1. Narcissism in postmodern society and schools : the influence of neoliberal values on the behavioral patterns of children and adolescentsBeno Arnejčič, 2026, original scientific article Abstract: Abstract This article examines narcissism as a cultural and educational problem in late-modern societies, arguing that neoliberal values— competition, performativity, and market-like evaluations shape children’s and adolescents’ self-understanding and behavior. Drawing on cultural and psychosocial perspectives (Lasch, Fromm, Twenge and Campbell, Verhaeghe, and Vesna V. Godina), the paper conceptualizes narcissism less as an individual pathology and more as a socially produced pattern of relating to oneself and others. Schools are approached as microcosms where broader cultural imperatives become daily practices through grading, comparison, and status competition, which may intensify external validation seeking and reduce empathic engagement. Two divergent student responses are highlighted: grandiose self-presentation, often linked to aggression under ego threat, and egoistic withdrawal, characterized by self-suppression and fear of standing out. The article concludes by outlining educational strategies that strengthen community, dialogue, and social-emotional learning as protective factors against narcissistic dynamics.
Keywords: narcissism, neoliberalism, school culture, dark triad, echoism, social-emotional learning
Published in RUP: 05.03.2026; Views: 87; Downloads: 1
 Full text (2,98 MB)
This document has more files! More... |
2. |
3. Image-based analysis of tourist destination perceptions : a deep learning and spatial–temporal study in SloveniaDejan Paliska, Aleksandra Brezovec, Gorazd Sedmak, 2026, original scientific article Abstract: In the context of fierce competition among tourist destinations and increasing difficulty of differentiation, developing a strong destination image is particularly important. A comprehensive understanding of how tourists perceive destinations through user-generated images can help destination management organizations (DMOs) design more effective marketing strategies. This is especially relevant for destinations with spatially and temporally dispersed tourism resources and strong seasonal dynamics. This paper analyses inbound tourist photographs by combining deep learning techniques with spatial analysis to examine the spatial and temporal distribution of photo scenes and shifts in scene preferences among tourists. The study focuses on three distinct types of destinations in Slovenia—urban (Ljubljana), nature-based/alpine (Bled), and coastal (Piran, Izola, Koper)—providing insights into how image-based spatial scene analysis can inform destination marketing strategies. The results reveal significant spatial and temporal heterogeneity of scenes across micro destinations. Nature-based destinations exhibit lower topic entropy and fewer topic changes per user, whereas urban destinations show higher variability, with users changing topics on average five times per day. Seasonal effects are moderate: nature-based destinations display lower topic entropy in winter and higher in autumn and spring, coastal destinations show less pronounced seasonal variation, and urban destinations show almost none. These findings provide valuable insights into the spatial and temporal distribution of tourist interests and offer practical guidance for DMOs in strategic marketing planning.
Keywords: tourist destination image, user-generated content, deep learning, spatial-temporal analysis, destination marketing strategy
Published in RUP: 18.02.2026; Views: 186; Downloads: 4
Full text (2,93 MB)
This document has more files! More... |
4. Designing the ideal political identity questionnaire using machine learning and ideology scalesAna Nikolić, Uroš Sergaš, Marko Tkalčič, 2025, published scientific conference contribution (invited lecture) Abstract: Political ideology shapes beliefs, behavior and attitudes toward society. Many existing questionnaires for measuring ideology are lengthy, repetitive, and misaligned with self-perception. This paper investigates whether a shorter, reliable, two-dimensional political identity questionnaire can be created using machine learning and psychometric methods. Sixty participants completed four ideological instruments (MFQ, SDO, RWA and 8values). Lasso regression and Random Forest with nested cross-validation identified predictive items, while psychometric evaluation included CFA and Cronbach’s alpha. Random Forest outperformed Lasso. Internal reliability was excellent and factor loadings supported a two-factor structure despite moderate model it. Findings show that ideology can be measured efficiently with reduced items, supporting applications in research, digital platforms and political psychology.
