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Fuentes</td><td style=3D"width:10.5pt; bord= er-right:0.75pt solid #000000; border-left:0.75pt solid #000000; border-bot= tom:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB= QAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAA2lJR= EFUOI21lEtoXVUUhr+197n3nNzbJN6omdTWRmwbikVq4iMxk0ZU2oGgA1FnQcQiUhUnFqo4cGIR= QUGd+KDY+Jp25ouiGCOxElqjhba0URPzsPQmuY9zzj1nLwcnuU1iQ0f+mzXZe///Wv/aiy1cBV/= 83NNOPr/VWHunqO5OVNuNlQVjmZCGjCVhcPHR3q8XrsaVNUITu/KSlu7LFeSpNNZ+57RTjDTvqF= M1IvNiGXWJvD/9x8yXB/efizYU/Hy8/0W/aA/Xq43r1P3nOCNIFsW2XLleTd648Z/863v3nkjWC= L59dp/fWSsfzOXNkShKacvfgnMRlXjqqqIrwr5vkihyhxbT+K2ne082AAzADZXyPj+wL8ehQ1NH= 700v0N35OAroutW0rxDF6rUUvddaJfdwM9HwqYFSa0GO1SrpfucUVUXEZCQcqQtZ0VEU1QTPtmA= kDygiEBTsTxq6hx65fWTOy7l0exjKgGrGcjTo2/oKS9EUZ2aPcf/Oo1iTI9UGqFJrzDF5+Sv+Wv= g+y+IEVXaGibsbOO5hGBSkbUUQdRT9zaQuBhE6Ct2MT7/D9OIIVnw6Ct3csfk52oNtTMwcBbE0Y= 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style=3D"line-height:150%; font-family:'Times New Roma= n'; font-size:10pt">https://orcid.org/0009-0008-6626-4486</a> <= /span></td></tr><tr><td style=3D"width:3.25pt; border-top:0.75pt solid = #000000; border-right:0.75pt solid #000000; border-bottom:0.75pt solid #000= 000; padding:0pt 5.03pt; vertical-align:top"> </td><td colspan=3D"3" style=3D"w= idth:407.25pt; border-top:0.75pt solid #000000; border-left:0.75pt solid #0= 00000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical-ali= gn:top">Universid= ad Bolivariana del Ecuador (UBE), Guayaquil, Ecuador.Maestr=C3=ADa en Educaci=C3= =B3n Entornos Digitalesckchugaf@ube.ed= u.ec</td></tr><tr><td style=3D"width:3.25pt; border-top:0.75pt s= olid #000000; border-right:0.75pt solid #000000; border-bottom:0.75pt solid= #000000; padding:0pt 5.03pt; vertical-align:top">2</td><td style=3D"width:171.95pt= ; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">Esmelinger Thais Magallanes Tabares</td><td st= 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#000000; border-bottom:0.75pt solid #000000; padding:0pt 5.= 03pt; vertical-align:top"> </td><td colspan=3D"3" style=3D"width:407.25pt; bord= er-top:0.75pt solid #000000; border-left:0.75pt solid #000000; border-botto= m:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">Universidad Bolivariana del = Ecuador (UBE), Guayaquil, Ecuador.Maestr=C3=ADa en Educaci=C3=B3n Entornos Digita= lesetmagallanest@ube.edu.ec= </td></tr><tr><td style=3D"width:3.25pt; border-top:0.75pt solid #000000; b= order-right:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:0pt 5.03pt; vertical-align:top">3</td><td style=3D"width:171.95pt; border:0.75pt= solid #000000; padding:0pt 5.03pt; vertical-align:top">Johana Del Carmen Parre=C3=B1o S=C3=A1nchez</td><td style=3D"= width:10.5pt; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-ali= gn:top"><img src=3D"data:image/png;base64,iVBORw0KGg= oAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAAD= 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solid #000000; padding:0pt 5.03pt; vertical-align:top">Universidad Bolivariana del Ecuador= (UBE), Guayaquil, Ecuador.<s= pan class=3D"Hyperlink" style=3D"font-family:'Times New Roman'">jdparre=C3= =B1os@ube.edu.ec</td></tr><tr><td style=3D"width:3.25pt; border-= top:0.75pt solid #000000; border-right:0.75pt solid #000000; border-bottom:= 0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top"><spa= n style=3D"line-height:150%; font-family:'Times New Roman'; font-size:6.67p= t; font-weight:bold; vertical-align:super">4</td><td style=3D"wi= dth:171.95pt; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-ali= gn:top">Hendy Maier P=C3=A9rez Barrera</td= ><td style=3D"width:10.5pt; border:0.75pt solid #000000; padding:0pt 5.03pt= ; vertical-align:top"><img src=3D"data:image/png;bas= e64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAl= wSFlzAAAOxAAADsQBlSsOGwAAA2lJREFUOI21lEtoXVUUhr+197n3nNzbJN6omdTWRmwbikVq4i= 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style=3D"margin-left:7.1pt; margin-bottom:0pt; text-indent:-7.1pt; text-al= ign:justify; line-height:normal">hmperezb@ube.edu.ec</td></tr></table> <table style=3D"width:436.35pt; margin= -left:0.25pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr= ><td colspan=3D"3" style=3D"width:156.4pt; padding:0pt 5.4pt; vertical-alig= n:top"><a id=3D"_Hlk92186133"> <= /span></a> = ;</td><td colspan=3D"2" style=3D"width:258.35pt; border-top:0.75= pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Art=C3=ADculo de In= vestigaci=C3=B3n Cient=C3=ADfica y Tecnol=C3=B3gica<sp= an style=3D"font-family:'Times New Roman'">Enviado: <s= pan style=3D"font-family:'Times New Roman'">Revisado: Aceptado: Publicado:DOI: </td></tr><tr s= tyle=3D"height:6.55pt"><td colspan=3D"3" style=3D"width:156.4pt; padding:0p= t 5.4pt; vertical-align:top"> </s= pan> </td><td cols= pan=3D"2" style=3D"width:258.35pt; border-bottom:0.75pt solid #000000; padd= ing:0pt 5.4pt; vertical-align:top"> 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border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">Palabras claves: Gamif= icaci=C3=B3n, motivaci=C3=B3n, aprendizaje significativo, herramienta Educa= play, estrategias pedag=C3=B3gicas.</td><td style=3D"width:4.3pt= ; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top"> </td><td style=3D"width:324.9p= t; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">Resumen Introducci=C3=B3n: La gamificaci=C3=B3n se ha consolidado como una e= strategia innovadora en el =C3=A1mbito educativo, permitiendo mejorar la mo= tivaci=C3=B3n y el aprendizaje significativo en los estudiantes. En context= os donde predominan metodolog=C3=ADas tradicionales, se evidencian bajos ni= veles de inter=C3=A9s y participaci=C3=B3n, lo que afecta el rendimiento ac= ad=C3=A9mico. Objetivo: El= aborar una estrategia de gamificaci=C3=B3n basada en la herramienta Educapl= ay que contribuya a incrementar la motivaci=C3=B3n hacia el aprendizaje sig= nificativo en la asignatura de estudios sociales. Metodolog=C3=ADa: <spa= n style=3D"font-family:'Times New Roman'">Se emple=C3=B3 un enfoque mixto, = y de tipo cuasiexperimental, permitiendo analizar tanto resultados cuantita= tivos como percepciones cualitativas mediante el uso de instrumentos como p= retest, postest, encuesta diagn=C3=B3stica y entrevistas. Los resultados se= evaluaron por medio de la escala de Likert y c=C3=A1lculo de medias, adem= =C3=A1s, se aplic=C3=B3 una prueba de hip=C3=B3tesis por medio del estad=C3= =ADstico Rho de Spearman. Resultados: Se evidencio que la implementaci=C3=B3n de actividades gamifica= das increment=C3=B3 significativamente la motivaci=C3=B3n, la participaci= =C3=B3n activa y la comprensi=C3=B3n de los contenidos, reflej=C3=A1ndose e= n mejoras en todas las dimensiones evaluadas. Se evidenci=C3=B3 una alta co= rrelaci=C3=B3n entre la gamificaci=C3=B3n y la motivaci=C3=B3n (Rho =3D 0,7= 35), as=C3=AD como relaciones significativas entre la motivaci=C3=B3n y el = aprendizaje significativo (Rho =3D 0,804). Estos resultados indican que el = uso de estrategias gamificadas incrementa la participaci=C3=B3n, el inter= =C3=A9s y el compromiso de los estudiantes, favoreciendo una comprensi=C3= =B3n m=C3=A1s profunda de los contenidos. Conclusi=C3=B3n: La propuesta resulta eficaz como estrate= gia innovadora para fortalecer el aprendizaje significativo, siempre que se= consideren aspectos como la adecuada planificaci=C3=B3n pedag=C3=B3gica, l= a capacitaci=C3=B3n docente y el acceso a recursos tecnol=C3=B3gicos que ga= ranticen su correcta implementaci=C3=B3n. =C3=81rea de estudio general: = Educaci=C3=B3n. =C3=81rea de estu= dio espec=C3=ADfica: T= ecnolog=C3=ADa educativa y did=C3=A1ctica. Tipo de estudio: Art=C3=AD= culos original.