SavedAdd your journal

Journals / Yükseköğretim Dergisi / 2026 / Cilt: 15 Sayı: 1

Early Warning Systems in the Context of Curriculum Flexibility and Agility: Systematic Review

Program Esnekliği ve Çevikliği Bağlamında Erken Uyarı Sistemleri: Sistematik Derleme

Asil Derin Kılıç(KAFKAS ÜNİVERSİTESİ)0000-0003-2195-9889
Emine Er(KAFKAS ÜNİVERSİTESİ)0000-0003-4738-9749
Adnan Küçükoğlu(ATATÜRK ÜNİVERSİTESİ)0000-0002-8522-258X
Pages
209–233
Publication Date
2025-04-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In conclusion, it is suggested that the early warning system should be further promoted, based on the findings of this review, which offers valuable insights into key issues and establishes priorities for future research.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This systematic review aimed to examine and introduce early warning systems from both contemporary and educational perspectives to identify university students who exhibit risky behaviors and to implement the necessary interventions.
📊
Key Findings & Data:To achieve this goal, a total of 31 studies—2 national and 29 international—were reviewed.
💡
Conclusion & Impact:In conclusion, it is suggested that the early warning system should be further promoted, based on the findings of this review, which offers valuable insights into key issues and establishes priorities for future research.
🔬Design:Review & Systematic Survey
📍Scope:2000 Verileri
⚙️Variables:Yükseköğretim · Erken Uyarı Sistemi · Program Esnekliği

Abstract

This systematic review aimed to examine and introduce early warning systems from both contemporary and educational perspectives to identify university students who exhibit risky behaviors and to implement the necessary interventions. To achieve this goal, a total of 31 studies—2 national and 29 international—were reviewed. These studies included various implementations of early warning systems at the higher education level, and 8 research questions were explored. The review revealed that studies on early warning systems have primarily been published after 2000. It was also found that quantitative research methods were most commonly used in these studies, which focused on university students. Additionally, learning management systems (such as Moodle, BookRoll, Learning Smart System, Teaching Assistant System, etc.) were the main data sources in these studies. The data from these systems included metrics such as the number of clicks, total time spent in the system, resources accessed, messages sent or read in forums, lecture notes, and book reading data. In terms of data analysis, data mining and regression analysis methods were predominantly used. Moreover, “early warning system” was commonly used as a keyword in the studies reviewed. In conclusion, it is suggested that the early warning system should be further promoted, based on the findings of this review, which offers valuable insights into key issues and establishes priorities for future research.

Özet

Sistematik derleme yöntemiyle yürütülen bu araştırma kapsamında, riskli davranışlar sergileyen üniversite öğrencilerinin belirlenebilmesi ve gerekli müdahalelerin gerçekleştirilebilmesi noktasında güncel ve eğitimsel bir perspektiften erken uyarı sistemlerinin incelenmesi ve tanıtılması hedeflenmiştir. Bu hedef doğrultusunda ilgili alan yazından yükseköğretimde erken uyarı sistemlerine ilişkin çeşitli uygulamaları içeren 2’si ulusal ve 29’u uluslararası olmak üzere toplam 31 araştırma incelenerek sekiz araştırma sorusuna yanıt aranmıştır. Araştırma sonucunda makale türünde yayımlanan araştırmaların genellikle 2000 yılından sonra gerçekleştirildiği gözlenirken, genel anlamda üniversite öğrencileriyle gerçekleştirilen bu araştırmalarda çoğunlukla nicel araştırma yöntemlerinin kullanıldığı gözlenmiştir. Öte yandan araştırmalarda veri kaynağı olarak en çok öğrenme yönetim sistemlerinden (Moodle, BookRoll, Öğrenme Akıllı Sistemi, Eğitim Yönetimi Sistemi ve Öğretim Asistanı Sistemi gibi kaynaklardan elde edilen tıklama sayısı, sistemde geçirilen toplam süre, kaynaklar, forumlarda gönderilen ve okunan mesajlar, ders notları, kitap okuma verileri vb.) yararlanıldığı saptanırken, verilerin analizi noktasında ise en çok veri madenciliği ve regresyon analizi yöntemlerinden yararlanıldığı saptanmıştır. Ayrıca araştırmalarda anahtar sözcük olarak genellikle erken uyarı sistem(ler)inin kullanıldığı belirlenmiştir. Sonuç olarak önemli konuların kapsamına dair açıklamaların yapılmasına ve daha fazla araştırma için önceliklerin belirlenmesine yönelik eşsiz fırsatların sunulduğu geleneksel ya da sistematik derleme türündeki çeşitli araştırmalar ışığında erken uyarı sistemlerinin yeterince tanıtılması gerektiği düşünülmektedir.

