Masters

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Open Access
A Comparative Study of AI-assisted Assessment and Teacher-Assessment in an EFL Writing Course
(SDU University, 2025) Fazilova A.
With the rapid development of artificial intelligence in education, automated writing evaluation (AWE) tools such as ChatGPT are becoming increasingly popular for providing feedback on students' written works. This study explores the perceptions of first-year EFL students about AI-assisted assessment compared to teacher assessment in an EFL writing course at a private Kazakhstani university. Over the period of one semester, 33 participants have written 4 essays and received both the teacher and ChatGPT feedback on each essay. Their perceptions have been compared and analyzed with a quantitative research design with elements of qualitative analysis. A questionnaire with Likert-scale closed-ended and open-ended questions was used. The results revealed that while students recognize the importance of AI-assisted assessment for surface-level corrections (grammar, vocabulary, structure), the majority of students prefer teacher feedback for its clarity, personalized support, and depth. Additionally, most students viewed the ideal approach as a combination of two types of feedback: AI for quick technical feedback and teachers for more complex aspects like structure, argumentation, and tone. The study concludes that although AWE tools have potential as supplementary support in EFL writing instruction, they cannot replace the human connection and pedagogical insight offered by teachers. Implications for integrating AI tools into classroom practice and teacher training are also discussed, along with recommendations for future research in this evolving field.
Open Access
A comparative study of air quality analysis in Almaty
(SDU University, 2025) Dauletkhan N.
Air pollution remains a pressing public health and environmental challenge in Almaty, Kazakhstan, where concentrations of fine particulate matter (PM2.5) frequently exceed World Health Organization limits. This study presents a comprehensive comparative analysis of statistical, machine learning (ML), deep learning (DL), and hybrid models for short-term PM2.5 forecasting using real-world meteorological and air quality data collected between 2020 and 2024. The methodology involved rigorous data preprocessing, including imputation techniques such as mean substitution, time-based mean, and Multiple Imputation by Chained Equations (MICE), followed by correlation analysis and normalization. Multiple models were implemented and evaluated: statistical models like Multiple Linear Regression (MLR), SARIMA, and Prophet; ML algorithms including Random Forest, Support Vector Regression (SVR), and XGBoost; DL architectures such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN); and hybrid combinations like CNN-ELM and CNN-LSTM. Model performance was assessed using MAE, RMSE, and R² across three imputation scenarios. Results indicated that LSTM consistently achieved the highest accuracy, particularly under the MICE imputation scenario, while Random Forest and XGBoost showed strong performance among ML models. Hybrid models like CNN-LSTM demonstrated promising results in capturing both spatial and temporal patterns. This research contributes to the development of robust, interpretable, and localized forecasting systems, offering valuable insights for environmental monitoring and public health planning in data-constrained urban regions
Open Access
A Comparison of Effect of Abstract Textbook and Real-Life–Based Physics Problems on Students’ Attitudes and Perceived Relevance
(SDU University, 2026) Rakhymberdiyev B.
The purpose of this study was to investigate the effects of physics problems set within real-life contexts on students' understanding of physics and the relevance of physics to their lives․ The study used a mixed-method design with 84 students responding to textbook physics problems and five context-based problems, and 52 of them participated the survey with multiple-choice, Likert-scale, and open-ended questions․ Their results in both types of problem solving activities were compared and analyzed to test if there is any difference in overall achievements of students. Thematic analysis was implemented in order to make sense of the qualitative data as results of the open-ended questions. Most studies found that context-based problems had a positive effect on students' interest and relevance to physics․ Student learning‚ as measured by conceptual understanding tests‚ often improved‚ but the effect on performance was more mixed․ Results of the study indicated positive tendency in both problem solving and survey. Due to open-ended questions results, the vast majority of participants posed that realistic problems are deemed more interesting and encourage meaningful learning of physics‚ though this is only the case with a balanced set of problems
Open Access
A Study of the Impact of the International Physics Olympiads on School Students
(SDU University, 2026) Akniyet I.