</td></tr><tr><td style=3D"width:73.75pt; border-= top:0.75pt solid #000000; border-right:0.75pt solid #5b9bd5; border-left:0.= 75pt solid #5b9bd5; border-bottom:0.75pt solid #5b9bd5; padding:0pt 5.03pt;= vertical-align:top">Keyword= s: </sp= an>Gamification, motivation, = meaningful learning, Educaplay tool, pedagogical strategies.</td= ><td colspan=3D"2" style=3D"width:5.3pt; border-top:0.75pt solid #000000; b= order-right:0.75pt solid #5b9bd5; border-left:0.75pt solid #5b9bd5; border-= bottom:0.75pt solid #5b9bd5; padding:0pt 5.03pt; vertical-align:top"> </td><td style=3D"width:324.9pt;= border-top:0.75pt solid #000000; border-right:0.75pt solid #5b9bd5; border= -left:0.75pt solid #5b9bd5; border-bottom:0.75pt solid #5b9bd5; padding:0pt= 5.03pt; vertical-align:top">Abstract<= span style=3D"font-family:'Times New Roman'; font-weight:bold">Introduction= : gamification has bee= n consolidated as an innovative strategy in the educational field, allowing= the improvement of student motivation and meaningful learning. In contexts= where traditional methodologies predominate, low levels of interest and pa= rticipation are evident, which negatively affect academic performance.</spa= n> Objectiv= e: to design a gamific= ation strategy based on the Educaplay tool to increase motivation toward me= aningful learning in the subject of social studies. Methodology: a mixed-method and quasi-experimenta= l approach was used, allowing the analysis of both quantitative results and= qualitative perceptions through instruments such as pretest, postest, diag= nostic survey, and interviews. The results were evaluated using the Likert = scale and mean calculation; additionally, a hypothesis test was conducted u= sing Spearman=E2=80=99s Rho statistic. Results: it was evidenced that the implementation of gamified = activities significantly increased motivation, active participation, and co= ntent comprehension, reflected in improvements across all evaluated dimensi= ons. A high correlation between gamification and motivation was found (Rho = =3D 0.735), as well as significant relationships between motivation and mea= ningful learning (Rho =3D 0.804). These results indicate that the use of ga= mified strategies increases student participation, interest, and engagement= , promoting a deeper understanding of the content. Conclusion: the proposal proves to be effective as= an innovative strategy to strengthen meaningful learning, if aspects such = as proper pedagogical planning, teacher training, and access to technologic= al resources are considered to ensure its correct implementation. <s= pan style=3D"font-family:'Times New Roman'; font-weight:bold">General Area = of Study: Education. <= /span>Speci= fic area of study: Edu= cational Technology and Didactics. Typ= e of study: Original article.</td></tr><tr st= yle=3D"height:0pt"><td style=3D"width:84.55pt"></td><td style=3D"width:1pt"= ></td><td style=3D"width:15.1pt"></td><td style=3D"width:335.7pt"></td></tr= ></table>1. &#= xa0; Introducci=C3=B3n<sp= an style=3D"font-family:'Times New Roman'">Hist=C3=B3ricamente el juego ha = formado parte fundamental de la vida infantil a lo largo del tiempo, convir= ti=C3=A9ndose en uno de los componentes clave del aprendizaje temprano, rep= resenta uno de los recursos m=C3=A1s relevantes para adquirir conocimientos= , debido a que permite al infante explorar, descubrir y comprender su entor= no (P=C3=A9rez & Barrio, 2021). Es una actividad natural del ser humano= que es especialmente importante en la ni=C3=B1ez debido a que se estimula = un acercamiento a la comprensi=C3=B3n de la realidad y la resoluci=C3=B3n d= e problemas. La gamificaci=C3=B3n se ha consolidado como una estrategia inn= ovadora en el =C3=A1mbito educativo, esta t=C3=A9cnica permite fomentar la = participaci=C3=B3n activa y despertar emociones positivas y reforzar su mot= ivaci=C3=B3n intr=C3=ADnseca (S=C3=A1nchez, 2023). Por ello se considera im= portante comprender los impactos en las din=C3=A1micas motivacionales y de = aprendizaje significativo resultantes de la gamificaci=C3=B3n al desarrolla= r nuevas habilidades.Uno de los desaf=C3=ADos m=C3=A1s importantes en la educaci=C3= =B3n infantil radica en mantener la atenci=C3=B3n, inter=C3=A9s y motivaci= =C3=B3n de los ni=C3=B1os; en el contexto educativo mundial persiste esta p= roblem=C3=A1tica adem=C3=A1s de promover aprendizajes verdaderamente signif= icativos (Miranda & Choez, 2024). A nivel latinoamericano el problema s= e acent=C3=BAa debido a brechas pedag=C3=B3gicas, tecnol=C3=B3gicas y forma= tivas; muchos sistemas educativos de la regi=C3=B3n enfrentan limitaciones = relacionadas con el acceso a recursos digitales, capacitaci=C3=B3n docente = y la resistencia al cambio metodol=C3=B3gico (N=C3=A1poles, 2023). En Ecuad= or estas dificultades se evidencian de forma concreta en el aula, en muchas= instituciones educativas, los estudiantes muestran desinter=C3=A9s, baja p= articipaci=C3=B3n y dificultades para relacionar los contenidos con situaci= ones reales (Abarca, 2025). <a id=3D"_Hlk223773627"><s= pan style=3D"font-family:'Times New Roman'">En la Unidad Educativa San= Agust=C3=ADn de Guayaquil</a>, se evidencia que las metodolog=C3=ADas tradicionales utilizadas, = basadas principalmente en la repetici=C3=B3n, la memorizaci=C3=B3n y la ins= trucci=C3=B3n directa, no logran captar la atenci=C3=B3n de los estudiantes= ni mantener su inter=C3=A9s durante las clases. Esta falta de motivaci=C3= =B3n se refleja en una participaci=C3=B3n limitada, desinter=C3=A9s por las= actividades acad=C3=A9micas y dificultades para comprender y relacionar lo= s contenidos con sus experiencias previas. Adem=C3=A1s, se observa que los = estudiantes presentan bajos niveles de compromiso y escasa disposici=C3=B3n= para involucrarse activamente en el aprendizaje, lo que afecta el desarrol= lo de procesos significativos. Aunque se han implementado algunas estrategi= as, a=C3=BAn no se cuenta con m=C3=A9todos