Ethical Educational Data Processing in Post-Soviet Countries: Addressing the Needs of Students with Special Needs
This is a research study conducted by the University of Jyväskylä and the Research Council of Finland.
Introduction
In the realm of educational data processing, ethical considerations play an important role, especially when addressing the needs of students with special needs [1]. Post-Soviet countries, having undergone significant transformations in their educational systems since the dissolution of the Soviet Union in 1991, present a unique case study. These countries have adopted varying strategies and policies for data governance where they often face challenges related to transparency, accuracy, and bias [2]. This article investigates the ethical data processing practices in these countries by emphasizing the disparities and the need for improved strategies to support students with special needs.
Background
The collapse of the Soviet Union led to diverse educational reforms across its former republics [4]. While countries like Estonia and Latvia embraced democratic reforms and implemented inclusive data governance practices, others in the Caucasus and Central Asia struggled with transparency and accuracy in data collection. This disparity is evident in international assessments like the Programme for International Student Assessment (PISA), where Estonia ranks high while countries like Uzbekistan, Georgia, and Azerbaijan lag behind [3].
One of the primary challenges in these regions is the bias in data collection, where certain groups of learners, especially those with special needs, are underrepresented [7]. This bias not only affects the accuracy of the data but also hinders the development of effective educational policies. Additionally, the lack of accountability and transparency in data governance further complicates the situation, making it difficult to align the collected data with the actual needs of the students.
Methodology
To understand the ethical data processing practices in post-Soviet countries, we analyzed various sources, including the Global Education Monitoring (GEM) Report and official education policies from these countries [5]. The thematic content analysis was employed to identify recurring patterns and challenges in data collection, categorized under accuracy, bias, fragmentation, and transparency [6].
Grouping of Post-Soviet Countries Based on Data Governance
Post-Soviet countries were grouped based on their data governance status:
- Group 1: Estonia, Latvia, Lithuania – EU countries adhering to GDPR requirements, exhibiting complex data reporting systems and higher transparency.
- Group 2: Moldova, Ukraine – Countries transitioning to GDPR compliance, with less complex data systems than Group 1.
- Group 3: Armenia, Azerbaijan, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan – Countries with strict rules and limited data sharing, leading to challenges in accessing detailed information.
- Group 4: Belarus, Russia – Countries with their own regulations similar to GDPR, providing restricted access to data.
Results
The analysis indicated several challenges in ethical data processing across these groups:
- Group 1: Estonia and Latvia demonstrate relatively transparent and accountable data processing practices. However, Latvia faces challenges in tracking detailed data of learners with special needs, highlighting the need for improved data quality for policy development. Lithuania struggles with accurate data on non-attendance and requires better involvement of special education experts in rural areas.
- Group 2: Ukraine and Moldova face challenges in cooperation between government units and the lack of a unified database for learners with special needs. Moldova’s GEM Report emphasizes the importance of a transparent database to facilitate collaboration with sectors like healthcare and social services.
- Group 3: These countries exhibit significant issues in data availability and accuracy. Azerbaijan and Georgia face discrepancies between official and independent data and this complicates the identification of out-of-school students. Armenia’s main challenge is the schools’ lack of interest in providing data input, affecting the use of data in policy development. Kazakhstan and Uzbekistan suffer from inconsistent databases which leads to doubts about data completeness.
- Group 4: Russia and Belarus have their own systems for data processing but face issues in data openness and validity. Russia’s data collection overlaps which makes it difficult to obtain accurate information about students with special needs. Belarus does not include individuals with psychophysical development challenges in their database that leads to underrepresentation.
Conclusion
The study highlights the critical challenges in ethical data processing in post-Soviet countries, particularly regarding the representation of students with special needs. The reliance on parents to register their children and the varying definitions of special needs across countries contribute to these challenges. Future research should focus on assessing the fairness of data collection and conducting interviews with government officials to understand their approaches to ethical data processing and policy development. Understanding and addressing these challenges is crucial for improving educational outcomes and ensuring that all students, especially those with special needs, receive the support they need. As post-Soviet countries continue to evolve their educational systems, a focus on ethical data processing will be essential for developing inclusive and equitable education.
References
[1] H. N. Prasetyo and S. F. S. Gumilang. Data governance strategy for e-government in bandung district governments. International Journal of Engineering & Technology, 8(1.9):254–258, 2019.
[2] I. Silova and S. Niyozov. Globalization on the margins: Education and post-socialist transformations in Central Asia. IAP, 2020.
[3] PISA. Pisa participants. https://www.oecd.org/pisa/aboutpisa/pisaparticipants.htm. Accessed: March 11, 2024.
[4] A. ˚Aslund, P. Boone, S. Johnson, S. Fischer, and B. W. Ickes. How to stabilize: Lessons from post-communist countries. Brookings papers on economic activity, 1996(1):217–313, 1996.
[5] UN-iLibrary. Global education monitoring report. https://www.unilibrary.org/content/periodicals/26180693. Accessed: March 11, 2024.
[6] R. Anderson. Thematic content analysis (tca). Descriptive presentation of qualitative data, 3:1–4, 2007.
[7] P. 2022. Pisa 2022 participants. https://www.oecd.org/pisa/aboutpisa/pisa-2022-participants.htm. Accessed: March 11, 2024.
