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

• Main Authors: Mariia S. Mazorchuk (Author), Tetyana S. Vakulenko (Author), Anna O. Bychko (Author), Olena H. Kuzminska (Author), Oleksandr V. Prokhorov (Author)
• Format: Book
• Published: Academy of Cognitive and Natural Sciences, 2021-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.55056/cte.302; 2833-5473
• Keywords: learning analytics; Cloud Technologies; PISA; web application; Technology; T; Education; L
• Source: CTE Workshop Proceedings, Vol 8 (2021)

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.