<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2007-5057</journal-id>
<journal-title><![CDATA[Investigación en educación médica]]></journal-title>
<abbrev-journal-title><![CDATA[Investigación educ. médica]]></abbrev-journal-title>
<issn>2007-5057</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Medicina]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-50572018000100061</article-id>
<article-id pub-id-type="doi">10.1016/j.riem.2017.11.003</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Big data y analítica del aprendizaje en aplicaciones de salud y educación médica]]></article-title>
<article-title xml:lang="en"><![CDATA[Big Data and Learning Analytics in health and medical education applications]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Peñaloza Báez]]></surname>
<given-names><![CDATA[Marcela Juliana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Autónoma de México Dirección General de Cómputo y de Tecnologías de Información y Comunicación ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2018</year>
</pub-date>
<volume>7</volume>
<numero>25</numero>
<fpage>61</fpage>
<lpage>66</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-50572018000100061&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-50572018000100061&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-50572018000100061&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Big datay la analítica del aprendizaje son herramientas que permiten aprovechar enormes cantidades de datos que hoy existen en formato digital, y que se generan durante la prestación de servicios de salud y cuando se incorporan plataformas digitales al proceso de enseñanza-aprendizaje en la formación de médicos. El objetivo es diseñar proyectos que exploten las ventajas de las capacidades técnicas actuales para el procesamiento automatizado de datos en grandes volúmenes y distintos formatos, para buscar su entendimiento y mejorar la toma de decisiones. Las asociaciones, los patrones y las tendencias que pueden emerger del análisis de datos generados en la educación médica pueden facilitar el seguimiento e incluso modelar la conducta de los estudiantes, y perfilar su desempeño. Conbig datay la analítica del aprendizaje es posible transformar grandes cantidades de datos en información de muy alta calidad. Lo anterior es un área que puede ser aprovechada por el educador médico, quien conoce la información que tiene disponible y la que puede obtener mediante alianzas y acuerdos legales, a partir de un problema que se requiera resolver; se necesita apoyo de expertos técnicos pero, en todo momento, el rumbo del proyecto le corresponde a quien ha definido el problema, pues es quien conoce el marco de referencia y las posibles acciones a tomar con los hallazgos y las ideas que surjan. Ciertamente se requiere infraestructura, pero con las posibilidades para el trabajo en la nube, y los recursos disponibles en las instituciones médicas y de educación superior, se puede partir de un primer nivel dehardwareysoftware, y escalar a plataformas más sofisticadas cuando la combinación de hallazgos en los datos y de los volúmenes a procesar lo justifique. Sin duda,big datay analítica del aprendizaje son herramientas en las que el educador médico debe poner atención.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Big Data and Learning Analytics are tools that allow to take advantage of enormous amounts of data that exist in digital format, and that are generated during the provision of health services and when digital platforms are incorporated into the teaching-learning process in the training of doctors. The objective is to design projects that exploit the advantages of current technical capabilities for the automated processing of data in large volumes and different formats, to seek their understanding and improve decision making. The associations, patterns and trends that can emerge from the analysis of data generated in medical education can facilitate the tracking and even modeling of student behavior, and shape its performance. With Big Data and Learning Analytics it is possible to transform large amounts of data into very high quality information. The above is an area that can be exploited by the medical educator, who knows the information that is available and what can be obtained through alliances and legal agreements, based on a problem that needs to be solved; technical support is needed but at all times, the direction of the project corresponds to the professional who has defined the problem, because he/she knows the frame of reference and the possible actions to take with the findings and ideas that arise. Certainly infrastructure is required but with the possibilities for working in the cloud and the resources available in medical institutions and higher education, projects can start from a first level of hardware and software, and scale to more sophisticated platforms when the combination of findings in the data and the volumes to be processed justify it. Without a doubt, Big Data and Learning Analytics are tools in which the medical educator should pay attention.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Big data]]></kwd>
<kwd lng="es"><![CDATA[Analítica del aprendizaje]]></kwd>
<kwd lng="es"><![CDATA[Procesamiento de datos automatizado]]></kwd>
<kwd lng="es"><![CDATA[Análisis de datos]]></kwd>
<kwd lng="es"><![CDATA[Toma de decisiones]]></kwd>
<kwd lng="en"><![CDATA[Big Data]]></kwd>
<kwd lng="en"><![CDATA[Learning Analytics]]></kwd>
<kwd lng="en"><![CDATA[Automatic data processing]]></kwd>
<kwd lng="en"><![CDATA[Data analysis]]></kwd>
<kwd lng="en"><![CDATA[Decision making]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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