Abstract
Feedback is one of the most effective tools for quality control in education, used by higher education institutions in Ukraine and around the world. It enables the collection of information regarding students' perception of educational materials and facilitates analysis of learning outcomes for a better understanding of the current effectiveness of a specific discipline or educational component within an academic program, with the goal of improving the quality of the discipline and, thus, the overall academic program. Feedback can relate to an entire discipline, a specific semester, the module in which the discipline is taught, or even an individual class session. A wide range of mathematical methods and models, as well as ready-made software libraries, allow for qualitative analysis of feedback results, with most requiring an adequate amount of information for processing. However, in Ukrainian higher education institutions, including Kherson State University, there are academic groups in certain specialties where the number of students is insufficient for applying most analytical methods, especially at the master’s level or higher, or in elective disciplines. To obtain information on learning effectiveness in these groups, equivalent to that in groups with a sufficient number of students, specialized analysis methods are needed. The article describes methods for analyzing feedback results in the form of surveys within the KSU24 system, allowing for consideration of relatively small student groups. It discusses approaches for the software implementation of relevant algorithmic methods and necessary architectural solutions for building a survey module within the KSU24 system. A key functional requirement of this module is ensuring anonymity in the process, as integrity among participants is critically important for obtaining high-quality feedback, especially when processing a limited number of results.
References
В. Биков, “Моделі організаційних систем відкритої освіти”, Київ: Атіка, 2009.
О. Співаковський, М. Львов та Г. Кравцов, “Інноваційні методи управління інформаційними активами вищого навчального закладу”, Комп’ютер у школі та сім’ї. Vol. 3. 2013.
М. Львов, О. Співаковський та Д. Щедролосьєв, “Інформаційна система управління вищим навчальним закладом як платформа реалізації управління академічним процесом”, Вісник Харківського національного університету Серія «Математичне моделювання. Інформаційні технології. Автоматизовані системи управління», 2005.
J.Heil and D.Ifenthaler, “Online assessment in higher education: A systematic review.” Online Learning, Vol. 27.pp. 187-218. doi:10.24059/olj.v27i1.3398.
A. Irawan, Tb. Moh. I., M. Saripudin and S.Fadilah. “Online Teaching and Learning in Higher Education during COVID-19: International Perspectives and Experiences”, Journal of Higher Education Policy and Management, pp. 113–115. Vol. 45.1. 2022. doi:1360080X.2022.2088644.
P.K. Butakor, T.Kakutia, S.M.M. Shah and E.Hunt. “Higher education challenges in the era of Covid-19, from the perspective of educators and students”. ESI Prepr.. 2022.
T.Maryon, V.Dubre, K.Elliott, J.Escareno, M.Fagan, E.Standridge and C.Lieneck. “COVID-19 academic integrity violations and trends: A rapid review”. Educ. Sci.. 2022.
R. Imran, A.Fatima, S.Elbayoumi, K. Allil. “Teaching and learning delivery modes in higher education: Looking back to move forward post-COVID-19 era”. Int. J. Manag. Educ. 2023.
T.D. Altindag, S. F. Elif and E. Tekin. "Is online education working?." Educational Evaluation and Policy Analysis, 2021.
N.Kerimbayev, Z.Umirzakova and R.Shadiev, “A student-centered approach using modern technologies in distance learning: a systematic review of the literature”. Smart Learn. Environ. Vol. 10. pp. 61. 2023. doi:10.1186/s40561-023-00280-8.
M. Lu, "Wilcoxon-Mann-Whitney statistics in randomized trials with non-compliance.", Electron. J. Statist. Vol.18. pp. 465 - 489. 2024. doi:10.1214/23-EJS2209.
Y.Li, Y.Xiao and K.Wang, “A systematic review of high impact empirical studies in STEM education”. IJ STEM Ed Vol. 9. pp. 72. 2022. doi.org:10.1186/s40594-022-00389-1.
K. J. Millman and M. Aivazis, "Python for Scientists and Engineers", Computing in Science & Engineering, Vol. 13, no. 2. pp. 9-12, 2011. doi: 10.1109/MCSE.2011.36.
