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AI-Powered Approach to Career Path Prediction for IT Students Using Academic and Behavior Data
(SDU University, 2025) Berilkozha B.A.
The integration of educational outcomes with workforce requirements is now a pressing issue facing economies worldwide in the age of digital transformation.In addition to changing industries, the fast development of information and communication technology (ICTs) has also changed the skills that future professionals must possess [1]. Higher education institutions are under more and more pressure to equip graduates with the technical and adaptable skills necessary to meet the quickly evolving demands of the industry as global economies move toward automation, artificial intelligence (AI), and datacentric operations [2]. Of them, the field of information technology (IT) has become one of the most dynamic, requiring students to constantly adjust to challenging interdisciplinary problems, data-driven workflows, and new programming paradigms [3]. The choice of a career route is still quite difficult for university undergraduates, even with the growth of IT education. Finding job paths that fit their interests, abilities, and long-term goals is a challenge for many recent graduates. According to research, a sizable percentage of IT graduates wind up working in fields unrelated to their degree of concentration [4]. The absence of individualized career counseling programs that may combine behavioral and academic markers into a logical framework for decision-making frequently causes this mismatch. In the age of big data and artificial intelligence, traditional academic advising-which mostly depends on human intuition and small datasets-cannot scale efficiently [5]. Recent developments in machine learning (ML) and artificial intelligence (AI) have created new avenues for enhancing educational decision-making. Large amounts of behavioral and academic data can be analyzed by AI-driven systems to produce tailored recommendations for pupils [6]. This is in line with the larger worldwide movement toward data-driven education, which is referred to as Learning Analytics (LA) and Educational Data Mining (EDM) [7]. Whereas LA analyzes educational data to aid in institutional decision-making, EDM concentrates on identifying patterns in the data to improve learning outcomes. Predictive modeling has become well-known in this context due to its capacity to predict career outcomes, dropout risks, and student performance [8]. In the context of career counseling, artificial intelligence (AI) makes it possible to build prediction systems that determine the best career pathways for students based on their academic records, extracurricular activities, motivating factors, and personality features [9]. To interpret both structured and unstructured data, these systems use machine learning methods including Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (GB), and Deep Neural Networks (DNNs). Research has indicated that models that employ ensemble techniques, such Gradient Boosting, frequently perform better than conventional classifiers because of their ability to manage intricate, nonlinear interactions between features [10]. However, there are also regional differences in how AI is incorporated into job counseling. Universities in developing nations, especially those in Central Asia, 7 are still in the early phases of adopting AI-powered academic support systems, whereas those in Europe, North America, and parts of East Asia have already done so [11]. Under the government's Digital Kazakhstan initiative, the creation of digital ecosystems in education has been designated as a strategic priority in Kazakhstan [12]. However, data-driven career advice is still completely unexplored despite significant improvements in digital infrastructure and e-learning platforms [
Analysis and development of algorithm and method for object recognition
(SDU University, 2025) Aitimov A.K.
In an era characterized by the proliferation of visual data, the ability to comprehend and interpret the content of images and videos is of paramount significance [1]. Object recognition, a cornerstone of computer vision, plays a pivotal role in this endeavor. It constitutes the bedrock upon which numerous applications, ranging from autonomous vehicles to augmented reality systems, rely for accurate perception and decision-making. The fundamental task of object recognition is to endow machines with the capability to identify and categorize objects within a given visual context, akin to the cognitive abilities of the human visual system [2]. Pattern identification, description, categorization, and grouping provide challenges for Artificial Intelligence (AI), medicine, biology, and other branches of engineering and science. There are numerous definitions of the term "pattern". A pattern is a group of things, happenings, or thoughts that have certain similarities or features. According to Norbert Wiener, the essence of a pattern is an arrangement [3]. It is distinguished by the arrangement of its constituent pieces, rather than by their intrinsic characteristics the pattern is sometimes regarded as the opposite of chaos and a substantially namable item [4]. It can also be defined by a factor shared by multiple instances of the same object. Similarity in fingerprint pictures creates fingerprint patterns; handwriting, audio signals, web pages, and the human face are further examples of patterns [5]. Object recognition remains a challenging problem in computer vision due to various factors that affect the reliability and generalization of recognition systems. According to Bansal et al. in 2021, key challenges include the variability in object appearance, where differences in size, shape, color, illumination, and orientation make it difficult for models to learn consistent representations. Another major issue is scale and resolution, as detecting small objects within large and complex scenes requires algorithms capable of handling multi-scale information effectively. Partial occlusion also hinders detection accuracy when objects are partially hidden by others or by their own parts, while intra-class variability causes significant differences in appearance among objects of the same category. Moreover, Bansal et al. highlight that deep learning approaches often depend on large annotated datasets, and limited training data remain a major obstacle to achieving stable recognition performance [6]. Therefore, the present work focuses on addressing the limitations associated with insufficient data in object recognition tasks
English as a Foreign Language Learners’ Experiences of Using ChatGPT in Academic Writing
(SDU University, 2025) Medimanov D.
Nowadays, the role of AI has increased rapidly in education, especially ChatGPT in higher education. ChatGPT is an AI-powered chatbot that was developed by using the technology which is called Large Language Model (Stevenson, 2024).This study examines the experiences of EFL learners who have used ChatGPT in academic writing using the Technology Acceptance Model as a theoretical framework (Davis,1989), which explains the acceptance of technology through usefulness and easiness of usage. Qualitative research design was used to gather data from 10 EFL postgraduate MA students. The semi-structured interview was conducted to explore the students’ experiences in using ChatGPT in academic writing. The findings in this study have been revealed into four main themes: 1. ChatGPT 3.5 as a supportive tool 2. Confidence and Motivation in academic writing. 3. Challenges and Limitations in using ChatGPT 3.5. 4. Ethical Consideration in using ChatGPT 3.5. Also, the findings demonstrated that the majority of participants found ChatGPT beneficial in brainstorming, correction with grammar and organization. In addition, it was reported that it was easy and simple to use by supporting the main concepts of the TAM. On the other hand, some students were concerned about overdependence, misleading information and reducing critical thinking. This research study contributes crucial information that suggests ChatGPT has the potential to become an effective tool for supporting EFL learners in academic writing, if it is used in an ethical way
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.
Modern Methods of Teaching the Periodic Table of Chemical Elements
(SDU University, 2025) Rakhmet D.
This master's thesis examines modern methods of teaching the periodic table of chemical elements in the general education curriculum of secondary education. The main purpose of the master's thesis is to assess the position of teaching the periodic table in Kazakhstan and in other countries. As well as based on the literature review, the development of modern methods of teaching the periodic table of chemical elements aimed at improving the effectiveness of teaching students in grades 7-8. The research includes both traditional and modern teaching methods such as gamification, the use of digital tools, visualization techniques, digital periodic tables, as well as learning using artificial intelligence. In order to achieve these goals, a survey was conducted among 250 students from two pedagogical universities in Kazakhstan to assess students' readiness to teach the periodic table using modern methods. At the second stage, a lesson was conducted in three schools in Kazakhstan using modern teaching methods where preliminary and sequential testing was used. The results demonstrated positive trends in understanding and memorizing the structure of periodic tables, as well as increased students' motivation to study chemistry. According to the conclusions, this dissertation can serve as a practical guide on the introduction of modern teaching methods for teachers seeking to adapt the educational process to the digital age and increase student engagement. According to the hypothesis, modern periodic table teaching methods have an effective effect on student academic performance compared to traditional teaching methods by providing a conceptual methodology that promotes memorization and use of the periodic table.