The increasing integration of digital technologies in higher education raises important questions about their real pedagogical value in teaching practice. The aim of this research was to examine students’ attitudes about the use of digital technologies, their representation in teaching, and their influence on the perceived pedagogical value of teaching at the technical faculty, examined through correlational and regression analyses. The research also included an analysis of the relationships among digital technologies, student engagement, and students’ assessments of teachers’ digital competence, particularly under conditions of limited technical resources. The research was conducted on a sample of 375 students in engineering study programs. Data were collected using a structured questionnaire and analysed using exploratory factor analysis, reliability analysis of the measurement scales (Cronbach’s alpha and, for the two-item scale, the Spearman-Brown coefficient), correlation analysis and regression models. The results show that, although students generally have a positive perception of digital technologies and teachers’ digital competence, exposure to advanced digital tools in teaching remains limited. Student engagement showed as the most significant predictor of the perceived pedagogical value of digital technologies, along with the institutional support and students’ digital competencies, which also made significant contributions. In contrast, teachers’ digital competence and level of exposure to digital technologies were not significant predictors when considered alongside other variables. These results demonstrate that the perceived pedagogical value of digital technologies primarily depends on the ability to encourage students’ interaction and active participation in the teaching process. The research also provides empirical evidence from the technical faculty context and contributes to understanding the conditions under which digital technologies enhance teaching quality.
Managerial learning and competence development are important for service microenterprises seeking to use business information effectively, adopt innovations, and improve their operations. This study examines managerial perceptions of information use, innovation, institutional support, and professional development needs in service microenterprises in Serbia, with particular attention to their implications for learning, competence development, and quality improvement. A descriptive survey was conducted from June to December 2024 using a convenience sample of 224 owners, directors, managers, and other representatives of microenterprises located in three municipalities of Belgrade. Data were collected through a structured closed-ended questionnaire and analysed using descriptive statistics. Results show that innovation management was highly valued as a contributor to competitiveness, while technological processes and customer-oriented changes received particularly favourable assessments. Respondents associated innovation with market expansion, service quality, operational flexibility, and efficiency, but also identified financial, technological, and knowledge-related constraints. Institutional support was considered particularly important through financial incentives, assistance with technology acquisition, and access to financing. Training preferences focused on export activities, public procurement, marketing and sales, quality standards, and financial management, indicating demand for practically applicable managerial competencies. Business information was also recognized as relevant to successful operations. Overall, the findings indicate that quality improvement in service microenterprises can be supported by strengthening managerial competencies, information use, innovation capacity, and continuing professional learning.
The abuse of artificial intelligence in the creation of pornographic content represents a modern form of socially dangerous behavior that requires an adequate criminal law response and an interdisciplinary approach. The development of generative technologies has enabled the creation of highly realistically manipulated or completely synthetic content, which opens up new types of violation of the right to privacy, dignity and personal safety. These forms of abuse include the use of “deepfake” technology, the digital manipulation of existing visual materials, as well as the generation of content that imitates real people without their knowledge or consent, thereby further complicating the determination of responsibility and the protection of victims. The current criminal legal framework is based on the protection of sexual freedom, honor and reputation, as well as the protection of minors, whereby the question of its normative completeness and effectiveness in the context of accelerated technological development and new forms of digital violence is raised. In addition to the legal aspect, this phenomenon is also characterized by pronounced psychological and victimological effects, which are reflected in the impairment of mental health, the feeling of stigmatization, the loss of a sense of security and the long-term consequences for the social functioning of the victims. The work is methodologically based on a theoretical analysis of relevant positions in domestic and foreign literature, a normative analysis of valid legal regulations, as well as on empirical research conducted among students using the survey method. Descriptive analysis was used in the processing and interpretation of collected data, with the application of inductive and deductive methods in order to draw conclusions. Special attention was paid to examining respondents’ views on the social danger of this phenomenon, their level of information, as well as the perception of the need to improve criminal law regulations. The aim of the work is to point out the shortcomings of the existing criminal legal framework, as well as the need for its normative improvement and the development of more effective preventive and protective mechanisms.
