scholarly journals Digital Competence Analysis of University Students Using Latent Classes

2021 ◽  
Vol 11 (8) ◽  
pp. 385
Author(s):  
Carmen Gloria Burgos-Videla ◽  
Wilson Andrés Castillo Rojas ◽  
Eloy López Meneses ◽  
Javiera Martínez
Keyword(s):  
University Students ◽  
Latent Class ◽  
Categorical Variables ◽  
The Internet ◽  
Latent Classes ◽  
Digital Competence ◽  
Data Set ◽  
National University ◽  
Spanish Universities ◽  
Age Range

The objective of this study is to characterize Latent Classes emerging from the analysis of the level of digital competences, use and consumption of applications and/or services through the Internet. For this purpose, the results of the survey Basic Digital Competences (Competencias Básicas Digitales-COBADI®) applied to university students, with more than 60 categorical variables, were considered. A total of 4762 undergraduate and graduate students from five Spanish universities participated in this survey: Complutense University of Madrid (UCM), Pablo de Olavide University (UPO), Almeria University (UAL), National University of Distance Education (UNED) and Rey Juan Carlos University (URJC). The application of the questionnaire was done through the Internet, from the Institute for Research in Social Sciences and Education of University of Atacama—Chile. The methodology used is mixed, because the questions of the questionnaire provide qualitative information that can be interpreted and elaborated from the results. It is also quantitative because basic statistical techniques are used for the exploratory analysis of the data, and later Latent Class Analysis (LCA), to complement the description of the data set and the variables considered in the study, thus allowing us to group the classes of variables that do not appear explicitly in the set of observed variables, but which nevertheless affect them. The results of the study show that regardless of the gender and age range of the participants, there are four clearly differentiated groups or classes in the use and consumption of ICTs in different ways for their activities, both personal and academic, which allows for identifying different developments of digital competences. This study allows establishing a baseline in order to be able to elaborate later, in the development of the digital competences currently needed, which should be developed by university students.

scholarly journals Building Latent Class Trees, With an Application to a Study of Social Capital

Methodology ◽  
2017 ◽  
Vol 13 (Supplement 1) ◽  
pp. 13-22 ◽  
Author(s):  
Mattis van den Bergh ◽  
Verena D. Schmittmann ◽  
Jeroen K. Vermunt
Keyword(s):  
Social Capital ◽  
Latent Class ◽  
Recursive Partitioning ◽  
Hierarchical Cluster ◽  
Categorical Variables ◽  
Latent Classes ◽  
Data Set ◽  
Clear Insight ◽  
Partitioning Procedure ◽  
Number Of Classes

Abstract. Researchers use latent class (LC) analysis to derive meaningful clusters from sets of categorical variables. However, especially when the number of classes required to obtain a good fit is large, interpretation of the latent classes may not be straightforward. To overcome this problem, we propose an alternative way of performing LC analysis, Latent Class Tree (LCT) modeling. For this purpose, a recursive partitioning procedure similar to divisive hierarchical cluster analysis is used: classes are split until a certain criterion indicates that the fit does not improve. The advantage of the LCT approach compared to the standard LC approach is that it gives a clear insight into how the latent classes are formed and how solutions with different numbers of classes relate. We also propose measures to evaluate the relative importance of the splits. The practical use of the approach is illustrated by the analysis of a data set on social capital.

Heavy Internet use and its associations with health risk and health-promoting behaviours among Thai university students

2014 ◽  
Vol 26 (2) ◽  
pp. 187-194 ◽  
Author(s):  
Karl Peltzer ◽  
Supa Pengpid ◽  
Tawatchai Apidechkul
Keyword(s):  
University Students ◽  
Health Risk ◽  
Health Outcomes ◽  
Internet Use ◽  
Risk Behaviour ◽  
The Internet ◽  
Post Traumatic Stress ◽  
Preventive Actions ◽  
Health Promoting ◽  
Age Range

