The contemporary development of education is marked by the rapid expansion of online educational applications and platforms. Consequently, it is becoming clear that teachers must consider them and in ...light of their changing role, know what they can offer in their field to be able to recommend them to students to get additional knowledge or even to integrate them into their teaching. As students in the field of computer science and informatics are particularly accustomed to considering online knowledge resources, we decided to investigate their perception of educational applications and platforms. Based on the analysis of the results of their testing of educational platforms and applications, we found that for them the most important is content, followed by personal preferences, reason, user experience, price, etc., and only in the last place is a certificate. The most frequent word in their research reports proved to be knowledge, which we included under the code reason, followed by research, content, time, free, variety, and quality. It also turned out that students’ experience of testing educational applications and platforms is predominantly positive and has even improved over the course of the last three years, which we attribute to the effects of the Covid-19 coronavirus epidemic. The comparison of pre-pandemic and post-pandemic data also revealed that positive sentiment came to the front, while students now prioritize user experience, reason, and quality over the content and personal preferences compared to the pre-pandemic period, while they are still aware of the need for exploration.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The paper presents a study of digital transformation at the global level, comparing the evolution of ICT spending and economic indicators, i.e., GDP per capita, employment, and labor productivity. ...The study aims to compare the two most recent global crises, the 2008-2009 economic crisis and the COVID-19 (2020-) pandemic crisis, while also predicting developments after the pandemic recession. The research results show that during the pandemic crisis, ICT spending declined more severely than during the previous economic downturn, although new ICTs showed more visible resilience to crisis impacts. The pandemic crisis also changed shares of traditional and new ICTs and led to four different deviations from classical diffusion, and as a result, new ICTs are expected to increase their share over all other main ICT segments by 2027.
This paper presents the research of the new technologies' impact on information and communication technology (ICT) spending and economy. We found that new technologies drive demand for ICT on ...general, while individual ICT segments' shares changed significantly due to development of the field and due to different stages of maturity process (diffusion) of individual ICT segments. New technologies also have impact on the longer-lasting growth of spending for traditional ICTs, as new technologies cannot be used and exploited without being connected to (and supported by) traditional ICTs. Moreover, considering traditional and total ICT spending, productivity of workers follows the same trend and consequently ICT spending has immediate impact on labor productivity, but this is not detected in relation to new ICTs' spending. Gross domestic product growth also stimulates only traditional and total ICT spending, having strong positive impact on increased ICT spending in the same year.
: The transformation to Industry 4.0 increases the number of robots installed within industries, which brings great shifts in industrial ecosystems. For this reason, our research goal was to analyze ...the key performance indicators to investigate the economic and social sustainability of the changes in production.
: The combination of official (World Bank, U.S. Bureau of Labor Statistics) and publicly available (Federal Reserve Economic Data, Industrial Federation of Robotics) data was used for statistical data processing, including comparison, correlation, cross-correlation and vector autoregression analysis, to present the past developments and also to predict future trends within the U.S. manufacturing sector.
In contrast to robust industry robotization observed in the 2008–2018 period, the share of manufacturing output and employment declined. Nonetheless, the vector autoregression model forecast shows, that the U.S. manufacturing sector has arrived at a turning point, after which robotization can increase employment and labor productivity of workers, while also stimulating further growth of their education levels.
The transition to Industry 4.0 has a major impact on increasing demands for new knowledge and skills for increased productivity. Accordingly, forecasted growths of analyzed manufacturing indicators suggest that negative impacts of robotization in the recent past were only temporary, due to the entrance to the Industry 4.0 era. Nonetheless, additional policies to support sustainable industry development are required.
Scientific network analysis takes at input large amounts of bibliographical data that are often incomplete. This leads to the introduction of different measurement errors in the scientific networks, ...which, in turn, influence the results of scientific networks analyses. Different authors have been studying the effects of measurement error on the results of network analysis, but these studies mostly rely on data gathered by survey questionnaires or on the study of incomplete data that are shown as random processes and emerge in unweighted undirected networks. This article aims at overcoming the limitations of these studies in three directions. First, we introduce measurement errors to network data following three most frequently present and well-known problems often present in bibliographic data: multiple authorship, homographs, and synonyms. Second, we apply missing data mechanisms to the identified incomplete data sources in order to link the latter with the probability of their occurrence. Third, we apply the incomplete data sources to different types of scientific networks and study the effects of measurement error in both, the weighted directed (i.e., citation) network and the weighted undirected (i.e., co-authorship) network. The results show that the most destructive incomplete data source is the problem of synonyms; it influences the accuracy and the robustness of the network structural measures the most. On the other hand, the multiple-authorship problem does not influence the results of network analysis at all.