Keywords: political ideology, questionnaire design, machine learning, psychometrics
Published in RUP: 30.01.2026; Views: 230; Downloads: 2
Full text (189,01 KB) |
5. Enhanced precision in axle configuration inference for bridge weigh-in-motion systems using computer vision and deep learningDomen Šoberl, Jan Kalin, Andrej Anžlin, Maja Kreslin, Klen Čopič Pucihar, Matjaž Kljun, Doron Hekič, Aleš Žnidarič, 2025, original scientific article Abstract: Heavy goods vehicles (HGVs) have a significant impact on road and bridge infrastructure, with overloaded vehicles accelerating structural deterioration and increasing safety risks. Bridge weigh-in-motion (B-WIM) systems estimate gross vehicle weight (GVW) using strain measurements, but inaccuracies in axle configuration recognition can reduce reliability. This study presents a low-cost computer vision (CV) extension for existing B-WIM installations that verifies strain-inferred axle configurations using traffic camera images and flags GVW estimates as reliable or unreliable. Experiments on a data set of over 30,000 HGV records show that by combining convolutional neural networks with strain-based heuristics, GVW reliability can improve from 96.7% to 99.89%, effectively excluding nearly all erroneous measurements. The approach operates without interrupting ongoing B-WIM operations and can be applied retrospectively to historical data. Limitations include the inability to detect raised axles (RAs), which the method excludes as unreliable. This method provides a practical, high-precision enhancement for structural health monitoring of bridges.
Keywords: B-WIM, computer vision, deep learning
Published in RUP: 16.01.2026; Views: 204; Downloads: 4
Full text (2,01 MB)
This document has more files! More... |
6. Upskilling older employees in the artificial intelligence eraTinkara Žabar, Aleksander Janeš, 2025, original scientific article Abstract: Research Question (RQ): What is the effect of new technologies, with an emphasis on artificial intelligence (AI), on the need to upskill older employees (50+ years). Purpose: The purpose of the research was to carry out a systematic literature review of existing research in the field of the effect of AI on the upskilling needs of older employees. Method: We performed a systematic literature review across six academic search engines: ProQuest, Emerald, Sage Journals, Springer, Research Gate, and Google Scholar. Results: Artificial intelligence is significantly transforming the labor market, as it requires constant adaptation to new skills and knowledge. AI has a significant effect on older employees, who are exposed to greater challenges due to a possible lack of digital skills and sensitivity to change. In this context, training and further education are key mechanisms to ensure that skills match the requirements of the work environment and the labor market. Organizations must quickly adapt to changing requirements by creating a culture of lifelong learning that encourages seniors and other employees to improve. Training programs must be based on the specific needs and challenges faced by older employees. Organization: The research emphasizes the importance of training older employees in the age of AI and encourages organizations to create a culture of lifelong learning as part of the organization's strategic directions and goals. Society: The importance of research for society is reflected in the insight into the involvement of all age groups in the possibility of improving knowledge, skills, and attitudes towards the use of modern technologies. Organizations and society itself bear the social responsibility to enable older employees to successfully integrate into the work environment in the AI era. Originality: The research addresses the need to improve the skills of a specific age group in the age of AI, where it simultaneously highlights the importance of fostering a culture of lifelong learning in a rapidly changing world. The research findings provide guidelines for policymaking in the field of training on the national level in the context of an aging workforce and new technologies. Limitations/further research: The literature review was limited to six publicly available databases. In the article, older employees were considered as people in the labor process older than 50 years. We must emphasize that older employees differ from each other in terms of education, economic, social, and other circumstances. Further research should investigate the effect of new technologies regarding the specific circumstances mentioned in this age group.
Keywords: knowledge society, upskilling, knowledge management, retraining, older employees, artificial intelligence, lifelong learning
Published in RUP: 12.01.2026; Views: 186; Downloads: 4
 Link to file |
7. Predictors of sustainable development outcomes in learning spaces of early childhood : Slovenian teachers' perspectivesJurka Lepičnik-Vodopivec, Adrijana Višnjić-Jevtić, Aleksandra Šindić, 2026, original scientific article Abstract: This research examined factors associated with sustainable development outcomes (O-SD) in early childhood learning settings, as perceived by preschool teachers, considering environmental, social, and economic aspects along with contextual elements (child age, eco-program affiliation, teacher experience). Data were gathered through an online survey targeting 114 Slovenian preschool teachers in 2024. The reliability of the instrument was high (α = .942), and principal component analysis confirmed its validity. Correlation and stepwise regression analyses identified the key predictors of O-SD. Education focused on economic sustainability and child age emerged as the primary predictors, accounting for approximately 55% of the variance. While social sustainability was frequently observed in practice, it did not prove to be a significant predictor. The findings highlight the importance of developmentally appropriate and age-sensitive strategies, alongside the intentional inclusion of economic considerations. Limitations include a convenience sample and reliance on self-reported data. Future research should aim to replicate these results in diverse settings and consider mixed-method approaches that incorporate children’s perspectives.
