The Eurasia Proceedings of Educational and Social Sciences
The Eurasia Proceedings of Educational and Social Sciences (EPESS) is a peer-reviewed and online free journal. As an open access journal, the Eurasia Proceedings of Educational and Social Sciences (EPESS) does not charge article submission or processing fees.The manuscripts which are accepted for publication in the EPESS are invited from the conferences. EPESS will be published in the last 8 months of the year (May, June, July, August, September, October, November and December) starting from 2025. The journal will continue its academic life by publishing 8 issues per year. The Eurasia Proceedings of Educational and Social Sciences (EPESS) welcomes any research papers on education and social sciences using techniques from and applications in any technical knowledge domain: original theoretical works, literature reviews, research reports, social issues, psychological issues, curricula, learning environments, and review articles. The articles should be original, unpublished, and not in consideration for publication elsewhere at the time of submission to the EPESS. The full paper versions of the conference papers are published in the EPESS. EPESS only publishes full texts of conferences organized by International Society for Research in Education and Science (ISRES) Period Months May June July August September October November December
ISRES Publishing - International Society for Research in Education and Science (ISRES)
The articles may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Authors alone are responsible for the contents of their articles. The journal owns the copyright of the articles. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of the research material. All authors are requested to disclose any actual or potential conflict of interest including any financial, personal or other relationships with other people or organizations regarding the submitted work.
Enhancing School Safety Through Technology: Analysis the Effectiveness, Efficiency, and Practicality of a Violence Prevention App
This study examines the effectiveness, efficiency, and practicality of a technology-based application for preventing and handling school violence, developed in response to Regulation No. 46 of 2023 by the Indonesian Ministry of Education. The research was conducted in six model schools—three senior high schools and three vocational high schools—in the Special Region of Yogyakarta (DIY), selected due to a two-year decline in their School Safety Climate Index reported in the National Education Report (Rapor Pendidikan). Using a Mixed Methods approach, the study involved 120 participants, including 30 members of the School Violence Prevention and Handling Teams (TPPK) and 90 students. Quantitative data were collected via a validated Likert-scale questionnaire assessing the three constructs, yielding high reliability (Cronbach’s Alpha = 0.92). Data were analyzed using descriptive and inferential statistics in SPSS. Qualitative data from semi-structured interviews were thematically analyzed using NVivo to explore user experiences and contextual insights. The application demonstrated high effectiveness (M = 4.18) in facilitating case reporting and response, efficiency (M = 4.06) in reducing administrative workload, and practicality (M = 4.10) in usability and accessibility. Qualitative findings reinforced these results, revealing improved awareness, collaboration, and transparency among teachers, students, and TPPK members. However, challenges remained in digital literacy, connectivity, and user training. The integration of quantitative and qualitative data indicates that the application strengthens school violence prevention systems by promoting timely response, accountability, and data-driven management. This study highlights the potential of technology-supported school governance to enhance safety and inclusiveness in educational environments, with recommendations for capacity building and infrastructure improvement for sustainable implementation.
Copyright (c) 2026 The Eurasia Proceedings of Educational and Social Sciences https://creativecommons.org/licenses/by/4.0
How Do Junior High School Students Comprehend the Concept of Function?
Function is an essential material in mathematics as it is connected to real life very much and cannot be mastered easily, as shown with the findings of researches on the learning difficulty faced by students in the material of function. This qualitative research with data triangulation is designed in case study aiming to explore learning obstacles based on students’ comprehension on concept of function and how students construct meaning related to their learning experience. This research involves 13 (thirteen) 9th (ninth) graders of Junior High School in Gunungkidul Regency having received the material of function. Data were collected through test and interview. Result of interview was transcribed to be analysed in-depth to find out how the students comprehend concept and to identify potential learning obstacles. Result shows that overall, the concept of function is defined as: (1) correspondence between two elements; (2) there is inconsistent/partial definition of function; and (3) some students can define completely but limited to the context of linear function and square function. In conclusion, there is learning obstacle in the concept of function, including epistemological, ontogenic, and didactical. Some attempts or further researches should be conducted to improve the learning of function to enable the students to comprehend completely through, among others: (1) completing the material with the concept map of function family tree; (2) conducting an analysis on the material of textbook using fraxeology; and (3) developing hypothetical learning trajectory and didactic design to overcome learning obstacle.
