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All Journal Sharia Oikonomia Law Journal Journal Neosantara Hybrid Learning
Maria Alexandrovna
Pavlodar State University

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ISLAMIC FINANCE AND CRISIS MANAGEMENT IN KAZAKHSTAN: LESSONS FROM THE COVID-19 PANDEMIC Maria Alexandrovna; Sergey Kuznetsov; Viktoria Sokolova
Sharia Oikonomia Law Journal Vol. 3 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/solj.v3i3.2200

Abstract

The COVID-19 pandemic exposed structural vulnerabilities in financial systems worldwide, prompting renewed interest in ethical and resilient financing models. In Kazakhstan, where Islamic finance is still nascent, the crisis highlighted both challenges and opportunities for faith-based financial instruments to contribute to national crisis response mechanisms. This study examines the role of Islamic finance in Kazakhstan’s financial resilience during the COVID-19 pandemic, assessing its potential integration into broader economic recovery frameworks. A mixed-methods approach was adopted, combining doctrinal review of Kazakhstan’s Islamic finance legislation with semi-structured interviews involving policymakers, Shariah scholars, and financial practitioners. The findings indicate that while Islamic finance institutions remained limited in size and scope, their emphasis on risk-sharing, social solidarity (zakat, waqf), and asset-backed structures offered valuable alternatives during economic shocks. Islamic microfinance and charitable models proved especially relevant for supporting vulnerable populations. The study concludes that with regulatory development, institutional support, and public awareness, Islamic finance can enhance Kazakhstan’s financial system diversification and crisis response capability. The research contributes to the discourse on ethical finance as a complementary mechanism for economic resilience in emerging markets.
HARNESSING PREDICTIVE ANALYTICS TO PERSONALIZE HYBRID LEARNING TRAJECTORIES IN UNDERREPRESENTED COMMUNITIES Andrei Romanov; Maria Alexandrovna; Sergey Kuznetsov
Journal Neosantara Hybrid Learning Vol. 4 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jnhl.v4i1.2232

Abstract

Educational inequality continues to affect underrepresented communities, particularly in the context of hybrid learning environments that often fail to account for students’ diverse socio-economic, cognitive, and technological backgrounds. Traditional instructional models rarely provide the necessary flexibility or responsiveness to address learning disparities at scale. This study explores the use of predictive analytics as a tool to personalize hybrid learning trajectories for students in underrepresented communities, aiming to enhance engagement, performance, and retention. The research employed a mixed-methods approach, combining machine learning-based predictive models with qualitative interviews and real-time learning analytics. Conducted across four public schools serving marginalized populations, the study analyzed data from over 300 students to identify risk factors and generate personalized intervention strategies. Results showed that predictive models accurately forecasted student disengagement and academic decline with 85% accuracy, allowing educators to implement timely, targeted instructional responses. Teachers reported improved decision-making and reduced dropout intentions among at-risk students. The study concludes that integrating predictive analytics into hybrid instruction offers a scalable pathway to equity-oriented education, enabling data-driven personalization that supports learners historically excluded from mainstream academic success.
Co-Authors Andrei Romanov Sergey Kuznetsov Sergey Kuznetsov Viktoria Sokolova