Iskanje po katalogu digitalne knjižnice

Opis: It is no longer just an opinion but a fact that the only way to prevent a catastrophic future for humanity on a planetary scale is to introduce sustainable practices in all areas of human endeavour. The key role in these processes is activity to education. The aim of this study is to investigate the perceptions of Slovenian secondary school students, parents, and teachers (SPTs) regarding the role of smartphones and tablets in promoting 21st-century skills. This study explores the views of Slovenian secondary school students, parents, and teachers (SPTs) on the value of smartphones and tablets in 21st-century skills education. The results show a consensus among participants that smartphones and tablets make a positive contribution to various aspects of 21st-century skills as a Prerequisite for Sustainable Future. Participants consistently rated the suggested benefits of smartphones and tablets above the middle of the scale, with a focus on internet, digital, and information literacy. However, there is still room for improvement in basic skills and higher-order thinking skills. The factorial analysis revealed three correlated factors: Holistic Learning skills, Higher-Level Cognitive skills, and Digital Information Literacy skills. Subsequent analysis revealed significant differences between the focus groups, with students showing stronger agreement with the positive impact of smartphones and tablets on a wide range of skills. While teachers recognized the value of smartphones and tablets for students’ digital literacy and engagement, the differences between teachers and other groups were relatively small. These findings underscore the importance of integrating smartphone strategies and technology tools to promote 21st-century skills as a Prerequisite for Sustainable Future. Educators and policymakers can use these findings to promote effective teaching and learning practices that meet the demands of the 21st century.
Ključne besede: 21st-century skills, smartphone, mobile learning
Objavljeno v DKUM: 18.09.2024; Ogledov: 0; Prenosov: 0
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Opis: Microbiota analysis can provide valuable insights in various fields, including diet and nutrition, understanding health and disease, and in environmental contexts, such as understanding the role of microorganisms in different ecosystems. Based on the results, we can provide targeted therapies, personalized medicine, or detect environmental contaminants. In our research, we examined the gut microbiota of 16 animal taxa, including humans, as well as the microbiota of cattle and pig manure, where we focused on 16S rRNA V3-V4 hypervariable regions. Analyzing these regions is common in microbiome studies but can be challenging since the results are high-dimensional. Thus, we utilized machine learning techniques and demonstrated their applicability in processing microbial sequence data. Moreover, we showed that techniques commonly employed in natural language processing can be adapted for analyzing microbial text vectors. We obtained the latter through frequency analyses and utilized the proposed hierarchical clustering method over them. All steps in this study were gathered in a proposed microbial sequence data processing pipeline. The results demonstrate that we not only found similarities between samples but also sorted groups’ samples into semantically related clusters. We also tested our method against other known algorithms like the Kmeans and Spectral Clustering algorithms using clustering evaluation metrics. The results demonstrate the superiority of the proposed method over them. Moreover, the proposed microbial sequence data pipeline can be utilized for different types of microbiota, such as oral, gut, and skin, demonstrating its reusability and robustness.
Ključne besede: machine learning, NLP, hierarchical clustering, microbial data, microbiome, n-grame
Objavljeno v DKUM: 04.09.2024; Ogledov: 38; Prenosov: 3
Celotno besedilo (4,48 MB)

Ključne besede: voltage sag (dip), source localization, supervised and unsupervised learning, convolutional neural network, time-sample-based features
Objavljeno v DKUM: 23.08.2024; Ogledov: 65; Prenosov: 4
Celotno besedilo (15,94 MB)

Ključne besede: waste management, smart waste bin system, central post-sorting, sensor technology, deep learning, convolutional neural networks
Objavljeno v DKUM: 23.08.2024; Ogledov: 51; Prenosov: 1
Celotno besedilo (3,64 MB)

Opis: This paper examines how values and professional socialization in business schools impact the formulation of students’ contextualized view of social responsibility. We propose the empirical concept of a mental gap between the existing and the wished-for level of a business school’s corporate social responsibility and estimate it empirically by using a sample of business school students from Central and South East Europe. Results show that students wish their business schools to reduce their current orientation toward economic outcomes and focus on environmental and social responsibilities. We interpret those empirical results in terms of the students’ wish to balance achieving economic prosperity and enjoyment of life with the prosocial outcomes of their education. New student generations’ perception of corporate social responsibility is not shaped by the professional socialization patterns but rather by the own perceptions, which can be influenced by experiential approaches to academic teaching and learning. Based on these empirical results, implications for academic practice and future research are explored.
Ključne besede: students, education, social responsibility, economy, culture, human learning, psychological attitudes
Objavljeno v DKUM: 06.08.2024; Ogledov: 91; Prenosov: 5
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