Keywords: quality education, early childhood education for sustainability, sustainable development outcomes, early childhood learning settings
Published in RUP: 07.01.2026; Views: 273; Downloads: 6
Full text (365,89 KB)
This document has more files! More... |
8. Digital Narrative Photography as a Method to Improve Empathy in Health SciencesJuan M. Leyva, 2025, independent scientific component part or a chapter in a monograph Abstract: This chapter explores narrative photography in health sciences education, highlighting its effectiveness in addressing the often-overlooked psychosocial needs of patients. Traditionally focused on immediate biological outcomes, healthcare has neglected the importance of empathy and reflective practice. Narrative photography, inspired by Photovoice and reflective practice, involves capturing and reflecting on patients’ real-life narratives through visual and written means. Students create self-made photographs or drawings, articulate their interpretations through short reflective narratives, and engage in group discussions to foster deeper empathy and reflection. Originally dependent on face-to-face interaction, narrative photography has been adapted to hybrid formats with digital tools, enhancing accessibility and cost-effectiveness. This chapter examines the origins, methodologies, technological advancements, and real-world applications of narrative photography, along with variations developed by the author. It also provides recommendations for assessing learning outcomes, evidence of effectiveness, and evaluations of student and faculty satisfaction.
Keywords: art-based methods, narrative photography, teaching innovation, active learning, hybrid learning
Published in RUP: 22.12.2025; Views: 211; Downloads: 2
Full text (1,24 MB) |
9. Exploring student perspectives on e-learning in nursing educationMirko Prosen, Sabina Ličen, 2025, independent scientific component part or a chapter in a monograph Abstract: E-learning has rapidly gained prominence in nursing education, offering flexible alternatives to traditional learning. This study aimed to explore nursing students’ experiences with e-learning, focusing on perceived benefits, challenges, and its impact on skill acquisition. Using a qualitative design, data were collected from four face-to-face focus groups comprising 20 nursing students. Thematic analysis was employed to examine the data, yielding six key themes: Flexibility and accessibility benefits, Impact on student engagement and interaction, Technological and infrastructure challenges, Effect on practical skills and learning outcomes, Diverse preferences in learning approaches, and Self-management and motivation in e-learning. The findings indicate that, while e-learning provides accessibility and flexibility, it poses challenges in practical skill development and engagement. This study emphasises the need for adaptive e-learning models to meet diverse learning requirements effectively.
Keywords: online learning, blended learning, thematic analysis, skill development, student engagement
Published in RUP: 22.12.2025; Views: 231; Downloads: 0
Full text (323,42 KB) |
10. Maximizing Nursing Students’ Engagement in Distance Learning: Strategies and InsightsBoris Ilić, Irena Kovačević, Danko Relić, Vesna Švab, Vedrana Vejzović, Seher Yurt, 2025, independent scientific component part or a chapter in a monograph Abstract: The increase of online nursing education, driven by technology and the demand for flexible learning, has emphasized the importance of student engagement. Participation plays a key role in shaping both academic achievement and the acquisition of crucial nursing skills. This chapter explores the impact of technological factors such as audio and video quality, as well as internet speed, on student engagement. Moreover, it delves into how the ERR framework – a teaching approach that includes Evocation, Realization of Meaning and Reflection, along with other interactive tools can be used in online learning settings to improve nursing students’ participation. This work aims to provide educators with practical insights for improving online nursing education by combining technological considerations with innovative teaching strategies.
Keywords: nursing education, distance learning, student engagement, ERR framework, Technological factors
Published in RUP: 22.12.2025; Views: 227; Downloads: 1
Full text (142,92 KB) |
