The "Igniting Curiosity: A STEAM Journey for Young Minds" (IC-SJYM) program integrates Science, Technology, Engineering, Art, and Mathematics (STEAM) into early childhood education to enhance linguistic and scientific engagement among 5 to 6-year-olds. This study uses a mixed-methods design to evaluate the program's effectiveness, utilizing the Kaufman Survey of Early Academic and Language Skills (K-SEALS) and the Teacher Rating Scale of Children's Motivation for Science (TRS-CMS), alongside qualitative feedback from educators. Results show that the experimental group, following the IC-SJYM program, demonstrated significant improvements in academic performance and motivation towards science compared to a control group with a traditional curriculum. Additionally, qualitative analyses highlight the program's positive impact on expressive language skills, innovative thinking, and a sustained interest in scientific inquiry. These findings suggest that an integrative STEAM curriculum can significantly enhance early learning experiences, advocating for its broader adoption. The IC-SJYM program's success in fostering intellectual curiosity and academic excellence underscores the critical role of STEAM in early childhood education and calls for further research into its potential to revolutionize educational paradigms for young learners.

The main purpose of the article is to highlight and determine the level of influence of the most significant threats from the use of artificial intelligence in the educational process in the context of ensuring information security. To achieve this goal, the research methodology involves the use of an expert analysis method, which, through the Delphi method, will help to identify the most significant threats to the use of artificial intelligence in the educational process, a paired comparison method, which is necessary to implement the hierarchical analysis method, which in turn aims to organize a certain list of experts. As a result of the study, the most significant modern threats to the use of artificial intelligence in the educational process in the context of ensuring information security were identified. The resulting matrix of hierarchical ordering of threats made it possible to divide them into those that require immediate intervention and those that are less important. The innovativeness of the results obtained is revealed through the established methodological approach to modeling the ordering of the influence of threats from the use of artificial intelligence on the educational process in the context of ensuring information security. The study is limited by taking into account only the specifics of the educational process in Ukraine.

The research problem is based on the study of the possibilities of expanding methodological approaches, educational technologies, and educational programs for the implementation of blended learning and increasing the level of its effectiveness in the educational system of Kazakhstan. This study aims to identify the best conditions for implementing blended learning that would meet the technical capabilities of the university, the educational programs, and the interests and needs of all participants of the educational process. For this, the following data collection methods were used: online surveys, quantitative and qualitative analyses, and facilitation tools, such as World Café, Future  
Search, ranking, and Spearman's correlation analysis. The results show that more than half of the students (58%) and teachers (65%) were not satisfied with the existing structure of blended learning at the university. This research suggests involving all participants in the educating process when adopting the blended mode of learning to enhance the efficacy of the blended learning program. The practical significance of this research lies in its determination of the optimal conditions for implementing blended learning in the university programs of Kazakhstan. The engagement of all stakeholders in the Learning pathway in decision-making regarding hybrid education, taking into account the technical capabilities of universities and the individual needs of students and instructors, aims not only to address current issues but also to enhance the quality of education and prepare graduates to meet the demands of the contemporary labor market. Such an approach to research and innovation implementation in Kazakhstan's education could foster the development of more flexible, adaptive, and effective educational systems that meet the requirements of the modern world.

The Simple Human Learning Optimization (SHLO) algorithm, drawing inspiration from human learning mechanisms, is a robust metaheuristic. This study introduces three tailored variations of the SHLO algorithm for optimizing the 0/1 Knapsack Problem. While these variants utilize the same SHLO operators for learning, their distinctiveness lies in how they generate new solutions, specifically in the selection of learning operators and bits for updating. To assess their efficacy, comprehensive tests were conducted using four benchmark datasets for the 0/1 Knapsack Problem. The results, encompassing 42 instances from three datasets, reveal that both SHLO and its proposed variations yield optimal solutions for small instances of the problem. Notably, for datasets 2 and 3, the performance of SHLO variations 2 and 3 outpaces that of the Harmony Search Algorithm and the Flower Pollination Algorithm. In particular, Variation 3 demonstrates superior performance compared to SHLO and variations 1 and 2 concerning optimal solution quality, success rate, convergence speed, and execution time. This makes Variation 3 notably more efficient than other approaches for both small and large instances of the 0/1 Knapsack Problem. Impressively, Variation 3 exhibits a remarkable 14x speed improvement over SHLO for large datasets.