D. Hooshyar, R.Azevedo, Y.Yang. “Augmenting Deep Neural Networks with Symbolic Educational Knowledge: Towards Trustworthy and Interpretable AI for Education”. Mach. Learn. Knowl. Extr. Vol.6,pp. 593-618, 2024. doi:10.3390/make6010028.
S.Raschka, J.Patterson and C.Nolet, “Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence”, Information. Vol.11. 2020. doi:10.3390/info11040193.
S.Vincent-Lancrin and R. Van der Vlies, “Trustworthy Artificial Intelligence (AI) in Education: Promises and Challenges”. OECD, Paris, France, 2020.
Günther, F., “Machine learning for real-world data from digital mental health”, Division of Informatics, Imaging & Data Sciences, The University of Manchester, 2024.
JA. Gómez-Pulido, Y. Park, R. Soto and JM. Lanza-Gutiérrez, “Data Analytics and Machine Learning in Education”. Applied Sciences. 2023. doi:10.3390/app13031418.
A. Wilson, F. Wedyan and S. Omari, "An Empirical Evaluation and Comparison of the Impact of MVVM and MVC GUI Driven Application Architectures on Maintainability and Testability," 2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), San Antonio, TX, USA, pp. 101-10. 2022. doi: 10.1109/IDSTA55301.2022.9923083.
R.Elmoazen, M.Saqr, and M.Khalil, “Learning analytics in virtual laboratories: a systematic literature review of empirical research”, Smart Learn. Environ. vol.10, 2023. doi: 10.1186/s40561-023-00244-y.
L.Guangping, F.Wenliang and W.Kaifa, “Analysis of t-test misuses and SPSS operations in medical research papers”, Burns & Trauma. Vol.7. 2019. doi:10.1186/s41038-019-0170-3.
E.Toma, and A.Sipica, “The adaptation to technology of teachers and students in the period 2020-2022: a non-parametric analysis”, Scientific Papers Series Management, Economic Engineering in Agriculture & Rural Development. Vol.23. 2023.
Л. Петухова, “Трисуб’єктна дидактика в моделі інноваційного розвитку освітніх систем”, Збірник наукових праць «Педагогічні науки», 2014.
S.Bird, N.Ellison and D.Klein, “The rise of Python: A survey of recent research.” ACM Computing Surveys, Vol. 53, pp. 1-36. 2020.
K. J. Millman and M. Aivazis, "Python for Scientists and Engineers", Computing in Science & Engineering, Vol. 13, no. 2. pp. 9-12, 2011. doi: 10.1109/MCSE.2011.36.
REFERENCES (TRANSLATED AND TRANSLITERATED)
REFERENCES (TRANSLATED AND TRANSLITERATED)
V. Bykov, “Models of the open education organizational systems”, Kyiv: Atika, 2009. (in Ukrainian)
О. Spivakovskiy, М. Lvov and H. Kravtsov, “Innovative methods of managing information assets of a higher educational institution”, Computer at school and home,Vol. 3. 2013. (in Ukrainian)
M. Lvov, О. Spivakovskiy and D. Shchedrolosev, “The information system of the management of a higher educational institution as a platform for implementing the management of the academic process”, Bulletin of the Kharkiv National University Series "Mathematical modeling. Information technologies. Automated control systems", 2005. (in Ukrainian)
J.Heil and D.Ifenthaler, “Online assessment in higher education: A systematic review.” Online Learning, Vol. 27.pp. 187-218. doi:10.24059/olj.v27i1.3398. (in English)
A. Irawan, Tb. Moh. I., M. Saripudin and S.Fadilah. “Online Teaching and Learning in Higher Education during COVID-19: International Perspectives and Experiences”, Journal of Higher Education Policy and Management, pp. 113–115. Vol. 45.1. 2022. doi:1360080X.2022.2088644. (in English)
P.K. Butakor, T.Kakutia, S.M.M. Shah and E.Hunt. “Higher education challenges in the era of Covid-19, from the perspective of educators and students”. ESI Prepr.. 2022. (in English)
