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    EFL TEACHERS’ PERCEPTIONS OF LESSON STUDY IMPLEMENTATION IN SECONDARY RURAL SCHOOLS THROUGH THE INTERDISCIPLINARY APPROACH
    (SDU University, 2023) Ichshanova Zh.; Kassymova G.
    Lesson study approach is widely used in various countries to develop teachers’ pedagogical and instructional knowledge. The purpose of this study is to explore how Kazakhstani EFL teachers perceive the effectiveness of Lesson Study implemented through an interdisciplinary approach. Five EFL teachers from a secondary rural school who were the members of the interdisciplinary group of the Lesson Study participated in semi-structured interviews. The respondents indicated that the Lesson Study through the interdisciplinary approach had significantly improved their pedagogical and instructional knowledge. However, they also indicated that they experienced some challenges in the process, such as time consumption, observation of case students, and support from the administration of school.
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    ANALYSIS OF THE KAZAKH CYBER TOURNAMENTS PLATFORM “PINGER.KZ”
    (СДУ хабаршысы - 2023, 2023) Alimanova M.; Karazhar B. ; Alseitova A. ; Orynbekova K.
    Abstract. Worldwide experts presume further growth of videogames field and suggest solutions according to the actual statistics. Despite the remarkable successes of the e-athletes from Kazakhstan in a worldwide arena, there is a lack of statistical data on the local cybersport. Therefore, the research was conducted to provide prospective researchers in fields like gamification, eSport and media with actual data. The given scientific paper uses the data collected and analysed from the largest cyber tournament platform in Kazakhstan “Pinger.kz”. The analysed information is about tournaments that took place since the website launched in 2019 to 2021. The libraries of Python for data analysis as BeautifulSoup, requests, matplotlib and Apache tools were used. The aim of the work is to collect and make data analysis for future research. The paper identifies the most popular games among kazakh players in order to use this data in the research on gamification. Parsed data results show the development of the local cybersport and confirm worldwide trends.
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    DATA COLLECTION TO IDENTIFY STUDENTS AT RISK OF NOT COMPLETING A COURSE USING MACHINE LEARNING
    (СДУ хабаршысы - 2023, 2023) Bairamova D.
    Abstract. One of the most important methods in the study of various subjects is the understanding at an early stage of the learning process on the part of both the teacher and the student that the student is in a risk group that will not complete the course successfully. Identifying this group of students at an early stage of learning increases the level of motivation of students to start studying well in time and can help the teacher individually determine which student needs help. Before identifying a group of students at risk of not completing the course successfully, an important part is to collect and prepare the necessary data (predictors) for teaching machine learning algorithms. Currently, this is necessary for both online and offline education. In the presented method of determining a group of students, various types of algorithms were used, where one of the best results of determining a group of students with risk and without risk was shown by Logistic Regression with a high AUC =0.8003. The SMOTE method was used in the work, which coped well with the problem of data imbalance of the "Pass" and "Not Pass" classes, while increasing the accuracy of the forecast for the minority class "Not Pass" by 11%. Using certain predictors of student performance, it is possible to derive additional information such as the level of interest in the lesson. the determination of the final score for the lesson, a certain category (A, B, C, D) of students with different characteristics and other indicators that contribute to the involvement of students in the lesson at the earliest stage of learning.
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    CODE-SWITCHING IN DIALOGUES AMONG YOUNG INDIVIDUALS FROM KAZAKHSTAN
    (SDU University, 2023) Myrzabek A.; Amangazina A.; Moldagaliyeva I.
    This research investigates the varieties and communicative purposes of codeswitching (CS) among the youth demographic in Kazakhstan. Centered around specific research questions, the researchers investigate the complex dynamics of CS within Kazakhstan's bilingual environment. Four young individuals studying abroad participated in a focus group interview, and the results showed a range of code-switching occurrences, from intra-sentential to tag-switching, and differing levels of ability to switch codes. There are various reasons why people switch codes, such as habit or choosing the best language for a conversation. Findings were discussed with a focus on the expressive and referential functions observed among young Kazakh adults. The comfort level of speakers in a given language and audience familiarity are factors that affect code- switching. The paper acknowledges its limits and proposes directions for future investigation, promoting the examination of other factors that may impact code - switching among young Kazakh adults.
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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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    ARTIFICIAL AI IN TEST AUTOMATION: SOFTWARE TESTING OPPORTUNITIES WITH OPENAI TECHNOLOGY - CHATGPT
    (СДУ хабаршысы - 2023, 2023) Talasbek A.