The skills and knowledge gained through participation in international physics Olympiads are a main point of analysis of this thesis․ The role of Olympiad learning as an effective way of promoting higher order thinking skills and a positive orientation to a future career in science has received increasing attention․ Nevertheless‚ there is little empirical evidence of its impact on students' competencies and aspirations‚ particularly in the case of Kazakhstan․ To bridge this gap‚ a mixed-methods approach was used to collect quantitative data with a structured survey adopting pre-tested standardized instruments for measuring 21st century skills and career aspirations‚ and qualitative data to triangulate‚ validate‚ and provide a deeper context of the quantitative findings․ Students who had previously participated in international physics Olympiads were included in the study․ Descriptive statistics‚ independent samples t-tests‚ and one-way ANOVAs were used to analyze gender‚ age‚ and school type differences between students in the study
Open Access
Academic Integrity: Students’ Awareness and Instructors’ Promoting Strategies in English
(SDU University, 2025) Nesterova A.
With the emergence of artificial intelligence, maintaining academic integrity has become one of the most discussed topics. Thus, universities make attempts to decrease academic dishonesty. The aim of this research is doublefold: first, it seeks to identify students’ awareness of academic integrity. Second, it describes the instructors’ roles and strategies they use to maintain academic integrity. To achieve these aims, the adapted version of M-AIS was distributed among 187 students, majoring in the specialty “6B1702: Foreign Language: Two Foreign Languages” at one private university in Kazakhstan. As for instructors, 12 of them participated in semi-structured interviews. The research followed a mixed method design, in which the quantitative part was analyzed based on mean, standard deviation, and Spearman’s rank correlation. Overall, it was found that the students were fairly familiar with academic integrity and held positive attitudes towards it. However, they still perceived plagiarizing home assignments, getting unpermitted help, and mispresenting sources as trivial plagiarism and admitted to being engaged in them. In addition, there was a correlation between the student’s year of study, GPA, perceived severity of academic dishonesty, and self-reported engagement in it. The findings were interpreted using Hatch’s (2002) framework of typological analysis. As regards the instructors, their roles in promoting academic integrity were moderately active (ambassador). They taught different techniques to avoid plagiarism, assigned authentic assignments, or developed their own materials. The other part was determined to be passive (casual and detached) because they believed in students’ responsibility to learn about academic integrity. As for strategies, the instructors considered the Turnitin application and authentic assessment as effective ways of maintaining academic integrity. In addition, they made presentations to teach about academic integrity. On the other hand, they did not believe in the usefulness of honor codes and online proctoring applications.
Open Access
AI chatbots in Second Language Acquisition and Learning: Teachers' and Students' Perspectives in Higher Education
(SDU University, 2026) Abykhanov B.
The emergence of Artificial Intelligence technology has affected most of the fields. The field of education is among them as well. The educators and students have access to artificial intelligence. It can be stated that it is being used for various purposes by educators and students alike. Therefore, there is a need for more research on artificial intelligence in education. This study aimed to investigate the perspectives of second foreign language teachers and students on the use of artificial intelligence chatbots. In order to investigate the perspectives of teachers and students, the study employed a qualitative descriptive research design. The researcher conducted semi – structured interviews and observations. The participants were 24 in total. Participants were seven second language teachers and were five second (18 - 19 year olds), five third (20 - 21 year olds), and seven fourth (21 - 22 year olds) year students. The major of the students were Foreign Language: Two Foreign Languages and Translation Studies. The data collected during the interviews and observations was analyzed thematically. The findings indicated that SFL teachers use AI chatbots such as ChatGPT, Gemini, Deepseek, Groc - 3, and Doubao. As regards to students, they use ChatGPT, Gemini, Deepseek, and Doubao. SFL teachers mostly used AI chatbots to generate tasks for the students and to correct their syllabuses. As per the students, the students used AI chatbots for grammar, vocabulary, speaking practice, preparation for the tests, and feedback. SFL teachers and students acknowledged the challenges of using AI chatbots such as inability to detect pronunciation, mistakes in grammar and identification of the context.
Open Access
AI in chemistry education and ethical considerations
(SDU University, 2025) Balkyibek K.
