Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X

Ahmet Başal; Mehmet Kayakuş; Cem Oktay Guzeller; Dilek Ilhan-Findikoglu; Fuat Fındıkoğlu

doi:10.17275/per.26.65.13.5

Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X

Abstract

The Türkiye Century Maarif Model, introduced by the Ministry of National Education in 2024, represents a major curriculum reform. Although previous research has examined teachers, administrators, and teacher candidates mainly through surveys and interviews, spontaneous digital reactions remain underexplored. This study investigates discourse surrounding the Maarif Model on X. Posts containing five reform-related hashtags were collected through the X API and analyzed in Python using a KDD-informed text-mining framework. The analysis combined BERT-based sentiment classification, word-frequency analysis, and Latent Dirichlet Allocation topic modelling. Data preparation included duplicate removal, relevance screening, and bot/noise filtering, while automated sentiment labels were interpreted cautiously because no independently coded gold-standard validation set was available. Positive posts constituted the largest sentiment category across all hashtag-based subsets, suggesting substantial symbolic support for the reform. However, negatively oriented posts contained more prominent references to projects, targets, administrative expectations, and implementation-related concerns. Topic patterns also linked the reform discourse to curriculum, teacher roles, student-centered approaches, institutional coordination, and local implementation. Broader hashtags were associated with relatively more critical or contested expressions, whereas institutionally aligned hashtags contained stronger positive discourse. These findings suggest that social media can provide an early source of feedback on large-scale educational reforms, although it should not be treated as representative public opinion. The study illustrates how computational analysis of digital discourse can complement traditional policy-evaluation methods.

Keywords

maarif model, education reform, social media discourse, sentiment analysis, topic modelling, education policy

Ethical Statement

No human subjects were included in the study. The data were collected through X Platform API and the dataset has been comprised of online tweets.

References

Acheampong, F. A., Nunoo-Mensah, H., & Chen, W. (2021). Transformer models for text-based emotion detection: A review of BERT-based approaches. Artificial Intelligence Review, 54(8), 5789–5829. https://doi.org/10.1007/s10462-021-09958-2
Artvin İl Millî Eğitim Müdürlüğü [Artvin Provincial Directorate of National Education]. (2025, January 8). Türkiye Yüzyılı Maarif Modeli eTwinning çalıştayı [Türkiye Century Maarif Model eTwinning workshop]. Millî Eğitim Bakanlığı [Ministry of National Education]. https://artvin.meb.gov.tr/www/turkiye-yuzyili-maarif-modeli-etwinning-calistayi/icerik/5431
Bağcivan, E., Kahraman, H., & Doğan, S. (2025). Teacher perceptions on the Century of Turkey Educational Model based on the functions of education. Kastamonu Education Journal, 33(4), 855–872. https://doi.org/10.24106/kefdergi.1797580
Baz, B. (2024). Examining the maarif model of century of Türkiye in the context of classroom teachers’ views. Journal of Educational Philosophy and Sociology, 5(2), 106-123. https://doi.org/10.29329/jeps.2024.1105.2
Binali, H., Potdar, V., & Wu, C. (2009, February). A state of the art opinion mining and its application domains. In IEEE International Conference on Industrial Technology (pp. 1–6). IEEE. https://doi.org/10.1109/ICIT.2009.4939640
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(Jan), 993–1022. https://www.jmlr.org/papers/v3/blei03a.html
Bruns, A., & Burgess, J. (2015). Twitter hashtags from ad hoc to calculated publics. In N. Rambukkana (Ed.), Hashtag publics: The power and politics of discursive networks [Digital Formations, Volume 103]. (pp. 13-27). Peter Lang Publishing.
Bucher, T. (2018). If... then: Algorithmic power and politics. Oxford University Press. https://doi.org/10.1093/oso/9780190493028.001.0001
Burgul Adıgüzel, F., & Karakuş Aktan, E. N. (2025). Teachers' views on the 2024 Türkiye century maarif model Turkish language and literature curriculum. Journal of Gazi Faculty of Education, 45(3), 819-840. https://doi.org/10.17152/gefad.1758777
Catelli, R., Pelosi, S., & Esposito, M. (2022). Lexicon-based vs. BERT-based sentiment analysis: A comparative study in Italian. Electronics, 11(3), 374. https://doi.org/10.3390/electronics11030374

