Periodicals of Engineering and Natural Sciences (PEN - International University of Sarajevo)
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Using big data analytics to assess educational outcomes at Ukrainian universities
Get PDFThe proposed article aims to analyze the perception of teachers and students regarding the use of Big Data analytics to assess educational outcomes at Ukrainian universities. The primary method of information collection is interviews with survey elements. A purposive method was used to find and attract participants. In total, 15 teachers and 89 students participated. The results show that all participants in the educational process are ready to work with modern technologies. Among the advantages of big data analytics are enhanced tracking of student results, data-driven decision-making, and personalization of curricula. The study also identifies certain risks: problems with security, the use of incomplete or biased data, dependence on algorithms, financial barriers, dehumanization of education, and dependence on technology. The conclusions indicate that further implementation of these technologies requires the introduction of moderate solutions to analyze information
Digital transformation of education: The legacy of Arab-Muslim pedagogy in the development of modern learning platforms
Get PDFDigital learning platforms often reflect western pedagogical traditions, potentially marginalizing learners from diverse cultural backgrounds. Consequently, the study examined the reflection of pedagogical principles of Arab-Muslim in modern digital learning platforms. In this research, a qualitative content analysis approach was employed, analyzing four digital learning platforms: Noon Academy, Bayyinah TV, Edraak, and Coursera. The findings revealed that Arab-Muslim pedagogical principles, including ijtihad (critical thinking), shura (collaborative learning), and adab (etiquette) are essential in modern Information Technology-based education. Notably, Arab-world platforms emphasized adab and moral engagement, highlighting the importance of cultural context in digital learning platform design. The findings of this study have implications for Edtech development, underscoring the need for culturally responsive design and development. The findings suggest that Arab-Muslim pedagogy provides valuable models for ethical and learner-centered digital education
The Effectiveness of Cooperative Learning in Enhancing Student Participation among Grade 10 English Students in Jordanian Public Schools
Get PDFThis study responded to the problem of classroom participation among Jordanian public school students, whose education has been mainly teacher-centered and has constrained students\u27 learning autonomy. This study seeks to explore the impact of cooperative learning methods for increasing classroom participation among Grade 10 students. A quasi-experimental pretest-posttest design was applied with 80 participants labeled as an experimental group (40 participants) and a control group (40 participants). The treatment group received cooperative learning approaches, including Think-Pair-Share (TPS), Numbered Heads Together (NHT), and Teams Games Tournament (TGT), while the control received regular instruction. Measures that were used to collect data included a 10-item observation checklist questionnaire, a 15-item student self-report questionnaire, and teacher reflection logs. The finding suggested that the experimental group had a marked improvement in mean participation, from 1.9 to 3.2 (+1.3 or +68.4%), while the control group had a minimal increase from 1.8 to 2.1 (+0.3 or +16.7%). A separate-samples t-test likewise determined that the improvement in the experimental group was significant (p < 0.05), and the students demonstrated more confidence and responsibility. In general, the application of cooperative learning techniques greatly increased student participation, which was a welcome change in the Jordanian academic setting, especially English lessons. However, study limitations include overreliance on subjective rating, single-site design, and self-reporting by teachers, with suggestions for future research including the incorporation of objective performance measures and exploration of digital substitutes to increase ease of deployment
COMB: A blockchain-based framework for secure military UAV swarm operations