que respondan de manera efectiva= a las necesidades motivacionales de los ni=C3=B1os, lo cual mantiene la pr= oblem=C3=A1tica vigente y afecta el rendimiento y la calidad del aprendizaj= e. Es por eso por lo que se plantea el siguiente problema de investigaci=C3= =B3n =C2=BFDe qu=C3=A9 manera se puede incrementar la motivaci=C3=B3n en lo= s estudiantes y favorecer el aprendizaje significativo?Esta investigaci=C3=B3n se = justifica debido a la necesidad de b=C3=BAsqueda de estrategias pedag=C3=B3= gicas innovadoras y eficientes que puedan fortalecer el aprendizaje en la p= rimera infancia. De esta forma la gamificaci=C3=B3n se plantea como una alt= ernativa capaz de transformar las din=C3=A1micas de ense=C3=B1anza tradicio= nales en una promoci=C3=B3n de experiencias m=C3=A1s significativas. Por el= lo, la creaci=C3=B3n de una estrategia de gamificaci=C3=B3n es necesaria pa= ra contribuir en la motivaci=C3=B3n hacia el aprendizaje significativo y pa= ra aportar evidencia que permitan a los docentes la implementaci=C3=B3n de = pr=C3=A1cticas educativas m=C3=A1s din=C3=A1micas. <= span style=3D"font-family:'Times New Roman'">En un contexto general se pued= e mencionar a la investigaci=C3=B3n de Prieto et al. (2022) quienes analiza= n la relaci=C3=B3n entre la gamificaci=C3=B3n, la motivaci=C3=B3n y el apre= ndizaje, con la finalidad de proporcionar estrategias pedag=C3=B3gicas y di= d=C3=A1cticas que se puedan aplicar con efectividad; es as=C3=AD como en ba= se a un exhaustivo estudio y revisi=C3=B3n bibliogr=C3=A1fica se constat=C3= =B3 que la gamificaci=C3=B3n tiene un efecto directo y positivo en cuanto a= la motivaci=C3=B3n y rendimiento del alumnado. Sep=C3=BAlveda & Mayora= l (2025) en su investigaci=C3=B3n encontraron que la gamificaci=C3=B3n esti= mula la motivaci=C3=B3n intr=C3=ADnseca, favorece el desarrollo de las habi= lidades sociales y mejora la calidad del aprendizaje; adem=C3=A1s mencionan= que esta estrategia ofrece un recurso valioso en cuanto al proceso de ense= =C3=B1anza =E2=80=93 aprendizaje siempre que se apoye en una planificaci=C3= =B3n cuidadosa y sobre todo en un profesorado con una formaci=C3=B3n adecua= da. Por = otro lado, en la investigaci=C3=B3n de Buestan et al. (2025) se muestra que= la gamificaci=C3=B3n, mediante aspectos clave como desaf=C3=ADos, recompen= sas y retroalimentaci=C3=B3n, puede fomentar habilidades emocionales clave = como la empat=C3=ADa, la toma de decisiones y la autorregulaci=C3=B3n. Por = medio de combinaci=C3=B3n de ecuaciones booleanas y operadores l=C3=B3gicos= se analiz=C3=B3 la relaci=C3=B3n entre gamificaci=C3=B3n, inteligencia emo= cional y resoluci=C3=B3n de problemas y se obtuvo que la gamificaci=C3=B3n = se presenta como una herramienta eficaz para la integraci=C3=B3n de la inte= ligencia emocional y contribuir en el desarrollo de habilidades sociales y = de resoluci=C3=B3n de problemas en contextos de alta presi=C3=B3n. <= /p>Pues bien, en t= =C3=A9rminos te=C3=B3ricos se explica que la gamificaci=C3=B3n pretende est= imular el pensamiento del alumnado por medio de t=C3=A9cnicas de juego que = plantean la resoluci=C3=B3n de problemas y que es aplicable en diversas =C3= =A1reas de estudio, adem=C3=A1s, como estrategia did=C3=A1ctica ha aportado= mejoras significativas del rendimiento acad=C3=A9mico, motivaci=C3=B3n y e= mocionales positivas (=E2=80=AALled=C3=B3= et al., 2023). La gamificaci=C3=B3n es una estrategia educativa que hace u= so de elementos y din=C3=A1micas de los juegos con la intenci=C3=B3n de inc= entivar la motivaci=C3=B3n, el compromiso y la mejora en el desempe=C3=B1o = acad=C3=A9mico; es as=C3=AD como, esta estrategia concentra diversos compon= entes como desaf=C3=ADos, recompensas y competencia amistosa, convirtiendo = experiencias tradicionales en procesos m=C3=A1s interactivos y atractivos (= Bastidas, 2024). =E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2= =80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80= =AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC= =E2=80=AC=E2=80=ACAdem=C3=A1s, se examina el concepto de =E2=80=9CGamificaci=C3=B3n= =E2=80=9D como una estrategia que incorpora principios, din=C3=A1micas y co= mponentes propios de los juegos en entornos que no son de car=C3=A1cter l= =C3=BAdico, con la finalidad de estimular la motivaci=C3=B3n, favorecer el = aprendizaje y promover la participaci=C3=B3n activa (Prieto et al., 2022). = Consiste en integrar mec=C3=A1nicas, elementos y din=C3=A1micas propias del= juego en contextos diversos, tales como la educaci=C3=B3n, los negocios o = la salud, con el fin de incrementar el compromiso y el rendimiento en difer= entes actividades (Gonz=C3=A1lez & Torres, 2024). Esta metodolog=C3=ADa= recurre a recursos como retos, sistemas de recompensas, niveles, retroalim= entaci=C3=B3n inmediata y din=C3=A1micas de competencia positiva, logrando = transformar actividades convencionales en experiencias din=C3=A1micas (Bast= idas, 2024). En cuanto al espacio educativo, la gamificaci=C3=B3n se ha con= solidado como una estrategia eficaz para fortalecer el aprendizaje signific= ativo, estimular la participaci=C3=B3n del estudiante y favorecer el desarr= ollo de sus habilidades cognitivas y sociales. En cuanto a la gamificaci=C3=B3n bas= ada en herramientas digitales consiste en la incorporaci=C3=B3n de elemento= s propios de los videojuegos como desaf=C3=ADos, recompensas, niveles, insi= gnias y retroalimentaci=C3=B3n inmediata, dentro de las plataformas tecnol= =C3=B3gicas dise=C3=B1adas para el aprendizaje (Rosado et al., 2024). Estas= herramientas permiten transformar actividades tradicionales en experiencia= s din=C3=A1micas, interactivas y motivadoras, facilitando la participaci=C3= =B3n activa del estudiante. Aplicaciones como Educaplay no solo ofrecen ent= ornos visualmente atractivos, sino que tambi=C3=A9n permite adaptar conteni= dos, monitorear el progreso y reforzar habilidades cognitivas mediante din= =C3=A1micas de juego (Chavez et al., 2025). De esta forma, la gamificaci=C3= =B3n digital se convierte en una estrategia pedag=C3=B3gica que potencia la= motivaci=C3=B3n intr=C3=ADnseca, favorece el aprendizaje significativo y p= romueve un rol m=C3=A1s atractivo y aut=C3=B3nomo por parte del estudiante.= Adem=C3= =A1s, se muestra a la motivaci=C3=B3n hacia el aprendizaje significativo pa= rtiendo desde la teor=C3=ADa de David Ausubel <a id=3D"_Hlk231821023= ">(1980) </a>quien plantea que el aprendizaje ocurr= e de manera profunda y duradera cuando los nuevos conocimientos se relacion= an de forma sustancial con las ideas previas del estudiante (V=C3=A1squez &= amp; Reynoso, 2025). A diferencia del aprendizaje memor=C3=ADstico o repeti= tivo, que se limita a almacenar informaci=C3=B3n sin una comprensi=C3=B3n r= eal, el aprendizaje significativo implica una integraci=C3=B3n cognitiva qu= e reorganiza y ampl=C3=ADa la estructura mental existente (Vasconez, 2025).