This paper evaluates the performance of selected containerization methods. As part of the re-search, infrastructure was created using Google Cloud public cloud, and then load tests were con-ducted for each containerization method using Apache JMeter. The study has shown that choosing the right containerization method depends on the service to be implemented. It was demonstrated that the highest performance was achieved by combining Podman with Docker. An example imple-mentation and performance of Kubernetes technology, together with Docker and autoscaling using Google Cloud, was demonstrated. The project demonstrates high educational value by combining theory with practice through cloud-based infrastructure testing using Google Cloud and Apache JMeter. It helps learners understand container behavior under load, resource usage, and orchestration techniques. Additionally, the study emphasizes critical decision-making in selecting appropriate technologies based on specific service requirements. It serves as a practical teaching tool for IT and computer science education, offering insights into real-world DevOps workflows, scalability strate-gies, and performance optimization.

The article provides a thorough analysis and systematization of key conceptual approaches to the integration of artificial intelligence (AI) technologies into the educational and professional training of teachers in the natural and mathematical disciplines, namely future teachers of mathematics, physics, and computer science. The strategic necessity of transforming traditional didactic models is substantiated, driven by the rapid development of generative AI tools and the high level of their adoption in contemporary Ukrainian society. The study identifies three main approaches—instrumental, didactic, and competency-based (AI-TPACK)—as a theoretical foundation for the development of educational programs. Based on an analysis of domestic and international experience, examples of effective positive practices are presented, such as the use of chatbots to simulate typical student errors, while critical negative aspects are also outlined, including ethical dilemmas related to algorithmic bias, violations of academic integrity, and data protection issues.

Generative AI enables personalized computer science education at scale, yet questions remain about whether such personalization supports or undermines learning. This scoping review synthesizes 32 studies (2023–2025) purposively sampled from 259 records to map personalization mechanisms and effectiveness signals in higher-education CS contexts. We identify five application domains—intelligent tutoring, personalized materials, formative feedback, AI-augmented assessment, and code review—and analyze how design choices shape learning outcomes. Designs incorporating explanation-first guidance, solution withholding, graduated hint ladders, and artifact grounding (student code, tests, and rubrics) consistently show more positive learning processes than unconstrained chat interfaces. Successful implementations share four patterns: context-aware tutoring anchored in student artifacts, multi-level hint structures requiring reflection, composition with traditional CS infrastructure (autograders and rubrics), and human-in-the-loop quality assurance. We propose an exploration-firstadoption framework emphasizing piloting, instrumentation, learning-preserving defaults, and evidence-based scaling. Four recurrent risks—academic integrity, privacy, bias and equity, and over-reliance—are paired with operational mitigation. Critical evidence gaps include longitudinal effects on skill retention, comparative evaluations of guardrail designs, equity impacts at scale, and standardized replication metrics. The evidence supports generative AI as a mechanism for precision scaffolding when embedded in exploration-first, audit-ready workflows that preserve productive struggle while scaling personalized support.

Editorial: Contemporary Issues and Challenges in Computer Science Education

This study examines how students enrolled in a two-subject teacher programme (computer science and mathematics) at the Faculty of Education, University of Ljubljana, develop a conceptual understanding of programming knowledge through the implementation, recognition and explanation of programming patterns. Based on over 500 programming solutions completed by first- and second-year students, we focus on four foundational patterns: linear search, guarded search, counting and extreme values. The study involved 70 students across different phases, with 17 of them tracked longitudinally over three to four years, examining their ability to recognise programming patterns, explain underlying logic and design related tasks. The results show that the students gradually improved their use of programming patterns, initially producing many redundant or incorrect solutions, which over time shifted towards correct implementations. However, this development was uneven across pattern types and programming constructs. Tasks involving while loops and guarded searches initially proved more challenging, with higher rates of incorrect or redundant solutions in the early phases. A consistent finding across all of the student groups was a substantial gap between the students’ ability to implement patterns and their ability to explain them conceptually. This demonstrates that for loop implementation skills do not automatically transfer to conceptual understanding, especially for more complex cases like guarded search and extreme values. This finding is particularly concerning for prospective educators. Importantly, explanation ability strongly predicted task design quality, underscoring the fact that conceptual mastery directly supports pedagogical competence. These findings highlight the need for explicit instruction on programming patterns in teacher education, not only to support correct implementation but also to build deeper explanatory and pedagogical skills. Emphasising patterns as conceptual tools can help future educators better analyse code, anticipate student difficulties and design effective, pattern-based programming tasks.

