A Business Intelligence Framework for Analyzing Educational Data

Currently, universities are being forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on students’ learning. These factors have forced universities to look for a solution that allows them to extract data from different information systems and convert them into the knowledge necessary to make decisions that improve learning outcomes. The information systems administered by the universities store a large volume of data on the socioeconomic and academic variables of the students. In the university field, these data are generally not used to generate knowledge about their students, unlike in the business field, where the data are intensively analyzed in business intelligence to gain a competitive advantage. These success stories in the business field can be replicated by universities through an analysis of educational data. This document presents a method that combines models and techniques of data mining within an architecture of business intelligence to make decisions about variables that can influence the development of learning. In order to test the proposed method, a case study is presented, in which students are identified and classified according to the data they generate in the different information systems of a university

Analysis of Educational Data in the Current State of University Learning for the Transition to a Hybrid Education Model

Currently, the 2019 Coronavirus Disease pandemic has caused serious damage to health throughout the world. Its contagious capacity has forced the governments of the world to decree isolation and quarantine to try to control the pandemic. The consequences that it leaves in all sectors of society have been disastrous. However, technological advances have allowed people to continue their different activities to some extent while maintaining isolation. Universities have great penetration in the use of technology, but they have also been severely affected. To give continuity to education, universities have been forced to move to an educational model based on synchronous encounters, but they have maintained the methodology of a face-to-face educational model, what has caused several problems in the learning of students. This work proposes the transition to a hybrid educational model, provided that this transition is supported by data analysis to identify the new needs of students. The knowledge obtained is contrasted with the performance presented by the students in the face-to-face modality and the necessary parameters for the transition to this modality are clearly established. In addition, the guidelines and methodology of online education are considered in order to take advantage of the best of both modalities and guarantee learning

Application of a Smart City Model to a Traditional University Campus with a Big Data Architecture: A Sustainable Smart Campus

Currently, the integration of technologies such as the Internet of Things and big data seeks to cover the needs of an increasingly demanding society that consumes more resources. The massification of these technologies fosters the transformation of cities into smart cities. Smart cities improve the comfort of people in areas such as security, mobility, energy consumption and so forth. However, this transformation requires a high investment in both socioeconomic and technical resources. To make the most of the resources, it is important to make prototypes capable of simulating urban environments and for the results to set the standard for implementation in real environments. The search for an environment that represents the socioeconomic organization of a city led us to consider universities as a perfect environment for small-scale testing. The proposal integrates these technologies in a traditional university campus, mainly through the acquisition of data through the Internet of Things, the centralization of data in proprietary infrastructure and the use of big data for the management and analysis of data. The mechanisms of distributed and multilevel analysis proposed here could be a powerful starting point to find a reliable and efficient solution for the implementation of an intelligent environment based on sustainability

Application of a Big Data Framework for Data Monitoring on a Smart Campus

At present, university campuses integrate technologies such as the internet of things, cloud computing, and big data, among others, which provide support to the campus to improve their resource management processes and learning models. Integrating these technologies into a centralized environment allows for the creation of a controlled environment and, subsequently, an intelligent environment. These environments are ideal for generating new management methods that can solve problems of global interest, such as resource consumption. The integration of new technologies also allows for the focusing of its efforts on improving the quality of life of its inhabitants. However, the comfort and benefits of technology must be developed in a sustainable environment where there is harmony between people and nature. For this, it is necessary to improve the energy consumption of the smart campus, which is possible by constantly monitoring and analyzing the data to detect any anomaly in the system. This work integrates a big data framework capable of analyzing the data, regardless of its format, providing effective and efficient responses to each process. The method developed is generic, which allows for its application to be adequate in addressing the needs of any smart campus

Inducible deletion of CD28 prior to secondary nippostrongylus brasiliensis infection impairs worm expulsion and recall of protective memory CD4 (+) T cell responses