🔖

Cite This Article

Kılıç, A. D., Er, E., Küçükoğlu, A. (2025). Early Warning Systems in the Context of Curriculum Flexibility and Agility: Systematic Review. Yükseköğretim Dergisi, Cilt: 15 Sayı: 1, 209–233. https://doi.org/10.53478/yuksekogretim.1442815

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (67)

  1. Aguilar, S., Lonn, S., & Teasley, S. D. (2014, March, 24–28). Perceptions and use of an early warning system during a higher education transition program [Bildiri Sunumu]. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, Indianapolis.
  2. Akçapınar, G. (2014). Çevrim içi öğrenme ortamındaki etkileşim verilerine göre öğrencilerin akademik performanslarının veri madenciliği yaklaşımı ile modellenmesi [Yayımlanmamış doktora tezi]. Hacettepe Üniversitesi.
  3. Akçapınar, G., Altun, A., & Aşkar, P. (2019). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1), 1–20. https://doi.org/10.1186/s41239-019-0172-z
  4. Akçapınar, G., Hasnine, M. N., Majumdar, R., Flanagan, B., & Ogata, H. (2019). Developing an early-warning system for spotting at-risk students by using eBook interaction logs. Smart Learning Environments, 6(1), 1–15. https://doi.org/10.1186/s40561-019-0083-4
  5. Alexandro, D. (2018). Aiming for success: Evaluating statistical and machine learning methods to predict high school student performance and improve early warning systems [Doktora Tezi, Connecticut Üniversitesi]. UCONN Library. https://opencommons.uconn.edu/dissertations/1982
  6. Bakırtaş, D., & Nazlıoğlu, M. (2021). Okul terkinin maliyeti: Kamu gelirleri kapsamında Türkiye değerlendirmesi. Alanya Akademik Bakış, 5(2), 671–691. https://doi.org/10.29023/alanyaakademik.847747
  7. Bañeres, D., González, M. E., Roldán, A. E., & Cortadas, P. (2023). An early warning system to identify and intervene online dropout learners. International Journal of Educational Technology in Higher Education, 20(1), 1–25. https://doi.org/10.1186/s41239-022-00371-5
  8. Bañeres, D., Rodríguez, M. E., Roldán, A. E., & Karadeniz, A. (2020). An early warning system to detect at-risk students in online higher education. Applied Sciences, 10(13), 4427–4455. https://doi.org/10.3390/app10134427
  9. Batool, S., Rashid, J., Nisar, M. W., Kim, J., Kwon, H. Y., & Hussain, A. (2023). Educational data mining to predict students’ academic performance: A survey study. Education and Information Technologies, 28(1), 905–971. https://doi.org/10.1007/s10639-022-11152-y
  10. Beck, H. P., & Davidson, W. D. (2001). Establishing an early warning system: Predicting low grades in college students from survey of academic orientations scores. Research in Higher Education, 42(6), 709–723. https://doi.org/10.1023/A:1012253527960
  11. Bloland, H. G. (2005). Whatever happened to postmodernism in higher education?: No requiem in the new millennium. The Journal of Higher Education, 76(2), 121–150. https://doi.org/10.1080/00221546.2005.11778908
  12. Brink, S., Carlsson, C. J., Enelund, M., Georgsson, F., Keller, E., Lyng, R., & McCartan, C. (2021, October 13–16). Curriculum agility: Responsive organization, dynamic content and flexible education [Bildiri Sunumu]. 2021 IEEE Frontiers in Education Conference (FIE), Lincoln.
  13. Brink, S., Carlsson, C. J., Georgsson, F., Keller, E., Lyng, R., & McCartan, C. (2020, June 8). Assessing curriculum agility in a CDIO engineering education [Bildiri Sunumu]. 16th International CDIO Conference, Chalmers University of Technology.
  14. Bruce, M., Bridgeland, J. M., Fox, J. H., & Balfanz, R. (2011). On track for success: The use of early warning indicator and intervention systems to build a grad nation. Johns Hopkins University, School of Education, Everyone Graduates Center. http://eric.ed.gov/?id=ED526421
  15. Cano, A., & Leonard, J. D. (2019). Interpretable multiview early warning system adapted to underrepresented student populations. Transactions on Learning Technologies, 12(2), 198–211. https://doi.org/10.1109/TLT.2019.2911079
