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All Journal International Journal of Electrical and Computer Engineering International Journal of Evaluation and Research in Education (IJERE)
Kaipova, Assemgul
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Computer Science & IT Education Electrical & Electronics Engineering

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Forecasting creditworthiness in credit scoring using machine learning methods Mukhanova, Ayagoz; Baitemirov, Madiyar; Amirov, Azamat; Tassuov, Bolat; Makhatova, Valentina; Kaipova, Assemgul; Makhazhanova, Ulzhan; Ospanova, Tleugaisha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5534-5542

Abstract

This article provides an overview of modern machine learning methods in the context of their active use in credit scoring, with particular attention to the following algorithms: light gradient boosting machine (LGBM) classifier, logistic regression (LR), linear discriminant analysis (LDA), decision tree (DT) classifier, gradient boosting classifier and extreme gradient boosting (XGB) classifier. Each of the methods mentioned is subject to careful analysis to evaluate their applicability and effectiveness in predicting credit risk. The article examines the advantages and limitations of each method, identifying their impact on the accuracy and reliability of borrower creditworthiness assessments. Current trends in machine learning and credit scoring are also covered, warning of challenges and discussing prospects. The analysis highlights the significant contributions of methods such as LGBM classifier, LR, LDA, DT classifier, gradient boosting classifier and XGB classifier to the development of modern credit scoring practices, highlighting their potential for improving the accuracy and reliability of borrower creditworthiness forecasts in the financial services industry. Additionally, the article discusses the importance of careful selection of machine learning models and the need to continually update methodology in light of the rapidly changing nature of the financial market.
Enhancing informatics teacher training in Kazakhstan through dual education and specialized educational platforms Seitaliyeva, Alima; Shyndaliyev, Nurzhan; Kalmanova, Dinara; Kaipova, Assemgul; Mukhtarkyzy, Kaussar
International Journal of Evaluation and Research in Education (IJERE) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v14i6.34236

Abstract

This study addresses the gap between traditional informatics teacher training in Kazakhstan and the practical demands of modern classrooms. It explores the integration of dual education and the informaticedu.kz digital platform as a means to enhance methodological and practical competencies among future teachers. A mixed-methods design was used, involving 24 students from Pavlodar Pedagogical University. Data were collected through structured questionnaires and qualitative interviews. Quantitative responses were analyzed using descriptive statistics, t-tests, and correlation analysis, while qualitative data underwent thematic analysis. The findings showed that the platform significantly supported lesson planning and methodological development, particularly among 4th-year students who rated the tool more positively than 3rd-year students. High correlations were found between understanding lesson structure and effective planning. However, participants reported a lack of interactive content such as case studies and problem-solving tasks. The results suggest that integrating dual education with specialized digital platforms enhances informatics teacher training. Still, to maintain relevance and effectiveness, platforms must evolve to include more interactive and adaptive features tailored to different training stages.
Co-Authors Amirov, Azamat Baitemirov, Madiyar Kalmanova, Dinara Makhatova, Valentina Makhazhanova, Ulzhan Mukhanova, Ayagoz Mukhtarkyzy, Kaussar Ospanova, Tleugaisha Seitaliyeva, Alima Shyndaliyev, Nurzhan Tassuov, Bolat