Chauhan, U., & Shah, A. (2021). Topic modeling using latent Dirichlet allocation: A survey. ACM Computing Surveys (CSUR), 54(7), 1–35. https://doi.org/10.1145/3462478
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4171–4186). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423
Duyul, S., Duyul, Y., Kesman, M., & Kesman, M. (2025). Teachers' views on the Turkish century education model. Ulusal Eğitim Dergisi [Journal of National Education], 5(3), 20262. https://doi.org/10.5281/zenodo.15002346
Düğmeci, H., & İlik, Ş. Ş. (2025). Preschool teachers' perspectives on the Türkiye century education model curriculum. International Journal of Social Sciences Academy (IJSA), 20, 1652-1678. https://doi.org/10.47994/usbad.1802755
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54. https://doi.org/10.1609/aimag.v17i3.1230
Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96-104. https://doi.org/10.1145/2818717
Fırat, Z. S. (2025). Examining the thoughts of preschool teachers on the Turkey century education model preschool education program. Journal of Gazi Faculty of Education, 45(3), 749-764. https://doi.org/10.17152/gefad.1743526
Fitzpatrick, J. L., Sanders, J. R., Worthen, B. R., & Wingate, L. A. (2023). Program evaluation: Alternative approaches and practical guidelines (5th ed.). Pearson.
Gardazi, N. M., Daud, A., Malik, M. K., Bukhari, A., Alsahfi, T., & Alshemaimri, B. (2025). BERT applications in natural language processing: A review. Artificial Intelligence Review, 58, 166. https://doi.org/10.1007/s10462-025-11162-5
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press.
Güven, G., & Yumugan, S. (2025). Skill-based preschool education: Teachers' perspectives on the 2024 maarif program. Western Anatolia Journal of Educational Sciences, 16(1), 1215-1242. https://doi.org/10.51460/baebd.1581408
Ishtiaq, A., Munir, K., Raza, A., Samee, N. A., Jamjoom, M. M., & Ullah, Z. (2024). Product helpfulness detection with novel transformer based BERT embedding and class probability features. IEEE Access, 12, 55905–55917. https://doi.org/10.1109/ACCESS.2024.3390605
Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59–68. https://doi.org/10.1016/j.bushor.2009.09.003
Karataş, F. (2025). The Turkish century education model (TCEM) through the lens of preservice teachers: A thematic analysis of evaluation reports. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi [Journal of the Institute of Social Sciences, Nevşehir Hacı Bektaş Veli University], 15(2), 1068–1087. https://doi.org/10.30783/nevsosbilen.1622630
Kemp, S. (2024, February 23). Digital 2024: Turkey. DataReportal. https://datareportal.com/reports/digital-2024-turkey
Keleş, F., & Cırık, İ. (2025). The experiences of curriculum development experts in the framework of the century of Türkiye education model. Millî Eğitim Dergisi [Journal of National Education], 54(1), 11-60. https://doi.org/10.37669/milliegitim.1707179
Konya İl Millî Eğitim Müdürlüğü AR-GE [Konya Provincial Directorate of National Education R&D Unit]. (2025, January 8). Türkiye Yüzyılı Maarif Modeli kapsamında 7 il çevrim içi eTwinning çalıştayı büyük bir katılımla başladı [Online eTwinning workshop with seven provinces started with strong participation within the scope of the Türkiye Century Maarif Model]. Millî Eğitim Bakanlığı [Ministry of National Education]. https://konyaarge.meb.gov.tr/www/turkiye-yuzyili-maarif-modeli-kapsaminda-7-il-cevrim-ici-etwinning-calistayi-buyuk-bir-katilimla-basladi/icerik/534
Küçük, Z. A., & Kurt, T. (2025). Evaluation of the Turkish century education model in the context of effectiveness of educational reforms according to the opinions of school administrators. International Journal of the Pursuit of Excellence in Social Sciences (IJPESS), 8, 2–27. https://izlik.org/JA99DC48BZ
Lee, J. H., & Ostwald, M. J. (2024). Latent Dirichlet Allocation (LDA) topic models for Space Syntax studies on spatial experience. City, Territory and Architecture, 11, 3. https://doi.org/10.1186/s40410-023-00223-3
Mellon, J., & Prosser, C. (2017). Twitter and Facebook are not representative of the general population: Political attitudes and demographics of British social media users. Research & Politics, 4(3), 1-9. https://doi.org/10.1177/2053168017720008
Mettler, S., & SoRelle, M. (2018). Policy feedback theory. In C. Weible & P. Sabatier (Eds.), Theories of the policy process (4th ed., pp. 103–134). Westview Press. https://doi.org/10.4324/9780429494284-4
Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., & Gao, J. (2021). Deep learning-based text classification: A comprehensive review. ACM Computing Surveys, 54(3), Article 62. https://doi.org/10.1145/3439726
Millî Eğitim Bakanlığı [Ministry of National Education]. (2024). Türkiye yüzyılı maarif modeli öğretim programları ortak metni [Common framework text of the Türkiye century maarif model curricula]. https://tymm.meb.gov.tr/ortak-metin
Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. M. (2013). Is the sample good enough? Comparing data from Twitter’s streaming API with Twitter’s firehose. Proceedings of the International AAAI Conference on Web and Social Media, 7(1), 400-408. https://doi.org/10.1609/icwsm.v7i1.14401
