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SPEAKING CHALLENGES FACED BY BACHELOR DEGREE STUDENTS IN KAZAKHSTAN
No full textAbstract. 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
Increase of Economic Potential of the Enterprise
No full textPurpose: The research aimed to test whether there is a distinction in entrepreneurial potential between entrepreneurs who are thriving and those who have failed; and whether there are variables that can predict entrepreneurial success or failure. Relevance: It presents an approach to entrepreneurship research tested by foreign academics, the main content of which is the empirical operationalization of success and failure in business to test a specific hypothesis and identify the prerequisites and consequences of entrepreneurial potential Key methodological aspects: The study was conducted using a descriptive and quantitative approach. Based on the Scale of Entrepreneurial Potential, the survey was completed by 61 entrepreneurs from Kazakhstan who satisfied the criteria for participation in the analysis, both successful entrepreneurs (n = 38) and entrepreneurs who failed (n = 23). The data were analyzed by using logistic regression and Student's t-test statistical methods. Summary of Key Findings: The results indicate that a successful entrepreneur scores higher on the Entrepreneurial Potential Scale than an unsuccessful entrepreneur. The main similarity between entrepreneurial potential and business success lies in setting business goals. In the study sample, gender has shown to be a strong predictor of business success, showing that men are 2.6 times more likely to be successful in business than women. Key conclusion: These findings point to vital elements in explaining business success and support recent findings from research on gender and entrepreneurship
Gamification in Programming Language Learning
Get PDFGamification is widely being used in every field including education to increase motivation as one of the reasons. Learning a programming language is generally difficult as understanding abstract notions and most teachers agree that students’ struggle with programming languages which then leads to lack of motivation to learn afterwards. To solve this issue a case study on the gamification of programming language class for lower secondary students studying computer science is provided and examined. During two-week experiment total of 32 8th grade students participated, and Codehs, a gamified online learning platform, employed an experimental group, whereas the control group received the traditional teaching approach. Students’ motivation level was measured using the Intrinsic Motivation Inventory before and after the experiment. The findings indicated that there was significant difference in the level of intrinsic motivation observed in the experimental group, thus rejecting the null hypothesis. These findings suggest that gamification may have a major impact on intrinsic motivation in the context of learning programming language in lower secondary school
Question Answering system on Regulatory Documents
Get PDFThe domain of legal text processing in the Kazakh language is currently underserved, presenting a unique challenge due to its specialized language and the relative scarcity of computational resources dedicated to it. This thesis explicitly identifies the problem: the need for an efficient model to process, understand, and generate meaningful insights from Kazakh legal texts. Addressing this problem, the thesis proposes a solution by developing and evaluating bespoke language models pre-trained on a vast corpus of Kazakh legal documents. The study begins with the assembly of a corpus, which comprises over 315 million words from Kazakh legal texts, alongside a benchmark dataset of 2500 multiple-choice questions for civil service examinations in Kazakhstan. Three language models based on the BERT architecture are then pre-trained. Among these, one model is pre-trained entirely from scratch. To emulate a real-world application in the legal domain, the performance of these models is assessed using the multiple-choice question-answering task. The BERT base model pre-trained from scratch, leveraging both Masked Language Modeling (MLM) and Next Sentence Prediction (NSP) tasks, achieves an accuracy of 56.11%. This result underlines the potential of custom pre training strategies on domain-specific corpora for enhancing the performance of language models in specialized areas. In conclusion, this research represents a significant advancement in using AI for legal text processing in the Kazakh language. It presents a promising solution to the problem, paving the way for more efficient and informed decision-making processes in legal and civil service settings
2D face recognition using PCA and triplet similarity embedding
Get PDFThe aim of this study is to propose a new robust face recognition algorithm by combining principal component analysis (PCA), Triplet Similarity Embedding based technique and Projection as a similarity metric at the different stages of the recognition processes. The main idea is to use PCA for feature extraction and dimensionality reduction, then train the triplet similarity embedding to accommodate changes in the facial poses, and finally use orthogonal projection as a similarity metric for classification. We use the open source ORL dataset to conduct the experiments to find the recognition rates of the proposed algorithm and compare them to the performance of one of the very well-known machine learning algorithms k-Nearest Neighbor classifier. Our experimental results show that the proposed model outperforms the kNN. Moreover, when the training set is smaller than the test set, the performance contribution of triplet similarity embedding during the learning phase becomes more visible compared to without it
БІЛІМАЛУШЫЛАРДЫҢ МӘТІН АРҚЫЛЫ МӘДЕНИТАНЫМДЫҚ ҚҰЗЫРЕТТІЛІГІН АРТТЫРУДЫҢ ПСИХОЛОГИЯЛЫҚ НЕГІЗДЕРІ
Get PDFАңдатпа. Қазіргі уақытта білім беру жүйесі жаңа деңгейге көтерілді. Заман талабына сай білім беру жүйесі де өз алдына мақсат қойды. Ол өз ойы бар, барлық құзыреттіліктерді меңгерген, сауатты, психологиялық негіздерге сай білім алған, дүниетанымы кең, еркін ойлы тұлға дайындау. Соңғы уақыттарда білім беру жүйесінде білімалушылардың дүниетанымын кеңейту, таным көкжиегін дамыту мақсатында «мәденитанымдық құзыреттілік» ұғымы жиі қолданылуда. Мәдениеттанымдық құзыреттілігі қалыптасқан, жетілген тұлға әрдайым сұранысқа ие. Оқыту процесінде оқушының қабылдауына, зейініне, танымына, түсінуіне мән берілу қажет. Мәденитанымдық мәтіндер арқылы баланы оқытудың мәні өте зор. Мәтін арқылы оқушылардың мәденитанымдық құзыреттілігі қалыптасатыны мақалада дәлелденіп отыр. Оқушылардың мәтін арқылы мәденитанымдық құзыреттілігін арттырудың психологиялық негіздері мақалада тереңірек қарастырылад
