<?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>2007-7467</journal-id>
<journal-title><![CDATA[RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educativo]]></journal-title>
<abbrev-journal-title><![CDATA[RIDE. Rev. Iberoam. Investig. Desarro. Educ]]></abbrev-journal-title>
<issn>2007-7467</issn>
<publisher>
<publisher-name><![CDATA[Centro de Estudios e Investigaciones para el Desarrollo Docente A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-74672018000200688</article-id>
<article-id pub-id-type="doi">10.23913/ride.v9i17.402</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Estrategia didáctica de enseñanza y aprendizaje para programadores de software]]></article-title>
<article-title xml:lang="en"><![CDATA[Teaching and learning didactic strategy for software programmers]]></article-title>
<article-title xml:lang="pt"><![CDATA[Ensino e aprendizagem de estratégia didática para programadores de software]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González Jaimes]]></surname>
<given-names><![CDATA[Elvira Ivone]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López Chau]]></surname>
<given-names><![CDATA[Asdrúbal]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Trujillo Mora]]></surname>
<given-names><![CDATA[Valentín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rojas Hernández]]></surname>
<given-names><![CDATA[Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma del Estado de México  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma del Estado de México  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Autónoma del Estado de México  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Autónoma del Estado de México  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2018</year>
</pub-date>
<volume>9</volume>
<numero>17</numero>
<fpage>688</fpage>
<lpage>712</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-74672018000200688&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-74672018000200688&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-74672018000200688&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Introducción Las estrategias didácticas utilizadas para la enseñanza y el aprendizaje de la programación de software son complejas porque se debe adquirir, codificar y recuperar información con base en el pensamiento lógico-matemático para diseñar instrucciones que serán ejecutadas por un ordenador con el fin de resolver problemas de diversa naturaleza. Esto justifica por qué estos contenidos tienen alta demanda en la educación superior de la actualidad, a pesar de que los índices de titulación sean, por el contrario, muy bajos.  Objetivo Proponer y evaluar estrategias didácticas generales y específicas para la enseñanza y aprendizaje de programación en software con el fin de crear una alternativa para disminuir los registros de bajo rendimiento y la deserción en las carreras que incluyan temas de la mencionada área.  Método Diseño cuasiexperiemtal, análisis cuantitativo no paramétrico, longitudinal (cuatro evaluaciones), con una muestra de 78 estudiantes de la carrera de ingeniería en Computación, con distribución al azar en dos grupos experimentales y dos grupos de control en escenarios de aulas inteligentes.  Material Programas de software con lenguajes C y C++ en plataforma educativa, así como uso de Autogradr y del Inventario de estrategias metacognitivas.  Resultados Se empleó una prueba de Kruskal-Wallis debido a que se tuvo una muestra sin distribución normal y con escala por jerarquías. Se observó la aceptación de la hipótesis nula en ambas pruebas: 1) H = 0.04, en rendimiento académico superior en los grupos experimentales, la interdependencia en conocimientos es regular alta 0.657, con diferencia de 1.7 en rendimiento promedio, y 2) H = 0.24, en estrategias metacognitivas superior en los grupos experimentales, la interdependencia en estrategias metacognitivas es baja 0.342. Se resalta el área de planificación como fase elemental para la solución de problemas.  Conclusiones  Se demostró que la estrategia didáctica expuesta en tres bloques específicos de enseñanza y aprendizaje elevó el rendimiento académico y la metacognición de los estudiantes.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Introduction The didactic strategies used for teaching and learning software programming are complex because information must be acquired, coded and retrieved based on logical-mathematical thinking to design instructions that will be executed by a computer in order to solve problems of diverse nature. This justifies why these contents are in high demand in today's higher education, despite the fact that the degree indices are, on the contrary, very low.  Objective Propose and evaluate general and specific didactic strategies for teaching and learning software programming in order to create an alternative to reduce low-performance records and dropout in careers that include subjects from the above area.  Method Design quasiexperiemtal, quantitative analysis not parametric, longitudinal (four evaluations), with a sample of 78 students of the career of engineering in Computation, with random distribution in two experimental groups and two groups of control in scenarios of intelligent classrooms.  Material Software programs with C and C++ languages in educational platform, as well as the use of Autogradr and the Inventory of metacognitive strategies.  Results A Kruskal-Wallis test was used because we had a sample without normal distribution and with scale by hierarchies. The acceptance of the null hypothesis was observed in both tests: 1) H = 0.04, in superior academic performance in the experimental groups, the interdependence in knowledge is regular high 0.657, with difference of 1.7 in average performance, and 2) H = 0.24, in superior metacognitive strategies in the experimental groups, the interdependence in metacognitive strategies is low 0.342. The planning area is highlighted as an elementary phase for the solution of problems.  Conclusions It was demonstrated that the didactic strategy exposed in three specific blocks of teaching and learning elevated the academic performance and the metacognition of the students.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo  Introdução As estratégias didáticas utilizadas para o ensino e aprendizagem de programas de software são complexas, pois devem adquirir, codificar e recuperar informações baseadas em raciocínio lógico-matemático para projetar instruções que serão executadas por um computador para solucionar problemas de palavras. natureza diversa. Isso justifica por que esses conteúdos estão em alta demanda no ensino superior de hoje, embora os índices de grau sejam, ao contrário, muito baixos.  Objetivo Propor e avaliar estratégias didáticas gerais e específicas para o ensino e aprendizagem de programação de software, a fim de criar uma alternativa para reduzir os registros de baixo desempenho e abandono em carreiras que incluam tópicos da área acima mencionada.  Método Projeto Cuasiexperiemtal, análise quantitativa não paramétrica, longitudinal (quatro avaliações), com uma amostra de 78 alunos da carreira de engenharia em Computação, com distribuição aleatória em dois grupos experimentais e dois grupos controle em ambientes de sala de aula inteligente.  Material Programas de software com linguagens C e C ++ na plataforma educacional, bem como o uso do Autogradr e do Inventário de Estratégias Metacognitivas.  Resultados Um teste de Kruskal-Wallis foi usado porque havia uma amostra sem distribuição normal e escala por hierarquias. A aceitação da hipótese nula foi observada em ambos os testes: 1) H = 0,04, em maior desempenho acadêmico nos grupos experimentais, a interdependência no conhecimento é alta regular 0,657, com diferença de 1,7 no desempenho médio, e 2) H = 0,24 , nas estratégias metacognitivas mais elevadas nos grupos experimentais, a interdependência nas estratégias metacognitivas é baixa 0.342. A área de planejamento é destacada como a fase elementar para resolver problemas.  Conclusões Foi demonstrado que a estratégia didática exposta em três blocos específicos de ensino e aprendizagem elevou o desempenho acadêmico e a metacognição dos alunos.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[estrategias de aprendizaje]]></kwd>
<kwd lng="es"><![CDATA[estrategia de enseñanza]]></kwd>
<kwd lng="es"><![CDATA[programas de computación]]></kwd>
<kwd lng="en"><![CDATA[learning strategies]]></kwd>
<kwd lng="en"><![CDATA[teaching strategy]]></kwd>
<kwd lng="en"><![CDATA[computer programs]]></kwd>
<kwd lng="pt"><![CDATA[estratégias de aprendizagem]]></kwd>
<kwd lng="pt"><![CDATA[estratégia de ensino]]></kwd>
<kwd lng="pt"><![CDATA[programas de computador]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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