FORMATIVE ASSESSMENT IN HIGHER EDUCATION
DOI:
https://doi.org/10.28925/2412-0774.2025.3.7Keywords:
artificial intelligence, formative assessment, higher education, individual learning trajectory, pedagogical technologies, pedagogical partnership, self-assessment.Abstract
The article addresses the pressing issue of the systematic implementation of formative assessment (FA) in Ukrainian higher education institutions in the context of contemporary challenges that require students to develop critical thinking and independent learning skills. The aim of the study is to clarify the state of implementation of formative assessment in higher education institutions and to determine the ways of increasing its effectiveness. To achieve this aim, a set of theoretical and empirical methods was applied, including the analysis of scientific literature as well as the generalization and systematization of data. A pedagogical survey was the key empirical method. A quantitative and comparative analysis of the results revealed significant discrepancies between the declared requirements and the actual practice. The findings confirmed the fragmented application of FA: most students reported little or no experience with systematic self-assessment, peer assessment, or consistent feedback from all lecturers. The authors highlight the positive experience of implementing FA in the teaching of chemistry at Dragomanov Ukrainian State University. The article also emphasizes that the use of digital resources, particularly AI, can enhance FA by automating data analysis and providing personalized feedback. It is argued that AI should serve as a complement rather than a replacement for the lecturer’s creative work, thereby contributing to the development of a high-quality and flexible educational environment. Overall, the study concludes that the integration of FA is a crucial factor in improving the quality of higher education, and further research should focus on designing models for its systematic application in the training of future specialists.
References
Kalinina, I. M. (2021). Formuvalne otsiniuvannia navchalnykh dosiahnen zdobuvachiv vyshchoi osvity [Formative assessment of higher education students’ learning achievements]. Grail of Science, 5, 285–290. https://doi.org/10.36074/grail-of-science.04.06.2021.051
Kostiuchenko, O. V., & Bryl, M. M. (2022). Formuvalne otsiniuvannia u vyshchii shkoli yak instrument rehuliuvannia dynamiky perezhyvannia studentamy tryvohy [Formative assessment in higher education as a tool for regulating students’ anxiety dynamics]. Scientific Notes of the Taurida V. I. Vernadsky National University. Series: Psychology, 33 (1), 99–105. https://doi.org/10.32838/2709-3093/2022.1/16
Honcharenko, S. U., Oliinyk, P. M. (Eds.), Fedorchenko, V. K., et al. (2003). Metodyka navchannia i naukovykh doslidzhen u vyshchii shkoli [Methods of teaching and scientific research in higher education]. Vyshcha shkola.
Ministerstvo osvity i nauky Ukrainy (2021, July 13). Metodychni rekomendatsii shchodo otsiniuvannia rezultativ navchannia uchniv 1–4-kh klasiv zakladiv zahalnoi serednoi osvity [Methodical recommendations on assessment of learning outcomes of students in grades 1–4 of general secondary education institutions] (Nakaz № 813). https://zakon.rada.gov.ua/rada/show/v0813729-21#Text
Morze, N. V., Barna, O. V., & Vember, V. P. (2013). Formuvalne otsiniuvannia: vid teorii do praktyky [Formative assessment: From theory to practice]. Informatyka ta informatsiini tekhnolohii v navchalnykh zakladakh, 6, 45–57. https://elibrary.kubg.edu.ua/id/eprint/6327/1/Morze_N_Barna_O_Vember_V_IITNZ_6_2013_IS.pdf
Prybora, N. A., Shafarenko, L. V., & Zhukova, D. S. (2023). Zahalna khimiia: laboratornyi praktykum-zoshyt [General chemistry: Laboratory practicum-workbook]. Vydavnytstvo Ukrainskoho derzhavnoho universytetu imeni Mykhaila Drahomanova. http://enpuir.npu.edu.ua/handle/123456789/43388
Ministerstvo osvity i nauky Ukrainy (2024, August 2). Pro zatverdzhennia rekomendatsii shchodo otsiniuvannia rezultativ navchannia [On approval of recommendations for assessing learning outcomes] (Nakaz № 1093). https://mon.gov.ua/npa/pro-zatverdzhennia-rekomendatsii-shchodo-otsiniuvannia-rezultativ-navchannia
Baker, T., & Smith, L. (2019, February 25). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://www.nesta.org.uk/report/education-rebooted/
Bloom, B. S. (1968). Learning for Mastery. Evaluation Comment, 1 (2), 1–12. https://files.eric.ed.gov/fulltext/ED053419.pdf
Chappuis, S., & Stiggins, R. J. (2017). An introduction to student-involved assessment for learning. Pearson. https://www.pearson.com/en-us/subject-catalog/p/introduction-to-student-involved-assessment-for-learning-an/P200000000879/9780137526130?srsltid=AfmBOooXBAI1DrFwBu9fkkC9bUaraZYQZqb-axQ-PHiON2AFe126fGIq#main-content-starts
Concina, E. (2022). The Relationship between Self- and Peer Assessment in Higher Education: A Systematic Review. Trends in Higher Education, 1, 41–55. https://doi.org/10.3390/higheredu1010004
Fleckney, P., Thompson, J., Vaz-Serra, P. (2025). Designing effective peer assessment processes in higher education: a systematic review. Higher Education Research & Development, 44, 386–401. https://doi.org/10.1080/07294360.2024.2407083
Hattie, J., & Timperley, H. (2018). The power of feedback. Review of Educational Research, 77 (1), 81–112. https://doi.org/10.3102/003465430298487
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AIED-Book-Excerpt-CCR.pdf
James, M. (2017). Embedding Formative Assessment in Classroom Practice. In R. Maclean (Ed.), Life in Schools and Classrooms. Education in the Asia-Pacific Region: Issues, Concerns and Prospects, vol. 38 (р. 509–525). https://doi.org/10.1007/978-981-10-3654-5_31
Luckin, R. (2017). Towards artificial intelligence-based assessment systems. Nature Human Behaviour, 1, 0028. https://doi.org/10.1038/s41562-016-0028
Rosé, C. P., McLaughlin, E. A., Liu, R., & Koedinger, K. R. (2019). Explanatory learner models: Why machine learning is the wrong way to think about AI in education. Interactive Learning Environments, 27 (2), 182–193. https://doi.org/10.1111/bjet.12858
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45 (3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
Yaroshenko, O., Korshevniuk, T., & Vashchenko, L. (2024). Formative assessment as a factor in modernizing the teaching of natural science subjects. Continuing Professional Education: Theory and Practice, 1, 56–73. https://doi.org/10.28925/1609-8595.2024.1.5
Zawacki-Richter, O., Marín, V. I., Bond, M. & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16 (1), 39. https://doi.org/10.1186/s41239-019-0171-0
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Наталія Прибора, Ірина Соколовська , Вікторія Богатиренко, Валентина Нечипоренко
This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License.
