Browsing by Author "Berlikozha B.A."

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    AI-Powered Approach to Career Path Prediction for IT Students Using Academic and Behavior Data
    (SDU University, 2025) Berlikozha 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 [
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    The Art of Personalized Student-Supervisor Matchings
    (ADAL Kitap, 2025) Serek A.G.; Berlikozha B.A.
    The process of student-supervisor matching is a critical yet complex task in higher education institutions, directly influencing research productivity, student satisfaction, and workload distribution. The ability to assign students to the most suitable supervisors is essential for fostering strong academic relationships, optimizing institutional resources, and improving research outcomes. However, traditional manual assignment methods often lead to inefficiencies, subjective biases, and an imbalance in workload distribution. As a result, automated recommendation systems have emerged as a promising solution to enhance the efficiency and fairness of student-supervisor pairings. This study evaluates three recommendation algorithms—Singular Value Decomposition (SVD)-based collaborative filtering, graph-based matching using the Hungarian Algorithm, and machine learning via Random Forest Regression—to determine their effectiveness in optimizing student-supervisor assignments. A rigorous empirical analysis is conducted across five key performance metrics: accuracy, fairness, stability, scalability, and computational efficiency. The findings reveal that while collaborative filtering performs well with established datasets, it struggles with novel cases due to its dependence on prior interactions. The Hungarian Algorithm guarantees optimal matching but faces scalability challenges, particularly in large academic institutions with thousands of students and supervisors. Meanwhile, Random Forest Regression effectively captures complex compatibility patterns but requires extensive labeled data, limiting its applicability in cases where historical matching data is sparse or unavailable. To overcome these limitations, the study proposes an adaptive hybrid framework that integrates the strengths of all three approaches. The hybrid model leverages collaborative filtering’s ability to recognize patterns in existing data, the Hungarian Algorithm’s precision in optimal pairings, and the predictive power of machine learning. By combining these methodologies, the proposed system enhances match accuracy, ensures fair workload distribution, and remains computationally efficient for large-scale institutional implementation. Additionally, the framework introduces dynamic adaptation mechanisms that allow the system to update recommendations based on real-time changes in student preferences and supervisor availability, making it more practical for real-world applications. The research contribution is a comprehensive, empirically validated hybrid framework that improves student-supervisor matching by balancing accuracy, fairness, and efficiency. This study provides educational institutions with actionable guidelines for scalable and equitable assignment processes, ultimately contributing to more effective mentorship experiences, improved research collaborations, and enhanced academic outcomes.