Virtual Laboratory–Assisted RADEC Courseware for Enhancing Conceptual Change in Elementary Teacher Education
Conceptual change is a crucial aspect of science learning, particularly for pre-service primary school teacher students who will become educators at the elementary level. However, science instruction in teacher education programs still faces various challenges, especially in helping students understand abstract concepts and overcome pre-existing misconceptions. This study aims to examine the effect of science courseware based on the RADEC learning model (Read, Answer, Discuss, Explain, Create) assisted by a virtual laboratory on students’ conceptual change in excretory system. A quasi-experimental method with a pretest–post-test non-equivalent control group design was employed. The research sample was selected using random sampling, resulting in 39 students from Class A as the experimental group, who received the RADEC model assisted by a virtual laboratory, and 39 students from Class B as the control group, who did not receive the treatment. Data were collected through a conceptual change test and analyzed using a paired-sample t-test with SPSS. The results showed that t-count > t-table (2.04 > 1.66) and p-value = 0.015 (p <0.05). It can be concluded that science courseware based on the RADEC learning model assisted by a virtual laboratory has a significant effect on students’ conceptual change. The integration of virtual laboratory activities within the RADEC stages effectively facilitates active engagement, reflection, and deeper conceptual understanding.
Guidelines For Automatic Grading of Student Essays Using Large Language Models
Automated essay evaluation using large language models (LLMs) has emerged as a promising approach to support scalable and consistent educational assessment. However, the effectiveness of LLM-based grading varies significantly across evaluation dimensions and is highly influenced by prompt design and model selection. In this study, we evaluate five state-of-the-art LLMs across five rubric-based categories: Relevance to Question, Reasoning and Critical Thinking, Evidence and Examples, Organization, and Clarity and Writing Quality. We systematically investigate the impact of three prompting strategies, including rubric-only prompting, exemplar-based prompting (with and without rubric guidance)(Original and Refined prompt designs) incorporating structured instructions. Additionally, a prompt ablation study is conducted to analyze the effect of instruction detail and reasoning guidance on grading accuracy. Our results demonstrate that no single model consistently outperforms others across all categories. Instead, each LLM exhibits strengths in specific evaluation dimensions, motivating a category-wise model selection approach. Furthermore, refined and structured prompting strategies significantly improve evaluation performance, with chain-of-thought-style instructions yielding the highest accuracy. These findings highlight the importance of both prompt engineering and task-specialized model allocation in developing robust LLM-based grading systems. The study provides evidence supporting multi-agent frameworks, where different LLMs can be assigned to distinct evaluation tasks to enhance overall grading reliability and alignment with human judgments.
An Investigation of Pre-Service Science Teachers’ Perceptions and Levels of Use of Mobile Computing and Mobile Learning
The aim of this study is to examine science pre-service teachers’ perceptions of mobile computing and mobile learning, as well as their levels of use of these technologies. The research was conducted with fourth-year pre-service teachers enrolled in the Science Education programme at the Faculty of Education, Nigde Omer Halisdemir University. A qualitative research approach was adopted in the study, and a semi-structured interview form consisting of open-ended questions was used as the data collection tool. The interview questions were designed to explore participants’ ownership of mobile devices, the purposes for which they use them, the frequency of their use in lessons, and their perceptions of mobile learning. The data obtained were analysed using a descriptive analysis method. The research findings indicate that the vast majority of pre-service teachers own mobile devices such as smartphones and laptops and actively use these devices in their daily lives. It was determined that participants most frequently used mobile technologies for social media use, accessing information online, and accessing digital content. However, it was observed that the use of mobile devices for learning purposes in lessons is relatively limited, and the potential of these technologies in learning processes is not being fully utilised. Furthermore, it was found that pre-service teachers hold positive perceptions regarding the advantages of mobile learning, such as providing rapid access to information, supporting learning, and making the learning process more flexible. Conversely, it was noted that mobile devices have certain drawbacks, such as potentially causing distraction during lessons, increasing the risk of unauthorised use, and creating difficulties in maintaining control during the learning process. Based on the findings, various recommendations are presented for the more effective, conscious and pedagogically appropriate use of mobile learning in educational settings.
Mapping Research on Mathematical Argument, Proof, and Reasoning in Indonesian Mathematics Education: A Bibliometric Study
Mathematical argument, proof, and reasoning are central practices in mathematics education, yet research addressing these constructs has developed unevenly across educational contexts. This study aims to map research trends on mathematical argument, proof, and reasoning in Indonesian mathematics education using a bibliometric approach. Publications were retrieved from the Scopus database through a structured query focusing on journal articles written in English and published between 2010 and 2024 by authors affiliated with Indonesian institutions. A total of 48 articles were analyzed using Biblioshiny in R to examine publication trends, citation patterns, productive sources, authorship, keywords, and thematic structures. The results indicate a gradual increase in research output since the mid-2010s, with a publication peak around 2020, reflecting growing scholarly interest in mathematical argument in Indonesia. Keyword and thematic analyses reveal that research in this field is predominantly framed within pedagogical contexts, with realistic mathematics education and instructional design emerging as central themes. In contrast, studies focusing on formal proof and abstract algebra appear more specialized and less central. Overall, this study provides a structured overview of the development and conceptual orientation of mathematical argument research in Indonesian mathematics education and highlights directions for future research, particularly in strengthening theoretical integration and international collaboration.