T.Maryon, V.Dubre, K.Elliott, J.Escareno, M.Fagan, E.Standridge and C.Lieneck. “COVID-19 academic integrity violations and trends: A rapid review”. Educ. Sci.. 2022. (in English)
R. Imran, A.Fatima, S.Elbayoumi, K. Allil. “Teaching and learning delivery modes in higher education: Looking back to move forward post-COVID-19 era”. Int. J. Manag. Educ. 2023. (in English)
T.D. Altindag, S. F. Elif and E. Tekin. "Is online education working?." Educational Evaluation and Policy Analysis, 2021. (in English)
N.Kerimbayev, Z.Umirzakova and R.Shadiev, “A student-centered approach using modern technologies in distance learning: a systematic review of the literature”. Smart Learn. Environ. Vol. 10. pp. 61. 2023. doi:10.1186/s40561-023-00280-8. (in English)
M. Lu, "Wilcoxon-Mann-Whitney statistics in randomized trials with non-compliance.", Electron. J. Statist. Vol.18. pp. 465 - 489. 2024. doi:10.1214/23-EJS2209. (in English)
Y.Li, Y.Xiao and K.Wang, “A systematic review of high impact empirical studies in STEM education”. IJ STEM Ed Vol. 9. pp. 72. 2022. doi.org:10.1186/s40594-022-00389-1. (in English)
K. J. Millman and M. Aivazis, "Python for Scientists and Engineers", Computing in Science & Engineering, Vol. 13, no. 2. pp. 9-12, 2011. doi: 10.1109/MCSE.2011.36.
D. Hooshyar, R.Azevedo, Y.Yang. “Augmenting Deep Neural Networks with Symbolic Educational Knowledge: Towards Trustworthy and Interpretable AI for Education”. Mach. Learn. Knowl. Extr. Vol.6,pp. 593-618, 2024. doi:10.3390/make6010028. (in English)
S.Raschka, J.Patterson and C.Nolet, “Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence”, Information. Vol.11. 2020. doi:10.3390/info11040193. (in English)
S.Vincent-Lancrin and R. Van der Vlies, “Trustworthy Artificial Intelligence (AI) in Education: Promises and Challenges”. OECD, Paris, France, 2020. (in English)
Günther, F., “Machine learning for real-world data from digital mental health”, Division of Informatics, Imaging & Data Sciences, The University of Manchester, 2024. (in English)
JA. Gómez-Pulido, Y. Park, R. Soto and JM. Lanza-Gutiérrez, “Data Analytics and Machine Learning in Education”. Applied Sciences. 2023. doi:10.3390/app13031418.(in English)
A. Wilson, F. Wedyan and S. Omari, "An Empirical Evaluation and Comparison of the Impact of MVVM and MVC GUI Driven Application Architectures on Maintainability and Testability," 2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), San Antonio, TX, USA, pp. 101-10. 2022. doi: 10.1109/IDSTA55301.2022.9923083. (in English)
R.Elmoazen, M.Saqr, and M.Khalil, “Learning analytics in virtual laboratories: a systematic literature review of empirical research”, Smart Learn. Environ. vol.10, 2023. doi: 10.1186/s40561-023-00244-y. (in English)
L.Guangping, F.Wenliang and W.Kaifa, “Analysis of t-test misuses and SPSS operations in medical research papers”, Burns & Trauma. Vol.7. 2019. doi:10.1186/s41038-019-0170-3. (in English)
E.Toma, and A.Sipica, “The adaptation to technology of teachers and students in the period 2020-2022: a non-parametric analysis”, Scientific Papers Series Management, Economic Engineering in Agriculture & Rural Development. Vol.23. 2023. (in English)
L. Petukhova, “Three subjective didactics in the model of innovation development of educational systems”, Collection of Research Papers «Pedagogical Sciences», 2014. (in Ukrainian)
S.Bird, N.Ellison and D.Klein, “The rise of Python: A survey of recent research.” ACM Computing Surveys, Vol. 53, pp. 1-36. 2020. (in English)
K. J. Millman and M. Aivazis, "Python for Scientists and Engineers", Computing in Science & Engineering, Vol. 13, no. 2. pp. 9-12, 2011. doi: 10.1109/MCSE.2011.36. (in English)

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