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FLIPPED CLASSROOM INTEGRATION AS AN INNOVATION IN TEACHING PRACTICES IN KAZAKHSTAN HIGHER EDUCATION: A PHENOMENOLOGICAL STUDY OF FACULTY EXPERIENCES
(Nazarbayev University Graduate School of Education, 2025-02-10) Bainova, Olga
Higher education institutions (HEIs) adopt innovations at varying paces, with centralized systems and limited autonomy often hindering progress, particularly in Kazakhstan. Traditional teacher-centered methods remain common, despite the growing need to develop critical 21st-century skills such as critical thinking and teamwork. The flipped classroom (FC) approach offers a way to transition to student-centered learning, yet research on its implementation has largely focused on students, leaving faculty perspectives underexplored. This gap highlights the need to understand faculty experiences to support the effective integration of FC in Kazakh higher education.
This research explores the rationale and experiences of faculty members in Kazakhstan as they integrate the FC model into higher education. Through semi-structured interviews with 13 faculty members who have at least one semester of experience using the FC approach, the study identifies key factors influencing the adoption and dissemination of this innovative teaching method.
The findings reveal that faculty motivations are driven by dissatisfaction with traditional lecture-based instruction, logistical challenges, and a desire to improve student engagement and learning outcomes. Key facilitators include institutional support, intrinsic motivation, and technological readiness, while barriers such as time constraints, resistance to change, and limited incentives hinder its implementation. The study also highlights the FC model’s transformative impact on teaching practices, fostering active learning, student-centered instruction, and skill development, particularly during the COVID-19 pandemic.
This research provides critical insights into the facilitators and impediments of FC adoption within Kazakhstan’s unique educational context paying special attention to the discussion of why faculty continue or discontinue innovation. It contributes to global discussions on pedagogical innovation and emphasizes the need for institutional frameworks that support faculty autonomy, professional development, and recognition. These findings inform strategies for the successful integration of innovative teaching approaches, enhancing both faculty practice and student learning outcomes.
PREDICTION OF DIFFUSION COEFFICIENT OF CARBON DIOXIDE USING ADVANCED MACHINE LEARNING MODEL IN BOTH BRINE AND HYDROCARBONS
(Nazarbayev University School of Mining and Geosciences, 2025-04-14) Khan, Qaiser
The diffusion coefficient (DC) of carbon dioxide (CO₂) in both brine and hydrocarbon play a critical role in geological carbon sequestration and CO₂-enhanced oil recovery (EOR), governing mass transfer efficiency and subsurface storage capacity. This study used three advanced machine learning (ML) algorithms — Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost) — for the prediction of CO₂ diffusion coefficient using a dataset of 176 experimental and simulation data spanning pressures (0.10–30.00 MPa), temperatures (286.15–398.00 K), salinities (0.00–6.76 mol/L), and DC values (0.13–4.50 × 10⁻⁹ m²/s) utilized for the storage purpose in brine. The dataset was divided into 80% training and 20% testing sets to evaluate model generalizability. Performance metrics revealed RF as the most robust model, achieving an R² of 0.95, RMSE of 0.03, and MAE of 0.11 on test data, outperforming GBR (test R²: 0.925) and XGBoost (test R²: 0.91). Feature importance analysis identified temperature as the dominant predictor of diffusion coefficient, followed by salinity and pressure. A parallel investigation focusing on CO₂-EOR in hydrocarbon systems demonstrated RF adaptability, achieving R² values of 0.95 (training) and 0.92 (testing) using temperature (292.65–473.15 K), pressure (1.72–8 MPa), and API gravity (8.517–13.97) as input parameters. A dataset of 313 points was used for the hydrocarbon case. Contrastingly, tornado chart analysis in this context highlighted pressure as the most influential parameter, followed by temperature and API, suggesting context dependent variable significance. These findings establish ML frameworks as powerful tools for optimizing CO₂ injection strategies and storage security, with RF emerging as a versatile model for diverse subsurface conditions. The study underscores the potential of data-driven approaches to replace costly experimental methods while providing actionable insights for industrial carbon
management.