The Effectiveness of Stellarium as an Interactive Digital Medium to Enhance Vocational High School Students’ Understanding of Earth and Space Science
This study investigates the effectiveness of Stellarium, an interactive digital planetarium software, in improving vocational high school students’ understanding of Earth and space science concepts. The research employed a one-group pre-test post-test design with 10 tenth-grade students from SMK Hamong Putera Pakem, Yogyakarta, Indonesia. Data were collected using cognitive tests (10 multiple-choice items), a student response questionnaire (10 Likert-scale items), and an observation sheet (11 indicators). The results showed a significant increase in students’ scores from pre-test to post-test, with an average N-Gain score categorized as medium. Observation data revealed that students actively participated, collaborated effectively, and demonstrated conceptual understanding of rotation, revolution, lunar phases, and eclipses. Furthermore, student responses were overwhelmingly positive, with more than 80% agreeing that Stellarium made learning more engaging, motivating, and enjoyable. These findings highlight the potential of Stellarium as a technology-enhanced learning tool to facilitate abstract scientific concepts, foster active learning, and support the digital transformation of vocational education. The study contributes to the growing literature on immersive and interactive learning environments and provides practical insights for science teachers in vocational schools.
Academic Staff Experiences Regarding the Use of Generative Artificial Intelligence in Teacher Training: A Phenomenological Study
This study aims to examine faculty members’ lived experiences regarding the use of generative artificial intelligence (AI) in teacher education from a phenomenological perspective, with a particular focus on uncovering the essence and meaning of these experiences. As generative AI technologies increasingly shape educational environments, they have begun to influence not only teaching practices but also the broader structure of teacher education programs. In this context, examining how academics interpret these technologies and integrate them into teaching practices is critical to understanding their pedagogical impacts. The research was designed within a qualitative framework using a phenomenological approach. The study group consisted of three faculty members selected through criterion sampling, all of whom had prior experience using generative AI tools in teacher education courses. Data were collected via a semi-structured forum, enabling an in-depth examination of the participants’ processes of meaning-making. The findings indicate that participants’ initial interactions with generative AI were largely driven by curiosity; however, this interaction evolved into a multifaceted experience encompassing pedagogical transformation, professional reflection, and ethical considerations. While generative AI was perceived as a tool that facilitates teaching processes and supports content development, concerns regarding the potential decline in academic integrity, originality, and critical thinking were also highlighted. Additionally, it was found that the integration of AI technologies contributes to the restructuring of faculty members’ professional identities and teaching roles. In conclusion, the study emphasizes that generative AI should be viewed not merely as a technical tool, but as a transformative element that reshapes pedagogical practices, professional identities, and the ethical dimensions of teaching. These findings underscore the need for structured pedagogical frameworks and ethical guidelines to support the effective integration of generative AI into teacher education.
Trends in Cognitive Diagnostic Research for Learning Evaluation in Educational Environment: A Bibliometric Analysis on Scopus Database
The analysis in this study aims to map the trends in research publications related to diagnostic cognitive assessment in the field of education in general, using bibliometric analysis. Using predefined keywords, the author analyzed 137 articles based on the Scopus database selected using the PRISMA methodology, followed by analysis using the R and VOSviewer programs. Based on the analysis, the topic of cognitive diagnostics in education began in 2006 and continued to grow rapidly until 2025, with the highest surge in publications occurring in 2023 and 2024. The United States and China dominate in terms of the number of publications and citations, indicating a significant impact in the field of cognitive diagnostics in education. Universities in Malaysia, China, and the United States stand out in publications related to cognitive diagnostic research. New trends are emerging in the grouping of keywords related to the application of cognitive diagnostics in education, marked by “educational data mining” and “educational computing,” indicating a new direction in cognitive diagnostic innovation in the education sector. These keywords can be used by subsequent researchers for further research to enrich the topic of cognitive diagnostics.
Digital Transformation in Secondary Mathematics Education in Albania: AI/ICT, STEM, and Teachers’ Professional Readiness
This study aims to analyze the integration of Artificial Intelligence (AI) and Information and Communication Technology (ICT) within the framework of STEM education in pre-university education in Albania. Particular attention is given to mathematics teachers’ perceptions of their professional readiness and to the influence of school infrastructure on the adoption of innovative technologies in the teaching process. The study is based on quantitative data collected through a questionnaire administered to secondary school mathematics teachers. This research instrument assesses teachers’ digital competencies, the necessary conditions for integrating innovative technologies into teaching, and their pedagogical attitudes toward these technologies. The data were analyzed using reliability indicators, descriptive analysis, and statistical correlation analysis to identify the relationships between teachers’ professional preparation and institutional technological support. Mathematics teachers report above-average knowledge and skills in integrating STEM, AI, and ICT into their teaching. However, they assess the current conditions in their schools somewhat more modestly. From this perspective, statistical analysis suggests that teachers’ professional readiness is strongly correlated to the quality of technical support and the availability of institutional resources. The study highlights that the digital transformation of mathematics teaching in Albania requires a strategic intervention that combines teachers’ professional development with infrastructural improvements to enable the effective integration of digital technologies and to support innovative educational policies and teaching practices.