Coding with ChatGPT: Empirical Evidence of Cognitive Offloading in Computer Science Education

The research aims to derive the positive and negative values and strange habits included in Ibn Battuta’s journey called “Tuhfat An-Nuzzar fi Ghara’ibal-Amsar wa-‘Aja’ib Al-Asfar” by Shams al-Din bin Abdullah al-Lawati, the Moroccan al-Tanji, known as Ibn Battuta (d. 1377 AD), presented and investigated by Ali al-Muntasir al-Katani (D.T), which was included in Ibn Battuta’s trip, to the peoples of the countries he visited on the African and Asian continents. A total of 440 respondents participated in the study: 195 teachers in the supplementary track and 245 fourth-year regular track students at an Arab College of Education from all disciplines: early childhood, Arabic language, science, mathematics and computer science, English language, and special education. The respondents were asked to select an enrichment text or a story of one or more pages from Ibn Battuta’s travels, with the aim of eliciting the positive and negative values and strange customs of the peoples and countries Ibn Battuta visited in Africa and Asia. The study results indicated that Ibn Battuta’s travelogue, “Tuhfat An-Nuzzar fi Ghara’ibal-Amsar wa-‘Aja’ib Al-Asfar,” is considered an important literary reference, rich with texts and stories from which we can deduce the values and customs of the people of the countries Ibn Battuta visited in Africa and Asia. Teachers can use this information for discussion and constructive dialogue with their students in schools, in various educational subjects such as social studies, religion, literature, Arabic language, history, and geography. Most of the study participants support the idea of integrating Ibn Battuta’s travelogue into various lessons. The study recommends the importance of integrating and expanding it to include other subjects in schools, colleges, and universities. This integration should be systematically built around various activities that achieve “meaningful learning,” ensure active student participation, and enhance value for the learner and society. In conclusion, I recommend conducting detailed studies and research on the educational values derived from travel literature.

In the field of computer science education, understanding algorithms and data structures poses a significant challenge for many learners due to their abstract and logical nature. The Code Visualizer project aims to bridge this gap by developing an interactive algorithm visualization platform that allows students and beginners to write, execute and visualize algorithms through step-by-step, real-time animations. The system provides a dynamic learning environment where users can observe how algorithms process data internally, such as during sorting, searching and graph traversal operations. By translating algorithmic logic into visual representations, the platform enhances conceptual clarity and fosters deeper comprehension. The integration of automatic algorithm detection enables the system to recognize code patterns and generate contextual visualizations along with explanations and complexity analysis. To make the learning experience engaging and effective, the application incorporates interactive controls—allowing users to pause, replay or adjust execution speed—and educational overlays that provide theoretical insights. The platform’s responsive and modular design, built using Flutter and integrated with Firebase, ensures accessibility across devices, real-time data handling and scalability for future algorithm additions. Beyond individual learning, the Code Visualizer also supports educators through tools that facilitate custom demonstrations, assignment sand performance tracking. By transforming complex algorithmic logic into intuitive visual feedback, this project empowers learners to connect theoretical knowledge with practical understanding. Ultimately, the Code Visualizer serves as a modern educational tool that promotes active learning, algorithmic thinking and computational problem-solving skills for the next generation of programmers

Educational institutions should promote student entrepreneurship to foster personal growth, skill development, and social change. Teachers should use collaborative learning approaches to stimulate entrepreneurial and leadership skills. The current paper aims to explore the importance of student leadership in the context of computer science education and proposes a business simulation learning approach that is related to motivation of students' creative thinking, stimulate learners' leadership skills, and increase motivation to achieve academic success. We conducted a study in Bulgarian university which tested a business simulation learning approach with 136 computer science students, focusing on teamwork, listening, trust, and helping teammates. Results showed that students prioritized team player, good listening, and teamwork over technical skills. The study suggests that business simulation can provide a comprehensive platform for developing leadership and technology skills essential for success in the technology sector.

