Meruert Serik

The rapid integration of neural network technologies into education has increased the need to assess future teachers’ professional readiness for the pedagogically appropriate, ethically responsible, and methodologically effective use of artificial intelligence tools in teaching practice. However, there remains a lack of validated instruments specifically designed to measure this form of professional readiness in teacher education. The aim of this study was to develop and validate a scale for assessing the professional readiness of future teachers to use neural network technologies in the educational process. The study involved 150 students enrolled in teaching programs at L.N. Gumilyov Eurasian National University. The developed instrument included 36 items organized into three theoretically derived content domains: cognitive, operational, and reflexive-motivational readiness. In the present study, the scale was validated as an overall measure of professional readiness using the Rasch model, while the internal multidimensional structure requires further verification through Exploratory Factor Analysis (EFA) or Confirmatory Factor Analysis (CFA) in future research. Data was analyzed using the Rasch measurement model to evaluate one-dimensionality, category functioning, item fit statistics, reliability and separation indices, Wright map targeting, and Differential Item Functioning (DIF). The results demonstrated acceptable psychometric properties of the scale. The main measured dimension explained 48.7% of the total variance, while all response categories functioned in an ordered manner. Most items showed acceptable Infit and Outfit statistics, with only two items demonstrating borderline fit values. Reliability indices were high for both items (0.95) and respondents (0.91), indicating stable measurement performance. DIF (Differential Item Functioning) analysis revealed no substantial gender bias across items. The findings suggest that the proposed scale is a reliable and valid instrument for assessing future teachers’ professional readiness to use neural network technologies in educational settings. The scale can be applied in teacher education research, curriculum evaluation, and the development of AI (Artificial Intelligence)-related pedagogical competencies in higher education.

The rising number of pupils with Special Educational Needs (SEN) and the underutilization of creative teaching strategies pose challenges to the integration of inclusive education in primary schools. The purpose of this research is to assess how well Project-Based Learning (PBL) teaches computer science in inclusive classroom environments. An inclusive pedagogical model was created by integrating interactive exercises, teaching aids, and digital learning materials. 151 Kazakhstani elementary school pupils participated in the study; they were split into two groups: one that used PBL as an experimental strategy and the other that used conventional techniques. Three components—motivation, content comprehension, and technical skills—were evaluated using a structured questionnaire to gather data. PBL has a positive effect on inclusive digital learning, as evidenced by statistical analysis showing significant improvements in the experimental group across all components. Future work will focus on scaling this model to other subjects and educational levels, as well as enhancing accessibility features for pupils with various types of disabilities.

This article presents the implementation of a machine learning-based face anti-spoofing method to enhance the security of an educational information portal for university students. The study addresses the challenge of preventing academic dishonesty by ensuring that only authorized individuals can complete intermediate and final assessment tasks. The proposed method leverages the Tiny neural network model, selected for its efficiency in compact data processing, alongside the dlib system in Python and the LCC_FASD dataset, which enables precise detection of 68 facial landmarks. Using a confusion matrix to evaluate performance, the method achieved a 94.47% accuracy in detecting spoofing attempts. These findings demonstrate the effectiveness of the proposed approach in safeguarding educational platforms and maintaining academic integrity.

Computer vision methods based on machine and deep learning are extensively used in information security for data protection and user authentication. Key applications include biometric authentication (face, iris, fingerprint recognition), which enhances security compared to traditional methods, and real-time video surveillance to detect suspicious behaviour and cyber threats like deepfakes and hacking attempts. Ensuring confidentiality also requires encryption at all processing stages. In the context of teaching computer science, particularly in areas related to cybersecurity and cyber-attacks this research serves as a valuable didactic method. By incorporating real-world applications of computer vision into education, instructors can enhance security measures to protect sensitive data. Through practical experiments, students gain hands-on experience with biometric technologies, deepening their understanding. This article examines modern computer vision methods for information security, focusing on facial recognition and anti-spoofing in student registration portals, with an emphasis on data protection. Experimental work with students from L.N. Gumilyev Eurasian National University and Almaty University of Technology compared facial recognition to traditional password-based registration. Results demonstrated over 95% accuracy in facial recognition with anti-spoofing, significantly reducing unauthorized access attempts and strengthening data security. These findings indicate that this technology streamlines registration, provides enhanced security, and reduces the risks associated with manual or password-based systems on educational platforms.