Teacher as Toolmaker: Developing AI-Assisted Oral Assessment in Mathematics
This study examines the phenomenon of teacher as toolmaker through the development of an AI-assisted oral assessment platform in mathematics education within a post-pandemic context. Addressing the misalignment between educational technologies and classroom realities, the study adopts a qualitative instrumental case study approach using development artefacts, AI interaction logs, and classroom implementation notes. The findings identify three key dimensions. First, pedagogical primacy ensures that instructional goals drive technological design. Second, a symbiotic human–AI collaboration emerges, where AI supports technical development while the teacher retains pedagogical authority. Third, the platform enables ecological integration by combining task delivery, response capture, recording, and analysis into a unified system, enhancing both efficiency and insight into students’ reasoning. The study demonstrates that teacher-led innovation can produce contextually responsive digital tools. It concludes that AI should function as a pedagogically grounded support system that extends, rather than replaces, teacher capacity, with implications for teacher development and educational technology design.
The Future of Learning: How Artificial Intelligence is Reshaping Education in the Middle East
Artificial Intelligence (AI) is increasingly recognized as a transformative force in education, reshaping learning environments, pedagogical practices, and institutional strategies worldwide. In the Middle East, this transformation is unfolding within distinct socio-cultural, economic, and policy contexts that shape both the opportunities and constraints of AI adoption. This study advances a regionally grounded conceptual synthesis of how AI is influencing learning processes, teaching roles, and institutional governance across the region. Drawing on a structured qualitative synthesis of peer-reviewed scholarship (2019–2025), the paper develops an interpretive framework that situates AI-driven change within broader debates on ethics, digital readiness, and educational reform. While AI offers significant potential for personalization, data-informed decision-making, and institutional modernization, its impact remains uneven and contingent upon governance capacity and contextual alignment. By integrating fragmented regional evidence into a coherent analytical perspective, this study contributes a context-sensitive conceptual framework that clarifies the structural, pedagogical, and policy dimensions of AI-enabled educational transformation in the Middle East (ME).
Student Engagement Patterns in STEM-Hybrid Research-Based Learning
Student engagement is a crucial element in meaningful science learning, particularly in teacher education programs that often face limited laboratory facilities. Although hybrid learning is widely implemented as a solution to infrastructure constraints, its ability to maintain active student engagement is highly dependent on the learning design used. This study aims to explore student engagement patterns and the implementation of the STEM-Hybrid Research-Based Learning (STEM-Hybrid RBL) model in a science course for 25 prospective elementary school teachers. This study is part of the Design and Development Research framework and specifically focuses on process evaluation during the field implementation phase. Data were collected through student activity observation sheets and a model implementation rubric. Descriptive analysis was conducted to identify student engagement patterns at each learning phase. The results showed that the model was implemented consistently across all learning phases and was categorized as well implemented. The level of student engagement varied across phases, with higher engagement in the hybrid research and data analysis phases. Collaborative experimentation, discussion, and data interpretation were the main drivers of behavioral, cognitive, and social engagement. Differences between offline and online participation were evident in the types of activities undertaken, but both groups remained actively engaged when the assignments were designed based on an authentic research process. These findings suggest that STEM-Hybrid RBL has the potential to sustain meaningful student engagement in hybrid science learning, particularly in teacher education contexts with limited laboratory facilities. This research underscores the importance of process-based analysis to understand how learning models operate in practice.
From Generation to Autonomy: A Framework for Understanding Generative, Agent, and Agentic AI in Higher Education
The rapid evolution of artificial intelligence (AI) in higher education has moved beyond content generation toward increasingly autonomous systems, necessitating a clearer conceptual understanding of their roles and implications. This paper introduces the Generation-to-Autonomy (GtA) Framework, which positions Generative AI, Agent AI, and Agentic AI along a multidimensional continuum defined by autonomy, control, and intelligence. Drawing on a functional definition of educational intelligence, the framework distinguishes AI systems based on their capacity for content generation, task execution, and self-directed decision-making. The study provides a structured analysis of how Generative AI enhances cognitive support yet raises challenges related to academic integrity and epistemic reliability; how Agent AI enables task-oriented automation and human–AI collaboration while introducing risks of accountability diffusion and task misspecification; and how Agentic AI represents a paradigm shift toward autonomous educational systems, raising complex ethical, governance, and control concerns. By synthesizing these categories into a unified framework, the paper contributes theoretically by clarifying the evolving human–AI relationship—from tool use to partnership and delegation—and practically by offering guidance for curriculum design, assessment transformation, and differentiated governance in higher education. The findings suggest that as AI systems progress toward greater autonomy, educational institutions must adopt nuanced, capability-sensitive strategies that balance innovation with ethical responsibility. The GtA Framework provides a foundation for informed decision-making and future research in AI-integrated education.