Get PDFThis study aims to enhance the performance of military drone swarms by implementing a practical technological model called the Blockchain-Based Military Consortium (COMB). Instead of a centralized command model, the COMB model distributes control across all drones in the swarm by use the Hyperledger Fabric platform. The drones record mission steps and coordinates in a shared, decentral log. This avoids the drawbacks of centralized control. Furthermore, smart contracts handle routine rules. An Intermediate File System (IPFS) is used to ensure that mission data remains available. To explore how this works, we created a virtual environment and simulated a swarm of up to 500 drones. The system was subjected to GPS spoofing, fake commands, and compromised nodes. On average, command delays remained around 2.3 milliseconds, blocks were confirmed in about 1.5 seconds, and a 15% resource margin was maintained as a reserve. In these tests, approximately 98.6% of impersonation attempts were detected and addressed. The testing approach reduced costs by about 83% compared to traditional live trials. Overall, the results suggest that the COMB system could provide a starting point for managing drone swarms. The syetem needs field testing, particularly on a larger scale, but these early steps provide a basic understanding for evolve this approach over time
Quantitative assessment of the effects of climate change on water resources in the Huancané River basin, Peruvian Andes
Get PDFThe purpose of this article is to evaluate the effects of climate change on surface runoff, aquifer recharge, percolation and renewable water resources in the Huancané River basin, Puno – Peru, using the SWAT hydrological model and the standardized precipitation index (SPI). The RCP 4.5, RCP 8.5, SSP1-2.6 and SSP3-7.0 climate scenarios of CMIP5 and CMIP6 were applied for the projected period 2025–2100. The model was calibrated and validated with historical data from the period 1981–2016. The results showed that surface runoff will decrease significantly in the most extreme scenarios, reaching only 8.09 m³/s in SSP3-7.0, while in RCP 8.5 a maximum of 12.59 m³/s is projected. The recharge of the aquifer will be reduced from 559.22 Mm³ to 179.09 Mm³ and the volume of renewable water will decrease by 51.2%, from 750 Mm³ to 366 Mm³. In addition, the average annual temperature in the basin could reach 14°C by the end of the 21st century, increasing evapotranspiration and further reducing water availability. The SPI index projects an intensification of droughts during the period 2025–2050. These scenarios show a growing vulnerability of the water system, which represents a critical challenge for agriculture, supply and sustainability. The integration of the SWAT model with climate projections is a key tool for water planning and adaptation in vulnerable Andean regions
Interactive learning in the digital age: Advancing STEM skills in primary school education through robotics
Get PDFThe purpose of the study is to analyze the effects of using robotics as elements of interactive learning on the development of STEM competencies in primary school students. The study is of the quasi-experimental-comparative type, the duration of the intervention (6–8 weeks), the assessment tools (test, questionnaire), and the statistical analysis (t-test). The main instruments: questionnaires, a STEM competency scale adapted for children, a didactic experiment based on observation (with the involvement of two groups of schoolchildren - 26 participants in the control group and 26 participants in the experimental group). The study showed insignificant changes in the level of STEM competencies in the control group, which studied according to the traditional program without the use of robotics: the increase was only 0.6 points (from 11.5 to 12.1), which is not statistically significant (p = 0.11). However, participants in the experimental group, who studied with the use of robotics, showed a statistically significant improvement in STEM competencies - the increase was 4.1 points (from 11.8 to 15.9), (t = 6.74; p < 0.001). This indicated a pedagogical effect and an increase in the practical component of learning. The conclusions noted that there was an increase in the general level of knowledge of students and their practical skills, which was accompanied by an increase in students\u27 self-confidence and desire to solve atypical tasks. Therefore, the study fills the existing empirical gap in assessing the effectiveness of STEM education using robotics in primary education and has the potential to be scaled up in other educational institutions.