This dissertation examines the integration of Artificial Intelligence (AI) tools, specifically ChatGPT, in chemistry education in Kazakhstan, aiming to identify challenges, opportunities, ethical considerations, and the practical effectiveness of these tools in enhancing learning outcomes. The study explored three key areas: the effectiveness of AI integration into chemistry education, the ethical and privacy concerns associated with student use of AI tools, and the primary advantages and disadvantages of employing AI in chemistry instruction. Using a mixed-method approach, the research combined quantitative surveys among 108 chemistry education students from Kazakh universities and qualitative expert evaluations of AI-generated chemistry solutions. The theoretical framework drew from existing literature on AI integration in education, ethical implications, and pedagogical impacts. Findings indicated that students strongly prefer ChatGPT due to its efficiency, clarity, and ability to facilitate independent learning, primarily utilizing it for problemsolving and exam preparation. However, significant limitations were observed, including accuracy issues, logical inconsistencies, and inadequate linguistic adaptation to the Kazakh language. Ethical concerns highlighted were academic integrity, dependency on technology, and unequal access to premium AI features. The dissertation contributes theoretically by providing empirical evidence of AI’s educational benefits and limitations, and practically by recommending structured AI integration strategies, specialized training, enhanced linguistic localization, and ethical guidelines. Ultimately, this research informs educators, policy-makers, and developers aiming to harness AI responsibly and effectively in chemistry education.
Open Access
AI-Powered Technology in Enhancing Biology Education
(SDU University, 2026) Bizhanova A.
This study investigated the impact of AI-supported learning on academic achievement, student perceptions, engagement, independent learning, and AI literacy in university biology education. The research was conducted among 52 undergraduate students enrolled in a biology course focusing on nervous system anatomy. A quasiexperimental design was employed, involving a control group receiving traditional instruction and an experimental group participating in AI-supported learning activities. Data were collected using pre-tests, post-tests, and a student perception questionnaire. The results demonstrated significant improvements in academic achievement within both groups. Although the AI-supported group achieved higher mean post-test scores, the difference between groups was not statistically significant according to independent-samples t-tests and ANCOVA analyses. Questionnaire findings revealed highly positive student perceptions of AI-supported learning. Students reported that AI facilitated understanding of complex biological concepts, supported independent learning, enhanced engagement, and helped identify knowledge gaps. Participants also demonstrated awareness of AI-related challenges, including misinformation, inaccuracies, and excessive reliance on AI-generated content. The findings suggest that AI-supported learning represents a valuable complement to traditional biology instruction. While no statistically significant achievement advantage was identified, AI contributed positively to student engagement, independent learning, conceptual understanding, and responsible.
Open Access
An Inclusive Analysis of Mathematics Achievement and Attitudes in Diverse Educational Environments
(SDU University, 2025) Yuzeyeva Z.
This dissertation presents an inclusive analysis of mathematics education by examining the development of inclusive competence in future mathematics teachers within diverse educational environments in the Republic of Kazakhstan. Rooted in national and international frameworks on inclusive education, the research explores how cultural, linguistic, psychological, and regional factors affect teacher preparedness and attitudes toward working with learners with special educational needs (SEN). The study focuses on Almaty and rural areas as case settings, analyzing how educational equity and inclusivity are addressed in mathematics classrooms. A structural-logical model was developed to support the formation of inclusive competence through a task-based, personalized, and culturally responsive methodology. The model integrates motivational, cognitive, practical, and reflective components, preparing future teachers to adapt mathematics instruction for learners with a wide range of abilities and backgrounds. A three-stage pedagogical experiment involving 180 pre-service mathematics teachers and 28 in-service educators were conducted. Quantitative and qualitative data were collected to assess initial readiness, track development of inclusive attitudes, and evaluate the effectiveness of proposed instructional strategies. Results revealed significant improvements in teachers’ motivation, adaptability, and use of inclusive methods following implementation of the model. The findings highlight the importance of localized, inclusive teacher education that reflects Kazakhstan’s evolving educational landscape. This work contributes to the broader discourse on equitable mathematics education and supports ongoing efforts to create accessible and high-quality learning environments for all learners.
Open Access
An Investigation into the Development of Intercultural Competence in TEFL: A Study of Master’s Students’ Perspectives
(SDU University, 2025) Ibrayeva A.