Modern artificial Intelligence (AI) technologies are increasingly shaping higher education, particularly in their use in training computer science students and integrating neural networks into the learning process. The research aims to evaluate modern approaches and challenges to teaching neural networks to computer science students in higher education. This study employs a quasi-experimental method involving 85 third-year students enrolled in the Computer Science programs at L.N. Gumilyov Eurasian National University and Buketov Karaganda University. The participants were divided into two groups: an experimental group and a control group. The experimental group received instruction with an enhanced curriculum that included modern tools such as TensorFlow, Keras, OpenCV, and Google Colab. Data were collected through pre-tests and post-tests, evaluating changes in student motivation, content comprehension, and technical competencies. Pearson’s chi-square test was utilized to analyze the data, which revealed statistically significant improvements in the experimental group compared to the control group. These results suggest that integrating updated content and hands-on technologies into teaching practices enhances students’ skills and learning outcomes in neural network education. The revised neural network curriculum had a positive impact on student learning outcomes. The research emphasizes the importance of continually updating the curriculum to meet the evolving demands of modern AI.

The subject of robotics in education has a significant impact on the development of skills of students. Studying the main factors motivating robotics to participate in competitions, project competitions, and win prizes is always relevant. There are many competitions in robotics. In particular, one of the competitions for developing robotics in education is WRO. However, it is essential to know and research several factors that can lead to a place in a competition and support young coaches and participants. Higher education institutions that already have courses in educational robotics can benefit from the proposed approach, which allows them to develop new projects with robotics elements.

The need for specialists with high-performance computing skills is growing day by day. This is due to the fact that the high-performance process of processing big data is one of the most pressing problems today. This is especially important in science, economics, physical modeling, medicine, bioinformatics, weather forecasting, etc. This article analyzes the conditions for teaching high-performance computing, the experience of leading universities in the world, and it is established that teaching high-performance computing requires study. High performance computing training was carried out on 3 different hardware equipment (a personal computer, a supercomputer “Param-Bilim” India – Kazakhstan Centre of Excellence in ICT [IKCOEICT] at L.N. Gumilyov Eurasian National University and a quantum computer in the cloud IBM Quantum Experience) using different algorithms in the C ++ and Phyton programming languages. The effectiveness of the calculation results in the educational process was determined as a result of the completed questionnaire.

The integration of software solutions in the content of the training courses as a result of the socio-economic demand has a positive effect on the improvement of training the students for future professional activities. However, the latest achievements of hardware and software are not included in the content of the high education in a timely manner. Therefore, in our research we set the task to improve the content of the training course on the client-server technology using modern software capabilities. The content of the article consists of the conceptual structure of the training course, settings and integration of local and public cloud-based servers, describes the approaches of its using in the learning process. Microsoft SQL Server 2016, Microsoft Azure SQL Database, ASP.NET MVC 5 Framework were used as teaching environments. Training course was held for the students in the specialty of Computer Science of L.N.Gumilyov Eurasian National University and Kh.Dosmukhamedov Atyrau State University in Kazakhstan.

In this paper presented one of the possibilities of using information technology to improve the quality of education. It examines approaches of using cloud resource in the content of high education, the capabilities of the Microsoft Azure cloud resource and it's applying in educational programs of informatics,. Ways to implement in client-server technologies course, the issues of server deployment and administration in the cloud; development, organization remote connections via different environments; creation web and mobile applications and their publishing in Azure. The proposed cloud resource can be a working tool in the process of training future informatics teachers and IT specialists.

The transition towards innovative cloud computing technologies will allow universities to reduce the costs of purchasing software licenses and expensive computer parks with large amounts of memory and disks, since the programs used in training sessions, as well as all the results of work done, can be stored in the cloud. The transfer of educational services to the cloud will facilitate switching to a format of "learning everywhere and all the time". This article considers the pedagogical possibilities of cloud technologies, which confirm the expediency of use in the education process. The definition of cloud computing is discussed. In addition, the main task of the research was considered: using cloud technologies as a tool in the organizing education process. The experience of using open source software in training is given. The basic methods of using cloud technology in the learning process are presented.