SENTIMENT ANALYSIS ON TWEETS ABOUT THE ELECTION CANDIDATES ISING TEXTBLOB
Get PDFAbstract. Sentiment analysis is the categorization of the speaker’s, writer’s, or other subject’s perspective on a certain issue. Since Kazakhstan's presidential election in 2022 is one of the most discussed events we distinguish people’s perspectives on the election and each candidate. The possible leader of the country will be influenced by the public’s perception of a candidate. A diverse data set illustrating the current public perceptions of the candidates are gathered from the Twitter platform. The collected tweets are examined using a lexicon- based methodology TextBlob to ascertain the public’s sentiments. In this study, we analyze the collected tweets to identify the polarity and subjectivity measures that provide light on the user perception of a certain candidate
Reinforcement Learning Methods for solving combinatorial optimization problems
Get PDFThe use of reinforcement learning approaches to resolve combinatorial optimization issues in the context of vehicle routing is examined in this paper. Three previously published articles’ insights are combined in one study. The Stochastic Dynamic Vehicle Routing Problem is addressed in the first paper with a deep reinforcement learning strategy. To learn the routing strategy for a single truck as customer orders come in over time, a fully attention-based model with a dynamic encoder and decoder is introduced. The model is taught using reinforcement learning, in which a reward signal influences the choice of nodes to visit. Computational analyses show that this tactic outperforms comparison algorithms. A thorough overview of machine learning techniques used to address NP-hard Vehicle Routing Problems is presented in the second paper. A variety of learning paradigms, solution structures, underlying models, and algorithms are included in the survey. It demonstrates the benefits of machine learning-based models that use the symmetry of VRP solutions and their parity with conventional approaches. The directions for future studies to address the problems with contemporary transportation systems are also described. The third paper provides a thorough analysis of the stochastic dynamic vehicle routing problem, examining state-of-the-art approaches and methodologies for solving this challenging optimization problem. The stochastic and dynamic restrictions that present significant challenges to efficient route planning and optimization are covered in detail. The study offers a thorough grasp of cutting-edge techniques and recommends prospective directions for further investigation. In addition, the use of Markov decision processes and sophisticated reinforcement learning techniques to address the issue of stochastic dynamic vehicle routing is examined. Combining these articles, this thesis advances knowledge of reinforcement learning methods for combinatorial optimization issues, particularly in the area of vehicle routing. The study demonstrates the effectiveness of deep reinforcement learning and machine learning techniques while also highlighting areas that need additional study and development in the field of transportation logistics
PERSPECTIVES OF CONSUMER LENDING IN KAZAKHSTAN
Get PDFConsumer lending is a critical aspect of the modern economy, providing individuals with access to credit for various purposes. As we look towards the future, it is essential to understand how consumer lending may evolve and what factors may shape its development. This survey aimed to gather perspectives on consumer lending in the future, including potential trends, concerns, and preferences. It is very important because the volume of consumer lending in Kazakhstan has increased rapidly in last 5 years.
The survey found that the majority of respondents were likely to use consumer lending services in the future, and they expected to see more personalized lending options, increased use of AI and machine learning in lending decisions, and more competition between lenders leading to lower interest rates. However, respondents also expressed concerns about potential risks associated with lending, such as high interest rates and debt accumulation, and were not sure on the potential popularity of alternative lending options.
When it comes to choosing a lender, respondents considered reputation of the lenders more than other factors, it shows that goodwill of lenders more important than other factors in choosing a lender. Finally, respondents were divided on how the COVID-19 pandemic has impacted consumer lending, with some expecting tighter lending requirements and higher interest rates in the future, while others anticipated more lenient lending requirements and lower interest rates.
Overall, these findings underscore the complex nature of consumer lending and the importance of considering multiple factors when assessing its development in the future.
The research paper uses qualitative research method, if more accurately, author used a survey method to know the opinions of people about what they think about future of consumer lending in Kazakhstan
STOCHASTIC DYNAMIC VEHICLE ROUTING PROBLEM SURVEY
Get PDFAbstract. The present article aims to offer an exhaustive and in-depth investigation of the Stochastic Dynamic Vehicle Routing Problem, which remains a significant challenge in the field of transportation logistics. To achieve this objective, we will undertake a meticulous analysis of the latest cutting-edge techniques and methodologies deployed to tackle this complex optimization problem. Furthermore, we will delve into the intricate and multifaceted stochastic and dynamic constraints that pose formidable obstacles to effective route planning and optimization. Through this survey paper, we seek to provide a comprehensive understanding of the current state-of-the-art approaches and highlight the potential avenues for future research in this critical area of transportation logistics. In addition, we will also analyze the application of advanced reinforcement learning methods and Markov decision processes to solve the problem of stochastic dynamic vehicle routing