analysis of institutional self-evaluation reports of state universities established in 2007. International Journal of Euroasian Researches. 2019;7(16):72–89. https://doi.org/10.33692/avrasyad.543723
Taşcı D, Lapçın HT. Quality assurance system in higher education: An evaluation based on institutional accreditation reports. Eskisehir Osmangazi University Journal of Social Sciences. 2023;24(1):1-16. https://doi.org/10.17494/ogusbd.1185953

Yıldırım E, Aydın MÇ. What Do Institutional Internal Evaluation Reports Say? Content Analysis of Fully Accredited Universities. Journal of University Research. 2025;8(2):292-305. https://doi.org/10.32329/uad.1687783
Jagannathan M, Roy D, Delhi VSK. Application of NLP-based topic modeling to analyse unstructured text data in annual reports of construction contracting companies. CSI Transactions on ICT. 2022;10(2):97-106. https://doi.org/10.1007/s40012-022-00355-w
Abimbola B, de La Cal Marin E, Tan Q. Enhancing legal sentiment analysis: A convolutional neural network–long short-term memory document-level model. Machine Learning and Knowledge Extraction. 2024;6(2):877-97. https://doi.org/10.3390/make6020041
Ahadi A, Singh A, Bower M, Garrett M. Text mining in education—A bibliometrics-based systematic review. Education Sciences. 2022;12(3):210. https://doi.org/10.3390/educsci12030210
Blei D, Ng A, Jordan M. Latent dirichlet allocation. Journal of machine Learning research. 2003;3(Jan): 993–1022. https://doi.org/10.7551/mitpress/1120.003.0082
Řehůřek R, Sojka P. Software framework for topic modelling with large corpora. Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. 2010;45–50. Available from: http://www.lrec-conf.org/proceedings/lrec2010/workshops/W10.pdf [Accessed 23 June 2026].
Bystrov V, Naboka-Krell V, Staszewska-Bystrova A, Winker P. Choosing the number of topics in LDA models–A Monte Carlo comparison of selection criteria. Journal of Machine Learning Research. 2024;25(79):1-30. Available from: https://jmlr.org/papers/volume25/23-0188/23-0188.pdf [Accessed 23 June 2026].
Hutto C, Gilbert E, editors. Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the international AAAI conference on web and social media; 2014. https://doi.org/10.1609/icwsm.v8i1.14550
Liu FT, Ting KM, Zhou Z-H. Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data (TKDD). 2012;6(1):1-39. https://doi.org/10.1145/2133360.2133363
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation. 1997;9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Taylor SJ, Letham B. Forecasting at scale. The American Statistician. 2018;72(1):37–45. https://doi.org/10.1080/00031305.2017.1380080
Gonzalez T, de la Rubia MA, Hincz KP, Comas-Lopez M, Subirats L, Fort S, et al. Influence of COVID-19 confinement on students' performance in higher education. PLoS ONE. 2020;15(10):e0239490. https://doi.org/10.1371/journal.pone.0239490
Benavides LMC, Tamayo Arias JA, Arango Serna MD, Branch Bedoya JW, Burgos D. Digital transformation in higher education institutions: A systematic literature review. Sensors. 2020;20(11):3291. https://doi.org/10.3390/s20113291

Details

Primary Language

English

Subjects

Natural Language Processing, Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Gamze Yılmaz ^*
0000-0002-1917-3740
Türkiye

İman Askerzade
0000-0003-4466-8128
Türkiye

Early Pub Date

June 23, 2026

Publication Date

June 30, 2026

Submission Date

June 1, 2026

Acceptance Date

June 19, 2026

Published in Issue

Year 2026 Volume: 6 Number: 1

DOI

https://doi.org/10.62189/ci.1961669

IZ

https://izlik.org/JA72AT23GC

Cite

RIS / Bibtex

APA

Yılmaz, G., & Askerzade, İ. (2026). AI-supported analysis of institutional self-assessment reports: A study on the example of Ankara University (2015-2024). Computers and Informatics, 6(1), 8-18. https://doi.org/10.62189/ci.1961669

AMA

1.Yılmaz G, Askerzade İ. AI-supported analysis of institutional self-assessment reports: A study on the example of Ankara University (2015-2024). Computers and Informatics. 2026;6(1):8-18. doi:10.62189/ci.1961669

Chicago

Yılmaz, Gamze, and İman Askerzade. 2026. “AI-Supported Analysis of Institutional Self-Assessment Reports: A Study on the Example of Ankara University (2015-2024)”. Computers and Informatics 6 (1): 8-18. https://doi.org/10.62189/ci.1961669.

EndNote

Yılmaz G, Askerzade İ (June 1, 2026) AI-supported analysis of institutional self-assessment reports: A study on the example of Ankara University (2015-2024). Computers and Informatics 6 1 8–18.

IEEE

[1]G. Yılmaz and İ. Askerzade, “AI-supported analysis of institutional self-assessment reports: A study on the example of Ankara University (2015-2024)”, Computers and Informatics, vol. 6, no. 1, pp. 8–18, June 2026, doi: 10.62189/ci.1961669.

ISNAD

Yılmaz, Gamze - Askerzade, İman. “AI-Supported Analysis of Institutional Self-Assessment Reports: A Study on the Example of Ankara University (2015-2024)”. Computers and Informatics 6/1 (June 1, 2026): 8-18. https://doi.org/10.62189/ci.1961669.

JAMA

1.Yılmaz G, Askerzade İ. AI-supported analysis of institutional self-assessment reports: A study on the example of Ankara University (2015-2024). Computers and Informatics. 2026;6:8–18.

MLA

Yılmaz, Gamze, and İman Askerzade. “AI-Supported Analysis of Institutional Self-Assessment Reports: A Study on the Example of Ankara University (2015-2024)”. Computers and Informatics, vol. 6, no. 1, June 2026, pp. 8-18, doi:10.62189/ci.1961669.

Vancouver

1.Gamze Yılmaz, İman Askerzade. AI-supported analysis of institutional self-assessment reports: A study on the example of Ankara University (2015-2024). Computers and Informatics. 2026 Jun. 1;6(1):8-18. doi:10.62189/ci.1961669