Obstacles and Supporting Factors for Women in Software Engineering Education and Professional Work: A Literature Review
Despite progress in gender equality across many disciplines, women remain significantly underrepresented in software engineering. This imbalance is not only a question of fairness, but also a limitation on diversity of perspectives, creativity, and innovation in the field. This review paper examines the barriers women face and the factors that can support their participation and success across two key contexts: higher education and the professional work environment. Drawing on foundational and recent literature, the paper identifies recurring challenges such as gender stereotypes, limited role models, biased evaluation practices, reduced sense of belonging, and unsupportive organizational cultures. At the same time, it highlights supportive practices associated with improved participation and persistence, including inclusive pedagogy, pair programming, mentorship, flexible working conditions, and inclusive team and workplace climates. The findings suggest that women’s participation in software engineering is shaped by interconnected individual, interpersonal, and structural factors, and that meaningful progress requires coordinated interventions across educational and professional settings.
Exploring Pupils’ Mathematical Understanding: The Role of Metacognitive-Discursive Activities in the Learning Process
This paper reports on a research project conducted in a secondary school on Sumba Island, Indonesia, aiming to enhance pupils’ understanding of mathematics. To achieve this, the learning environment was designed to reconstruct certain properties of mathematical objects and their related operations, with a specific focus on equations. At the beginning of the learning sequence, pupils engaged with number puzzle stories that prompted them to represent the given situations in algebraic form and solve the resulting equations. Throughout the lessons, metacognitive and discursive activities were applied to foster reflection, reasoning, and communication. Technological tools supported the implementation of these principles: a document camera and projector were used so that pupils could display and discuss their solutions with their classmates accurately. Through this setting, pupils learned that they were individually responsible for presenting and justifying their mathematical arguments in front of the class. This technological mediation using a document camera, fostered an interactive discussion environment that promoted both individual accountability and collective reasoning. After the learning sequence, a written test was administered to assess pupils’ understanding of the mathematical concepts addressed. The test items were aligned with the targeted mathematical competencies. Selected pupil solutions are presented and discussed as illustrative examples. The findings indicate that metacognitive-discursive activities effectively supported pupils in developing a deeper and more connected understanding of mathematics.
Application of Artificial Intelligence in Educational Evaluation: A Bibliometric Analysis
The purpose of this study's analysis is to map trends in research publications on the application of artificial intelligence to educational evaluation, using bibliometric analysis of data from Scopus. After entering keywords related to artificial intelligence, evaluation, and education at the data collection stage in Scopus, the author analysed 159 articles selected using the PRISMA method. The next step was to conduct the analysis using R and VOSviewer software. The results of the study show that research on the application of artificial intelligence in educational evaluation began to emerge in 2021 and increased significantly through 2025, with the highest number of publications recorded in 2025. The United States, Australia, and China occupy the top three positions in terms of the number of publications and citations, confirming their significant influence in research on the application of artificial intelligence in educational evaluation. Meanwhile, the most active institutions in this research are the University of the Health Sciences and Rensselaer Polytechnic Institute. In the study of artificial intelligence applications in educational evaluation, new trends have emerged, including ‘AI literacy’ and ‘artificial intelligence education,’ which still require further exploration.
Ethical-AI Pedagogy: Developing Moral Reasoning and Digital Responsibility Among Students in the Age of Intelligent Systems
The development of intelligent systems that are increasingly integrated into academic activities has transformed how students seek information, assess source credibility, and make decisions. However, this increased access and efficiency are not always accompanied by ethical readiness, especially concerning algorithmic bias, data privacy, automated decision-making, and the boundaries of AI use in academic contexts. This study aims to analyse how AI Ethics education can shape students’ moral thinking and digital responsibility in the era of intelligent systems. Using a mixed-methods explanatory design, this research combines quantitative surveys with focus group discussions and individual reflections to capture experiences, risk perceptions, and moral dilemmas arising from the use of AI technology. The results indicate that understanding AI Ethics significantly influences students’ digital responsibility. Although students actively use various AI-based applications, their ethical understanding remains moderate and somewhat ambiguous, particularly regarding digital plagiarism, system bias, and algorithmic mechanisms. Case-based AI Ethics educational interventions have been shown to enhance moral sensitivity, digital caution, and students’ reflective capacity. These findings underscore the urgency of integrating AI ethics into higher education curricula as a strategic effort to prepare a generation that is not only skilled in utilising technology but also morally responsible within a complex and often opaque digital ecosystem.