This study investigated the relationships between computer science (CS) course effectiveness and academic performance in a Guangdong Province private school through an integrated environmental and social psychology framework, examining collaborative learning environment and technology environment perception as mediating mechanisms. A quantitative cross-sectional design surveyed several hundred Grade 4 international high school students enrolled in computer science courses at Guangdong Country Garden School during the 2024-2025 academic year. Results revealed that all five CS course effectiveness dimensions—hands-on experiences, real-world applications, collaborative learning, problem-solving abilities, and technology landscape readiness—demonstrated strong positive correlations with academic performance (r=.790-.879, p<.01), with problem-solving abilities emerging as the strongest predictor (β=.342). Mediation analysis using Hayes' PROCESS macro with bootstrapping procedures confirmed that collaborative learning environment (14.7%-23.7% mediation) and technology environment perception (14.7%-23.9% mediation) functioned as significant partial mediators, with real-world applications showing strongest social-psychological mediation and hands-on experiences exhibiting strongest environmental mediation. Hierarchical regression demonstrated that environmental and social-psychological factors contributed unique variance beyond course effectiveness dimensions (ΔR²=.043, p<.001). Findings provide empirical support for person-environment fit theories and social cognitive frameworks, highlighting the importance of optimizing both technological infrastructure and interpersonal climate for CS education effectiveness in private school contexts.

The world is characterised by volatility, uncertainty, complexity and ambiguity. Disruptive online technology is impacting education. Students in higher education should be adaptive, resilient, deeper self-directed learners who take ownership of their learning and transfer knowledge and skills to solve problems in new contexts. The challenge is to apply cooperative learning (CL) and to promote deeper self-directed learning (DSDL) in online environments. With this research we aimed to determine how technology-supported cooperative learning (TSCL) could be implemented to enhance DSDL. A sequential explanatory mixed methods research design was applied. The self-rating scale of self-directed learning (SRSSDL) and the cooperative learning perception questionnaire (CLPQ) were administered before and after a TSCL intervention. Reflective journals were used as a qualitative data-collection instrument. The population consisted of 31 second-year computer science education pre service teacher students enrolled in a course on computer networks. The quantitative data indicate an improvement in self directed learning with a medium effect. The qualitative data support the quantitative results and suggested that the TSCL intervention contributed to fostering DSDL. Guidelines covering group composition, cooperative learning structure, class preparation, reflection, and task design are proposed, although their transferability to other populations requires further research.

The paper presents research and preliminary findings aimed at improving curricula so that digital competencies are aligned with the required Artificial Intelligence (AI) literacy. The research was conducted at the Faculty of Technical Sciences in Čačak, University of Kragujevac (Serbia). The participants in the research were future computer science teachers and IT engineering students. The research tool for self-evaluation of AI literacy was a questionnaire based on the Serbian version of the AILS (Artificial Intelligence Literacy Scale), while digital competencies, based on the DigComp framework, were determined by objective testing. The research took into account the socioeconomic status of the students, demographic characteristics, and English language proficiency. Preliminary results indicated the persistence of significant relationships between certain digital competencies (such as programming, digital signal processing, and creative thinking) and all four constructs of AI literacy. The research findings highlight the impact of AI literacy on data analysis performance and problem solving.

