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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
Teacher’s Feedback on Students’ Written Work: Teachers’ and Students’ Perspectives
(SDU University, 2025) Kazybay M.
This qualitative study aimed to explore Kazakhstani EFL teachers’ and students’ perspectives on feedback in writing instruction. Data was collected using semi-structured interviews with 5 EFL teachers and 15 11th grade students in schools of Almaty, Kazakhstan. Thematic inductive analysis used to analyze the collected data. The study's findings indicate that teachers and students held both similar and differing views on feedback strategies and focus. Both groups acknowledged that feedback is essential for improving writing skills. Teachers preferred feedback that was selective and direct. Conversely, detailed and indirect feedback was preferred by the majority of the students, since they believed that it helps them to learn at a deeper level. Teachers preferred selective and direct feedback due to the challenges, such as limited time, a huge workload, student plagiarism, and overreliance on AI. Moreover, findings revealed that positive feedback that points out the strengths and that values the students' work made them feel more motivated and engaged in writing. Finally, recommendations based on participants' responses, limitations of the study, and directions for future research are also included in the study.
Identifying career aspirations of STEM Chemistry Teachers
(SDU University, 2025) Zainy A.
STEM is an integrated educational concept that combines the fields of Science (Science), Technology (Technology), Engineering (Engineering) and Mathematics (Mathematics). The term was first widely used in the 2000s in the United States for the purpose of reforming the education sector, and later became one of the areas of Education recognized at the global level. The STEM educational model differs from traditional subject teaching in that it organizes the educational process in a complex, interdisciplinary way, that is, it is based on teaching, combining several disciplines in solving real-life problems. Its significance lies not only in preparing individuals for specialized careers but also in cultivating a broader set of skills essential for navigating the challenges of the modern world.There are several reasons why STEM education is important. First, it prepares students for careers in STEM-related fields, which are growing rapidly and offer good job prospects. Second, it helps students develop skills that are valuable in all fields of work, such as creativity, innovation, and adaptability. Third, it promotes scientific literacy and understanding of important scientific issues. Despite its many benefits, STEM education faces several challenges. One challenge is the lack of qualified STEM teachers.
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.
THE TASK DEVELOPMENT OF PHYSICS SUBJECT TEACHERS USING CHATGPT.
(SDU University, 2025) Amirkhan D.
This study explores the effectiveness of ChatGPT in solving of physics problems of varying difficulty levels across different topics as a generative AI language model. We mainly do aim to evaluate just how the model performs as well as integrate the model into physics education. The succeeding queries provide guidance to the research: With what accuracy does ChatGPT solve the physics problems at a variety of difficulty levels? Do certain specific physics topics vary in performance regarding amount? ChatGPT performs to a greatly better degree on low-difficulty problems, the study hypothesizes, and shows a stronger conceptual understanding rather than a numerical accuracy. Theoretical frameworks grounding the research are Technological Pedagogical Content Knowledge (TPACK), as well as the SAMR model (Substitution, Augmentation, Modification, Redefinition) used to assess the pedagogical value of AI integration in teaching. ChatGPT was in fact tested on a total of 105 physics problems, drawn from a total of seven topical areas, and a design which was quantitative and descriptivecomparative was adopted. Each response was scored through use of a structured rubric for conceptual understanding. The final accuracy was also able to be scored. Data were analyzed by descriptive statistics and t-tests. ChatGPT performs well enough on formula-based, low-difficulty problems, especially inside mechanics, as findings indicate, yet struggles upon optics and electromagnetism, abstract, complex topics. With consistency, conceptual understanding in scores happened to be higher than the accuracy of answers. The study offers several practical recommendations for integrating ChatGPT into STEM instruction. The study does also contribute in a theoretical way by applying both TPACK and also SAMR to AIassisted learning. It concludes instruction that is teacher-led should be supplemented, and not replaced, while ChatGPT can improve learning through its guided use.