  16. Cardona, T., Cudney, E. A., Hoerl, R., & Snyder, J. (2023). Data mining and machine learning retention models in higher education. Journal of College Student Retention: Research, Theory & Practice, 25(1), 51–75. https://doi.org/10.1177/1521025120964920
  17. Chui, K. T., Fung, D. C. L., Lytras, M. D., & Lam, T. M. (2020). Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Computers in Human Behavior, 107, 1–7. https://doi.org/10.1016/j.chb.2018.06.032
  18. Chung, J. Y., & Lee, S. (2019). Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, 96, 346–353. https://doi.org/10.1016/j.childyouth.2018.11.030
  19. Desai, M. S., Hart, J., & Richards, T. C. (2008). E-learning: Paradigm shift in education. Education, 129(2), 327–334. https://research.ebsco.com/c/f57n4n/search/details/lcqvyjsrar?db=asn
  20. Dibek, M. İ., & Toptaş, B. (2023). Bütün parçaların toplamından daha fazladır: Eğitim bilimlerinde sistematik derleme nasıl yazılır? Erzincan Üniversitesi Eğitim Fakültesi Dergisi, 25(3), 538–550. https://doi.org/10.17556/erziefd.1176257
  21. Doğan, K., & Arslantekin, S. (2016). Büyük veri: önemi, yapısı ve günümüzdeki durum. Ankara Üniversitesi Dil ve Tarih-Coğrafya Fakültesi Dergisi, 56(1), 15–36. http://dtcfdergisi.ankara.edu.tr/index.php/dtcf/article/view/4
  22. Domínguez, M., Rodríguez, M., Nistal, M., & Mikic-Fonte, F. (2019, June 27–28). Predictors and early warning systems in higher education: A systematic literature review [Bildiri Sunumu]. Learning Analytics Summer Institute Spain 2019, Spain.
  23. Foo, Y. Z., O’Dea, R. E., Koricheva, J., Nakagawa, S., & Lagisz, M. (2021). A practical guide to question formation, systematic searching and study screening for literature reviews in ecology and evolution. Methods in Ecology and Evolution, 12, 1705–1720. https://doi.org/10.1111/2041-210X.13654
  24. Frazelle, S., & Barton, R. (2013). Implementing early warning systems. Principal’s Research Review, 8(4), 1–7. https://educationnorthwest.org/sites/default/files/resources/Principal’s%20Research%20Review,%20July%202013.pdf
  25. FuSheng, Z., Ling, Y., ZongFu, J., XinJun, Q., & Mingkui, Z. (2017, August 22–25). The monitoring and early-warning system of students’ learning based on the campus cloud [Bildiri Sunumu]. 12th International Conference on Computer Science and Education, Houston.
  26. Hanover Research (2014). Early alert systems in higher education. https://www.hanoverresearch.com/wp-content/uploads/2017/08/Early-Alert-Systems-in-Higher-Education.pdf
  27. Heppen, J. B., & Bowles-Therriault, S. B. (2008). Developing early warning systems to identify potential high school dropouts. National High School Center. https://files.eric.ed.gov/fulltext/ED521558.pdf
  28. Hew, K. F., & Cheung, W. S. (2014). Students’ and instructors’ use of massive open online courses (MOOCs): Motivations and challenges. Educational Research Review, 12, 45–58. https://doi.org/10.1016/j.edurev.2014.05.001
  29. Howard, E., Meehan, M., & Parnell, A. (2018). Contrasting prediction methods for early warning systems at undergraduate level. The Internet and Higher Education, 37, 66–75. https://doi.org/10.1016/j.iheduc.2018.02.001
  30. Hoyos, J. K., & Daza, G. (2023). Predictive model to identify college students with high dropout rates. Revista Electrónica de Investigación Educativa, 25, 1–10. https://doi.org/10.24320/redie.2023.25.e13.5398
  31. Hu, Y. H., Lo, C. L., & Shih, S. P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 36, 469–478. https://doi.org/10.1016/j.chb.2014.04.002
  32. Johnson, E., & Semmelroth, C. (2010). The predictive validity of the early warning system tool. NASSP Bulletin, 94(2), 120–134. https://doi.org/10.1016/j.childyouth.2018.11.030