The current study aims to investigate the perspective of master’s students on the development of intercultural competence (IC) in the Kazakhstani teaching English as a foreign language (TEFL) and to identify key challenges they face in practical application. The study involved ten working master’s students enrolled in TEFL programs at two Kazakhstani institutions through purposive sampling. Semi-structured interviews were used in a qualitative research design to acquire detailed information about the experiences and reflections of the participants. The research questions were addressed through thematic analysis of the data. The findings reveal the theoretical intercultural competence awareness of students, yet there is a lack of structured and practical inclusion of intercultural aspects in their academic curriculum. Participants reported that little experiential learning and intercultural engagement are incorporated into core teaching modules, and that intercultural competence development is frequently restricted to quick discussions in elective courses. The insufficient academic training, strict curriculum, and lack of support from the institution emerged as key barriers to intercultural competence integration in teaching. The students suggested the incorporation of practical tasks and intercultural competence integration mechanisms in the TEFL courses. This study contributes to the growing body of literature on IC development in the Kazakhstani TEFL context.
Open Access
Application of CLIL teaching methods in chemistry lessons
(SDU University, 2025) Yntymakkyzy K.
This dissertation explores the use of the CLIL (Content and Language Integrated Learning) method in chemistry classes, focusing on its impact on students` academic performance and teachers` perceptions of its implementation in the classroom. The main research question of this study is: "how does the use of CLIL methods affect students` performance in chemistry and how do teachers perceive their use in the classroom?" approach was used to use mixed methods that combined quality interviews with seven teachers with CLIL experience and quantitative data from pre-and post-test tests conducted on experimental and control groups in three different schools. Data sources included academic databases such as Scopus, Research Gate, and Google Scholar. The results show that the effectiveness of CLIL varies depending on the educational context, student motivation, and pre-impact on CLIL. In school 1, where CLIL was newly introduced and motivation was high due to the preparation for the national test, students showed a significant improvement in post-test scores. School 2 (IB school) has shown moderate but statistically significant advances, indicating that CLIL can improve understanding of the subject even when the knowledge of the content is initially low. On the contrary, the 3rd school (Lyceum) showed the least improvement, which is probably due to the high base performance and previous experience of CLIL. However, achievements in all schools were statistically significant, confirming the overall positive impact of CLIL on students ` achievements in chemistry. The study concludes that while CLIL can be a powerful learning approach, its effectiveness depends on the specific learning environment and student background.
Open Access
Applications of the differentiation method in the study of the topic of trigonometric equations and inequalities
(SDU University, 2025) Kazhmukhanov A.
This dissertation comprehensively explores the effectiveness of applying the differentiation method in teaching trigonometric equations and inequalities. In today's educational system, the variation in students’ preparedness levels has become a pressing issue, especially when dealing with complex mathematical topics like trigonometry. The theoretical part of the study discusses the concept and importance of differentiated instruction, along with a review of both domestic and international practices. The methodological section presents approaches for designing level-specific tasks on trigonometric equations and inequalities. Levels A, B, and C were introduced, and customized tasks were developed using graphical methods, function properties, and identity transformations. A pedagogical experiment was conducted with students from grades 5, 6, and 11. The findings demonstrate a positive impact of differentiation on student performance, motivation, and independent learning. Even students with lower academic performance achieved success through assignments tailored to their capabilities. The study also considers the integration of artificial intelligence (AI) tools to support differentiated learning. Digital platforms allow teachers to accurately assess student levels and assign personalized tasks accordingly. The research concludes that the differentiation method is effective not only in teaching trigonometry but also applicable to other areas of mathematics. Therefore, broader implementation and enhanced teacher training in differentiation strategies are recommended.
Open Access
Assessing the accessibility of virtual chemistry laboratories for school students with special educational needs
(SDU University, 2026) Absattarova A.
Students with special educational needs (SEN) often face limitations in studying natural science subjects, including chemistry, where laboratory practice must be constantly conducted to consolidate theoretical knowledge. The main purpose of this study was to study the effectiveness of the use of virtual chemical laboratories (VCL) as an educational tool for this category of students. The study examined the following issues, such as how a student SEN perceives the virtual environment, what difficulties they face during work, and how the VCL affects their academic performance. The hypothesis is that a virtual chemistry laboratory needs to remove laboratory barriers from students with SEN, as well as help improve their academic performance. The research methodology is based on mixed methods of analysis: quantitative (pre- and post-test) and qualitative (structured interview and observation). The study involved 17 students with different categories of inclusion, enrolled in grades 6-9. The results of the data confirmed the positive impact of the virtual laboratory (VL) on students with SEN. Many students have recorded an increase in test scores. The qualitative analysis data confirms the quantitative results. The scientific novelty of this study lies in the simultaneous coverage of several inclusive groups, and the results of the study can serve as a basis for further research in this area.