In order to strengthen the cooperation between humans and machines, empathic artificial intelligence is increasingly emerging as a key player. This cooperation is particularly evident in corporate cybersecurity and digital forensics. Today, thanks to the inclusion of emotional awareness in artificial intelligence systems, many organizations can more easily monitor the cognitive load of their analysts during critical security situations. Empathic artificial intelligence in corporate cybersecurity is a new force for proactive threat detection, compliance and risk management. It helps analysts to respond to constant cyber threats more effec-tively, and in forensic investigations it offers great support in emotional and behavioral analysis. In this way, in cybercrime inves-tigations, through the observation of anomalies and their patterns, decisions are made with a higher degree of accuracy. How-ever, this technology still faces numerous challenges, such as accurately reading emotional cues, protecting sensitive data, and ensuring ethical management. In any case, well-defined frameworks that promote cooperation between artificial intelligence and humans are the necessity of the present, in order to remove these barriers. It is undeniable that empathic artificial intelligence has the potential to create security ecosystems that are more adequate, reliable and above all focused on human needs. Therefore, in the near future, it will revolutionize the field of corporate cybersecurity operations and digital forensics, but only if it is smartly implemented.
This paper introduces a new scale in the social sciences that combines two types of measurements: a binary choice and a relative interval measurement consisting of 10+10 intervals. The scale’s relative interval nature allows for differentiation between intervals of 10±10, expressed based on the principle of deciles. This approach provides a more nuanced differentiation between degrees compared to traditional Likert scales. The paper also discusses a coding method and includes graphical representations of the data. An important aspect of the scale is its capacity to categorize responses into positive and negative attitudes (NEGATT and POSATT), enabling separate descriptive and inferential statistical analyses.
Increasing cultural diversity and globalization require higher education institutions to prepare future geography teachers who can work effectively in culturally diverse environments. Developing cultural competence has therefore become an important objective of geography teacher education, requiring instructional approaches that combine disciplinary knowledge with authentic learning experiences and digital innovation. This study investigated the effectiveness of integrating project-based learning (PBL) and digital technologies into a Cultural Geography course to enhance university students’ cultural competence. A mixed-methods quasi-experimental design was employed involving 73 second-year geography students, including 35 students in the control group and 38 students in the experimental group. The intervention was implemented over one academic semester. Quantitative data were collected using the Cultural Competence Questionnaire and analysed through mixed repeated-measures ANOVA, while qualitative data from classroom observations, reflective journals, and project reports were analysed using thematic analysis. The results showed that students in the experimental group achieved significantly greater improvements than the control group across all dimensions of cultural competence, including cultural knowledge, cultural sensitivity, cultural skills, and cultural interaction. Qualitative findings further indicated that project-based learning, cultural mapping, and digital technologies, including NetLogo, AnyLogic, and Cesium, enhanced students’ engagement, spatial thinking, collaborative inquiry, and understanding of cultural phenomena. Although the study was limited by its relatively small sample from a single university and the one-semester duration of the intervention, the findings provide empirical evidence that integrating project-based learning with digital technologies represents an effective pedagogical approach for strengthening cultural competence in geography teacher education and offers practical implications for competence-oriented curriculum development in higher education.
Introduction/Objective: Lexical development in early childhood is important for later literacy and academic success. This experimental study investigated the effectiveness of the NTC (Nikola Tesla Center) learning system, a pedagogical approach integrating movement, associative thinking, and play, on mother tongue vocabulary acquisition in preschool-age children. Methodology: A pre-test/post-test control group design was employed with 40 children (aged 22–34 months; 20 boys and 20 girls), equally divided into experimental and control groups. Participants completed a 20-item picture vocabulary test at three time points: initial, middle, and final. During four weeks, the experimental group participated in 12 NTC-based workshops involving motor-cognitive polygons, riddles, and associations, while the control group received standard curricular activities covering the same concepts. Due to non-normal data distribution, non-parametric statistics were used. Results: Groups did not differ significantly at initial testing (p > .05). Significant improvement over time was found for 19 of 20 target words in the experimental group (p < .05). The experimental group also demonstrated significantly greater vocabulary gains than the control group (p < .05), particularly for initially less familiar concepts, including iron, garlic, hyacinth, and nail. Progress was independent of age and gender, while dialectal forms were frequently replaced with standard forms by final testing. Conclusion: The findings suggest that integrating movement, associative thinking, and cognitive challenges may support vocabulary acquisition in early childhood. The greatest progress on less familiar words indicates potential benefits for deeper semantic learning. NTC-based activities may therefore represent a useful pedagogical approach for supporting lexical development and standard language competence in preschool children.