EVALUATION OF THE EU-FUNDED PROJECT ON THE EMPOWERMENT OF WOMEN FROM AFGHANISTAN
(Nazarbayev University Graduate School of Public Policy, 2025-04-04) Altenova, Madina; Goncsarenko, Karina; Hussain, Safdar
The purpose of this study is to examine the 2019 EU-funded project on the empowerment of Afghan women via training and education. In this research, we analyze the project's development, implementation, stakeholder issues and challenges, as well as broader implications for gender equality in post-conflict areas, such as that of Afghanistan. A qualitative methodology is used to analyze documents, as well as conduct in-depth interviews with key stakeholders, including EU representatives, UNDP officials, partner university administrators, who are all part of the initiative. The two-phased project was aimed at providing Afghan females with higher education opportunities and future career prospects. Findings show that the implementation of the project was rather smooth and the participants who participated were successful in their studies. However, following the Taliban’s return to power in Afghanistan in 2021, the project changed immensely and the reintegration of females into Afghan society and job market became close to impossible; thus, most females seek to build a new life and use their skills abroad. Findings highlight the need for enhanced post-graduation support for participants, as well as more flexible and adaptable implementation strategy to accommodate shifting political landscapes. This study serves as a project evaluation paper, with a target demographic of scholars and policymakers actively involved in international development and gender studies. This research also enhances the discussion on women's empowerment and provides valuable insights into the intricacies of implementing extensive programs in developing nations.
DOES ANTI-CORRUPTION TRAINING MAKE A DIFFERENCE?
(Nazarbayev University Graduate School of Public Policy, 2025-04-04) Duisebayeva, Laura; Edigeev, Arislan; Azhibekova, Nurizat
The research paper aims to address the issue of corruption and fill the literature gap about the effectiveness of anti-corruption training in Kazakhstan. This study investigates the effectiveness of anti-corruption training among bachelor students in Astana, Almaty, and Shymkent. The research analyzes whether participation in anti-corruption training affects students’ tolerance toward corrupt behavior. By using a survey-based approach, respondents were asked to rate ethics scenarios from 1(non-corrupt) to 7(very corrupt) to assess perceived corruption. 100 responses were collected from bachelor students aged 18-27+ by conducting an online survey through Google Forms. This methodological approach, introduced by Mancuso (1995) and later refined by Pelizzo and others (2008, 2019, 2023), has been utilized to analyze ethical standards in the public sector. The key analysis compares these findings with those of Pelizzo and Knox (2023), and by employing descriptive statistics, correlation analysis, and logistic regression, the results illustrate that there is almost no effect of training on the tolerance of corruption among students. The research findings raise concerns about the effectiveness of current anti-corruption educational measures and suggest the necessity for improved or alternative training methods. The research provides valuable insights for anticorruption organizations in Kazakhstan, highlighting the need to refine the anti-corruption training to foster ethical decision-making and integrity among the students, and further the
population.
MOONLIGHTING IN PUBLIC HEALTH SECTOR OF KAZAKHSTAN
(Nazarbayev University Graduate School of Public Policy, 2025-04-11) Zhexekeyeva, Kamila; Umirova, Aikerim; Akyzhanov, Madi
Moonlighting, the practice of holding multiple jobs, has become widespread globally, including Kazakhstan. Among other work sectors, public healthcare has unique factors that encourage moonlighting, including non-standard shifts, irregular work hours, and low wages in the public sector. The main purpose of this paper is to analyze the push and pull factors of moonlighting in public healthcare in Kazakhstan. We conducted an online survey among public hospitals and polyclinics in three big cities—Astana, Almaty, and Shymkent—that included healthcare providers. Logistic regression was applied with moonlighting as the binary outcome variable. We found that age was a major predictor, whereby younger professionals (less than 35) were over twice the probability to moonlight. Experience also comes into play; those who had more than three years of work experience tended to have secondary employment. Even though more women did moonlight as well, gender was not found to be statistically significant, although younger women in some specialties were less likely to moonlight, suggesting potential institutional or individual constraints. We recommend policymakers not to restrict moonlighting because it predominantly occurs in the same healthcare sector, not in irrelevant jobs. Enabling equitable access to regulated dual employment—especially among young and female professionals—may improve workforce experience and increase the supply of healthcare services in Kazakhstan's public healthcare sector.