1. Digital Narrative Photography as a Method to Improve Empathy in Health SciencesJuan M. Leyva, 2025, samostojni znanstveni sestavek ali poglavje v monografski publikaciji Opis: 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.
Ključne besede: art-based methods, narrative photography, teaching innovation, active learning, hybrid learning
Objavljeno v RUP: 22.12.2025; Ogledov: 112; Prenosov: 0
 Celotno besedilo (1,24 MB) |
2. Exploring student perspectives on e-learning in nursing educationMirko Prosen, Sabina Ličen, 2025, samostojni znanstveni sestavek ali poglavje v monografski publikaciji Opis: 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.
Ključne besede: online learning, blended learning, thematic analysis, skill development, student engagement
Objavljeno v RUP: 22.12.2025; Ogledov: 120; Prenosov: 0
Celotno besedilo (323,42 KB) |
3. Maximizing Nursing Students’ Engagement in Distance Learning: Strategies and InsightsBoris Ilić, Irena Kovačević, Danko Relić, Vesna Švab, Vedrana Vejzović, Seher Yurt, 2025, samostojni znanstveni sestavek ali poglavje v monografski publikaciji Opis: 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.
Ključne besede: nursing education, distance learning, student engagement, ERR framework, Technological factors
Objavljeno v RUP: 22.12.2025; Ogledov: 117; Prenosov: 0
Celotno besedilo (142,92 KB) |
4. Digital standard for the design of inclusive and effective online courses in higher education : an integrative literature reviewSabina Ličen, Mirko Prosen, 2025, samostojni znanstveni sestavek ali poglavje v monografski publikaciji Opis: This integrative literature review examines models and frameworks for digital education in higher education, synthesizing their key strengths and limitations. By analysing nine existing frameworks covering different aspects of digital education, including pedagogical approaches, technological solutions and assessment mechanisms, the study identifies gaps in the current literature. The findings show that while the individual models provide valuable insights, none of them independently offer a complete approach to the design, implementation and assessment of digital education. Therefore, this study proposes the development of an integrated digital standard that combines theoretical and practical perspectives to promote inclusive and effective online learning. Such a standard could increase the adaptability to students' needs, improve assessment mechanisms and increase the flexibility of digital learning environments. This study contributes to the development of sustainable and adaptable solutions for the future of digital education.
Ključne besede: innovative teaching methods, e-learning, sustainable education, effectiveness of online teaching, digital education
Objavljeno v RUP: 22.12.2025; Ogledov: 102; Prenosov: 1
Celotno besedilo (174,03 KB) |
5. Psychological factors and mechanisms of digital learningMaša Černelič Bizjak, Sabina Ličen, 2025, samostojni znanstveni sestavek ali poglavje v monografski publikaciji Opis: The digital environment is different from natural and social environments. Technologies are evolving to support new methods of collaborative learning and interaction. A key challenge is to ensure that technology-enhanced education is effective and creates a supportive environment for students. This requires considering adaptive motivations, emotions, and psychological factors such as intrinsic motivation, cognitive load and self-regulation, all which influence student engagement and success in digital learning. This chapter provides an overview of literature on psychological processes important in digital learning. Factors such as motivation, cognitive management, and self-regulation shape student performance in these environments. The psychology of digital learning explores the cognitive, emotional, and social dimensions of education in the digital age. Research aims to optimize these environments for better learning outcomes. Understanding these psychological elements is essential for educators to create more effective, engaging, and enjoyable digital learning experiences, though the field is still developing, and many aspects remain to be explored.
Ključne besede: digital learning, intrinsic motivation, cognitive load, self-regulation, technology-enhanced education
Objavljeno v RUP: 19.12.2025; Ogledov: 157; Prenosov: 3
Celotno besedilo (161,71 KB) |
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7. Reinforcement learning for graph theory, II. Small Ramsey numbersMohammad Ghebleh, Salem Al-Yakoob, Ali Kanso, Dragan Stevanović, 2025, izvirni znanstveni članek Opis: We describe here how the recent Wagner’s approach for applying reinforcement learning to construct examples in graph theory can be used in the search for critical graphs for small Ramsey numbers. We illustrate this application by providing lower bounds for the small Ramsey numbers R(K_{2, 5}, K_{3, 5}), R(B₃, B₆) and R(B₄, B₅) and by improving the lower known bound for R(W₅, W₇).
Ključne besede: Ramsey number, critical graph, reinforcement learning, cross-entropy method
Objavljeno v RUP: 03.11.2025; Ogledov: 210; Prenosov: 2
Celotno besedilo (291,40 KB) |
8. Deep learning for brain MRI tissue and structure segmentation : a comprehensive reviewNedim Šišić, Peter Rogelj, 2025, pregledni znanstveni članek Opis: Brain MRI segmentation plays a crucial role in neuroimaging studies and clinical trials by enabling the precise localization and quantification of brain tissues and structures. The advent of deep learning has transformed the field, offering accurate and fast tools for MRI segmentation. Nevertheless, several challenges limit the widespread applicability of these methods in practice. In this systematic review, we provide a comprehensive analysis of developments in deep learning-based segmentation of brain MRI in adults, segmenting the brain into tissues, structures, and regions of interest. We explore the key model factors influencing segmentation performance, including architectural design, choice of input size and model dimensionality, and generalization strategies. Furthermore, we address validation practices, which are particularly important given the scarcity of manual annotations, and identify the limitations of current methodologies. We present an extensive compilation of existing segmentation works and highlight the emerging trends and key results. Finally, we discuss the challenges and potential future directions in the field.
Ključne besede: magnetic resonance imaging, brain, image segmentation, deep learning
Objavljeno v RUP: 10.10.2025; Ogledov: 527; Prenosov: 6
Celotno besedilo (956,69 KB)
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