Mujahid, M., Lee, E., Rustam, F., Washington, P. B., Ullah, S., Reshi, A. A., & Ashraf, I. (2021). Sentiment analysis and topic modeling on tweets about online education during COVID-19. Applied Sciences, 11(18), 8438. https://doi.org/10.3390/app11188438
Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(1), 81. https://doi.org/10.1007/s13278-021-00776-6
Oceno, M. (2025). How social desirability bias impacts the expression of emotions. Political Science Research and Methods, 1–10. https://doi.org/10.1017/psrm.2025.10016
Orabi, M., Mouheb, D., Al Aghbari, Z., & Kamel, I. (2020). Detection of bots in social media: A systematic review. Information Processing & Management, 57(4), Article 102250. https://doi.org/10.1016/j.ipm.2020.102250
Özkaral, T. C. (2026). Opinions of social studies teacher candidates on the Türkiye Century Education Model (TYMM) and its implementation. Journal of Teacher Education and Lifelong Learning, 8(1), 59–69. https://doi.org/10.51535/tell.1884578
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1–135. https://dl.acm.org/doi/10.1561/1500000011
Papacharissi, Z. (2014). Affective publics: Sentiment, technology, and politics. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199999736.001.0001
Pierson, P. (1993). When effect becomes cause: Policy feedback and political change. World Politics, 45(4), 595–628. https://doi.org/10.2307/2950710
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Ruths, D., & Pfeffer, J. (2014). Social media for large studies of behavior. Science, 346(6213), 1063–1064. https://doi.org/10.1126/science.346.6213.1063
Sarigoz, O., Gucuk, C., & Yildiz, E. N. (2025). 21st century Türkiye maarif model: Teachers’ opinions on the renewed preschool education program. Educational Policy Analysis and Strategic Research, 20(3), 21-46.
Seyyar, F., & Aydın, M. (2025). Turkey’s century maarif model through the eyes of teachers: A descriptive survey research. International Journal of Education Science and Technology, 11(3), 201–224. https://doi.org/10.47714/uebt.1789014
Singh, V. K., Piryani, R., Uddin, A., & Waila, P. (2013, March). Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification. In 2013 International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s) (pp. 712–717). IEEE. https://doi.org/10.1109/iMac4s.2013.6526500
Spillane, J. P., Reiser, B. J., & Reimer, T. (2002). Policy implementation and cognition: Reframing and refocusing implementation research. Review of Educational Research, 72(3), 387–431. http://www.jstor.org/stable/3515992
Talim ve Terbiye Kurulu Başkanlığı [Board of Education]. (2025, January 11). Talim ve Terbiye Kurulu Başkanlığı Temmuz-Aralık 2024 genel değerlendirme toplantısı [Board of Education July-December 2024 general evaluation meeting]. Millî Eğitim Bakanlığı [Ministry of National Education]. https://ttkb.meb.gov.tr/www/talim-ve-terbiye-kurulu-baskanligi-temmuz-aralik-2024-genel-degerlendirme-toplantisi/icerik/702
Toçoğlu, M. A., & Onan, A. (2021). Sentiment analysis on students’ evaluation of higher educational institutions. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. Sari, S. Cebi, & A. Tolga (Eds.), Intelligent and fuzzy techniques: Smart and innovative solutions (Advances in Intelligent Systems and Computing, Vol. 1197). Springer. https://doi.org/10.1007/978-3-030-51156-2_197
Topaloğlu, D., & Polat, O. (2025). Trends and methods in intensive care unit (ICU) research using machine learning: Latent Dirichlet Allocation (LDA)-based thematic literature review. BMC Medical Informatics and Decision Making, 25(1), 282. https://doi.org/10.1186/s12911-025-03132-2
Trivedi, S. K., Patra, P., Singh, A., Deka, P., & Srivastava, P. R. (2023). Analyzing the research trends of COVID-19 using topic modeling approach. Journal of Modelling in Management, 18(4), 1204–1227. https://doi.org/10.1108/JM2-02-2022-0045
Tubishat, M., Al-Obeidat, F., & Shuhaiber, A. (2023, July). Sentiment analysis of using ChatGPT in education. In 2023 International Conference on Smart Applications, Communications and Networking (SmartNets) (pp. 1–7). IEEE. https://doi.org/10.1109/SmartNets58706.2023.10215977
Uludağ Kırçıl, R., & Uluçınar Sağır, Ş. (2025). Reflections of the Türkiye century education model in the classroom: Teachers' views. Erciyes Journal of Education, 9(1), 24-46. https://doi.org/10.32433/eje.1673094
Uygun, K., & Akgül, G. (2024). Opinions of social studies teachers on the Turkish century education model. Dumlupınar University Graduate School of Education Journal, 8(2), 81–102. https://izlik.org/JA72PF46DD
Vijayarani, S., Ilamathi, M. J., & Nithya, M. (2015). Multimedia mining research – An overview. International Journal of Computer Graphics & Animation (IJCGA), 5(1), 7–16. https://doi.org/10.5121/ijcga.2015.5105
Zhou, H., Yu, H., & Hu, R. (2017). Topic evolution based on the probabilistic topic model. A review. Frontiers of Computer Science, 11(5), 786-802. https://doi.org/10.1007/s11704-016-5442-5