ABSTRACT Background and Context The rapid expansion of computer science (CS) as a mandatory subject in K-12 education has created an urgent need for qualified teachers. Alternative certification pathways have led to a diverse CS teaching workforce with varied educational and professional experiences. Teachers’ beliefs – encompassing their attitudes, assumptions, and perceptions – are pivotal in shaping classroom practices and student outcomes. However, little research explores how diverse certification pathways influence these beliefs. Objective This study investigates subject- and educational related beliefs of German CS teachers from diverse professional backgrounds, examining how career pathways shape belief profiles and identifying key belief aspects in CS education. Method Twenty-three German CS teachers from traditional teacher training, advanced training programs, and lateral entry pathways participated in semi-structured interviews. Beliefs were analysed using a content-structuring form of qualitative content analysis based on Mo’s (2020) framework, categorizing them into theoretical, action and contextual dimensions. Findings Eight belief aspects were identified, including programming, thinking skills, practical orientation, and holistic understanding. Teachers exhibited three belief profiles: traditional degree holders emphasized thinking skills and holistic understanding; advanced training participants showed diverse profiles, often prioritizing media literacy; lateral entrants focused on practical applications and content matter. Implications The findings highlight how different teacher training pathways shape beliefs and instructional approaches, underscoring the need for integrative teacher training that combines technical, pedagogical, and socio-cultural competencies. Such efforts are crucial for addressing teacher shortages and ensuring a balanced, comprehensive approach to CS education.

This article examines the current requirements for the professional competencies of computer science teachers in the context of digitalization of education. It emphasizes that the training of future teachers in this field should incorporate not only a thorough knowledge of computer science and information technology but also the ability to integrate digital tools into the educational process and develop students' critical thinking, digital literacy, and teamwork skills. Regulatory documents defining the key competencies of a modern teacher are analyzed, and professional, methodological, communicative, and research competencies are highlighted as priorities for computer science teachers. Particular attention is paid to the need to foster pedagogical flexibility, a willingness to engage in continuous self-education, and adapt to innovative changes in the educational environment.

This article identifies the problems of the development of mathematical and natural science education in general education institutions in Russia, which forms the basis for the training of highly qualified personnel in priority areas of scientific and technological development of the national economy in the medium and long term. Based on the developed multivariate regression models, the quality of mathematical and natural science education in Russian schools is predicted until the 2027/28 academic year. The negative trends identified during the analysis and prediction are correlated with current measures of regulatory state policy in the field of general education and supplemented with recommendations for their elimination. It has been established that the most relevant areas for regulation are the regulation of the content and conditions of school education in computer science and information and computer technologies, as well as the development of the human resources potential of computer science teachers.

Objectives In calls for excellent and equitable Computer Science (CS) education, the word rigor often appears, but it often goes undefined. The goal of this work is to understand how CS teachers, instructors, and students conceive of rigor. Research Questions: 1) What do CS instructors think rigor is? and 2) What do students think rigor is? Methods: Using the principles of phenomenological research, we conducted a semi-structured interview study with 10 post-secondary CS students, 10 secondary CS teachers, and 9 post-secondary CS instructors, to understand their conceptions of rigor. Results: Analysis showed that no participants had the same understanding of rigor. We found that participants had abstract Principles of Rigor which included: Precision, Systematic Thought Process, Depth of Understanding, and Challenge. They also had concrete Observations of Rigor that included Time and Effort, Intrinsic Drive, Productive Failure, Struggle, Outcomes, and Gatekeeping. Participants also shared Conditions for Rigor which included Expectations, Standards, Community Support, and Resources. Implications: Our data supports prior work that educators are using different definitions of rigor. This implies that each educator holds different expectations for students, without necessarily communicating these expectations to their students. In the best case, this might confuse students; in the worst case, it reinforces hegemonic norms which can lead to gatekeeping which prevents students from fully participating in the CS field. Based on these insights, we argue that to commit to the idea of quality CS learning, the community must discard the use of this concept of rigor to justify student learning and re-imagine alternate benchmarks.

ABSTRACT The rapid expansion of computer science (CS) education in early childhood education requires robust professional development (PD) to adequately train educators to teach CS. Traditional pedagogies often prioritize technical proficiency without respect to human values and character development. The present work employed a mixed methods design to examine whether a self-paced online PD course improved teachers’ coding skills and expanded their beliefs on virtues in CS education. Findings showed teachers significantly improved in their coding proficiency and expanded their understanding of virtues and character strengths in the teaching of coding. Implications for further research are presented.