Learning Obstacles in Constructing Proofs of Geometric Transformation: A Didactic Transposition Analysis
This study aims to identify and analyze the learning obstacles experienced by preservice mathematics teachers in constructing proofs of geometric transformations through a didactic transposition analysis from scholarly knowledge to learnt knowledge. A descriptive qualitative approach was employed, involving textbook analysis, classroom observations, diagnostic tests, and interviews with 19 preservice teachers. The findings indicate that learning obstacles are systemic and layered, emerging through the transformation of knowledge across transposition stages. These obstacles comprise epistemological, didactical, and ontogenetic dimensions that are interrelated. Epistemologically, obstacles arise from a reduction in deductive structure and conceptual formality, reflected in students’ difficulties with quantifiers, proof logic, and the relationships among formal definitions of functions, injective functions, and surjective functions. Proof is often reduced to empirical generalization and visual verification, while formal representations such as two-variable functions and the domain ℝ² remain conceptually underdeveloped. Didactically, obstacles are reinforced by instructional practices characterized by dominant visual representations, limited proof strategies, and minimal opportunities for students to construct deductive arguments. Ontogenetically, students exhibit insufficient readiness for formal abstraction, shaped by predominantly procedural and illustrative learning experiences. These findings demonstrate that learning obstacles are not merely individual difficulties but are produced through mismatches across levels of knowledge in the didactic transposition process, highlighting the need for instructional designs that preserve epistemological coherence.
The Programmatic Framework of Initial Reading, Writing, and Language Instruction within the Subject Macedonian Language
In order for students to achieve the goals set forth in the curricula for the subject Macedonian Language, the programs are structured into four or, in some cases, five thematic areas. The research focus of this paper is directed toward the examination of two of these areas: Initial Reading and Writing and Language for grades one through three. To that end, a comparative analysis is conducted between the current curricula and those established in 2007. The aim of this paper is to analyze the programmatic framework of the thematic areas Initial Reading and Writing and Language, with the overarching goal of improving the teaching of Macedonian Language. For the successful realization of the objectives and assessment standards set within these areas for grades one to three, adequate preparation, application, and classification of teaching materials are required. These materials should serve as pedagogical support both for teachers and for students, assisting them in learning the Cyrillic and Latin alphabets, practicing reading and writing, mastering linguistic and orthographic rules, and properly using punctuation marks. The study’s findings indicate that the complexity of the assessment standards and the content (including conceptual scope) that students are expected to master begins to increase as early as the first grade under the new curriculum. However, this increase is not accompanied by a corresponding expansion of instructional hours allocated to the Macedonian Language subject. On the contrary, while assessment standards and content in the first grade become more demanding, the number of instructional hours decreases. This discrepancy raises questions about the feasibility of achieving the prescribed assessment standards, as formalized in the Concept for Primary Education adopted in 2021.
Analysis of Students’ Visual, Symbolic, and Verbal Representations as Indicators of Mathematical Representation Ability in a Quadratic Function Test
This study aims to analyze students’ visual, symbolic, and verbal representations as indicators of mathematical representation ability in solving a quadratic function test. The study employed a descriptive quantitative approach with a cross-sectional design. The research subjects consisted of 127 tenth-grade students from three senior high schools who had studied quadratic functions. Data were collected through an essay test consisting of six items to measure the indicators of visual, symbolic, and verbal representation. The data were analyzed using descriptive statistics through answer scoring, score grouping based on representation indicators, and conversion into percentage form. The results showed that students’ mathematical representation ability was still relatively low and had not developed evenly, with visual representation achieving the highest percentage at 36.68%, followed by verbal representation at 13.92%, while symbolic representation was the lowest indicator at 10.02%. The findings also showed that students already possessed basic abilities in familiar problems, but still experienced difficulties when they had to interpret graphs, understand function notation, connect different representations, and relate quadratic functions to other topics such as function transformations. These difficulties indicate that students’ understanding still tends to be partial and procedural, so that conceptual connections among representations have not been formed as a whole. Thus, the teaching of quadratic functions needs to be designed in a more balanced way by emphasizing conceptual understanding, the interconnection among representations, and the development of students’ mathematical argumentation.
An Examination of Teachers’ Perspectives and Metaphorical Perceptions Regarding AI
The purpose of this study is to examine awareness, usage experiences, concerns, future predictions, and metaphorical interpretations of AI among teachers working in different subject areas and educational levels. The study was designed within the framework of a qualitative research approach. A survey consisting of 10 open-ended questions, designed to develop a multidimensional perspective on AI, was used as the data collection tool. The study sample consists of 16 teachers working in different subjects and educational levels. Data were collected in written form and analyzed using content analysis. During the analysis process, the data were categorized and coded. The findings indicate that teachers are generally familiar with the concept of AI and are most aware of generative AI tools such as ChatGPT and Gemini. Teachers predominantly described AI as a technology that provides rapid access to information, supports learning, and reduces workload. However, some teachers expressed concerns about the potential negative impacts of AI on data security, the generation of misinformation, ethical use, and creativity. The study also included a metaphor analysis, which revealed that teachers described AI using metaphors such as machine, robot, guide, computer, and ocean. These metaphors indicate that AI is perceived both as a functional tool and as a complex and unpredictable entity. Consequently, while teachers view AI as a supportive tool in education, they believe it cannot replace the human teacher in terms of pedagogical and emotional dimensions. In this context, it is recommended that professional development programs aimed at enhancing teachers’ AI literacy be expanded.