In 2004, the European Commission implemented the Decision No 1608/2003/EC of the European Parliament and of the Council concerning the production and development of Community statistics on ...innovation. This triggered the awareness of the role of innovation and R&D on national and European level and thus the opportunity to step towards in-depth monitoring innovation performance through various indicators. The paper aims to investigate the trends in the selected innovation indicators (i.e., public funding, expenditures and innovation activities, types of innovation and products introduced, hampered innovation activities) to outline the development direction on the enterprise level using the Community innovation survey data for the 2002–2016 period. Using the basic time series analysis, the paper evaluates the progress according to the European Strategy on research and innovation. Furthermore, using the autocorrelation and autoregression methods, the paper also outlines the future direction in innovation performance on European level.
This is a long-term follow-up clinical study of adolescents and adults, survivors of childhood cancer. We evaluate and analyze the late somatic sequelae of childhood cancer treatment. Many such ...studies are susceptible to a strong selection bias, i.e., they employ a limited non-systematic sample of patients, based on a clinical hospital that provided the cancer treatment or performed the follow-up. To address the issue of selection bias, we perform here an analysis of late sequelae on a systematic database of the entire population of the children treated for cancer in Slovenia. Due to the specifics of cancer treatment procedures in Slovenia, they have all been treated and followed-up in the same clinic.
The data are based on the centralized registry of cancer patients in Slovenia and present a controlled and homogeneous collection. Late sequelae are evaluated following a modified CTCAE, i.e., the National Cancer Institute's Common Terminology Criteria for Adverse Events version 3.0. We use survival analysis method to estimate the incidence of and risk for late sequelae, where the time variable is measured in years from the diagnosis date, while we follow the event of incidence of late sequelae scored other than none. Survival analysis is performed using Kaplan Meier estimator and Cox regression model.
The incidence of mild, moderate, or severe late sequelae of childhood cancer treatment significantly decreased from 75% in the group of patients diagnosed before 1975 to 55% for those diagnosed after 1995. The Cox regression analysis of the risk factors for the incidence of late sequelae identifies three significant factors: treatment modalities, age at diagnosis, and primary diagnosis.
The change of treatment modalities in terms of replacement of surgery and radiotherapy with chemotherapy is the main reason for the decrease of the incidence and the risk for late sequelae of childhood cancer treatment; treatment modalities including surgery significantly increase the risk ratio of late sequelae, while those based on chemotherapy only significantly decreases the risk. Risk of late sequelae increases with the diagnosis age: younger children are more susceptible to late effects of treatment. Finally, primary diagnosis significantly influences the risk for late sequelae, but mostly due to the dependency of the treatment modality on the primary diagnosis.
The paper applies social network analysis techniques to the task of analysis the dynamics and structure of the e-government research community. From the bibliographic data about papers published in ...the proceedings of this conference (International Conference on e-Government), we build a co-authorship network representing collaboration patterns among community members in the period from 2005 to 2009. The co-authorship network analysis helps us identify the most productive and central authors in EGR community, as well as delineate the community structures through finding its sub-groups and core parts. In this way, several sub-communities are revealed in sense of the thematic topics, affiliations, and geographical origins of authors.
About fifteen years of e-government research (EGR) lead to a research field that is looking forward to define an identity as a proper and autonomous scientific discipline. This paper proposes the use ...of social network analysis as a methodology for building a map of EGR and consequently contributing to the process of establishing EGR identity. The paper analyzes the network of citation between authors induced by papers published in the four proceedings of this conference (International Conference on e-Government) in the period from 2005 to 2008. The analysis helps us identify the authors that had most influence on the EGR field development and relate them to the thematic topics that prevailed the conference papers in the last four years.
In 2004, the European Commission implemented the Decision No 1608/2003/EC of the European Parliament and of the Council concerning the production and development of Community statistics on ...innovation. This triggered the awareness of the role of innovation and R&D on national and European level and thus the opportunity to step towards in-depth monitoring innovation performance through various indicators. The paper aims to investigate the trends in the selected innovation indicators (i.e., public funding, expenditures and innovation activities, types of innovation and products introduced, hampered innovation activities) to outline the development direction on the enterprise level using the Community innovation survey data for the 2002–2016 period. Using the basic time series analysis, the paper evaluates the progress according to the European Strategy on research and innovation. Furthermore, using the autocorrelation and autoregression methods, the paper also outlines the future direction in innovation performance on European level.
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