Details

Primary Language

English

Subjects

Curriculum and Instration (Other)

Journal Section

Research Article

Authors

Ahmet Başal
0000-0003-4295-4577
Türkiye

Mehmet Kayakuş
0000-0003-0394-5862
Türkiye

Cem Oktay Guzeller
0000-0002-2700-3565
Türkiye

Dilek Ilhan-Findikoglu ^*
0000-0003-1663-5169
Türkiye

Fuat Fındıkoğlu
0000-0002-4480-353X
Türkiye

Publication Date

August 30, 2026

Submission Date

March 11, 2026

Acceptance Date

May 31, 2026

Published in Issue

Year 2026 Volume: 13 Number: 5

DOI

https://doi.org/10.17275/per.26.65.13.5

IZ

https://izlik.org/JA73UR95JM

APA

Başal, A., Kayakuş, M., Guzeller, C. O., Ilhan-Findikoglu, D., & Fındıkoğlu, F. (2026). Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X. Participatory Educational Research, 13(5), 70-93. https://doi.org/10.17275/per.26.65.13.5

AMA

1.Başal A, Kayakuş M, Guzeller CO, Ilhan-Findikoglu D, Fındıkoğlu F. Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X. PER. 2026;13(5):70-93. doi:10.17275/per.26.65.13.5

Chicago

Başal, Ahmet, Mehmet Kayakuş, Cem Oktay Guzeller, Dilek Ilhan-Findikoglu, and Fuat Fındıkoğlu. 2026. “Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X”. Participatory Educational Research 13 (5): 70-93. https://doi.org/10.17275/per.26.65.13.5.

EndNote

Başal A, Kayakuş M, Guzeller CO, Ilhan-Findikoglu D, Fındıkoğlu F (August 1, 2026) Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X. Participatory Educational Research 13 5 70–93.

IEEE

[1]A. Başal, M. Kayakuş, C. O. Guzeller, D. Ilhan-Findikoglu, and F. Fındıkoğlu, “Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X”, PER, vol. 13, no. 5, pp. 70–93, Aug. 2026, doi: 10.17275/per.26.65.13.5.

ISNAD

Başal, Ahmet - Kayakuş, Mehmet - Guzeller, Cem Oktay - Ilhan-Findikoglu, Dilek - Fındıkoğlu, Fuat. “Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X”. Participatory Educational Research 13/5 (August 1, 2026): 70-93. https://doi.org/10.17275/per.26.65.13.5.

JAMA

1.Başal A, Kayakuş M, Guzeller CO, Ilhan-Findikoglu D, Fındıkoğlu F. Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X. PER. 2026;13:70–93.

MLA

Başal, Ahmet, et al. “Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X”. Participatory Educational Research, vol. 13, no. 5, Aug. 2026, pp. 70-93, doi:10.17275/per.26.65.13.5.

Vancouver

1.Ahmet Başal, Mehmet Kayakuş, Cem Oktay Guzeller, Dilek Ilhan-Findikoglu, Fuat Fındıkoğlu. Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X. PER. 2026 Aug. 1;13(5):70-93. doi:10.17275/per.26.65.13.5

27707

Creative Commons Atıf 4.0 International Lisance

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Digital Reactions, Discourse and Sentiment on Educational Reform: A Text Mining Study of the Türkiye Century Maarif Model on X

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