Open Access
Creating the Integrative STEM Lesson Unit Plan for 11th Grade Students
(SDU University, 2026) Adilzhan A.
This study investigates the development, implementation, and evaluation of an integrative STEM lesson unit plan for 11th grade chemistry students across three secondary schools in Almaty, Kazakhstan a private school, a state school, and a specialized school. The study addresses three research questions concerning the development of an effective STEM unit plan, its impact on academic performance, and the challenges associated with its integration. Grounded in constructivism, the Zone of Proximal Development, inquiry-based learning, and the 5E Instructional Model, the research employed a mixed-methods design combining a quasi-experimental pre-test and post-test with semi-structured online interviews. A total of 117 students participated in the pedagogical experiment, and 15 chemistry teachers were interviewed. Research instruments were validated by five experts using Kendall's W analysis (W = 0.82 and W = 0.79, both p < .05). Paired samples t-tests confirmed statistically significant improvement in all six groups (p < .05). ANCOVA revealed significant advantages for the experimental group at the state school (F(1) = 9.34, p = .004) and the specialized school (F(1) = 9.14, p = .004). Qualitative findings indicated positive impacts on student engagement, motivation, and 21st-century skill development, while four systemic barriers were identified: infrastructural limitations, curriculum rigidity, inadequate teacher preparation, and student resistance. The study concludes that integrative STEM education has genuine and statistically demonstrable potential to enhance chemistry learning in Kazakhstan, and that successful implementation requires sustained investment in teacher professional development, school infrastructure, and curriculum reform.
Open Access
Designing chemistry project in an integrative STEM manner
(SDU University, 2025) Daribay A.
STEM (science, technology, engineering, and mathematics) was integrated into chemistry education; this study focused on project-based learning (PBL) as a pedagogical tool. The study examined how chemistry teachers conceptualize, design, and implement STEM-integrated projects and how these practices affect student engagement, teacher professional development, and student learning. A total of 79 chemistry teachers in Kazakhstan participated in the study using a mixed-method approach, including surveys, interviews, and classroom observations. The results indicate that the chemistry teachers are increasingly adopting STEM principles and focusing on real-world applications to improve student motivation and learning. The researchers noticed that teachers with advanced qualifications (i.e., pedagogical researchers) were more successful at implementing STEM practices, as they encouraged student-centered learning approaches, such as collaboration, experimental design, and integrating digital technology. With the aforementioned projects, the students exhibited increased interest, improved understanding of chemical concepts, and developed their critical thinking and problem-solving skills. Despite the successes of the STEM projects and activities, the teachers experienced significant challenges related to working in an un-coordinated education system, limited resources, not enough time to implement integrated PBL, and lack of institutional and administrative support. These issues inhibited the project-based teachers from fully embedding STEM practices into their curriculum, particularly in creating interdisciplinary opportunities and obtaining materials for project implementation. However, this research indicated that there were several benefits for both the students' learning and teacher professional development practices by employing STEM projects regardless of the limitations and challenges presented. The researchers concluded that programs for professional development for teacher support, better access to educational resources, and eventually a supportive framework by the institution were needed to integrate STEM for best practice in chemistry education. The recommendations offered by the research could provide insight into the challenges of STEM education and development for policymakers and educators in Kazakhstan and similar contexts to maximize the opportunities offered by STEM education.
Open Access
Detecting social conflicts in kindergartens using deep learning and computer vision
(SDU University, 2025) Kengesbay D.
