International Journal of Innovative Research and Scientific Studies

ISSN

2617-6548

2790-7600

Ключевые слова

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Публикации в журнале

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An educational mobile application for children with hearing impairment
(2025-09-19)
G.I. Salgarayeva
;
Aigul Makhanova
;
Fariza Lakhanova
The purpose of this study is to assess the effectiveness of an educational mobile application in improving algorithmic thinking and academic performance among children with hearing impairments. The research was conducted in two stages: (1) the development of the application using an inclusive design approach with game-based learning elements, and (2) an experimental implementation in the educational process. The participants were 15 sixth-grade students from a specialized school for children with hearing impairments, divided into an experimental group (n = 7) and a control group (n = 8). The experimental group used the mobile application as a supplementary tool for programming lessons, while the control group was taught using traditional methods. Pre-test and post-test assessments were conducted to evaluate changes in algorithmic thinking and academic outcomes. The results demonstrated that students in the experimental group showed significant improvement in algorithmic thinking and academic performance compared with those in the control group. The use of the mobile application increased student engagement and facilitated more effective learning. The study concludes that the developed mobile application is an effective educational tool for enhancing algorithmic thinking and improving academic outcomes among students with hearing impairments. This research highlights the potential of accessible digital technologies in supporting inclusive education. The findings can guide educators, developers, and policymakers in integrating inclusive digital resources into curricula to promote equal learning opportunities for students with special educational needs.
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Improving university students’ engagement in physical education through differentiated instruction and motivational strategies
(2025-06-02)
Nurzhan Temirbay
;
Aychurok Kaldybaeva
;
Baikulova Aigerim Meirkhanovna
;
Nurlan Beknurmanov
;
Zhambyl Beknazarov
This study aims to enhance university students’ engagement in physical education (PE) by applying a set of differentiated instructional strategies and motivational interventions. Declining levels of student participation in PE present a significant challenge in higher education institutions, necessitating pedagogically sound solutions. A quasi-experimental study was conducted at M.Kh. Dulaty Taraz University with 473 undergraduate students from four faculties. The intervention comprised individualized grouping based on fitness levels, theoretical modules on health literacy, flexible class formats (including outdoor activities), and motivational events such as sports competitions. Engagement was measured using pre- and post-intervention questionnaires and statistically analyzed via t-tests and ANOVA. The intervention yielded statistically significant increases (p < 0.001) in student engagement across all measured dimensions, including participation frequency, intrinsic motivation, and satisfaction with PE. Differentiated, student-centered instructional models combined with motivational support significantly improve engagement in university PE settings. The study provides a scalable framework for educators and administrators to improve PE program effectiveness, support student well-being, and foster lifelong healthy behaviors through targeted pedagogical innovations.
Цитирования (OpenAlex) 3Цитирования (OpenCitations) 1
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Integration of ethical categories of al-farabi and abai into civic and historical education in kazakhstan: An innovative educational model
(2025-09-19)
Кazbek Raissov
;
Karlygash Zhylkyshybaeva
This study is dedicated to the development and validation of an innovative model of civic and historical education based on the integration of key ethical categories from the Kazakh philosophical tradition. These categories include Qaiyrymdy qala (virtuous society), Tolyq adam (whole person), fazilat (virtue), aqyl (reason), and zhürek (heart). They serve as pedagogically operational foundations for shaping value-based subjectivity. In the context of cultural diversity and increasing global fragmentation, the research offers an alternative to universalist approaches by proposing a rooted philosophical framework capable of bridging the humanitarian tradition and contemporary educational practices. The methodological foundation combines a design-based research approach, philosophical componential analysis, and qualitative methods of pedagogical diagnostics. The model was piloted in three institutional contexts: in the school education system (Nazarbayev Intellectual Schools, NIS), in higher education (Kazakh National Women's Pedagogical University), and in digital learning environments (Moodle platform). Empirical results confirm significant effects in developing ethical reflection, empathy, resistance to manipulation, moral reasoning, and civic responsibility. A comparative analysis with control groups demonstrates the consistent advantage of axiologically rich instruction over conventional content-based approaches. The scientific novelty of the study lies in the pedagogical transformation of philosophical categories into a value-action model applicable across various formats of educational interaction. The practical significance is reflected in the scalability potential of the model, its adaptability to teacher training programs, digital platforms, and policy strategies aimed at humanizing education. In the long term, this model may serve as a foundation for developing a culturally sensitive, morally responsible, and conceptually coherent system of civic education.