= Su teor=C3=ADa se sostiene en tres componentes fundamentales: la significa= tividad l=C3=B3gica del material, la significatividad psicol=C3=B3gica del = aprendizaje y la actitud y disposici=C3=B3n para aprender (motivaci=C3=B3n)= , que interact=C3=BAan para garantizar un proceso educativo eficaz <= /p>La significativi= dad l=C3=B3gica del material se refiere a la coherencia interna, claridad c= onceptual y estructura organizada que posee el contenido que se ense=C3=B1a= ; para que un material sea l=C3=B3gicamente significativo, debe estar elabo= rado de manera secuencial, precisa y comprensible, facilitando conexiones c= laras entre conceptos generales espec=C3=ADficos (Bertossi et al., 2022). E= sto implica que el dise=C3=B1o curricular y los recursos pedag=C3=B3gicos d= eben organizarse intencionalmente para permitir que el estudiante identifiq= ue patrones, relaciones y jerarqu=C3=ADas conceptuales; la estructura del m= aterial no solo facilita la comprensi=C3=B3n, sino que tambi=C3=A9n posibil= ita la asimilaci=C3=B3n de ideas nuevas dentro de los esquemas cognitivos p= reexistentes, favoreciendo as=C3=AD la construcci=C3=B3n de aprendizajes du= raderos (Revelo & Y=C3=A1nez, 2023). Por otra parte, la significatividad psic= ol=C3=B3gica del aprendizaje se relaciona con la capacidad del individuo pa= ra vincular los nuevos conocimientos con su estructura cognitiva previa, se= destaca la importancia de evaluar y activar los saberes que posee el estud= iante, ya que la comprensi=C3=B3n depende de la disponibilidad de conceptos= clave o ideas previas relevantes (V=C3=A1squez & Reynoso, 2025). Cuand= o el estudiante puede relacionar el nuevo contenido con experiencias, conoc= imientos o representaciones ya interiorizadas, se genera un proceso de asim= ilaci=C3=B3n en el que la informaci=C3=B3n adquiere sentido personal (Moren= o et al., 2025). En este aspecto el rol del docente es fundamental pues deb= e identificar el punto de partida cognitivo del estudiante y promover estra= tegias que faciliten el enlace entre lo nuevo y lo conocido, garantizando u= n aprendizaje aut=C3=A9nticamente significativo. <span= style=3D"font-family:'Times New Roman'">Tambi=C3=A9n, se toma en cuenta a = la actitud favorable o motivaci=C3=B3n constituye el tercer componente esen= cial del aprendizaje significativo y se refiere a la disposici=C3=B3n inter= na del estudiante para aprender (Prieto et al., 2022). Ausubel (1980) se=C3= =B1ala que aun cuando el material sea claro exista conocimiento previo pert= inente, el aprendizaje no ocurrir=C3=A1 se el alumno no est=C3=A1 motivado = o no muestra inter=C3=A9s (V=C3=A1squez & Reynoso, 2025). La actitud po= sitiva hacia la tarea acad=C3=A9mica, la curiosidad, la expectativa de =C3= =A9xito y la valoraci=C3=B3n del contenido son factores clave que potencian= la participaci=C3=B3n activa y la integraci=C3=B3n cognitiva (Miranda &= ; Choez, 2024). Adem=C3=A1s, la motivaci=C3=B3n puede fortalecerse mediante= ambientes de aprendizaje estimulantes, actitudes retadoras, metodolog=C3= =ADas activas y una relaci=C3=B3n pedag=C3=B3gica que genere confianza (N= =C3=A1poles, 2023). De esta manera, la disposici=C3=B3n para aprender act= =C3=BAa como un catalizador que permite que los otros componentes se articu= len eficazmente en el proceso educativo. Finalmente se considera importante mencionar a una dimensi=C3= =B3n propuesta por D=C3=ADaz y Hern=C3=A1ndez sobre el aprendizaje signific= ativo desde su enfoque pedag=C3=B3gico, se centra en la capacidad del estud= iante para aplicar de manera efectiva los aprendizajes y situaciones reales= o simuladas, demostrando que el conocimiento adquirido tiene utilidad m=C3= =A1s all=C3=A1 de aula (Villadiego, 2025). Esto es importante al evidenciar= que el estudiante utiliza los contenidos para resolver problemas pr=C3=A1c= ticos, transferir conceptos a otros contextos o =C3=A1reas del conocimiento= y participa activamente en actividades como proyectos, juegos educativos o= tareas funcionales que requieren poner en acci=C3=B3n lo aprendido. Es as= =C3=AD como el objetivo de esta investigaci=C3=B3n es: Elaborar una estrate= gia de gamificaci=C3=B3n basada en la herramienta Educaplay, que contribuya= a incrementar la motivaci=C3=B3n hacia el aprendizaje significativo en la = materia de estudios sociales.2.</= span> Metodolog=C3=ADa= La presente investi= gaci=C3=B3n se desarroll=C3=B3 bajo un enfoque mixto, que integra procedimi= entos cuantitativos y cualitativos para obtener una compresi=C3=B3n amplia = y profunda del fen=C3=B3meno estudiado. El enfoque cuantitativo permite med= ir el efecto de la estrategia de gamificaci=C3=B3n basada en Educaplay sobr= e la motivaci=C3=B3n y el aprendizaje significativo; mientras que el enfoqu= e cualitativo posibilitar=C3=A1 analizar las percepciones, experiencias y a= ctitudes de los estudiantes durante la implementaci=C3=B3n de la estrategia= . El dis= e=C3=B1o metodol=C3=B3gico es de tipo cuasiexperimental, debido a que se tr= abaj=C3=B3 con dos grupos, el grupo control al cual se le aplic=C3=B3 la me= todolog=C3=ADa pedag=C3=B3gica de forma tradicional y al grupo experimento = que se le aplic=C3=B3 la propuesta pedag=C3=B3gica de esta investigaci=C3= =B3n, comparando los resultados obtenidos por medio de pretest y un postest= . Este dise=C3=B1o permiti=C3=B3 evaluar los cambios generados por la estra= tegia sin necesidad de conformar grupos completamente aleatorizados, lo cua= l se ajusta a la realidad escolar de la Unidad Educativa San Agust=C3= =ADn de Guayaquil.</sp= an>Por su alcan= ce, la investigaci=C3=B3n se considera exploratoria, descriptiva y correlac= ional. Es exploratoria porque aborda un campo poco investigado en el contex= to espec=C3=ADfico de la instituci=C3=B3n, como es la gamificaci=C3=B3n med= iante herramientas digitales para el fortalecimiento del aprendizaje signif= icativo. A su vez, es descriptiva porque busca detallar las caracter=C3=ADs= ticas del proceso de motivaci=C3=B3n, las conductas observadas y los nivele= s de aprendizaje antes y despu=C3=A9s de la intervenci=C3=B3n basada en gam= ificaci=C3=B3n. Finalmente, es correlacional debido a que permite identific= ar, analizar y predecir relaciones entre variables sin manipularlas, propor= cionando informaci=C3=B3n =C3=BAtil para la toma de decisiones. Es as=C3=AD= como se plantea la siguiente hip=C3=B3tesis: La estrategia de gamificaci= =C3=B3n basada en Educaplay aplicada en la Unidad Educativa San Agust= =C3=ADn de Guayaquil e= n estudiantes de 8vo a=C3=B1o permiti=C3=B3 incrementar la motivaci=C3=B3n = hacia el aprendizaje significativo. Para el desarrollo de este estudio hubo la nece= sidad de implementar m=C3=A9todos te=C3=B3ricos como en an=C3=A1lisis y la = s=C3=ADntesis; por un lado, el an=C3=A1lisis permiti=C3=B3 descomponer los = conceptos centrales como la gamificaci=C3=B3n, motivaci=C3=B3n y aprendizaj= e significativo para comprender sus caracter=C3=ADsticas y relaciones. Por = su parte, la s=C3=ADntesis facilit=C3=B3 la integraci=C3=B3n de dicha infor= maci=C3=B3n para fundamentar y estructurar la propuesta pedag=C3=B3gica.