Large language models in medicine: A systematic review of applications in medical, healthcare, and educational contexts
Get PDFLarge language models have emerged as transformative tools in medicine and medical education, offering applications in aided diagnosis, automation of clinical assessments, and optimization of healthcare workflows. This article critically reviews 112 relevant publications analyzing the use of LLMs in these fields. It explores their applications in specific tasks such as biomedical classification, automated clinical assessment, medical question answering, medical report generation, and enhancement in medical education through exam simulation and personalized tutoring. Despite their advances, LLMs continue to face significant challenges, including data privacy issues, clinical validation, and algorithmic biases. However, their integration into clinical and educational settings demonstrates considerable potential to improve efficiency, accuracy, and accessibility in health care, provided these models adhere to technical and ethical rigor. This article offers a comprehensive overview for healthcare professionals and researchers who aim to adopt these models responsibly
Assessment of impact velocity effects on damage in armor materials through experimental and FEM analysis
Get PDFIn this study, the deformation behavior of armor (plate) systems composed of high-strength ballistic steels MARS 190 and MARS 240 arranged in various thicknesses and combinations was investigated under different projectile impact velocities and angles using both experimental methods and the finite element method (FEM). In the experimental stage, four different plate configurations were prepared: single steel plate, double steel plates, steel–rubber–steel, and steel–air gap–steel combinations. These configurations were subjected to ballistic impacts at varying velocities. For each configuration, the level of deformation, energy absorption capacity, and structural integrity were analyzed.
The same test scenarios were modeled numerically using FEM, and the simulation results were validated through comparison with the experimental findings. Upon validating the analysis model, the number of composite plate models was increased. The results highlight the influence of impact velocity and plate composition on deformation behavior, revealing that multilayered and gapped structures effectively optimize energy distribution. The performance of different combinations of MARS 190 and MARS 240 materials was comparatively evaluated, and recommendations for effective armor design were provided. This study aims to contribute to engineering applications concerning the design of ballistic protection systems and material selection
Using big data to increase the efficiency of business processes in the digital economy of Ukraine
Get PDFThis study explores the transformative role of big data tools in enhancing business efficiency within Ukraine\u27s digital economy. Using a cross-sectional design, data were collected from 200 managers and experts across diverse industries through a semi-structured questionnaire. The analysis encompassed descriptive statistics, reliability testing, exploratory factor analysis (EFA), regression analysis, and cluster analysis to examine the adoption of predictive analytics, business intelligence, and process automation. Results highlight process automation as the most significant efficiency driver, followed by predictive analytics and business intelligence, enabling streamlined workflows, faster decision-making, and reduced operational costs. Cluster analysis identified three distinct groups of organizations: high adopters achieving notable efficiency gains, moderate adopters facing substantial barriers, and low adopters with targeted benefits but limited efficiency gains. Barriers such as skill shortages, infrastructure gaps, and organizational resistance were prominent among moderate adopters, underscoring the need for targeted interventions. Larger organizations and those led by experienced managers demonstrated greater efficiency, highlighting the importance of resources and leadership in digital transformation. The study emphasizes the need for investment in infrastructure, workforce development, and tailored support for SMEs to unlock the full potential of big data. Future research should focus on longitudinal impacts, sector-specific challenges, and integrating emerging technologies such as AI and IoT. These findings provide actionable insights for policymakers and organizations to foster a data-driven, competitive, and inclusive digital economy in Ukraine
Human-AI collaboration in the analysis of literary narratives:a new pedagogical paradigm
Get PDFThe digitization of education in Kazakhstan has demonstrated the importance of using AI in the teaching of literature. The purpose of this article is to evaluate the effectiveness of collaboration between humans and AI in improving students\u27 analytical and interpretive skills when studying literary narratives in the context of higher education. The study applied a true experimental design with a randomized controlled trial (RCT) with a pre-test/post-test control group structure. A voluntary purposeful sample (40 participants in total) with elements of randomization was used (all participants were randomly divided into two groups).The results showed that the t-test results indicated the importance of using ChatGPT, as it showed a statistically significant improvement in literary analysis skills among students. There is also a high level of positive perception of learning using AI elements. This indicates the effective introduction of AI technologies as an important factor in increasing student motivation and engagement. The main challenges to the further integration of AI systems into literary education are technical unpreparedness, further integration of AI systems into literary education are technical unpreparedness, lack of experience and skills in interacting with special programs. The advantages of using AI include the formation of individual educational trajectories, increased access to information sources, interactivity and support. The conclusions emphasize the importance of scaling up the study to obtain new empirical data