Browsing by Author "Zhalgassova Zh."

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    ANALYSIS OF PROGRAMMING EDUCATION AT THE PRIMARY EDUCATION LEVEL
    (СДУ хабаршысы - 2023, 2023) Saimassay G. ; Zhaparov M. ; Mukhiyayeva A. ; Zhalgassova Zh.
    Abstract. Programming education has traditionally been provided at the undergraduate level worldwide. However, in recent years, there has been a growing trend in developed countries to introduce programming education at earlier ages with the aim of promoting software literacy, improving programming skills, and making programming education accessible to a wider audience. While some countries are updating their informatics lessons to include programming, others are incorporating programming lessons into their primary education curriculum for the first time. The level at which programming training is offered also differs between countries. The objective of this research is to explore how countries have integrated programming education into their curricula and to identify the differences between countries in terms of programming education. The study aims to answer the question of how programming education is provided at the primary education level both domestically and abroad. The research has found that programming education is increasingly recognized as important and many countries are now allowing programming lessons in their education curriculum, with some countries even introducing programming education in kindergarten. However, there are variations in the programming languages used and the skills taught to students across different countries.
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    Personalized Career-Path Recommender System for STEM Students
    (Faculty of Engineering and Natural Science, 2024) Zhalgassova Zh.
    This dissertation introduces a Personalized Career-Path Recommender System (PCRS) designed to help high school students in Kazakhstan, particularly those interested in STEM (Science, Technology, Engineering, and Mathematics) fields. The system uses the Myers-Briggs Type Indicator (MBTI) personality types and students’ academic performance to offer personalized recommendations for university specializations. The research addresses the common challenges faced by students, such as high dropout rates and frequent changes in majors, often due to the lack of structured career guidance. To tackle these issues, the study collected a variety of data, including students’ demographics, academic records, and personal attributes, as well as detailed profiles of university majors. Advanced machine learning techniques, including content-based filtering, collaborative filtering, fuzzy logic, and hybrid approaches, were used to process this data and generate accurate recommendations. The effectiveness of the PCRS was tested with real data from students at SDU University. The results show that the system can provide relevant and personalized career guidance, significantly improving students’ decision-making processes and satisfaction with their chosen specializations. By combining MBTI personality assessments with academic performance data, this research offers a fresh approach to educational technology and career counseling. The insights and methods developed in this study can be adapted for use in other regions facing similar challenges, ultimately helping more students make informed and satisfying career choices.