The current task of the modern education system is the digital transformation of information and educational resources, their substantive and methodological filling of the information and educational environment as educational content, which fully complies with the requirements of the Federal State Educational Standard. Digital representation of educational information through electronic educational resources has a complex impact on the educational process and the personality of the student through modern telecommunications. This requires ensuring a high degree of mobility of navigation in the information and communication educational environment (ICEE), unimpeded access and prompt interaction with educational resources of various formats of their presentation. It is noted that the transition to a new ICEE requires the study and analysis of its didactic potential in order to apply adequate organizational forms and teaching methods that contribute to the achievement of relevant educational results. In this regard, the key innovative function of a computer science teacher is the ability to use ICT to design an ICEE focused on the introduction of modern approaches to organizing the educational process in the context of modernization of school education. The issues of integration of digital technologies and pedagogical approaches in the organization of educational and cognitive activities and the principles of functioning of the IEE for a balanced combination of traditional and electronic forms of training, real and virtual learning and communication environments are considered. The innovative functions of an IT teacher can be defined as his involvement in innovative pedagogical activities in the context of implementing the requirements of the Federal State Educational Standard, which determine the transformation of the substantive and procedural components of educational practice.

Introduction This study examines how a research-practice partnership (RPP) involving university researchers and local education agency leaders across California engaged in the ongoing work of collaboratively defining and operationalizing “equity” in computer science (CS) education implementation. Grounded in Freire’s concepts of praxis, words-as-praxis, and dialog, this qualitative research explores how sustained engagement with defining equity became a transformative practice rather than a preliminary planning activity. Methods Over 4 years, the RPP iteratively developed three versions of an equity definition, responding to changing socio-political contexts. The RPP expanded from 5 to 17 leaders, ultimately scaling to influence a state-sponsored initiative encompassing 38 county offices of education. Data sources for the study include RPP meeting notes, interviews with RPP members, and analysis of evolving equity documents. Results Findings reveal four key themes: (1) productive tensions between CS content focus and equity emphasis that forced deeper examination of assumptions; (2) the necessity of iterative equity definition as an ongoing process responsive to socio-historical contexts; (3) inclusion/exclusion dynamics within the partnership that shaped both representation and understanding; and (4) how collaborative equity definition built capacity for sustained systemic change. Significantly, resistance to equity conversations paradoxically validated the need for sustained dialog, revealing underlying assumptions about CS education’s “neutrality” that required examination. Discussion The study demonstrates how collaborative equity definition serves dual functions: developing shared language for collective action while transforming participants’ professional identities and commitments. Participants became leaders of California’s statewide CS education equity initiatives, creating tools and approaches that continue to influence practice years later. This work contributes to research-practice partnership literature by showing how treating equity definition as ongoing praxis—rather than preliminary consensus-building— can create conditions for sustained educational transformation, with implications for STEM education partnerships seeking to center equity while navigating political resistance and changing contexts.

The integration of Artificial Intelligence (AI) into educational assessment has revolutionized how learners are evaluated and supported. This study, Artificial Intelligence-Driven Adaptive Testing: A Psychometric Approach to Personalized Learning in Computer Science Education, investigates the fusion of psychometric modeling and AI-based adaptive testing systems to enhance individualized learning pathways for students in computer science. The research adopts a hybrid framework combining Item Response Theory (IRT) and reinforcement learning algorithms to dynamically adjust question difficulty based on learner performance in real time. A dataset of undergraduate computer science learners was used to develop and validate the adaptive system through parameters such as accuracy, response time, and knowledge progression. The psychometric evaluation demonstrated high reliability and discriminant validity, while the AI model optimized test adaptivity and reduced assessment bias. Findings indicate that AI-driven adaptive testing significantly improves learning engagement, reduces cognitive overload, and enhances conceptual retention compared to static assessment methods. The study contributes to the growing discourse on intelligent educational systems, presenting a scalable, data-driven psychometric model that fosters personalized, equitable, and efficient learning environments in computer science education.