IL-13 driven Th2 immunity is indispensable for host protection against infection with the gastrointestinal nematode Nippostronglus brasiliensis. Disruption of CD28 mediated costimulation impairs development of adequate Th2 immunity, showing an importance for CD28 during the initiation of an immune response against this pathogen. In this study, we used global CD28−/− mice and a recently established mouse model that allows for inducible deletion of the cd28 gene by oral administration of tamoxifen (CD28−/loxCre+/−+TM) to resolve the controversy surrounding the requirement of CD28 costimulation for recall of protective memory responses against pathogenic infections. Following primary infection with N. brasiliensis, CD28−/− mice had delayed expulsion of adult worms in the small intestine compared to wild-type C57BL/6 mice that cleared the infection by day 9 post-infection. Delayed expulsion was associated with reduced production of IL-13 and reduced serum levels of antigen specific IgG1 and total IgE. Interestingly, abrogation of CD28 costimulation in CD28−/loxCre+/− mice by oral administration of tamoxifen prior to secondary infection with N. brasiliensis resulted in impaired worm expulsion, similarly to infected CD28−/− mice. This was associated with reduced production of the Th2 cytokines IL-13 and IL-4, diminished serum titres of antigen specific IgG1 and total IgE and a reduced CXCR5+ TFH cell population. Furthermore, total number of CD4+ T cells and B220+ B cells secreting Th1 and Th2 cytokines were significantly reduced in CD28−/− mice and tamoxifen treated CD28−/loxCre+/− mice compared to C57BL/6 mice. Importantly, interfering with CD28 costimulatory signalling before re-infection impaired the recruitment and/or expansion of central and effector memory CD4+ T cells and follicular B cells to the draining lymph node of tamoxifen treated CD28−/loxCre+/− mice. Therefore, it can be concluded that CD28 costimulation is essential for conferring host protection during secondary N. brasiliensis infection

Arquitectura para la gestión de datos en un campus inteligente

En la actualidad, las tecnologías de la información y comunicación (TIC) se han convertido en herramientas invaluables en el desarrollo de la sociedad. Estas tecnologías están presentes en las empresas, la medicina, la educación, etc. Prácticamente la sociedad ha llegado a un punto en que el principal asistente en cada una de las actividades son las TIC. Esto ha permitido la globalización de todas las áreas donde estas son aplicadas. Las ventajas del uso de las TIC han permitido mejorar y automatizar los procesos en todo nivel, sea en una empresa, una ciudad, una universidad, un hogar, etc. Para hacerlo, las tecnologías se ajustan a las necesidades del usuario y son capaces de interactuar con él, incluso, están en capacidad de interactuar entre sí sin la intervención de un humano. ¿Pero cómo lo hacen y para qué? Las nuevas tecnologías ahora integran varios sistemas y plataformas que están en la capacidad de adquirir información de las personas y sus entornos, analizar esta información y tomar decisiones con base en los resultados del análisis. Estas decisiones se ven plasmadas, por ejemplo, en la mejora de las ventas de una empresa o en la mejora de los procesos de manufactura. Como estos, existen muchos ejemplos que son resultado de numerosas investigaciones que tienen como objetivo mejorar la calidad de vida de las personas en ecosistemas sostenibles. Uno de estos ecosistemas que ha adquirido gran importancia recientemente son las ciudades inteligentes. El valor de las ciudades inteligentes se basa en satisfacer las necesidades de los miembros de su comunidad en armonía con la naturaleza. Esto involucra una mejor administración de los servicios como el transporte, la generación y consumo energético, la seguridad, la gobernabilidad, etc. Sin embargo, transformar una ciudad común en una ciudad inteligente requiere de muchos esfuerzos y recursos, tanto económicos como humanos. Ante este problema, es necesario contar con escenarios similares que incluso sirvan como un banco de pruebas para la implementación de tecnologías y que su implementación en entornos más grandes sea efectiva y con los recursos adecuados. Las universidades, como generadoras de conocimiento, son las llamadas a realizar los procesos de implementación, pruebas y generación de nuevas tecnologías. Su ambiente, administración y organigrama estructural, sumada a extensas áreas que conforman sus campus, permite compararlas con pequeñas ciudades. Esto permite establecer una línea base donde se apliquen todos los componentes necesarios para transformarlos en campus inteligentes (smart campus). Los campus inteligentes buscan mejorar la calidad de la educación a través de la convergencia de nuevas tecnologías. Es importante establecer que un campus universitario pone a disposición de los estudiantes y los miembros de la comunidad todas las condiciones para garantizar la calidad de la educación. Los campus inteligentes, al igual que las ciudades inteligentes, basan sus entornos en satisfacer las necesidades de sus miembros; para esto, es necesario crear procesos o sistemas que adquieran información sobre ellos. Es por esto, que el Internet de las cosas (IoT, acrónimo en inglés de Internet of Things) se convierte en uno de los componentes necesarios para la transformación de un campus tradicional. La información recolectada necesariamente debe convertirse en conocimiento para ejecutar acciones con base en este conocimiento. Estas acciones responden a una toma de decisiones efectiva y eficiente que satisfaga las necesidades de las personas. Para realizar el análisis de datos es necesario contar con una arquitectura que gestione un gran volumen de datos independientemente de su formato. La tecnología que ofrece estas capacidades es el big data, su integración al campus inteligente genera una estructura lo suficientemente robusta para soportar toda la carga del IoT y el análisis de datos requerido por los usuarios. Estas tecnologías, en compañía de la computación en la nube (cloud computing), permiten a los miembros del campus inteligente desarrollar sus actividades en total armonía con los recursos y la naturaleza. Este trabajo de investigación está enfocado en proponer una arquitectura para la gestión de datos en un campus universitario. Este enfoque trata todas las variables que influyen en la educación universitaria. Descubrir estas variables, tratarlas y establecer sus relaciones entre sí, requiere de la integración de las tecnologías mencionadas incluso con modelos de inteligencia artificial que permitan tomar acciones sobre los resultados del análisis de datos

