Cloud technologies and learning analytics: web application for PISA results analysis and visualization

Authors

DOI:

https://doi.org/10.55056/cte.302

Keywords:

learning analytics, Cloud Technologies, PISA, web application

Abstract

This article analyzes the ways to apply Learning Analytics, Cloud Technologies, and Big Data in the field of education on the international level. This paper provides examples of international analytical researches and cloud technologies used to process the results of those researches. It considers the PISA research methodology and related tools, including the IDB Analyzer application, free R intsvy environment for processing statistical data, and cloud-based web application PISA Data Explorer. The paper justifies the necessity of creating a stand-alone web application that supports Ukrainian localization and provides Ukrainian researchers with rapid access to well-structured PISA data. In particular, such an application should provide for data across the factorial features and indicators applied at the country level and demonstrate the Ukrainian indicators compared to the other countries’ results. This paper includes a description of the application core functionalities, architecture, and technologies used for development. The proposed solution leverages the shiny package available with R environment that allows implementing both the UI and server sides of the application. The technical implementation is a proven solution that allows for simplifying the access to PISA data for Ukrainian researchers and helping them utilize the calculation results on the key features without having to apply tools for processing statistical data.

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References

Andriushchenko, K., Rudyk, V., Riabchenko, O., Kachynska, M., Marynenko, N., Shergina, L., Kovtun, V., Tepliuk, M., Zhemba, A. and Kuchai, O., 2019. Processes of managing information infrastructure of a digital enterprise in the framework of the «Industry 4.0» concept. Eastern-european journal of enterprise technologies, 1(3 (97)), p.60–72. Available from: https://doi.org/10.15587/1729-4061.2019.157765. DOI: https://doi.org/10.15587/1729-4061.2019.157765

Arikan, S., 2014. A regression model with a new tool: IDB analyzer for identifying factors predicting mathematics performance using PISA 2012 indices. Us-china education review, 4(10), pp.716–727. DOI: https://doi.org/10.17265/2161-623X/2014.10A.004

Brown, M., McCormack, M., Reeves, J., Brooks, D.C., Grajek, S., Alexander, B., Bali, M., Bulger, S., Dark, S., Engelbert, N., Gannon, K., Gauthier, A., Gibson, D., Gibson, R., Lundin, B., Veletsianos, G. and Weber, N., 2020. 2020 EDUCAUSE Horizon Report: Teaching and Learning Edition Available. Louisville, CO: EDUCAUSE. Available from: https://library.educause.edu/-/media/files/library/2020/3/2020_horizon_report_pdf.

The Digital Enterprise: moving from experimentation to transformation, 2018. Available from: http://www3.weforum.org/docs/Media/47538_Digital%20Enterprise_Moving_Experimentation_Transformation_report_2018%20-%20final%20(2).pdf.

EIT 2008-2020: zvity, 2020. Available from: https://testportal.gov.ua/ofzvit/.

El-Seoud, M.S.A., El-Ssofany, H.F., Taj-Eddin, I.A.T.F., Nosseir, A. and El-Khouly, M.M., 2013. Implementation of Web-Based Education in Egypt through Cloud Computing Technologies and Its Effect on Higher Education. Higher education studies, 3(3), pp.62–76. Available from: https://doi.org/10.5539/hes.v3n3p62. DOI: https://doi.org/10.5539/hes.v3n3p62

Heyer, O., 2019. From Learning to Data Analytics: Some Implications for IT Strategy and Transformation. Educause review, 54(4).

Kiv, A., Shyshkina, M., Semerikov, S., Striuk, A., Striuk, M. and Shalatska, H., 2020. CTE 2019 - When cloud technologies ruled the education. Ceur workshop proceedings, 2643, pp.1–59. 7th Workshop on Cloud Technologies in Education, CTE 2019 ; Conference Date: 20 December 2019. Available from: http://ceur-ws.org/Vol-2643/paper00.pdf.