    Abstract. One of the most important and significant stages of the software development life cycle is software testing. Automated testing reduces testing costs and increases productivity, resulting in a high-quality and stable end product. To ensure that software is bug-free and delivers the desired user experience, test automation is critical. As technology advances, test automation becomes more complex. However, advanced Al technologies will soon become commonplace thanks to powerful tools like ChatGPT that, among countless other things, can chat with you and teach you how to read and write code like a human. In this article, we will try to consider the possibilities of automation with chatgpt. Starting with writing test plans, and test scripts by using Python and Selenium WebDriver. How ChatGPT can improve our tasks and make them more feasible. By leveraging the capabilities of ChatGPT, software testing engineers can enhance their skills, improve testing capabilities, and achieve better quality and accuracy of test results. The opportunities presented by ChatGPT can lead to improved efficiency, productivity, and overall performance in the field of software testing engineering.
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    KAZAKH HANDWRITING RECOGNITION
    (СДУ хабаршысы - 2023, 2023) Bazarkulova A.; Mutalivev Y.; Chazhabayev A.; Telman D. ; Bazarkulova D.
    Abstract. Recognition of handwritten text is one aspect of object recognition and known as handwriting detection cause of a computer’s potential to recognize and comprehend readable handwriting from resources including paper files, touch smart devices, images, etc. Data is categorized into a number of classes or groups using pattern recognition. The paper presents a successful experiment in recognizing handwritten Kazakh text using Convolutional Recurrent Neural Network based architectures and the Kazakh Autonomous Handwritten Text Dataset. The proposed algorithm achieved an overall accuracy of 86.36% and showed promising results. However, the paper suggests that further research could be conducted to improve the model, such as correlating and enlarging the database or incorporating other models and libraries. Additionally, the paper emphasizes the importance of considering language specifics when building a text recognition model, as modern algorithms that work well in one language may not guarantee the same performance in another.
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    CLASSIFICATION OF REVIEWS, ERROR REPORTS AND PRODUCT FEATURE REQUESTS USING MACHINE LEARNING METHODS
    (СДУ хабаршысы - 2023, 2023) Tolbassy B.
    Abstract. This article proposes a solution for filtering and categorizing user feedback on software products, which can be overwhelming in quantity and often includes uninformative or fake reviews. The proposed approach involves using machine learning methods for classifying reviews into categories such as error reports, product feature requests, and other reviews. The article compares the performance of different classification ML algorithms and investigates the impact of preprocessing options on classification accuracy. Additionally, the article addresses the task of identifying groups of similar reviews in each category, which can be useful for detecting duplicates and identifying patterns. The proposed solution is tested on a dataset and compared with existing solutions. The article concludes by highlighting the novelty and potential benefits of the proposed approach for improving the quality of user feedback and enhancing the reputation of software products.
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    SPEAKING CHALLENGES FACED BY BACHELOR DEGREE STUDENTS IN KAZAKHSTAN
    (СДУ хабаршысы - 2023, 2023) A. Suleimenova; Zh .Zhyltyrova.T
    Abstract. In the context of Kazakhstani university of Narxoz, the current study tried to explore the difficulties faced by Bachelor Degree students and the possible causal factors of these difficulties while speaking English as a foreign language. In this study, the researcher applied two qualitative approaches: open-ended questionnaire and semi-structured interview as a research tool to collect data. 10 students from the Faculty of Digital Engineering at the University of Narxoz were invited to participate for both interview and questionnaire. The collected data were analyzed through thematic analysis in terms of two major categories: challenges and causes which were divided into four subcategories of broad terms. Moreover, the study attempted to reveal some ideas and ways of solving these speaking difficulties suggested by students themselves. The findings found out the most common speaking difficulties which were divided into linguistic, personal and social problems , while teacher and teaching, course content, poor schooling and classroom environment were found to be the primary causes of speaking deficiency. The most common ideas to improve speaking skills and to overcome their language barriers, students were advised to attend language courses or to be actively involved in classes at the university as well as to do more self-study work. As regards suggestions for teachers, they were recommended to changes their current methods to more effective and to more practical ways of teaching. Also, students wished that their educators focus on more practice of oral production which can be provided in form of different competitive games and activities.
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    KAZAKH LANGUAGE-BASED QUESTION ANSWERING SYSTEM USING DEEP LEARNING APPROACH
    (СДУ хабаршысы - 2023, 2023) Bilakhanova A. ; Ydyrvs A.; Sultanova N.
    Abstract. Deep learning advances have resulted in considerable gains in a variety of natural language processing applications, including questionanswering (QA) systems. QA systems are intended to retrieve data from big datasets and respond to user queries using natural language. Deep learning-based techniques have yielded encouraging results in the development of QA systems capable of providing consistent answers to a wide range of inquiries. This research presents a deep learning-based Kazakh language-based QA system. A pre-processing module is also included in the proposed system to improve the quality of the input text and the accuracy of the final output. The results reveal that the system has a high level of accuracy. This study promotes to the advancement of question-answering technology and contributes to the development of natural language processing tools in the Kazakh language.