</s= pan>En cuanto a= la recopilaci=C3=B3n de datos primarios se tomar=C3=A1 en cuenta a la pobl= aci=C3=B3n de estudiantes conformada por 33 hombres y 15 <a id=3D"_H= lk224048309">mujeres de 8vo a= =C3=B1o, paralelos a y b de la asignatura de estudios sociales. Para la mue= stra se aplic=C3=B3 una muestra censal tomando a toda la poblaci=C3=B3n com= o parte del estudio dando un total de 48 participantes</a>. La estrategia para aplicarse se enfoc= a en la implementaci=C3=B3n de juegos con Educaplay orientados a temas de e= studios sociales, se llev=C3=B3 a cabo la evaluaci=C3=B3n por medio de inst= rumentos como pruebas pretest y postest para conocer el nivel de motivaci= =C3=B3n y el rendimiento sobre el aprendizaje significativo, adem=C3=A1s, s= e hizo un registro de interacci=C3=B3n por medio de una ficha de observaci= =C3=B3n, se aplic=C3=B3 una entrevista a los docentes para establecer la ap= licaci=C3=B3n de la estrategia. Por otra parte, para el tratamiento de datos primarios se hizo uso del= programa estad=C3=ADstico SPSS =E2=80=93 Statics para la evaluaci=C3=B3n de instrumentos y la prese= ntaci=C3=B3n de datos. Finalmente se desarroll=C3=B3 una operacionalizaci= =C3=B3n de variables para entender la aplicaci=C3=B3n de la estrategia tal = y como se muestra en la Tabla 1.Ta= bla 1Operacionalizaci=C3=B3n de variables</sp= an><table style=3D"width:101.68%; margin-bottom:0pt; padding:0pt; borde= r-collapse:collapse"><tr style=3D"height:14.4pt"><td style=3D"width:14.22%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:0pt 3.5pt; vertical-align:bottom">Variable</td><td style=3D"width:21.66%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt= 3.5pt; vertical-align:bottom">Dimensi=C3=B3n</td><td style=3D"width:64.12%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt= 3.5pt; vertical-align:bottom">Indicador</td></tr><tr style=3D"height:14.4pt"><td = rowspan=3D"12" style=3D"width:14.22%; border-top:0.75pt solid #000000; bord= er-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertical-align:middle"><= p style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">VI: Gamificaci=C3=B3n basada en la her= ramienta Educaplay</td><td rowspan=3D"4" style=3D"width:21.66%; = border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddin= g:0pt 3.5pt; vertical-align:middle">U= so de elementos de gamificaci=C3=B3n</td><td style=3D"width:64.1= 2%; border-top:0.75pt solid #000000; padding:0pt 3.5pt; vertical-align:bott= om"><spa= n style=3D"font-family:'Times New Roman'">Puntuaciones</td></tr>= <tr style=3D"height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; v= ertical-align:bottom">Retroalimentaci= =C3=B3n inmediata</td></tr><tr style=3D"height:14.4pt"><td style= =3D"width:64.12%; padding:0pt 3.5pt; vertical-align:bottom">Recompensas visuales (insignias, niveles)= </td></tr><tr style=3D"height:14.4pt"><td style=3D"width:64.12%; border-bot= tom:0.75pt solid #000000; padding:0pt 3.5pt; vertical-align:bottom">Competencias entre estudiantes</t= d></tr><tr style=3D"height:14.4pt"><td rowspan=3D"3" style=3D"width:21.66%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:0pt 3.5pt; vertical-align:middle">= Dise=C3=B1o de actividades gamificadas</td><td style=3D"width:64= .12%; border-top:0.75pt solid #000000; padding:0pt 3.5pt; vertical-align:bo= ttom"><s= pan style=3D"font-family:'Times New Roman'">Actividades creadas en Educapla= y</td></tr><tr style=3D"height:14.4pt"><td style=3D"width:64.12%= ; padding:0pt 3.5pt; vertical-align:bottom">Adecuaci=C3=B3n al curr=C3=ADculo de estudios</td></tr><t= r style=3D"height:14.4pt"><td style=3D"width:64.12%; border-bottom:0.75pt s= olid #000000; padding:0pt 3.5pt; vertical-align:bottom">Nivel de interacci=C3=B3n del estudiante con la activida= d</td></tr><tr style=3D"height:14.4pt"><td rowspan=3D"2" style= =3D"width:21.66%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 3.5pt; vertical-align:middle">Interactividad y usabilidad</td><td style=3D"wi= dth:64.12%; border-top:0.75pt solid #000000; padding:0pt 3.5pt; vertical-al= ign:bottom">Facilidad de uso por part= e del estudiante </td></tr><tr style=3D"height:14.4pt"><td style= =3D"width:64.12%; border-bottom:0.75pt solid #000000; padding:0pt 3.5pt; ve= rtical-align:bottom">Tiempo de intera= cci=C3=B3n por actividad</td></tr><tr style=3D"height:14.4pt"><t= d rowspan=3D"3" style=3D"width:21.66%; border-top:0.75pt solid #000000; bor= der-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertical-align:middle">= Integraci=C3=B3n pedag=C3=B3gica</spa= n></td><td style=3D"width:64.12%; border-top:0.75pt solid #000000; padd= ing:0pt 3.5pt; vertical-align:bottom">Pertinencia pedag=C3=B3gica de las actividades</td></tr><tr sty= le=3D"height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; vertical= -align:bottom">Relaci=C3=B3n con los = aprendizajes significativos</td></tr><tr style=3D"height:14.4pt"= ><td style=3D"width:64.12%; border-bottom:0.75pt solid #000000; padding:0pt= 3.5pt; vertical-align:bottom">Adecua= ci=C3=B3n al nivel de los estudiantes</td></tr><tr style=3D"heig= ht:14.4pt"><td rowspan=3D"15" style=3D"width:14.22%; border-top:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertical-= align:middle">VD: Motivaci=C3=B3n hac= ia el aprendizaje significativo</td><td rowspan=3D"5" style=3D"w= idth:21.66%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:0pt 3.5pt; vertical-align:middle">Actitud favorable/Motivaci=C3=B3n hacia el aprendizaje</= p></td><td style=3D"width:64.12%; border-top:0.75pt solid #000000; padding:= 0pt 3.5pt; vertical-align:bottom">Int= er=C3=A9s y curiosidad por los contenidos</td></tr><tr style=3D"= height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; vertical-align= :bottom">Participaci=C3=B3n activa en= las actividades</td></tr><tr style=3D"height:14.4pt"><td style= =3D"width:64.12%; padding:0pt 3.5pt; vertical-align:bottom">Atenci=C3=B3n sostenida durante el proceso de aprend= izaje</td></tr><tr style=3D"height:14.4pt"><td style=3D"width:64= .12%; padding:0pt 3.5pt; vertical-align:bottom">Persistencia frente a tareas o dificultades </td></tr= ><tr style=3D"height:14.4pt"><td style=3D"width:64.12%; border-bottom:0.75p= t solid #000000; padding:0pt 3.5pt; vertical-align:bottom">Disposici=C3=B3n positiva para aprender significativa= mente</td></tr><tr style=3D"height:14.4pt"><td rowspan=3D"5" sty= le=3D"width:21.66%; border-top:0.75pt solid #000000; border-bottom:0.75pt s= olid #000000; padding:0pt 3.5pt; vertical-align:middle">Significatividad