Global Trends and Collaborations in AI-Based Chemistry Education Research: A Bibliometric Analysis
In recent years, the integration of artificial intelligence (AI) into chemistry education has attracted increasing attention, leading to a growing body of research in this field. This study offers an in-depth bibliometric examination of publications focusing on artificial intelligence (AI) in chemistry education during the period 2021–2025. The data were retrieved from the Web of Science database, and the analysis was conducted using VOSviewer. A total of 972 publications meeting the defined criteria were identified. The yearly distribution of publications reveals a significant upward trend in research output, reaching its highest level in 2025. At the country level, the United States, China, and Germany were identified as the primary contributors to the field. In addition, the most productive authors were M. Cooper, R. L. Stowe, and R. Blonder. With respect to journal productivity and citation impact, the Journal of Chemical Education was identified as the most prominent source. University of South Florida, Michigan State University and University of Iowa are the most productive universities. Keyword co-occurrence analysis revealed that the most frequently used terms included “Undergraduate Education”, “Chemistry Education” and “General Chemistry”. Overall, these findings provide valuable insights into recent developments and emerging trends in AI research within chemistry education. The study is expected to support researchers in identifying key research directions and to inform future investigations in this rapidly evolving field.
Demographic Differences in IT Security Risk-taking: Implications for Targeted Training and Prevention
This paper examines the demographic determinants of risk-taking behavior related to IT security, with a particular focus on the role of age and gender. The analysis is based on a modified DOSPERT questionnaire supplemented with an IT security subscale, which allows for statistical control of general risk-taking dimensions. The empirical study involved 772 university students. The results of multivariate regression analyses show that both age and gender have independent and significant explanatory power in the case of IT security risk-taking, even when controlling for perceived benefits, perceived risks, and probability of action. These results suggest that IT security decisions are not solely based on psychological decision-making mechanisms but are also structured by stable demographic characteristics. The study draws practical conclusions for human resource management, IT security training, and targeted education, emphasizing the need to take into account differences between user groups.
Auditing AI-Based Systems in Banking Information Security: Auditors’ Perceptions and Expectations
The application of artificial intelligence (AI) in banking information systems poses new types of security, control and oversight challenges. The research aims to explore how IT auditors perceive and evaluate the tasks, risks and expectations related to auditing AI-based systems. The study uses a qualitative methodology to collect data from the professional community, with a particular focus on issues of transparency, verifiability, explainability and information security. The research presents the main risks identified by auditors – such as opacity of models, uncertainties in data quality, lack of reproducibility of decision processes and potential biases – as well as the opportunities that arise from the application of AI, such as increased efficiency, automated controls or improved anomaly detection. It also identifies the factors that determine the auditability of AI systems in the field of banking information security, including the quality of documentation, the regulation of model development processes, monitoring mechanisms and organizational maturity. The results contribute to the professional discourse on the methodological and practical frameworks of AI auditing and may inform the development of future regulatory, supervisory and audit practices. The study also highlights that the role and competencies of auditors are also changing with the spread of AI-based systems, creating new knowledge and skill needs in the profession.
The Impact of Employee Well-being on Job Performance and Organisational Development
As employment expectations continue to rise due to competitive pressures, technology advancements, and changing work environments, employee well-being has grown in importance in contemporary organisations. While productivity and efficiency were the main focus of traditional management approaches, modern organisations are realising that employees psychological, physical, social and workplace experiences have a significant impact on job performance and long-term organisational development. This study explores employee well-being as an idea encompassing psychological, physical, social, and occupational well-being, based on recent research in organisational behaviour and management. Job performance is considered a broad concept involving task-related, contextual and adaptive performance. To demonstrate how various aspects of employee well-being affect motivation, engagement, flexibility, and spontaneous performance behaviours, the paper develops a conceptual model. A qualitative research method will be employed, in which open-ended questions will be used to explore employees’ perceptions and experiences regarding well-being and its impact on job performance. The aim of the study is to help understand how well-being influences workplace behaviour and organisational outcomes. The findings are expected to contribute to management and business development literature by highlighting employee well-being as a strategic organisational resource and by offering practical implications for managers seeking to enhance performance through sustainable, employee-centred practices.