This study aims to identify the types of students’ mathematical representation errors using Newman’s error classification and to analyze their relationships with the stages of cognitive development in the Action, Process, Object, and Schema theory. The study employed a qualitative descriptive approach involving 42 tenth-grade students from a senior high school. Data were collected through a mathematical representation ability test and in-depth interviews, and were analyzed qualitatively by mapping the types of errors and students’ cognitive development stages. The results indicate that students’ mathematical representation ability remains low, with symbolic representation the most challenging aspect across all ability levels. Comprehension and transformation errors were dominant among low-ability students, most of whom were at the Action stage. Moderate-ability students showed an emerging uneven pattern of cognitive attainment, with Schema-level attainment in the visual representation and, to a limited extent, in the verbal representation, whereas no Schema attainment was observed in the symbolic representation. Meanwhile, high-ability students demonstrated Schema-level attainment in the visual and verbal representations, whereas their symbolic performance remained at the Object stage because of persistent process-skill difficulties. These findings indicate that cognitive development is specific to the type of representation and does not occur uniformly across representations. This pattern of uneven cognitive attainment across representational forms is conceptualized in this study as asymmetrical representational development. The implications of this study suggest that the teaching of quadratic functions should emphasize coordination among visual, symbolic, and contextual representations through multi-representation approaches and gradual scaffolding to consistently support students’ conceptual understanding.
The development of practical competence is an important component of biology teacher education, particularly in laboratory, field, and animal-related studies. The aim of this study was to evaluate the effectiveness of adapting selected practice-oriented pedagogical approaches identified during a research internship at Hacettepe University (Turkey) for developing the practical competence of biology students in Kazakhstan. A mixed-methods research design was employed. The Turkish stage included analysis of scientific and methodological literature from the Hacettepe University Library, a structured questionnaire survey of 25 undergraduate students, student interviews, classroom observations and examination of laboratory-, research- and field-oriented teaching practices. These activities, together with the findings of our previous studies, were used to identify instructional elements suitable for adaptation. Selected approaches, including laboratory and experimental learning, applied animal-biology tasks, collaborative learning, digital visualization, virtual laboratories and research-oriented instruction, were integrated with an author-developed electronic learning resource. The adapted approach was tested during a semester-long pedagogical experiment at Abai Kazakh National Pedagogical University involving an experimental group (n = 42) and a control group (n = 35). The experimental group received practice-oriented instruction, while the control group studied through conventional teaching methods. Subsequently, the developed lesson plans and didactic materials were incorporated into an approved 80-hour professional development course completed by 25 school and university teachers from different regions, who provided feedback on their applicability in real biology teaching practice. The findings support the effectiveness of adapting practice-oriented pedagogical approaches for strengthening practical competence in biology teacher education in Kazakhstan.
This pilot study examined whether incongruent facial expressions and Vietnamese emotion labels were associated with longer reaction times than congruent pairings in lower secondary school students. Six Vietnamese students in Grades 6 to 8 completed a 130-trial Emotional Face–Word Stroop Task comprising 70 congruent and 60 incongruent trials. Reaction time was the primary measure, while accuracy was summarized separately. Mean reaction time was longer for incongruent trials than for congruent trials, with an average difference of 175.87 milliseconds and a 95 percent confidence interval from 104.28 to 247.46 milliseconds. Overall accuracy was 81.3 percent and was descriptively lower for incongruent trials than for congruent trials. An exploratory analysis across three broader stimulus groups produced a condition-by-group pattern, but this result was interpreted as task-specific because trial frequencies were unequal and the corresponding cells differed in emotional composition. All participants completed the task, with completion times ranging from 7.76 to 11.12 minutes. The findings showed an observed within-sample congruency pattern in this Vietnamese-language task, but they do not support population-level or stable emotion-specific conclusions. Future studies should use larger and more diverse samples, balanced stimulus categories, prespecified reaction-time preprocessing rules, controlled testing conditions, and sensitivity analyses restricted to correct responses. This pilot provides procedural and behavioral information for refining the task and designing future developmental, educational, and cross-cultural research.