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Researching and determination of the strain sensitivity of the fabricated Bragg gratings
(2025-02-18)
Aliya Kalizhanova
;
Айнур Козбакова
;
Murat Kunelbayev
;
Zhalau Aitkulov
;
Ulghan Imanbekova
This article presents a systematic study of the stretching characteristics of polymer fiber Bragg gratings. Experimental data demonstrate that applying mechanical stretching to polymer fiber Bragg gratings enables a wide tuning range of the Bragg wavelength (over 50 nm) with high reproducibility, reversibility, and repeatability. It was also established that the strain sensitivity of polymer fiber Bragg gratings is 1.46 pm/με, which significantly exceeds the strain sensitivity of silica fiber Bragg gratings. Additionally, it is shown that the relationship between strain and the Bragg wavelength shift exhibits high linearity (correlation coefficient R² = 0.9995), ensuring the reliability of the gratings under mechanical loads. The study confirms the potential of polymer fiber Bragg gratings for use in WDM optical communication systems, fiber optic strain sensors, and medical sensors.
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Experimental studies of sensors based on fiber Bragg gratings embedded in the internal structure of composite plates
(2024-10-04)
Aliya Kalizhanova
;
Айнур Козбакова
;
Murat Kunelbayev
;
Beibut Amirgaliyev
;
Zhalau Aitkulov
The main purpose of this article is to study the structural characteristics of carbon fiber samples printed on a 3D printer, including embedded sensors, during 3D printing, through numerical and experimental studies. There is a widespread tendency to replace metals with modern composite materials. Various methods can be used to measure the destruction of the composite structure. Fiber Bragg array sensors are known as intelligent localized and global structural condition prediction devices for all kinds of structural applications, especially those using composite materials. In this work, we created control plates by impregnating a selected number of layers of carbon fabric with epoxy resin. Fiber-optic Bragg sensors were additionally located between the layers of carbon fabric. In the course of experimental studies, a fiber Bragg grating sensor was embedded in the middle of the sample, and a second sensor was attached to the finished surface of the sample. We conducted environmental tests to examine the durability of the additively manufactured standard’s components and the impact of integrated sensors on the composite sample. We applied the concept of equations, known as conjugate modes, and used the transition matrix method to solve them numerically. The scientific novelty of the work is the development of a new method and technology for measuring spatial deformation in composite materials using fiber-optic Bragg gratings.
Цитирования (OpenAlex) 1
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Integrating satellite data and machine learning algorithms into a flood prediction system
(2025-07-19)
Gulzat Z. Ziyatbekova
;
Dauren Darkenbayev
;
Shyraigul Shekerbayeva
;
Aisulu Zhaksymbet
The objective of this study is to develop an intelligent flood forecasting system based on the integration of satellite and ground-based hydrometeorological data using machine learning algorithms in the Orange visual analytical environment. The aim of the article is to improve the accuracy and efficiency of forecasting extreme hydrological events, as well as to simplify the process of building forecasting models through the use of an interface that does not require programming. The methodological basis of the study is the formation of a synthetic multivariate dataset combining satellite vegetation indices NDVI and LST temperature with meteorological parameters such as precipitation, temperature, humidity, and water level. Random forest, gradient boosting (XGBoost), and multilayer perceptron (MLP) algorithms were used to build and validate the models. All stages from data loading and pre-processing to visualization and interpretation of results are implemented in the Orange environment using cross-validation and feature significance assessment. The results obtained demonstrated high forecast accuracy (up to 94%), especially when using ensemble and deep models. The significance of satellite data is confirmed by analyzing the contribution of features to the final classification. The developed forecasting model can be adapted to various geographical conditions and integrated into existing monitoring and early warning platforms. The proposed approach has high applied value and demonstrates the potential for using modern big data analysis technologies and artificial intelligence methods in the tasks of reducing the risk of natural floods.
Цитирования (OpenAlex) 1Цитирования (OpenCitations) 1
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Shaping digital communication culture in prospective teachers: The role of digital etiquette training in Kazakhstan
(2025-02-24)
Ulzharkyn M. Abdigapbarova
;
Aigul Syzdykbayeva
;
Elmira A. Aitenova
;
Sabira Nishanbayeva
;
Saniya Nurgaliyeva
