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]

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

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

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

La inclusión financiera analizada desde una técnica de reducción de dimensiones

Objetivo: Analizar el nivel de los índices de inclusión financiera en los diferentes productos del sistema financiero como son los ahorros, el crédito y los seguros en el Departamento de Sucre, estudiando los factores que influyen en dicho nivel, mediante la técnica de reducción de dimensiones. Método: el estudio realizado fue de tipo exploratorio, descriptivo y transversal. Se realizaron 541 encuestas  en todo el Departamento de Sucre. Con la información obtenida se construyó una base de datos y se calcularon las variables principales del estudio. Resultados: los indicadores de inclusión seleccionados aunque se encuentran correlacionados, su variabilidad es explicada en gran parte por un solo factor. Discusiones: aún se encuentra gran exclusión de la población, especialmente en áreas rurales y en regiones con atraso económico como es el Departamento de Sucre. Conclusiones: con el presente estudio se analizaron los niveles de inclusión financiera de los diferentes productos del sistema financiero como son los ahorros, el crédito y los seguros en el Departamento de Sucre, estudiando los factores que influyen en dicho nivel, mediante la técnica de reducción de dimensiones: Análisis de Componentes Principales–ACP. Como resultado, se identificó que la variabilidad de los niveles de inclusión responde a un solo factor en común

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

UNA MIRADA AL LIDERAZGO INNOVADOR EN UNIVERSIDADES PÚBLICAS COLOMBIANAS.: A LOOK AT INNOVATIVE LEADERSHIP IN COLOMBIAN PUBLIC UNIVERSITIES

El artículo tiene como objetivo analizar el liderazgo innovador en las universidades públicas colombianas. De tipo documental, con diseño bibliográfico. Para la recolección de datos fue utilizada la ficha de trabajo y se realizó un análisis cualitativo. Como resultado de este análisis, se concluyó, que el liderazgo innovador constituye una herramienta para la optimización del funcionamiento de las universidades públicas colombianas, además de ser promotor de la investigación como fundamento e impulso del desarrollo, a través de la exaltación de la capacidad innovativa que caracteriza el talento humano de las Instituciones de Educación Superior. Por lo tanto, se requiere continuar estudiando detalladamente todos los aspectos relacionados con liderazgo e innovación, para alcanzar recomendables niveles en cuanto a competitividad y creatividad dentro del  circuito de crecimiento y progreso que hace funcionar concatenádamente la inversión, desarrollo e investigación

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