  33. Jokhan, A., Sharma, B., & Singh, S. (2019). Early warning system as a predictor for student performance in higher education blended courses. Studies in Higher Education, 44(11), 1900–1911. https://doi.org/10.1080/03075079.2018.1466872
  34. Kaban, A., & Bilen, Ö. (2021). Eğitimde kullanılan erken uyarı sistemleri konusunda yapılmış çalışmaların incelenmesi. MANAS Sosyal Araştırmalar Dergisi, 10(2), 788–797. https://doi.org/10.33206/mjss.816902
  35. Krumm, A., Waddington, R., Teasley, S. D., & Lonn, S. (2014). A learning management system-based early warning system for academic advising in undergraduate engineering. In A. Larusson & B. White (Eds.), Learning analytics. https://hdl.handle.net/2027.42/107974
  36. Li, F. (2012, October 20–21). Study on emergency early warning system for university students [Bildiri Sunumu]. International Conference on Information Management, Innovation Management and Industrial Engineering, Sanya.
  37. Liz-Domínguez, M., Caeiro-Rodríguez, M., Llamas-Nistal, M., & Mikic-Fonte, F. (2019, June 27–28). Predictors and early warning systems in higher education: A systematic literature review [Bildiri Sunumu]. Learning Analytics Summer Institute Spain 2019: Learning Analytics in Higher Education, Spain.
  38. Luo, J. (2020, September 11–13). Study on academic early warning system for information engineering vocational students [Bildiri Sunumu]. 2020 International Conference on Machine Learning and Computer Application, Shangri-La, China.
  39. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599. https://doi.org/10.1016/j.compedu.2009.09.008
  40. Manathunga, C. (2005). Early warning signs in postgraduate research education: A different approach to ensuring timely completions. Teaching in Higher Education, 10(2), 219–233. https://doi.org/10.1080/1356251042000337963
  41. Metin, F. (2023). Yükseköğretimden istihdama geçişte COVID-19 etkisi. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 25(1), 143–168. https://doi.org/10.26745/ahbvuibfd.1196890
  42. Mi, C. (2019). Student performance early warning based on data mining. International Journal of Performability Engineering, 15(3), 822–833. https://doi.org/10.23940/ijpe.19.03.p11.822833
  43. Organisation for Economic Co-operation and Development (OECD). (2020). Population with tertiary education. https://data.oecd.org/eduatt/population-with-tertiary-education.html
  44. Organisation for Economic Co-operation and Development (OECD). (2021). Education at a glance 2021: OECD indicators. https://www.oecd-ilibrary.org/docserver/1125513b-en.pdf
  45. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., & Moher, D. (2021). Updating guidance for reporting systematic reviews: Development of the PRISMA 2020 statement. Journal of Clinical Epidemiology, 134, 103–112. https://doi.org/10.1016/j.jclinepi.2021.02.003
  46. Pamuk, Y., Hamurcu, H., & Armağan, B. (2014). Sınıf öğretmeni adaylarının durumluk ve sürekli kaygı düzeylerinin incelenmesi (İzmir-Buca Örneği). Bartın University Journal of Faculty of Education, 3(2), 293–316. https://doi.org/10.14686/BUEFAD.201428183
  47. Petticrew, M., & Roberts, H. (2006). Systematic reviews in the social sciences: A practical guide. Blackwell Publishing. https://fcsalud.ua.es/en/portal-de-investigacion/documentos/tools-for-the-bibliographicresearch/guide-of-systematic-reviews-in-social-sciences.pdf
  48. Plak, S., Cornelisz, I., Meeter, M., & van Klaveren, C. (2022). Early warning systems for more effective student counselling in higher education: Evidence from a Dutch field experiment. Higher Education Quarterly, 76(1), 131–152. https://doi.org/10.1111/hequ.12298
  49. Raffaghelli, J. E., Rodríguez, M. E., Roldán, A. E., & Bañeres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in higher education. Computers & Education, 182, 1–14. https://doi.org/10.1016/j.compedu.2022.104468