psicol=C3=B3gica del aprendizaje= </td><td style=3D"width:64.12%; border-top:0.75pt solid #000000; paddin= g:0pt 3.5pt; vertical-align:bottom">A= ctivaci=C3=B3n de conocimientos previos del estudiante</td></tr>= <tr style=3D"height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; v= ertical-align:bottom">Relaci=C3=B3n e= ntre el saber previo y los nuevos conceptos</td></tr><tr style= =3D"height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; vertical-a= lign:bottom">Capacidad del estudiante= para integrar, comparar y diferenciar ideas</td></tr><tr style= =3D"height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; vertical-a= lign:bottom">Elaboraci=C3=B3n propia = (explicar con sus palabras, producir ideas)</td></tr><tr style= =3D"height:14.4pt"><td style=3D"width:64.12%; border-bottom:0.75pt solid #0= 00000; padding:0pt 3.5pt; vertical-align:bottom">Comprensi=C3=B3n profunda m=C3=A1s all=C3=A1 de la memorizaci= =C3=B3n</td></tr><tr style=3D"height:14.4pt"><td rowspan=3D"5" s= tyle=3D"width:21.66%; border-top:0.75pt solid #000000; border-bottom:0.75pt= solid #000000; padding:0pt 3.5pt; vertical-align:middle">Pr=C3=A1ctico - funcional</td><td style=3D"= width:64.12%; border-top:0.75pt solid #000000; padding:0pt 3.5pt; vertical-= align:bottom">Aplica los aprendizajes= en situaciones reales o simuladas</td></tr><tr style=3D"height:= 14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; vertical-align:bottom= ">Utiliza conocimientos para resolver= problemas pr=C3=A1cticos</td></tr><tr style=3D"height:14.4pt"><= td style=3D"width:64.12%; padding:0pt 3.5pt; vertical-align:bottom">Transfiere conceptos a otras =C3=A1reas o co= ntextos </td></tr><tr style=3D"height:14.4pt"><td style=3D"width= :64.12%; padding:0pt 3.5pt; vertical-align:bottom">Participa en actividades activas (Proyectos, juegos o tareas = funcionales)</td></tr><tr style=3D"height:14.4pt"><td style=3D"w= idth:64.12%; border-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertica= l-align:bottom">Demuestra autonom=C3= =ADa para usar lo aprendido</td></tr></table>3. </spa= n>ResultadosLa fiabilidad del instrumento se demostr=C3=B3 con el uzo del coefi= ciente Alfa de Cronbach, que alcanz=C3=B3 un valor de 0.851 en el cuestiona= rio de diagn=C3=B3stico y 0.816 en el cuestionario de pretest y postest apl= icados a los estudiantes. Estos resultados indican niveles de consistencia = elevado y significa que los =C3=ADtems del cuestionario miden de forma cohe= rente las variables de estudio. Por ello, se garantiza la solidez y confian= za de los datos recogidos de los estudiantes que se muestran a continuaci= =C3=B3n. <s= pan style=3D"font-family:'Times New Roman'; font-style:italic">3.1.</= span> Diagn=C3=B3stico inicial Los resultados del diagn=C3=B3stico inicial evidencian = caracter=C3=ADsticas generales de la poblaci=C3=B3n estudiantil encuestada.= En cuanto a la edad, los estudiantes se encuentran en una etapa de desarro= llo cognitivo y social en la que las estrategias din=C3=A1micas y participa= tivas suelen tener mayor impacto en el aprendizaje. En relaci=C3=B3n con la= motivaci=C3=B3n hacia las clases de estudios sociales la Tabla 2 muestra que la mayor=C3=ADa de estu= diantes presenta niveles de motivaci=C3=B3n moderados o bajos, lo que sugie= re que existe la necesidad de fortalecer las estrategias pedag=C3=B3gicas q= ue promuevan mayor inter=C3=A9s y participaci=C3=B3n en la asignatura. </sp= an><a id=3D"_Hlk223960946"></a><di= v style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA= AuQAAAABCAYAAACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABx= JREFUSIntwTEBAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width= =3D"740" height=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-6= 3.3pt; position:absolute" /> = ; Nombre 1 </div></div> <div class=3D"Section_2"= >T= abla 2 Diagn=C3=B3stico inicial<tabl= e style=3D"width:97.84%; margin-bottom:0pt; padding:0pt; border-collapse:co= llapse"><tr><td style=3D"width:52.82%; border-top:0.75pt solid #000000; bor= der-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Pregunta</= td><td style=3D"width:32.6%; border-top:0.75pt solid #000000; border-bottom= :0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"><s= pan style=3D"font-family:'Times New Roman'">Categor=C3=ADa</td><= td style=3D"width:14.58%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"><span= style=3D"font-family:'Times New Roman'">Porcentaje</td></tr><tr= ><td rowspan=3D"2" style=3D"width:52.82%; border-top:0.75pt solid #000000; = padding:0pt 5.4pt; vertical-align:middle">Edad</td><td style=3D"width:32.6%; border-top:0.75pt solid = #000000; padding:0pt 5.4pt; vertical-align:top">12</td><td style=3D"width:14.58%; border-top:0.75pt s= olid #000000; padding:0pt 5.4pt; vertical-align:top">39,6%</td></tr><tr><td style= =3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top">13</td><td style=3D"width:14.58%; padding:0pt= 5.4pt; vertical-align:top">60,4%</td></tr><tr><td style=3D"width:52.82%; padding:= 0pt 5.4pt; vertical-align:middle">&#x= a0;</td><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-al= ign:top"> </td><td st= yle=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top"><spa= n style=3D"font-family:'Times New Roman'"> </td></tr><tr><t= d rowspan=3D"2" style=3D"width:52.82%; padding:0pt 5.4pt; vertical-align:mi= ddle"><s= pan style=3D"font-family:'Times New Roman'">G=C3=A9nero</td><td = style=3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top">Masculino</td><td style=3D"width:14.58%;= padding:0pt 5.4pt; vertical-align:top">68,8%</td></tr><tr><td style=3D"width:32.6= %; padding:0pt 5.4pt; vertical-align:top">Femenino</td><td style=3D"width:14.58%; padding:0pt 5.4pt; = vertical-align:top">31,2%</td></tr><tr><td style=3D"width:52.82%; padding:0pt 5.4p= t; vertical-align:middle"> </spa= n></td><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top"= > </td><td style=3D"w= idth:14.58%; padding:0pt 5.4pt; vertical-align:top"> </td></tr><tr><td rowsp= an=3D"5" style=3D"width:52.82%; padding:0pt 5.4pt; vertical-align:middle"><= p style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">=C2=BFTe encuentras motivado al estar = en clases de estudios sociales?</td><td style=3D"width:32.6%; pa= dding:0pt 5.4pt; vertical-align:top">= Nunca</td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical= -align:top">0%</sp= an></td></tr><tr><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-= align:top">Casi nunca</td>= <td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top">12,5%</td></tr>= <tr><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top">A veces</td><td style=3D"width:1= 4.58%; padding:0pt 5.4pt; vertical-align:top">56,3%</td></tr><tr><td style=3D"widt= h:32.6%; padding:0pt 5.4pt; vertical-align:top">Casi siempre</td><td style=3D"width:14.58%; padding:0= pt 5.4pt; vertical-align:top">25,0%</td></tr><tr><td style=3D"width:32.6%; padding= :0pt 5.4pt; vertical-align:top">Siemp= re</td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-al= ign:top">6,3%</spa= n></td></tr><tr><td style=3D"width:52.82%; padding:0pt 5.4pt; vertical-= align:middle"> </td><= td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top"> </td><td style=3D"width:14.58%;= padding:0pt 5.4pt; vertical-align:top"> </td></tr><tr><td rowspan=3D"3" styl= e=3D"width:52.82%; padding:0pt 5.4pt; vertical-align:middle">=C2=BFEl docente de estudios sociales ense=C3=B1a a= trav=C3=A9s de qu=C3=A9 m=C3=A9todo?