The growing use of social networks and the steady popularity of online communication make the task of detecting gender from posts necessary for a variety of applications, including modern education, political research, public opinion analysis, personalized advertising, cyber security and biometric systems, marketing research, etc. This study aims to develop information technology for gender voice recognition by sound based on supervised learning using machine learning algorithms. A model, methods and means of recognition and gender classification of voice speech samples are proposed based on their acoustic properties and machine learning. In our voice gender recognition project, we used a model built based on the neural network using the TensorFlow library and Keras. The speaker’s voice was analysed for various acoustic features, such as frequency, spectral characteristics, amplitude, modulation, etc. The basic model we created is a typical neural network for text classification. It consists of the input layer, hidden layers, and the output layer. For text processing, we use a pre-trained word vector space such as Word2Vec or GloVe. We also used such techniques as dropout to prevent model overtraining, such activation functions as ReLU (Rectified Linear Unit) for non-linearity, and a softmax function in the last layer to obtain class probabilities. To train a model, we used the Adam optimizer, which is a popular gradient descent optimization method, and the “sparse categorical cross-entropy” loss function, since we are dealing with multi-class classification. After training the model, we saved it to a file for further use and evaluation of new data. The application of neural networks in our project allowed us to build a powerful model that can recognize a speaker’s gender by voice with high accuracy.  The intelligent system was trained using machine learning methods with each of the methods being analysed for accuracy: K-Nearest Neighbours (98.10%), Decision Tree (96,69%), Logistic Regression (98.11%), Random Forest (96.65%), Support Vector Machine (98.26%), neural networks (98.11%). Additional techniques such as regularization and optimization can be used to improve model performance and prevent overtraining.

Big data such as social network data, financial data, and disease data have multiple dimensions that are more complicated to interpret by the human brain. In this regard, the concept of three-dimensional metaphor-based information visualization and navigation has become very important for big data visualization. The three-dimensional visual metaphors can be used to represent information allowing dealing with more abstract data of larger volumes. Therefore new three-dimensional metaphors are needed for the visualization of multidimensional attributes into easily readable and understandable forms. When compared with 2D data representations, 3D brings many advantages in complex data visualization. But most of the existing 3D visualizations result in complex Graphical User Interfaces that require high cognitive efforts to clearly understand these datasets. Therefore this paper presents a novel 3D user interface metaphor for visual analytics of multidimensional data which leads to drawing better conclusions on the datasets. The proposed system represents information in a more realistic 3D setting. The concept of the 3D water fountain metaphor is adopted to implement the novel data exploration mechanism in 3D space. This paper provides an outline of the proposed conceptual design. Employing a Vector-Borne Disease dataset as a case study, a proof-of-concept prototype based on this conceptual design is developed. The applicability of the conceptual metaphor is showcased through two distinct experiments, each involving four groups engaged in decision-making scenarios within the realm of multidimensional data visualization. Key findings reveal that 85% of the data analysis tasks were efficiently completed using the proposed 3D metaphor. Notably, user satisfaction levels including feedback on learnability, interface aesthetics, ease of use, and overall user experience were graded high. These key findings of the evaluation underscore the heightened potential of 3D user interface metaphors for facilitating visual analytics of multidimensional datasets.

The rise of online education has changed the way students usually learn by making educational materials easier to get to and creating a global learning community. While online education offers numerous benefits, it is also crucial to acknowledge its certain drawbacks, such as the potential reduction in interaction between students and teachers, which might increase signs of isolation among students and impede opportunities for collaborative learning. Therefore, Student Evaluations of Teaching (SET) play a critical role in identifying areas for improvement from the students' standpoint, thereby promoting constructive communication between students and teachers. This research conducts a comparison among the traditional Educational Data Mining (EDM) techniques to find out the best-performing classifier for analyzing student evaluations of teaching online. It is accomplished by first extracting the dataset from the student evaluations of teaching at X-University and then applying six different classifiers to the dataset that were extracted. The results demonstrated that Logistic Regression, Naive Bayes, and K-Nearest Neighbors (KNN) exhibited a notably high level of accuracy compared to other classification techniques. The findings of this research will provide guidance for future researchers in applying a wider range of classification techniques to extensive datasets and in implementing the necessary adjustments to achieve superior results.