Status, Belonging, and Digital Engagement: Interpreting the Influencer Phenomenon
This study examines the motivational foundations of aspiring to become an influencer by focusing on different dimensions of online presence. Rather than interpreting the influencer phenomenon as a purely media or marketing outcome, the paper conceptualises influencer aspiration as a motivational process embedded in online social environments. Using data from a voluntary and anonymous online survey (N = 1,146), the motivation to become an influencer was operationalised as a binary outcome variable and analysed in relation to three online presence-related factors: perceived belonging to online communities, the desire for strong online visibility and follower base, and economic engagement with online content creators. To explore these relationships, a binary logistic regression model was estimated. The results indicate that all three dimensions of online presence are significantly associated with influencer aspiration, with the strongest effect observed for the desire for online visibility. At the same time, the model's limited predictive performance suggests that aspiring to become an influencer is not a deterministic outcome of online engagement, but rather a complex, multi-dimensional motivational phenomenon. Overall, the findings highlight that influencer aspiration is closely linked to the quality of online experiences, including community attachment, status-oriented visibility, and engagement with the digital content economy, rather than to the mere intensity of online activity.
The Impact of Business Communication on Crypto Market Prices
Blockchain technology has gained increased prominence in recent years, as more people follow developments in crypto-market prices, and the business communication of blockchain developers can presumably also influence price movements. In many cases, it is said that the market has already priced in the value of a given cryptocurrency in advance, something that is very likely driven by business communication. This research aims to examine the main channels of price-formation mechanisms from the perspective of business communication, such as roadmap scheduling, the relationship between sentiment and price, credibility, and who communicates the news and how consistent that communication is in a given case. In addition, the study investigates the typical channels through which forks affect prices, such as fundamentals, uncertainty, and volatility. Based on the research results, it can be stated that if communication was accurate and a hard-fork upgrade genuinely improved the blockchain as developers announced, this may support price appreciation in the long term, while the effect is less noticeable in the short term. In the case of a soft fork, price increases are less perceptible because they involve a smaller-scale modification of the blockchain’s structure.
Transforming Financial Services in Sub Saharan Africa: The Interplay of Fintech, Insurtech and Financial Inclusion
This paper examines the evolution of Financial Technology (FinTech) and Insurance Technology (InsurTech) within the financial services industry and their role in promoting financial inclusion in Sub-Saharan Africa (SSA).The study focuses on a balanced panel of 22 SSA countries observed over a ten-year period from 2011 to 2021.Financial inclusion is analysed through indicators related to banking efficiency, resource allocation, labour investment, training efficiency, and green credit development. To address potential endogeneity issues inherent in panel data analysis, the Generalized Method of Moments (GMM) estimation technique is employed. The empirical findings reveal that the adoption of FinTech significantly enhances labour investment efficiency within banks across SSA. Results further indicate improved resource allocation and greater training efficiency, suggesting that digital financial innovations support more productive banking operations. The study also finds evidence that FinTech contributes positively to the expansion of green credit, supporting environmentally sustainable financing in the region. In contrast, the rapid growth of digital technologies combined with low levels of digital literacy, particularly among rural populations, limits the effectiveness of InsurTech in reducing insurance service costs. These challenges hinder the full potential of InsurTech to improve insurance accessibility and affordability in SSA. The study concludes that greater investment in financial education and workforce training programs related to InsurTech is essential to ensure smoother adoption, improved insurance penetration, and sustainable financial inclusion across Sub-Saharan Africa.
The U.S.-China Textile and Apparel Trade: A Trade Competitiveness Analysis for the Period 1992-2022
This study looks at how the U.S. and China have interacted in the textile and apparel sector over a thirty-year period, from 1992 to 2022, and tries to make sense of the structure and nature of that relationship. Instead of focusing only on trade volumes, the paper uses three different tools “TCI (the trade complementarity index), ESI (the export similarity index) and Balassa’s RCA (revealed competitive advantage, RCA)” to see whether the two countries behave more like competitors or whether their trade tends to fit together in a complementary way. One of the evidences comes from the TCI results: China’s export profile in textiles and clothing lines up quite closely with import of the U.S., suggesting a relationship in which the U.S. mostly plays the role of import country and China the supplier. The ESI results, on the other hand, show that the two countries’ export structures are not very similar, especially in third markets, which implies that head-to-head competition between them is not particularly strong outside their bilateral trade.The policy implications that follow from these findings point in different directions for each country. On China’s side, the evidence shows that it holds a strong position in this sector and continues to meet much of the demand coming from the United States. At the same time, heavy depency on the U.S. market also appears to carry certain risks. Diversifying its export destinations, rather than becoming overly dependent on a single large buyer, would help China protect itself against potential changes in U.S. demand or shifts in American trade policy.For the United States, the analyses revealed that the situation was almost the exact opposite. The persistent comparative disadvantage in textiles and apparel, combined with its low export similarity with China, suggests that competing directly with Chinese producers on cost or product range is unlikely to yield strong results. A more practical path for the U.S. would be to redirect its resources toward industries where it already holds stronger advantages, instead of trying to match China within a sector that has long been structurally unfavorable for American producers.