</td><td style=3D"width:32.= 6%; padding:0pt 5.4pt; vertical-align:top">Memorizaci=C3=B3n mec=C3=A1nica</td><td style=3D"width:14.= 58%; padding:0pt 5.4pt; vertical-align:top">14,6%</td></tr><tr><td style=3D"width:= 32.6%; padding:0pt 5.4pt; vertical-align:top">Construcci=C3=B3n de conocimiento</td><td style=3D"widt= h:14.58%; padding:0pt 5.4pt; vertical-align:top">68,8%</td></tr><tr><td style=3D"w= idth:32.6%; padding:0pt 5.4pt; vertical-align:top">Juegos din=C3=A1micos</td><td style=3D"width:14.58= %; padding:0pt 5.4pt; vertical-align:top">16,7%</td></tr><tr><td style=3D"width:52= .82%; padding:0pt 5.4pt; vertical-align:middle"> </td><td style=3D"width:32.6%; padding:0pt 5.4p= t; vertical-align:top"> <= /p></td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top"><= p style=3D"margin-bottom:0pt; text-align:center; line-height:normal; font-s= ize:10pt"> </= td></tr><tr><td rowspan=3D"5" style=3D"width:52.82%; padding:0pt 5.4pt; ver= tical-align:middle">=C2=BFEl docente = de estudios sociales utiliza herramientas tecnol=C3=B3gicas para impartir l= as clases?</td><td style=3D"width:32.6%; padding:0pt 5.4pt; vert= ical-align:top">Nunca</td>= <td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top">0%</td></tr><tr= ><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top">Casi nunca</td><td style=3D"width:= 14.58%; padding:0pt 5.4pt; vertical-align:top">10,4%</td></tr><tr><td style=3D"wid= th:32.6%; padding:0pt 5.4pt; vertical-align:top">A veces</td><td style=3D"width:14.58%; padding:0pt 5= .4pt; vertical-align:top">56,3%</td></tr><tr><td style=3D"width:32.6%; padding:0pt= 5.4pt; vertical-align:top">Casi siem= pre</td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-a= lign:top">22,9%</s= pan></td></tr><tr><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical= -align:top">Siempre</td><t= d style=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top">= 10,4%</td></tr><tr= ><td style=3D"width:52.82%; padding:0pt 5.4pt; vertical-align:middle"> </td><td style=3D"width= :32.6%; padding:0pt 5.4pt; vertical-align:top"> </td><td style=3D"width:14.58%; padding:0pt 5.4p= t; vertical-align:top"> </td></tr><tr><td rowspan=3D"2" style=3D"width:52.82%= ; padding:0pt 5.4pt; vertical-align:middle">=C2=BFTe gustar=C3=ADa aprender por medio de juegos interactivos en = plataformas digitales?</td><td style=3D"width:32.6%; padding:0pt= 5.4pt; vertical-align:top">S=C3=AD</= span></td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:= top">100,0%= </td></tr><tr><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-ali= gn:top">= No</td><td style= =3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top">0%</td></tr><tr><td style= =3D"width:52.82%; padding:0pt 5.4pt; vertical-align:middle"> </td><td style=3D"width:32.6%; padd= ing:0pt 5.4pt; vertical-align:top">&#= xa0;</td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-= align:top"> <= /span></td></tr><tr><td rowspan=3D"2" style=3D"width:52.82%; border-bot= tom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:middle">=C2=BFEl docente ha utilizado la plataforma = Educaplay u otras similares en sus clases?</td><td style=3D"widt= h:32.6%; padding:0pt 5.4pt; vertical-align:top">S=C3=AD</td><td style=3D"width:14.58%; padding:0pt 5.= 4pt; vertical-align:top">47,9%</td></tr><tr><td style=3D"width:32.6%; border-botto= m:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">No</td><td style=3D"width:14.58%; bord= er-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">52,1%</td><= /tr></table>Nota: datos obtenidos de l= a investigaci=C3=B3n en <a id=3D"_Hlk224050000">la Unidad Educativa San Agust=C3=ADn de Guayaquil= </a></div> <div class=3D"Section_3"><div style=3D"clear:both"><img src=3D"data:image/png;ba= se64,iVBORw0KGgoAAAANSUhEUgAAAUIAAABPCAYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAA= lwSFlzAAAOxAAADsQBlSsOGwAADrlJREFUeJztnX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNz= KRxWdwMMcYY48o6Qlc2M6KNq1kXBbuprdYOWjsnRarir4l2jNZag6jMabV0astgprMtiqICVVEQ= 2BW4XMTX/nieL/dzP9/zPT++98KF8bwScr/n8+P5POccvs/3nM/nnOctBUEQBEEQBEEQBEEQBEE= QBEEQBEEQBEEQBEEQBEEQBEEQBNUAaxjNkJefCawFdgN7gB8AFwG3Zv17tgO+lNjdA3wq63tnNv= aSBv6eAawD9gKDwIoe7SYBi4FngZ3Aw0ndSuAt9/mhfuxnfWYDz7i9fcAm4MqsTdWYTfpvopvL6= 3wLgqABwADwj/7FehKY7OXPAi8B5wNnAdcDrwO3Z/1r23kdwAvAQNb/POBN4JyG/j5bEhDuyNqc= C7zodT8F/hmY5nXf8/JFwC3+eUUb+9lYU4DtwH7gH4BPA/+LBdIz6sZs2H9OiU9bmxyvIAgaApz= 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de conocimien= to; no obstante, aunque predomina un enfoque orientado a la construcci=C3= =B3n del conocimiento, los resultados muestran que el uso de metodolog=C3= =ADas l=C3=BAdicas o gamificadas a=C3=BAn es limitado dentro del aula. Resp= ecto al uso de herramientas tecnol=C3=B3gicas en las clases de estudios soc= iales se evidencia que la integraci=C3=B3n de recursos tecnol=C3=B3gicos en= el proceso educativo a=C3=BAn no es constante ni sistem=C3=A1tica. Un aspe= cto relevante del diagn=C3=B3stico es la percepci=C3=B3n positiva de los es= tudiantes hacia el aprendizaje mediado por tecnolog=C3=ADas interactivas, l= os estudiantes manifestaron que les gustar=C3=ADa aprender mediante juegos = interactivos en plataformas digitales, lo cual demuestra una alta aceptaci= =C3=B3n hacia estrategias pedag=C3=B3gicas innovadoras basadas en la gamifi= caci=C3=B3n. No obstante, aunque se ha empezado a hacer uso de la plataform= a Educaplay no est=C3=A1 completamente integrada en el proceso de ense=C3= =B1anza. Por otra parte, los resultados obtenidos en las entrevistas aplicadas a lo= s docentes refuerzan y complementan la informaci=C3=B3n del diagn=C3=B3stic= o cuantitativo; en general, los docentes coinciden en que las metodolog=C3= =ADas tradicionales, basadas principalmente en la exposici=C3=B3n del docen= te y la memorizaci=C3=B3n de contenidos, tienden a generar bajos niveles de= motivaci=C3=B3n en los estudiantes. Igualmente, se=C3=B1alan que uno de lo= s principales desaf=C3=ADos para mantener la atenci=C3=B3n en el aula es la= presencia de distractores como el uso del celular, el exceso de contenido = te=C3=B3rico y la falta de conexi=C3=B3n entre los temas abordados y la rea= lidad de los estudiantes. Los docentes tambi=C3=A9n destacan que las activi= dades din=C3=A1micas