Early conflict detection in kindergartens plays a significant role in ensuring a harmonious learning atmosphere and in promoting the social growth of young children. While most previous works have only addressed conflict detection through adults, in this paper, we specifically address conflict detection in kindergartens using deep learning, utilizing both spatial and temporal information to improve performance. The application of deep learning and computer vision in automatically detecting and analyzing early conflicts among young children is discussed in this paper. Using video footage, we leverage state-of-theart RNNs and 3D CNNs for high-accuracy detection of conflict instances. Crucial visual cues—facial expressions, gestures, poses, vocal tone, and movement—are examined for the extraction of tension or aggression signs. The model is evaluated on real kindergarten video data, with promising conflict detection and classification results. The findings indicate the potential of AI-supported tools in assisting teachers in class management, child behavior monitoring, early intervention mechanisms, and the fostering of a good social environment
Open Access
Determining the challenges of chemistry teachers in Integrative STEM Teaching
(SDU University, 2025) Zhibekaiym I.
This study examines the attitudes of school chemistry teachers toward integrated STEM education and the challenges they face when implementing STEM approaches in chemistry lessons. As STEM education continues to gain global importance, understanding teachers’ experiences and barriers is essential for improving pedagogical strategies and professional development in science education. A mixed-methods approach was used, combining quantitative data from a teacher survey and qualitative data from semi-structured interviews. Survey results were analyzed using descriptive statistics, factor analysis, independent samples t-tests, and one-way ANOVA to explore differences based on teachers’ demographic characteristics such as experience, academic degree, professional category, and school level. Interview data were analyzed using inductive content analysis to capture teachers’ authentic views and the specific difficulties they face in practice. The findings show that although chemistry teachers generally express positive attitudes toward STEM education, they encounter significant challenges due to a lack of methodological tools, resources, and difficulties with interdisciplinary integration. Differences in perceived challenges were also noted based on teaching experience and education level. Additionally, teachers reported struggling with implementing complex STEM activities such as project-based tasks, laboratory work, and interdisciplinary collaboration. The results emphasize the need for targeted professional development and systematic support to ensure effective integration of STEM practices in chemistry education. These findings are valuable for policymakers, curriculum developers, and teacher education institutions seeking to enhance STEM implementation in secondary education.
Open Access
Developing beginner-level young learners’ reading skills in Kazakhstani public schools
(Suleyman Demirel University, 2020-12-25) Karakat Dauletkyzy
This dissertation focuses on examining how English teachers develop reading skills of primary EFL learners in public schools in Kazakhstan. This qualitative study was carried out using semi-structured interviews with 15 English teachers working in primary public schools across Almaty, Kazakhstan. The findings suggest that a lot of English teachers use the phonics method to teach reading to primary school learners. Teachers find the phonics method to be more effective than other methods. The results of the study indicate that teacher training and gaining experience in the primary EFL classroom play the biggest role in teaching reading better. Teacher training programs should pay more attention to teaching methods aimed at primary EFL learners. The phonics method should be part of the English teacher-training curriculum.
Open Access
Developing students’ grammar skills using “Learning English grammar tenses” application at the intermediate level
(Suleyman Demirel University, 2021-06-18) Karman A.
This research looks specifically at using the mobile application as a material resource and how to prepare and manage the grammar lesson at the intermediate level, at the secondary school. This dissertation presents practical tasks aimed at developing grammar skills. The main goal of thіs study is to identify effective tasks that can be performed with the help of mobile devices as well as with the “Learning English grammar tenses” application.
Open Access
Development and optimization of physics-informed neural networks for solving partial differential equations
(SDU University, 2025) Sharimbayev B.
This thesis talks about using physics-informed neural networks (PINNs) to solve Poisson equations in both one-dimensional and two-dimensional areas. These equations are common in many physical problems, like heat transfer and electrostatics. The results from PINNs are compared to the finite difference method (FDM), which is a classical numerical method often used to solve these kinds of equations. The study shows that PINNs can give results that are close to those from FDM, with the added benefit of being more flexible for different types of problems. Another part of this work focuses on using multi-task learning with PINNs. In this part, the neural network does more than one job. It not only finds the solution of the differential equation, but it also learns unknown values or parameters that are part of the equation. For example, in one test problem, the equation had a source term and a coefficient that changes depending on the position. The PINN was able to learn both of them correctly while still solving the equation with a low training error. The results show that PINNs can work well even when the equation is more complex or has unknown parts. The model showed good performance on new or unseen data and was able to find the correct hidden values in the system. Because of this, PINNs may be very useful in future applications for solving advanced problems in science and engineering, especially where traditional methods might be harder to use.