This study examines the impact of digital etiquette training on the development of a digital communication culture among future teachers by providing new perspectives and access to scientific knowledge in the field of digital etiquette. A mixed-methods approach was used both before and after the test. The study included 115 respondents. The preliminary testing between the groups showed that both groups were at the same level in terms of communicative culture indicators. Additionally, participants had no prior knowledge of digital etiquette. An authors' program was created specifically for the course and implemented to help students become more skilled with technology. A set of measures was carried out to develop a digital communicative culture through the means of netiquette and the introduction of technology for individualization and personalization of learning. This study found the impact of digital etiquette on the development of a digital communication culture in future teachers by providing new perspectives and access to scientific knowledge in the field of digital etiquette, as well as the practical application of this knowledge to support cultural digital interaction.
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Machine learning-based predictive maintenance system for urban heating networks for real-time failure detection and analysis
(2025-09-19)
Dauren Darkenbayev
;
Uzak K. Zhapbasbayev
;
Gulnar Tultayevna Balakayeva
This study presents a comprehensive framework for predictive maintenance of urban heat supply networks utilizing advanced machine learning algorithms. The primary objective is to enable early detection of potential system failures, thereby improving operational reliability and minimizing unplanned downtimes. A synthetically generated dataset of 10,000 records was employed, simulating real – world operational parameters such as temperature, pressure, flow rate, and vibration, sampled at 5–minute intervals to replicate actual monitoring conditions. Data preprocessing involved outlier removal using the interquartile range (IQR) method, normalization through Min-Max scaling, and imputation of missing values, ensuring data quality and consistency. Feature importance was further analyzed using SHAP values to enhance interpretability and identify critical predictors influencing system behavior. Five machine learning models – Logistic Regression, Support Vector Machine (SVM), Random Forest, Artificial Neural Networks (ANN), and Gradient Boosting (LightGBM) – were implemented and evaluated using 10 – fold cross – validation. The Gradient Boosting model demonstrated superior performance, achieving an accuracy of 99.9%, F1-score of 0.999, ROC-AUC of 1.0, and LogLoss of 0.004. Logistic Regression and Random Forest also performed well (AUC = 1.0, F1 = 0.999), whereas SVM and ANN exhibited limited predictive capabilities (AUC ≈ 0.50, F1 = 0.038 and 0.632, respectively). These results underscore the robustness of Gradient Boosting in modeling complex nonlinear relationships and its applicability for real-time anomaly detection in heating systems. The proposed framework holds significant practical potential for integration into existing monitoring infrastructures, facilitating proactive maintenance planning, optimizing resource allocation, and reducing operational costs. Future research will focus on validating the approach with real – world datasets and exploring hybrid machine learning architectures to enhance model generalizability and resilience.
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Flood forecasting using machine learning methods in a visual programming environment
(2025-07-17)
Gulzat Z. Ziyatbekova
;
Dauren Darkenbayev
Floods are among the most destructive natural phenomena, significantly impacting the environment, economy, infrastructure, and public safety. Effective forecasting of such emergencies is crucial, especially in the context of global climate change and the increasing population density in coastal and low-lying areas. This work aims to develop and analyze flood forecasting models using machine learning algorithms within the Orange visual software environment. The study employed five algorithms: Random Forest, Decision Tree, Gradient Boosting, AdaBoost, and K-Nearest Neighbors. A comparative analysis of these models was conducted using key classification metrics, including Accuracy, Precision, Recall, and AUC. Special emphasis was placed on visualizing the results and assessing the usability of models within the Orange environment. The findings can be valuable for educational purposes, such as teaching students to work with real environmental data, as well as for practical applications in early warning systems and flood monitoring.
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Theory and methodology in vocational education
(2025-04-03)
Nurziya B. Rakhmetova
;
Saya Sakenova
;
Mariya Derbissova
;
Serik Kanapyanov
;
K. Suleimenova
Vocational schools focus on bridging the gap between theory and practice. The concepts of theory and practice are often discussed in debates about how to address them in the classroom, particularly as they pertain to vocational and technological learning. This research adds empirical evidence to the ongoing debate over vocational education philosophy and methodology. Starting from the heterogeneous phenomenology of the lived world, this study seeks to answer questions about the theoretical and practical experiences of supervisors, instructors, and students in vocational education concerning the teaching and learning process. Thematic analysis has been applied to the narratives of students, instructors, and supervisors gathered via qualitative interviews, with an emphasis on information pertaining to theory and practice. The findings point to both theoretical and practical experiences with a more interconnected perspective and a more typical dualistic one. A more nuanced and thorough comprehension of the concepts allows for their handling in a more integrated fashion.
Цитирования (OpenAlex) 12

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