It has always been an issue in the human resource management selecting the right rewards for the employees especially in the public institutions. Many public institutions are unable to identify the types of rewards which are best used to foster employees’ work satisfaction. The study aims are to evaluate how reward systems in public institutions influence employee perception and to analyze their impact on job satisfaction, quality of work, and employee retention in the public sector of North Macedonia. Data were collected via self-administered questionnaire of 153 employees in public institutions in North Macedonia. Reliability and validity of the measurement instrument are confirmed through Cronbach’s alpha (0.777) and factor analysis. Descriptive statistics, correlation analysis, and regression analysis are applied to test the proposed hypotheses. The results indicate that the re-ward system has a positive but weak effect on job satisfaction (β = 0.142, p = 0.079), quality of work (β = 0.146, p = 0.071), and employee retention (β = 0.144, p = 0.075), with none of the effects reaching statistical significance. Additional hierarchical regression analysis suggests that demographic characteristics partially influence these relationships, while the contribution of re-ward systems remains limited. The findings imply that reward systems in public institutions play a secondary role in shaping employee outcomes, while other factors such as job security, intrinsic motivation, and institutional stability may be more influential. The study contributes to the limited empirical literature on public sector reward systems in North Macedonia and offers practical insights for policymakers and managers seeking to improve human resource management practices.
The increasing adoption of immersive technologies in higher education has transformed digital learning environments by ena-bling interactive, experiential, and learner-centered pedagogies. Despite the growing body of research, existing evidence re-mains fragmented across technologies, disciplines, and educational outcomes. Therefore, this study systematically reviews em-pirical research on virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR) in higher educa-tion to synthesize patterns of technology adoption, learning outcomes, disciplinary applications, and geographical distribution while identifying existing research gaps. Following the PRISMA guidelines, a systematic literature review was conducted on 61 empirical studies published between 2018 and 2025 and indexed in Scopus and Web of Science. Descriptive and thematic analyses revealed that research is concentrated in education, health sciences, and engineering, with VR emerging as the most widely adopted immersive technology, followed by XR and AR, whereas MR remains relatively underexplored. The findings further indicate that immersive technologies predominantly enhance cognitive learning outcomes, professional competencies, and affective engagement through experiential learning approaches, while behavioral engagement, self-belief, psychomotor abilities, social interaction, and communication skills receive comparatively limited scholarly attention. Geographically, research output is heavily concentrated in Europe and East Asia, highlighting significant regional disparities. Overall, immersive technologies demonstrate substantial potential to improve higher education learning experiences, but important gaps remain in technology diversity, disciplinary coverage, engagement measurement, and global representation.
The aim of this study is to explore pre-service teachers’ perceptions regarding methodologies for enhancing the cognitive activities of future mathematics teachers through the use of educational resources. The study was designed within the framework of mixed research methodology integrating both quantitative and qualitative approaches. The participant group consisted of 80 pre-service mathematics teachers enrolled at various universities in Almaty, Kazakhstan. Data were collected through a semi-structured interview form developed by the researchers, which included both closed-ended items to capture participants’ self-reported cognitive awareness, cognitive flexibility, and mathematical thinking levels, and open-ended questions to explore their perceptions and experiences. The findings indicate that the cognitive awareness levels of the participating pre-service mathematics teachers are at a moderate level. Similarly, their levels of cognitive flexibility were found to be moderate. In contrast, the participants demonstrated a high level of mathematical thinking. Despite this strength, the majority of participants reported that they perceive themselves as only partially competent in terms of utilizing methodologies aimed at improving their cognitive activities through educational resources. Based on these findings, it is concluded that there is a need for systematic improvement in teacher education programs. In particular, curricula in faculties of education should be revised and restructured to better integrate methodologies that support the development of cognitive activity through effective use of educational resources.