Abstract The Internet provides significant benefits for learning about the world, but excessive Internet use can lead to negative outcomes. The aim of this study was to determine the associations between heavy Internet use and health-promoting behaviour, health risk behaviour and health outcomes among university students. The sample included 860 undergraduate university students chosen at random from Mae Fah Luang University in Thailand. Of the participants, 27.3% were male and and 72.7% were female in the age range of 18–25 years (M age=20.1 years, SD=1.3). Overall, students spent on average 5.3 h (SD=2.6) per day on the internet, and 35.3% engaged in heavy internet use (6 or more hours per day). In multivariate logistic regression adjusting for sociodemographics, lack of dental check-ups, three health risk behaviours (sedentary lifestyle, illicit drug use and gambling) and three health outcomes [being underweight, overweight or obese and having screened positive for post-traumatic stress disorder (PTSD)] were found to be associated with heavy Internet use. The results from this study may support the importance of developing early protective and preventive actions against problematic Internet use to promote university student health.

scholarly journals Didactic conditions for the formation of digital competence of students of pedagogical universities

2020 ◽  
Vol 54 (2) ◽  
pp. 165-178
Author(s):  
Mykhailo V. Moiseienko ◽  
Natalia V. Moiseienko ◽  
Arnold E. Kiv
Keyword(s):  
University Students ◽  
Educational Environment ◽  
The Internet ◽  
Digital Information ◽  
Digital Competence ◽  
Educational Trajectories ◽  
Scientific Analysis ◽  
The Creation ◽  
Operational Activity

The article is devoted to the formation of digital competence of pedagogical university students. The scientific analysis of the essence of the “digital competence” concept is carried out, its components are defined. In the course of the research the components of digital competence of pedagogical university students are determined, namely: motivational-value (target), cognitive, operational-activity and personal-reflexive components. The article defines and substantiates didactic conditions of digital formation competences of students of pedagogical universities: actualization of motivational value training of students of pedagogical universities; organization of interaction between students and teachers of pedagogical universities on the Internet through the creation of digital information educational environment; creation of individual educational trajectories of students.

scholarly journals Latent class analysis for identification of occupational accident casualty profiles in the selected Polish manufacturing sector

2021 ◽  
Vol 16 (4) ◽  
pp. 485-499
Author(s):  
M. Nowakowska ◽  
M. Pajecki
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Manufacturing Sector ◽  
Statistical Modelling ◽  
Occupational Accidents ◽  
Latent Classes ◽  
Occupational Accident ◽  
Class Analysis ◽  
Data Set ◽  
Wood Processing

The objective of the analysis is identifying profiles of occupational accident casualties as regards production companies to provide the necessary knowledge to facilitate the preparation and management of a safe work environment. Qualitative data characterizing employees injured in accidents registered in Polish wood processing plants over a period of 10 years were the subject of the research. The latent class analysis (LCA) method was employed in the investigation. This statistical modelling technique, based on the values of selected indicators (observed variables) divides the data set into separate groups, called latent classes, which enable the definition of patterns. A procedure which supports the decision as regards the number of classes was presented. The procedure considers the quality of the LCA model and the distinguishability of the classes. Moreover, a method of assessing the importance of indicators in the patterns description was proposed. Seven latent classes were obtained and illustrated by the heat map, which enabled the profiles identification. They were labelled as follows: very serious, serious, moderate, minor (three latent classes), slight. Some recommendations were made regarding the circumstances of occupational accidents with the most severe consequences for the casualties.

scholarly journals Multilevel Latent Transition Mixture Modeling: Variance Decomposition and Application

2021 ◽  
Vol 6 ◽  
Author(s):  
Grant B. Morgan ◽  
R. Noah Padgett
Keyword(s):  
Latent Class ◽  
Rating Scale ◽  
Unobserved Heterogeneity ◽  
Heterogeneous Data ◽  
Variance Decomposition ◽  
Latent Classes ◽  
Data Set ◽  
Transition Analysis ◽  
Latent Transition ◽  
The Hierarchical Structure

