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

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

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

Factores de la gestión del desempeño y las estrategias de marketing en las pequeñas empresas

Objetivo: Analizar la asociación entre los factores de la gestión del desempeño y las estrategias de marketing en las pequeñas empresas para comprender cómo estas dos variables se asocian dentro del contexto empresarial. Método: Se planteó un estudio cuantitativo, correlacional de campo, en el cual se aplicó una encuesta tipo likert de 61 reactivos en cuatro dimensiones: dos referidas a la gestión del desempeño y las otras dos referidas a estrategias de marketing. Este instrumento se aplicó a una muestra de 94 pequeñas empresas de la ciudad de Barranquilla (Colombia). Resultados: Los hallazgos permiten evidenciar que de las pequeñas empresas encuestadas hay una proporción importante que tienen un desarrollo adecuado de estrategias de marketing y de gestión del desempeño. Ademas, se evidencia un nivel elevado de asociación positiva entre ambas variables. Estos resultados ciertamente demuestran y se contrastan con otros contextos donde las estrategias de marketing se convierten en variables sumamente asociadas (en diversos estudios moderadores) con la gestión del desempeño. Conclusiones: El panorama encontrado dentro de las pequeñas empresas de la ciudad de Barranquilla incluidas en el estudio muestra un nivel representativo de la gestión del desempeño y medio de las estrategias de marketing; realidad debe ser superada para el alcance de una verdadera competitividad dentro del mercado actual al evidenciarse que al mejorar dichas estrategias se lograría una mejor gestión de desempeño

I Encuentro de la Red de Investigación en Gestión y Desarrollo Organizacional

Memories of the event "I Encounter of the Research Network in Organizational Management and Development"Memorias del evento "I Encuentro de la Red de Investigación en Gestión y Desarrollo Organizacional

Retracción: patrones de consumo eléctrico a través del aprendizaje automático

This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation. This work was originally published in Spanish (1) and has been translated and published without permission or acknowledgement to the original authors. IOP Publishing Limited has discovered other papers within this volume that have been subjected to the same treatment. This is scientific misconduct. Misconduct investigations are ongoing at the author’s institutions. IOP Publishing Limited will update this notice if required once those investigations have concluded. IOP Publishing Limited request any citations to this article be redirected to the original work (1). Anyone with any information regarding these papers is requested to contact [email protected] artículo, y otros dentro de este volumen, ha sido retirado por IOP Publishing tras una clara evidencia de plagio y manipulación de citas. Este trabajo fue publicado originalmente en español (1) y ha sido traducido y publicado sin permiso o reconocimiento a los autores originales. IOP Publishing Limited ha descubierto otros artículos dentro de este volumen que han sido sometidos al mismo tratamiento. Esta es una mala conducta científica. Las investigaciones sobre faltas de conducta están en curso en las instituciones del autor. IOP Publishing Limited actualizará este aviso si es necesario una vez que esas investigaciones hayan concluido. IOP Publishing Limited solicita que las citas de este artículo se redirijan al trabajo original (1). Cualquier persona que tenga información sobre estos documentos debe comunicarse con [email protected]

Herramientas gerenciales para la sostenibilidad de las organizaciones de la Costa Caribe Colombiana

Barranquilla has a thriving business network of which Micro, Small and Medium Enterprises stand out, known by their acronym as Mipymes, many of them dedicated to providing tourism services, in which, despite governmental, organizational and academic efforts, it is evident an exhaustion in terms of managerial skills of business leaders, a situation that affects the effective use of tourist potentialities that characterize it.Barranquilla cuenta con un pujante entramado empresarial del cual destacan las Micro, Pequeñas y Medianas Empresas, conocidas por sus siglas como Mipymes, muchas de ellas dedicadas a proporcionar servicios turísticos, en las cuales, pese a los esfuerzos gubernamentales, organizacionales y académicos, se evidencia un agotamiento en cuanto a las habilidades gerenciales de los líderes empresariales, situación ésta que incide en el aprovechamiento efectivo de las potencialidades turísticas que le caracterizan