Navigating the complexities of modern organizational landscapes, particularly in the context of cybercrime and economic – financial challenges, remains a critical issue for industries. Despite advancements in hybrid intelligence, cloud-based platforms, and algorithmic solutions, gaps persist in integrating data-driven, entropy-based approaches into next-generation management systems tailored for cybercrime prevention and economic optimization. This study addresses these gaps by proposing a novel framework that integrates a hybrid algorithmic model with entropy-based optimization techniques. Utilizing four datasets—three publicly available and one originally collected through online sources—this research explores how real-time data and adaptive decision-making can enhance cybercrime detection and economic – financial forecasting. The theoretical novelty lies in combining entropy-based modeling with a rule-based neural network to achieve superior accuracy, explainability, and scalability in complex settings. The proposed system delivers practical benefits, including improved cyberthreat identification, economic anomaly detection, and resource optimization, fostering resilient and adaptive management frameworks. Experimental results demonstrate statistically significant improvements in accuracy (p < 0.05) compared to baseline models, particularly in dynamic, resource-intensive environments. This study contributes to the literature by offering a comprehensive empirical evaluation, discussing integration with existing enterprise systems, and addressing scalability and cost-effectiveness in the context of cybercrime and economic management. By bridging these research gaps, we present an approach with both theoretical significance and practical utility for combating cybercrime and optimizing economic and financial performance.
Various factors, including knowledge, beliefs, and instructional practices, influence the quality of pre-service and in-service mathematics teachers’ instruction. The relationships among these factors have been investigated in many previous studies. This systematic review investigated the relationships among knowledge, beliefs, and instructional practices of pre-service and in-service mathematics teachers by analyzing included studies from 2015 to 2023. The study used systematic review methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, along with descriptive and qualitative statistical analyses. A collection of 38 relevant studies, selected from databases such as Google Scholar, Mendeley, ScienceDirect, Scopus, and Taylor & Francis Online, was analyzed to answer the research questions after a screening and eligibility assessment using inclusion and exclusion criteria. The research results on the relationships among knowledge, beliefs, and instructional practices of pre-service and in-service mathematics teachers reported in the included studies are relatively diverse. In particular, most research shows that teachers’ knowledge affects their beliefs and the quality of teaching in their instructional practices. In contrast, their instructional practices promote the development of their knowledge, reflect, and enhance their beliefs. However, the proportion of studies finding the relationships among these factors to be insignificant remains relatively low. Based on these findings, the study makes recommendations to stakeholders for in-service mathematics teachers’ professional development and training, as well as pre-service mathematics teacher training programs. In addition, the study highlights limitations and suggests new research directions for the future.
The aim of this study was to examine the perceptions of the teaching profession, satisfaction with the choice of a study programme, and desirable work values in students in their final years of various areas of teacher education studies. The study participants were 443 students in their final year of teacher education studies in the Republic of Croatia. The participants filled out the questionnaires designed to examine their intentions of finding a job in their professional field, their satisfaction with the chosen study programme, the perception of the support received from their surroundings, their satisfaction with a study programme, and the perception of the teaching profession and desirable work values. The findings have shown that students tend to rank highly expert career and high demand of the teaching profession, while they give the lowest ranking to the social status of teachers and their salary. Statistically significant differences were found in almost all of the examined variables with regard to the area of study (interdisciplinary, humanistic, STEM, social). The perceptions of students who intend to find employment in schools are statistically different in comparison with those of students who do not intend to or who are not sure whether they would work in schools, with regard to their satisfaction with the chosen study programme and studies, and their perceptions of the important work values. The logistic regression analysis results indicate that satisfaction with the chosen study programme is the most important factor contributing to students’ intentions to find employment in school. The findings highlight the importance of strengthening professional orientation and providing support to students during their studies, while future research should further examine the factors underlying satisfaction with the choice of a study programme.