Person-centered methodologies generally refer to those that take unobserved heterogeneity of populations into account. The use of person-centered methodologies has proliferated, which is likely due to a number of factors, such as methodological advances coupled with increased personal computing power and ease of software use. Using latent class analysis and its extension for longitudinal data, [latent transition analysis (LTA)], multiple underlying, homogeneous subgroups can be inferred from a set of categorical and/or continuous observed variables within a large heterogeneous data set. Such analyses allow researchers to statistically treat members of different subgroups separately, which may provide researchers with more power to detect effects of interest and closer alignment between statistical modeling and one’s guiding theory. For many educational and psychological settings, the hierarchical structure of organizational data must also be taken into account; for example, students (i.e., level-1 units) are nested within teacher/schools (i.e., level-2 units). Finally, multilevel LTA can be used to estimate the number of latent classes in each structured unit and the potential movement, or transitions, participants make between latent classes across time. The transitions/stability between latent classes across time can be treated as the outcome in and of itself, or the transitions/stability can be used as a correlate or predictor of some other, distal outcome. The purpose of the paper is to discuss multilevel LTA, provide considerations for its use, and demonstrate variance decomposition, which requires numerous steps. The variance decomposition steps are presented didactically along with a worked example based on analysis from the Social Rating Scale of ECLS-K.

scholarly journals Comparison of the Prevalence of addiction internet in Brazilian University Students: Online cognition scale versus internet addiction test

2019 ◽  
pp. 09-19 ◽  
Author(s):  
Pedro Anderson Ferreira Quirino ◽  
Rubiane Maria Costa Pininga ◽  
Mateus Mourato Barros ◽  
Polyana Felipe Ferreira da Costa ◽  
Priscila Maria de Barros Rodrigues ◽  
...  
Keyword(s):  
University Students ◽  
Internet Addiction ◽  
Statistical Tests ◽  
Categorical Variables ◽  
The Internet ◽  
Growth Trend ◽  
Significant Linear Trend ◽  
Health Area ◽  
Keywords Internet ◽  
Cognition Scale

Introduction: Some internet users lose the ability to control the duration and / or frequency of their use, leading to the phenomenon of internet addiction. In Brazil, there is no data about the prevalence of this phenomenon. Aim: To estimate and compare the prevalence of Internet addiction among university students in the health area. Method: A comparison of the prevalence was evaluated among undergraduate students from the health area of the University of Pernambuco, Brazil. Three instruments were applied: the Portuguese (Brazil) versions of the Internet Addiction Test, the Online Cognition Scale and a questionnaire characterizing socio-demographic and habits of use from Internet. The data were submitted to bivariate statistical tests, test of association for categorical variables and analysis of linear growth trend. Results: At the end of the study, 359 students participated in the study, 75.5% women with a mean age of 19.49 years (± 2.33 years). According to Internet Addiction Test, 44.28% of the sample had Internet Addiction, with a higher prevalence in males (51.1%). Concerning Online Cognition Scale, the prevalence of the disorder was 62.9%, higher in females (65.7%). This prevalence rates obtained were higher when compared to previous studies, which may be related to the variety of instruments. Conclusion: The prevalence of Internet Addiction in the sample studied varied according to the instrument used. There was also a significant linear trend between the weekly connection time and the severity of the addiction to the internet. Keywords: Internet; Addictive Behavior; Prevalence; Cross-Sectional Studies

scholarly journals University students’ digital competence in three areas of the DigCom 2.1 model: A comparative study at three European universities

2020 ◽  
pp. 69-88 ◽  
Author(s):  
Eloy López-Meneses ◽  
Fabrizio Manuel Sirignano ◽  
Esteban Vázquez-Cano ◽  
José Manuel Ramírez-Hurtado
Keyword(s):  
University Students ◽  
Digital Literacy ◽  
Intermediate Level ◽  
Digital Content ◽  
Quantitative Methodology ◽  
Digital Competence ◽  
Content Creation ◽  
European Universities ◽  
Student Profiles ◽  
Spanish Universities

