Using Big Data to determine potential dropouts in higher education

In higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of university success is the time to graduation (TTG), which is directly related to student dropout [1]. Global estimates put this dropout rate at 42% [2]. In the United States, this rate is around 30% and represents a loss of 9 billion dollars in the education of these students [3]. However, desertion not only affects the quality of education and the economy of a country, but also has effects on the development of society, since society demands the contributions derived from the population with higher education such as: innovation, knowledge production and scientific discovery [4]. Using basic statistical learning techniques, this paper presents a simple way to predict possible dropouts based on their demographic and academic characteristics

Data mining applied in school dropout prediction

In recent years, many studies have emerged about regarding the topic of school failure, showing a growing interest in determining the multiple factors that may influence it [1]. Most of the researches that attempt to solve this issue [2] are focused on determining the factors that most affect the performance of students (dropout and failure) at the different educational levels (basic, middle and higher education) through the use of the large amount of information that current computer equipment allows to store in databases. All these data constitute a real gold mine of valuable information about students. But, identifying and finding useful and hidden information in large databases is a difficult task [3]. A very promising solution to achieve this goal is the use of knowledge mining techniques or data mining in education, which has resulted in so-called Educational Data Mining (EDM) [4]. This new area of research is concerned with the development of methods for exploring data in education, as well as the use of these methods to better understand students and the contexts where they learn [5]

Electrical consumption patterns through machine learning

Electricity distribution companies have been incorporating new technologies that allow them to obtain complete information in real time about their customers´ consumption. Thus, a new concept called "Smart Metering" has been adopted, giving way to new types of meters that interact in an interconnected system. This will allow to make data analysis, accurate forecasts and detecting consumption patterns that will be relevant for the decision-making process. This research focuses on discovering common patterns among customers from data collected by smart meters

Impact of leadership on the development of organizational communication

Fundamental leadership in communication has been approached from science as a thematic focus of great relevance both academically and for the industry. In this way, the present study is developed in order to identify the bibliometric applications of the impact of leadership in the development of organizational communication. At the methodological level, a documentary research based on scientometric processes is presented, where the Scopus databases are consulted during the period from 1958 to 2022. The results allow us to show 512 results within the database, observing a significant growth of the scientific prediction, where the most relevant sources are Journal of Business Communication, Corporate Communications, Journal of Communication Management, International Journal of Business Communication and Business Communication Quarterly and in turn observing that 47% of the publications come from the United State

Retraction: using Big Data to determine potential dropouts in higher education

In higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of university success is the time to graduation (TTG), which is directly related to student dropout [1]. Global estimates put this dropout rate at 42% [2]. In the United States, this rate is around 30% and represents a loss of 9 billion dollars in the education of these students [3]. However, desertion not only affects the quality of education and the economy of a country, but also has effects on the development of society, since society demands the contributions derived from the population with higher education such as: innovation, knowledge production and scientific discovery [4]. Using basic statistical learning techniques, this paper presents a simple way to predict possible dropouts based on their demographic and academic characteristics

Digital literacy in Sucre schools

The present investigation explains the results of a descriptive bibliometric analysis on the field of study "Digital literacy in the schools of Sucre" aimed at identifying trends and methodology techniques used in this area of ​​research. The period of the study was delimited from the years 2013 to 2023. The exploration of the information was carried out in the largest database of abstracts and citations of the peer-reviewed literature such as Scopus, from which a file with 846 records was downloaded in csv format, made up of manuscript, books, chapters of books, conference papers and conference abstracts, among others; The analysis of the data and the creation of graphs, tables and maps were carried out with the R-Studio integrated development environment, specifically with the Biblioshiny application from the Bibliometric library and with the Excel software. The results of this analysis allowed us to obtain a holistic view of this area of ​​study and its evolution over time

El Mercado Integrado Latinoamericano – Mila – en tiempo de covid -19. Análisis enero – mayo 2020

This work aims to describe the behavior of the indices of the stock exchanges that make up the Latin American Integrated Market in the first five months of 2020 in an approximation to establish the impact of the pandemic generated by the COVID-19 virus that entailed to the implementation of isolation and quarantine measures in several countries that ended up affecting the markets, through a horizontal analysis the monthly percentage variation of infections was established for each country in the same way the relative change of the different stock market indices, it was appreciated how it impacted significantly the global spread in the behavior of the stock markets studied, evidencing the sharp drop specifically in the aftermath of the announcement by the World Health Organization that implied the confinement and therefore the paralysis of different economic activities, it is important stand out as a conjunctural factor reverberates in the regional and global economy.Este trabajo tiene como objetivo la descripción del comportamiento de los índices de las bolsas de valores que conforman el Mercado Integrado Latinoamericano en los primeros cinco meses del año 2020 en una aproximación por establecer el impacto de la pandemia generada por el virus COVID-19 que conllevó a la implementación de medidas de aislamiento y cuarentena en varios países que terminó afectando los mercados, mediante un análisis horizontal se estableció la variación porcentual mensual de los contagios para cada país de igual forma el cambio relativo de los diferentes índices bursátiles, se aprecia cómo impacta de manera significativa la propagación a nivel global en el comportamiento de los mercados accionarios estudiados, evidenciando la fuerte caída específicamente en las postrimerías del anuncio por la Organización Mundial de la Salud que implicaba el confinamiento y por ende la parálisis de diferentes actividades económicas, es importante destacar como un factor coyuntural repercute en la economía regional y mundial

Analysis of innovation and economic growth through the development of renewable energies

Sustainability has caused industries to generate new technologies based on renewable energy; resulting in innovation and development processes within the sector. From this panorama, this article is carried out in order to analyze the scientific production related to renewable or alternative energies as a source of innovation and economic development. At the methodological level, a bibliometric-based documentary study is proposed, which is carried out in the scopus database with a search equation that starts from the variables: "Economic development", "Renewable energies" and "Innovation". The results of the search carried out show a total of 674 documents in the time window of 1994-2023; and observing Environmental Science and Pollution Research, Renewable Energy, Journal of Environmental Management, Sustainability (Switzerland), Energies and Journal of Cleaner Production as main sources. It is concluded that scientific production has grown since 2015, possibly linked to subsequent programs to the sustainable development goals, and with an exponential peak since 2020. This is linked to the new achievements and findings of the energy industry in the field of sustainability

Temporary variables for predicting electricity consumption through data mining

In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results