In this qualitative study, I analysed the magnitude of cognitive overload among professors employed in Indian state-run universities and its impairment of their academic performance. The research was conducted in the Indian higher education framework, where the ever-increasing cognitive overload has significantly impacted academic performance and role expectations of professors. This qualitative study examines the causes of cognitive overload among professors at state-run universities, as well as its impact on their academic performance. This study offers a clearer understanding aimed at establishing cognitive overload as an obstacle to work efficiency within the Indian micro-environment. Semi-structured interviews were conducted with 40 assistant professors, associate professors and professors working at 20 state-run universities in India. The collected data were then examined thematically. Seven interrelated themes surfaced: (1) Task switching and interruptions - continuous juggling between tasks; (2) Pedagogical impact - effects on teaching quality; (3) cognitive symptoms - difficulty in thinking and understanding; (4) Social symptoms - difficulty in interaction and collaboration; (5) Behavioural manifestations - altering behavioural patterns; (6) Physical and emotional toll - complete physical and mental exhaustion and (7) Coping and support - dealing with the problem and assistance. These themes were persistently mirrored across participants, indicating robust data saturation. The study provides insights on the challenges faced by professors in managing their job-related responsibilities in the context of rising cognitive load arising from the administrative setup of Indian universities. The study reveals the impact of cognitive overload on the effectiveness of academic and research performance of Indian professors.
This paper examines the role of digital communication platforms in institutional branding in higher education, with particular attention to learning processes, digital literacy, and the integration of artificial intelligence (AI) and machine learning (ML). Based on a structured review and theoretical synthesis of interdisciplinary literature, the study conceptualizes institutional branding as a dynamic and relational process shaped by digitally mediated interactions among multiple stakeholders.The analysis integrates three interrelated dimensions: digital communication platforms and institutional strategies, stakeholders’ digital literacy as a mediating factor in engagement and trust formation, and the role of AI/ML systems in personalizing content, shaping visibility, and quantifying engagement, including their ethical implications. The paper argues that digital platforms function as infrastructural spaces for the co-creation of institutional reputation, while digital literacy enables meaningful engagement and trust. At the same time, AI/ML systems enhance communicative efficiency but introduce reputational and ethical risks. The study proposes a conceptual framework to support future empirical research and the development of responsible communication strategies in higher education.
This paper examines how Artificial Intelligence in Education (AIED) is reshaping teaching and learning, drawing on a systematic literature review alongside policy analysis to explore practical applications, the theories behind them, and their governance consequences. Adopting the EU AI Act’s risk-based lens, we investigate the ways in which regulatory demands—ranging from transparency and data stewardship to human oversight and provider accountability—influence how AIED tools are built and taken up in practice. We group current uses into four areas—adaptive learning, intelligent assessment, learner profiling, and emerging tools—and read them through the prism of well-known learning theories such as constructivism. The analysis underscores that while these technologies hold real promise, several prominent use cases—automated grading and learner profiling, for instance— fall squarely within the EU AI Act’s higher-obligation categories, which means equity, explainability, and genuine human control are not optional but essential for public trust. On the basis of these findings, we put forward concrete, compliance-oriented recommendations aimed at helping educators, institutions, and policymakers deploy AI responsibly across varied educational settings.
AI integration in applied cognitive psychology demands critical evaluation beyond efficiency metrics. Despite widespread institutional adoption, emerging research reveals concerning patterns including high hallucination rates, deteriorating retention with prolonged exposure, and a consistent tendency to support surface-level task completion at the expense of deeper cognitive processing. These findings align with established principles regarding desirable difficulties, metacognitive monitoring, skill acquisition, and vigilance, suggesting that applications prioritising task completion over cognitive development risk undermining the adaptive expertise essential for complex professional contexts. Methodological weaknesses in existing research, including brief interventions, inadequate control comparisons, and reliance on satisfaction measures, further constrain confident conclusions. Nonetheless, several domains including cognitive accessibility, rehabilitation, vigilance, and adaptive tutoring represent areas of genuine promise where AI’s architecture may complement rather than conflict with established cognitive science. This commentary synthesises emerging evidence, examines methodological limitations, proposes research priorities for responsible integration, and reflects on where cautious optimism is warranted.