This study analysed the digital competence of 1,073 students at one Italian and two Spanish universities using the COBADI 2.0 (Basic Digital Competences/Registered Trademark 2970648) questionnaire. A quantitative methodology was applied to university students’ use of, and competence in, three areas of DigCom 2.1: information and data literacy, communication and collaboration, and digital content creation. The results showed that these future graduates had an upper intermediate level of competence in information and digital literacy, and communication and collaboration, but a lower intermediate level in terms of digital content creation, particularly in the creation and dissemination of multimedia content using different tools. Two student profiles were identified for time spent online: those who dedicated a lot of their time to gaming or interacting on social media, and those who used most of their online time to searching for information and completing academic work.

scholarly journals Strategies for the communication and collaborative online work by university students

Comunicar ◽  
2018 ◽  
Vol 26 (54) ◽  
pp. 91-100 ◽  
Author(s):  
Isabel Gutiérrez-Porlán ◽  
Marimar Román-García ◽  
Maria-del-Mar Sánchez-Vera
Keyword(s):  
University Students ◽  
The Internet ◽  
Quantitative Methodology ◽  
Information And Communications Technologies ◽  
Redes Sociales ◽  
Communication Processes ◽  
Spanish Universities ◽  
Effective Development ◽  
La Red ◽  
The Impact

The impact that Information and Communications Technologies have in the way today’s young people communicate and interact is unquestionable. This impact also affects the educational field, which is required to respond to the needs of twenty first century students by training them in acquiring new skills and strategies to deal with a changing and uncertain future. In this study, which involved 2,054 university students from all Spanish Universities, it delved into the knowledge of networking strategies and tools used by these students for the effective development of communication processes and the implementation of strategies for collaboration and communication. It has been developed a nonexperimental quantitative methodology and the technique used for collecting information was a questionnaire. The results show that all of them use the Internet to communicate and they have a great use of basic tools to collaborate and interact, but they prefer social networks for being in contact with their peers and establishing relationships. It has been found that students do not have the idea of the Internet as a place to learn. This fact implies new challenges to be solved by Universities, to optimize the possibilities of the networks and institutional platforms as an environment to learn collaboratively. El impacto que las tecnologías de la comunicación tienen en la forma en la que los más jóvenes de hoy en día se comunican y relacionan es incuestionable. Dicho impacto afecta también al campo educativo, al que se le exige que dé respuesta a las necesidades de los estudiantes del siglo XXI, formándoles en la adquisición de habilidades y estrategias para afrontar un futuro cambiante y lleno de incertidumbre. En este estudio, en el que han participado 2.054 estudiantes universitarios de todas las universidades españolas, se profundiza en el conocimiento de las estrategias y herramientas en red empleadas por estos estudiantes para el desarrollo efectivo de los procesos comunicativos y colaborativos. Se ha realizado un diseño de investigación no experimental, de tipo exploratorio basado en el uso del cuestionario como instrumento de recogida de información. Los resultados muestran un mayor uso por parte del alumnado de herramientas básicas de Internet para el trabajo colaborativo mientras que para estar en contacto con sus compañeros y establecer relaciones prefieren las redes sociales. Se ha encontrado que no existe por parte de los estudiantes una concepción de la Red como espacio de aprendizaje, por lo que se plantean nuevos retos a resolver por parte de la institución universitaria de cara a que sus estudiantes optimicen las posibilidades de la Red como lugar en el que aprender colaborativamente.

scholarly journals Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregoire Preud’homme ◽  
Kevin Duarte ◽  
Kevin Dalleau ◽  
Claire Lacomblez ◽  
Emmanuel Bresso ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Latent Class ◽  
Latent Class Model ◽  
Real Life ◽  
Heterogeneous Data ◽  
Mixed Data ◽  
Categorical Variables ◽  
Clustering Methods ◽  
Model Based ◽  
Partitioning Around Medoids

AbstractThe choice of the most appropriate unsupervised machine-learning method for “heterogeneous” or “mixed” data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of “ready-to-use” tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.

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