Ukraine higher education based on data-driven decision making (DDDM)

Author:

Bondar KaterynaORCID,Shestopalova OlenaORCID,Hamaniuk VitaORCID,Tursky VyacheslavORCID

Abstract

This article presents a theoretical review and empirical study of the assessment of the quality of BA and MA online education during the Russian military invasion of Ukraine in 2022. A qualitative theoretical analysis and comparison are made with a study of higher education quality assessment across Ukraine during the war in 2022 (\textit{N} = 12019). The article analyzes the tools and structure of student feedback on the evaluation of the educational process. The undergraduate and graduate programs are modified based on the analysis of this data. In the Ukrainian higher education space, the National Qualifications Framework requirements for each specialty have been under development since 2014. The National Qualifications Framework developed a list of interdisciplinary competencies that should be transferred to academic courses. According to the Ukrainian higher education standard, universities must provide empirical evidence that their students actually acquire these competencies. Baseline data on the assessment of the quality of online education by student teachers during the war in Ukraine is presented using the case study of the Kriviy Rih State Pedagogical University (\textit{N} = 688). Further modifications of the questionnaire to assess the quality of teaching in order to improve data-driven decision-making and testing ethics are proposed.

Publisher

Academy of Cognitive and Natural Sciences

Reference38 articles.

1. Alekseyev, A.N., Konovalova, N.A., Lozova, C.A. and Trofimrnko, P.E., 2015. Importance and complexity of test tasks at evaluation of activity of university professors. Bulletin of the Kyiv National University of Technologies and Design. Technical Science Series, 2(84), pp.240–246. Available from: https://tinyurl.com/2p8ph7rf.

2. Ang, K.L.M., Ge, F.L. and Seng, K.P., 2020. Big Educational Data & Analytics: Survey, Architecture and Challenges. IEEE Access, 8, pp.116392–116414. Available from: https://doi.org/10.1109/ACCESS.2020.2994561.

3. Beatty, B.J., 2019. Hybrid-Flexible Course Design: Implementing student-directed hybrid classes. Provo, Utah: EdTech Books. Available from: https://edtechbooks.org/hyflex.

4. Beauducel, A. and Wittmann, W.W., 2005. Simulation Study on Fit Indexes in CFA Based on Data With Slightly Distorted Simple Structure. Structural Equation Modeling: A Multidisciplinary Journal, 12(1), pp.41–75. Available from: https://doi.org/10.1207/s15328007sem1201_3.

5. Bloom, B.S., 1994. Reflections on development retrospective. In: L.W. Anderson and L.A. Sosniak, eds. Bloom’s Taxonomy: A Forty-year Retrospective. Chicago, IL: The National Society for the Study of Education, Yearbook of the National Society for the Study of Education, pp.1–8.

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