Proposal for an Implementation Guide for a Computer Security Incident Response Team on a University Campus

Currently, society is going through a health event with devastating results. In their desire to control the 2019 coronavirus disease, large organizations have turned over the execution of their activities to the use of information technology. These tools, adapted to the use of the Internet, have been presented as an effective solution to the measures implemented by the majority of nations where quarantines are generalized. However, the solution given by information technologies has several disadvantages that must be solved. The most important in this regard is with the serious security incidents that exist, where many organizations have been compromised and their data has been exposed. As a solution, this work proposes the design of a guide that allows for the implementation of a computer incident response team on a university campus. Universities are optimal environments for the generation of new technologies; they also serve as the ideal test bed for the generation of security policies and new treatments for incidents in an organization. In addition, with the implementation of the computer incident response team in a university, it is proposed to be part of a response group to any security incident at the national level

Academic Activities Recommendation System for Sustainable Education in the Age of COVID-19

Currently, universities are going through a critical moment due to the coronavirus disease in 2019. To prevent its spread, countries have declared quarantines and isolation in all sectors of society. This has caused many problems in the learning of students, since, when moving from a face-to-face educational model to a remote model, several academic factors such as psychological, financial, and methodological have been overlooked. To exactly identify the variables and causes that affect learning, in this work a data analysis model using a Hadoop framework is proposed. By processing the data, it is possible to identify and classify students to determine the problems they present in different learning activities. The results are used by an artificial intelligence system that takes student information and converts it into knowledge, evaluates the academic performance problems they present, and determines what type of activity aligns with the students. The artificial intelligence system processes the information and recommends activities that focus on each student’s abilities and needs. The integration of these systems to universities creates an adaptive educational model that responds to the new challenges of society

Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment

Currently, data are a very valuable resource for organizations. Through analysis, it is possible to profile people or obtain knowledge about an event or environment and make decisions that help improve their quality of life. This concept takes on greater value in the current pandemic, due to coronavirus disease 2019 (COVID-19), that affects society. This emergency has changed the way people live. As a result, the majority of activities are carried out using the internet, virtually or online. Education is not far behind and has seen the web as the most successful option to continue with its activities. The use of any computer application generates a large volume of data that can be analyzed by a big data architecture in order to obtain knowledge from its students and use it to improve educational processes. The big data, when included as a tool for adaptive learning, allow the analysis of a large volume of data to offer an educational model based on personalized education. In this work, the analysis of educational data through a big data architecture is proposed to generate learning based on meeting the needs of students

A Business Intelligence Framework for Analyzing Educational Data

Currently, universities are being forced to change the paradigms of education, where knowledge is mainly based on the experience of the teacher. This change includes the development of quality education focused on students’ learning. These factors have forced universities to look for a solution that allows them to extract data from different information systems and convert them into the knowledge necessary to make decisions that improve learning outcomes. The information systems administered by the universities store a large volume of data on the socioeconomic and academic variables of the students. In the university field, these data are generally not used to generate knowledge about their students, unlike in the business field, where the data are intensively analyzed in business intelligence to gain a competitive advantage. These success stories in the business field can be replicated by universities through an analysis of educational data. This document presents a method that combines models and techniques of data mining within an architecture of business intelligence to make decisions about variables that can influence the development of learning. In order to test the proposed method, a case study is presented, in which students are identified and classified according to the data they generate in the different information systems of a university