  50. Santoso, L. W. (2018, October 26–28). Early warning system for academic using data mining [Bildiri Sunumu]. Fourth International Conference on Advances in Computing, Communication & Automation, Malaysia.
  51. Santoso, L. W. (2020). Predicting student performance in higher education using multi-regression models. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 18(3), 1354–1360. https://doi.org/10.12928/TELKOMNIKA.v18i3.14802
  52. Shapiro, D., Dundar, A., Huie, F., Wakhungu, P. K., Bhimdiwali, A., Nathan, A., & Youngsik, H. (2018). Transfer and mobility: A national view of student movement in postsecondary institutions, fall 2011 cohort (Signature Report No. 15). National Student Clearinghouse Research Center. https://nscresearchcenter.org/wp-content/uploads/Signature-Report-15.pdf
  53. Siemens, G., & Baker, R. S. D. (2012, April). Learning analytics and educational data mining: Towards communication and collaboration [Bildiri Sunumu]. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, University of British Columbia.
  54. Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40. https://er.educause.edu/-/media/files/article-downloads/erm1151.pdf
  55. Snow-Andrade, S. (2018). A responsive higher education curriculum: Change and disruptive innovation. D. Parrish & J. Joyce-McCoach (Ed.), Innovations in higher education: Cases on transforming and advancing practice (s. 1–10) içinde. IntechOpen. https://doi.org/10.5772/intechopen.80443
  56. Şenay, H. H., Şengül, M., & Seggie, F. N. (2020). Türkiye’de yükseköğretim çalışmaları: Eğilimler ve öneriler. Üniversite Araştırmaları Dergisi, 3(1), 1–13. https://dergipark.org.tr/en/download/article-file/1051854
  57. Tampke, D. R. (2013). Developing, implementing, and assessing an early alert system. Journal of College Student Retention: Research, Theory & Practice, 14(4), 523–532. https://doi.org/10.2190/CS.14.4.e
  58. Till, H. (2000). Towards the development of an early warning system for the identification of the student at risk of failing the first year of higher education [Doktora tezi, University of South Africa]. South African National ETD Portal. https://api.semanticscholar.org/CorpusID:146221209
  59. Trussel, J. M., & Smalley, L. (2018). Demography and student success: Early warning tools to drive intervention. Journal of Education for Business, 93(8), 363–372. https://doi.org/10.1080/08832323.2018.1496893
  60. Türkiye İstatistik Kurumu (TÜİK). (2022). İşgücü istatistikleri, Ağustos 2022. https://data.tuik.gov.tr/Bulten/Index?p=İşgücü-İstatistikleri-II.-Çeyrek:-Nisan-Haziran-2022-45652&dil=1
  61. United States Department of Education. (2016, September). Issue Brief: Early warning systems. https://www2.ed.gov/rschstat/eval/highschool/early-warning-systems-brief.pdf
  62. Viberg, O., Hatakka, M., Balter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027
  63. Waddington, R., Nam, S., Lonn, S., & Teasley, S. D. (2016). Improving early warning systems with categorized course resource usage. Journal of Learning Analytics, 3(3), 263–290. https://doi.org/10.18608/jla.2016.33.13
  64. Wang, Z., Zhu, C., Ying, Z., Zhang, Y., Wang, B., Jin, X., & Yang, H. (2018, November 10–12). Design and implementation of early warning system based on educational big data [Bildiri Sunumu]. 5th International Conference on Systems and Informatics, Nanjing.
  65. Yang, C. Y., Liu, J. Y., & Huang, S. (2020, September 2). Research on early warning system of college students’ behavior based on big data environment [Makale Sunumu]. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Nice.
  66. Yurdakul, S., Yıldırım, B., & Şahin, S. A. (2021). Eğitime bakış 2021: İzleme ve değerlendirme raporu (Yayın No. 117). https://www.ebs.org.tr/ebs_files/files/yayinlarimiz2021/EgitimeBakis2021.pdf
  67. Yükseköğretim Kurulu (YÖK). (2022). Yükseköğretim Kurulu Büyük Veri Projesi. https://www.yok.gov.tr/Documents/Yayinlar/Yayinlarimiz/2022/yuksekogretim-kurulu-buyuk-veri-projesi.pdf