Kiv, A., Soloviev, V. and Semerikov, S., 2019. CTE 2018 – How cloud technologies continues to transform education. Ceur workshop proceedings, 2433, pp.1–19. Available from: http://ceur-ws.org/Vol-2433/paper00.pdf. DOI: https://doi.org/10.55056/cte.352

Kiv, A.E., Soloviev, V.N. and Semerikov, S.O., 2021. XII International Conference on Mathematics, Science and Technology Education. Journal of physics: Conference series, 1840(1), p.011001. Available from: https://doi.org/10.1088/1742-6596/1840/1/011001. DOI: https://doi.org/10.1088/1742-6596/1840/1/011001

Kiv, A.E., Soloviev, V.N., Semerikov, S.O., Striuk, A.M., Osadchyi, V.V., Vakaliuk, T.A., Nechypurenko, P.P., Bondarenko, O.V., Mintii, I.S. and Malchenko, S.L., 2021. XIII International Conference on Mathematics, Science and Technology Education. Journal of physics: Conference series. DOI: https://doi.org/10.1088/1742-6596/2288/1/011001

Ko, H.W. and Chan, Y.L., 2009. Family factors and primary students’ reading attainment. 493 CTE Workshop Proceedings, 2021, Vol. 8: CTE-2020, pp. 484-494 Chinese education & society, 42(3), pp.33–48. Available from: https://doi.org/10.2753/CED1061-1932420302. DOI: https://doi.org/10.2753/CED1061-1932420302

Pavlenko, V., Prokhorov, A., Kuzminska, O. and Mazorchuk, M., 2017. Competence approach to modeling and control of students’ learning pathways in the cloud service. Ceur workshop proceedings, 1844, pp.257–264.

PISA-2018: zvity, 2020. Available from: http://pisa.testportal.gov.ua/pisa-2018-zvity/.

Vasyl’ieva, D.V., Holovko, M.V., Zhuk, Y.O., Kozlenko, O.H., Liashenko, O.I., Naumenko, S.O. and Novos’olova, V.I., 2020. UROKY PISA-2018: metodychni rekomendatsii. Instytut pedahohiky NAPN Ukrainy, Kyiv: Pedahohichna dumka, p.96.

Walker, S., Olney, T., Wood, C., Clarke, A. and Dunworth, M., 2019. How do tutors use data to support their students? Open learning: The journal of open, distance and e-learning, 34(1), pp.118–133. Available from: https://doi.org/10.1080/02680513.2018.1554476. DOI: https://doi.org/10.1080/02680513.2018.1554476

Wickham, H., 2020. Mastering Shiny. Available from: https://mastering-shiny.org/index.html.

Yang, G., Badri, M., Al Rashedi, A. and Almazroui, K., 2018. The role of reading motivation, self-efficacy, and home influence in students’ literacy achievement: a preliminary examination of fourth graders in Abu Dhabi. Large-scale assessments in education, 6(1), p.10. Available from: https://doi.org/10.1186/s40536-018-0063-0. DOI: https://doi.org/10.1186/s40536-018-0063-0

Zhang, J.H., Zhang, Y.X., Zou, Q. and Huang, S., 2018. What learning analytics tells us: Group behavior analysis and individual learning diagnosis based on long-term and large-scale data. Journal of educational technology & society, 21(2), pp.245–258. Available from: http://www.jstor.org/stable/26388404.

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Published

2021-03-19

Issue

Section

Educational Data Mining and Social Analytics in Education

How to Cite

Mazorchuk, M.S., Vakulenko, T.S., Bychko, A.O., Kuzminska, O.H. and Prokhorov, O.V., 2021. Cloud technologies and learning analytics: web application for PISA results analysis and visualization. CTE Workshop Proceedings [Online], 8, pp.484–494. Available from: https://doi.org/10.55056/cte.302 [Accessed 19 April 2024].
Received 2020-10-16
Accepted 2020-12-18
Published 2021-03-19

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