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Variety of special Tortken algebras
(SDU University, 2026) Baigali M.
This dissertation studies special Tortken algebras arising from the symmetrization of Novikov algebras. The main object is the space of symmetric elements in the free Novikov algebra generated by one element. Using the differential realization of free Novikov algebras, the symmetrized product is written as a ◦ b = (ab) ′ , which allows us to expand elements in the ◦-language into differential monomials and study their linear relations by methods of linear algebra. For the homogeneous component Tn of degree n, we describe the dimensions and obtain dim T1 = dim T2 = 1 and dim Tn = p(n − 2) for n ≥ 3, where p(n) is the partition function. Low-degree components are constructed explicitly, and special attention is given to relations in degrees 6 and 7. The computations suggest a recursive structure for the spaces Tn and contribute to the study of free special Tortken algebras.
Dynamic Obstacle Avoidance in Autonomous Robot Navigation Using Deep Reinforcement Learning
(SDU University, 2026) Parmash A.
Existing indoor navigation systems for autonomous robots have proven their success in geometric mapping and avoidance of obstacles in the local environment, but they lack semantic reasoning capabilities; thus, they cannot comprehend instructions issued in natural language. In contrast, utilizing state-of-the-art vision and language foundation models in the robot control loop would create too much computational delay, making the implementation impractical for the purpose of reactive execution of tasks. This thesis attempts to fill this gap through a two-stage hierarchy. During the initial phase, a mobile robot performs an exploration task within the unknown environment by employing an SLAM pipeline based on LiDAR, while the robot’s on-board VLM performs object detection to build a structured, small global 3D semantic database. During the second phase, the robot takes up any natural-language instructions (for example, “go to the bookshelf”), extracts the coordinates of the target from its memory, and performs autonomous navigation. Here, the navigation process is entirely mapless and does not involve any VLM inference cost overhead. Control at the lower level is done by the robot’s DRL-based policy trained using TD3 algorithms. Assessed under ROS-integrated Gazebo simulation, the proposed architecture attains an accuracy rate of 85% in navigation with a lightweight Moondream VLM, substantially superior to cloud-based GPT-4o mini versions and classical approaches. The trained policy for depth proves to have excellent generalization performance with respect to unknown objects, dense obstacles, and even multiple rooms, as well as a success rate of 80% in evading moving obstacles without the need for further training. Achieving a processing rate of 10 Hz on board, the framework manages to bypass foundation model delay constraints.
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.
The Impact of STEM Integration on Secondary School Students' Conceptual Understanding in Physics
(SDU University, 2026) Tursumbekova A.
This study examines the effect of STEM-integrated instruction on 9th-grade students' conceptual understanding of Newtonian mechanics in Kazakhstan. Using a quasi-experimental pretest-posttest design with 74 students at Al-Farabi Specialized Lyceum, the experimental group (n = 34) received six weeks of STEM-integrated instruction while the control group (n = 40) followed traditional teaching. Conceptual understanding was measured via the Force Concept Inventory (FCI). Results showed that the STEM group significantly outperformed the control group, achieving a medium normalised gain compared to a low gain for controls. A pre-test gender gap closed entirely by post-test, confirming equitable outcomes. The study provides the first controlled evidence from Kazakhstani secondary schools that STEM integration substantially outperforms traditional physics instruction.
Using Simple Chemistry Experiments to Improve Students' Understanding of Biochemical Processes in Biology
(SDU University, 2026) Abilova A.
One of the main problems in secondary education is the “separation effect", i.e. the teaching of biology and chemistry without any connection. Consequently, students consider biology as mere memorization of facts and chemistry as a set of tough formulas. Students often don't know that the two disciplines are a system that connect each other. To solve this problem, this dissertation proposed an experimental teaching method called "Four Main Pillars". The goal of this approach is to increase the interest and understanding of students by removing the boundaries between the disciplines and clearly demonstrating the biochemical relationships. The main benefit of this research is that its results can be applied in real life. Four main experiments were carried out during the work. They were: determining the acidity of the environment (pH), monitoring gas exchange, explaining how enzymes work, and demonstrating the distribution of molecules (diffusion). For such experiments, not expensive laboratory equipment, but simple everyday objects were used. Thanks to this, it was proven that science is very understandable and that it is closely related to our lives. The data collected shows that this change was quite effective. The quantitative analysis results showed that students' self-confidence increased 73%. In particular, the percentage of students who could explain biological processes in chemical terms increased from 9% to 82%. The numbers went up a lot for how much students understood the link between biology and chemistry. It went from twenty eight percent to ninety one. That stands out. Also more of them started thinking science could be done with basic stuff which went up by fifty six percent I think.