y participativas generan mayor inter=C3=A9s, participa= ci=C3=B3n y comprensi=C3=B3n por parte de los estudiantes, en comparaci=C3= =B3n con las actividades memor=C3=ADsticas. En este sentido, todos coincide= n en que el aprendizaje significativo se produce cuando el estudiante logra= comprender los contenidos, relacionarlos con su entorno y aplicarlos en si= tuaciones reales, m=C3=A1s all=C3=A1 de la simple memorizaci=C3=B3n de info= rmaci=C3=B3n. En relaci=C3=B3n con la gamificaci=C3=B3n, los entrevistados reconoce= n que la incorporaci=C3=B3n de elementos de juego como retos, recompensas, = niveles o competencias sanas puede aumentar significativamente la motivaci= =C3=B3n, el compromiso y la participaci=C3=B3n estudiantil. Algunos docente= s manifestaron haber utilizado herramientas digitales como cuestionarios in= teractivos, juegos de rol o plataformas como Educaplay, obteniendo resultad= os positivos en cuanto a la participaci=C3=B3n y comprensi=C3=B3n de los co= ntenidos. No obstante, tambi=C3=A9n se identifican algunas limitaciones par= a la implementaci=C3=B3n de estas estrategias, entre las que se mencionan l= a falta de conocimiento o capacitaci=C3=B3n en el uso de herramientas digit= ales, problemas de conectividad a internet y el riesgo de distracciones se = no se utilizan de manera pedag=C3=B3gicamente planificada.En s=C3=ADntesis, tanto e= l diagn=C3=B3stico inicial como las entrevistas docentes evidencian la nece= sidad de fortalecer las estrategias did=C3=A1cticas utilizadas en la ense= =C3=B1anza de estudios sociales mediante la incorporaci=C3=B3n de metodolog= =C3=ADas innovadoras que incrementen la motivaci=C3=B3n y favorezcan el apr= endizaje significativo. En este contexto, la gamificaci=C3=B3n apoyada en h= erramientas digitales como Educaplay se presenta como una alternativa pedag= =C3=B3gica viable, ya que permite transformar el proceso de aprendizaje en = una experiencia m=C3=A1s interactiva, participativa y atractiva para los es= tudiantes. = 3.2.= Actividades de gamificaci=C3=B3n basada en la herramienta Educaplay</s= pan>Para el des= arrollo de las actividades did=C3=A1cticas se utiliz=C3=B3 la plataforma Ed= ucaplay, la cual permite crear juegos educativos multimedia como cuestionar= ios, crucigramas, sopas de letras, mapas interactivos y ejercicios de asoci= aci=C3=B3n. Esta herramienta digital facilit=C3=B3 la gamificaci=C3=B3n del= aprendizaje al incorporar elementos propios del juego como puntuaciones, t= iempo limitado, retos y retroalimentaci=C3=B3n inmediata, lo que contribuye= a aumentar la motivaci=C3=B3n y participaci=C3=B3n de los estudiantes en e= l proceso educativo. Las actividades aplicadas en el aula se estructuraron = en varias tem=C3=A1ticas del =C3=A1rea de estudios sociales: los continente= s, el clima, mares y oc=C3=A9anos del mundo, regiones y etnias del Ecuador.= En cada tem=C3=A1tica se utilizaron diferentes tipos de juegos interactivo= s que permitieron reforzar los contenidos te=C3=B3ricos mediante el aprendi= zaje activo. Uno de los recursos m=C3=A1s utilizados fue el mapa interactivo, en el= cual los estudiantes deben identificar ubicaciones o elementos se=C3=B1ala= dos sobre una imagen o mapa geogr=C3=A1fico; en este tipo de actividad el e= studiante observa un mapa y debe seleccionar la ubicaci=C3=B3n correcta de = un pa=C3=ADs, regios o continente, lo que fortalece la orientaci=C3=B3n esp= acial y comprensi=C3=B3n geogr=C3=A1fica; adem=C3=A1s, estos juegos pueden = incluir informaci=C3=B3n adicional, explicaciones o im=C3=A1genes que apare= cen despu=C3=A9s de cada respuesta. La Tabla <= span style=3D"font-family:'Times New Roman'; font-weight:bold">3<spa= n style=3D"font-family:'Times New Roman'"> presenta un conjunto de activida= des gamificadas orientadas al aprendizaje de los continentes del mundo, des= arrolladas mediante la plataforma Educaplay, estas actividades incluyen cue= stionarios, juegos de identificaci=C3=B3n, mapas interactivos y ejercicios = de relaci=C3=B3n de conceptos, los cuales permiten reforzar los conocimient= os geogr=C3=A1ficos de los estudiantes de forma din=C3=A1mica. <= div style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUg= AAAuQAAAABCAYAAACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAA= BxJREFUSIntwTEBAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" widt= h=3D"740" height=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-= 63.3pt; position:absolute" /> = 0; Nombre 1 </div></div> <div class=3D"Section_4= ">Tabla 3</p= >Tem=C3=A1tica: Los continentes<table style= =3D"margin-bottom:0pt; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:0pt; border-spacing:1.5pt"><thead><tr><td style=3D= "padding:0.75pt">J= uego</td><td style=3D"padding:0.75pt">Actividad</td><td style=3D"paddin= g:0.75pt">Funciona= miento</td></tr></thead><tbody><tr><td style=3D"border-top:0.75p= t solid #000000; padding:0.75pt">Am= =C3=A9rica: Generalidades</td><td style=3D"border-top:0.75pt sol= id #000000; padding:0.75pt">Cuestiona= rio interactivo</td><td style=3D"border-top:0.75pt solid #000000= ; padding:0.75pt">Los estudiantes res= ponden preguntas sobre caracter=C3=ADsticas generales del continente americ= ano, como ubicaci=C3=B3n, pa=C3=ADses y caracter=C3=ADsticas geogr=C3=A1fic= as. Cada respuesta correcta suma puntos.</td></tr><tr><td style= =3D"padding:0.75pt">El continente ame= ricano</td><td style=3D"padding:0.75pt">Relaci=C3=B3n de conceptos</td><td style=3D"paddin= g:0.75pt">Se presentan conceptos o ca= racter=C3=ADsticas y los estudiantes deben asociarlos con el continente cor= respondiente.</td></tr><tr><td style=3D"padding:0.75pt">Continente africano</td><td style= =3D"padding:0.75pt">Cuestionario</spa= n></td><td style=3D"padding:0.75pt">Los estudiantes identifican datos importantes de =C3=81frica como pa=C3= =ADses, caracter=C3=ADsticas geogr=C3=A1ficas y cultura.</td></t= r><tr><td style=3D"padding:0.75pt">Co= ntinente africano (actividad adicional)</td><td style=3D"padding= :0.75pt">Juego de preguntas</p= ></td><td style=3D"padding:0.75pt">Se= refuerzan conocimientos mediante preguntas r=C3=A1pidas sobre ubicaci=C3= =B3n y caracter=C3=ADsticas del continente.</td></tr><tr><td sty= le=3D"padding:0.75pt">Mapa interactiv= o de Europa</td><td style=3D"padding:0.75pt">Mapa interactivo</td><td style=3D"padding:0.7= 5pt"><sp= an style=3D"font-family:'Times New Roman'">Los estudiantes observan el mapa= del continente europeo y deben seleccionar correctamente pa=C3=ADses o reg= iones se=C3=B1aladas en el mapa.</td></tr><tr><td style=3D"paddi= ng:0.75pt">Generalidades de Europa</s= pan></td><td style=3D"padding:0.75pt">Cuestionario</td><td style=3D"padding:0.75pt">Preguntas sobre caracter=C3=ADsticas f=C3=ADsicas,= culturales y geogr=C3=A1ficas del continente europeo.</td></tr>= <tr><td style=3D"padding:0.75pt">Cont= inente europeo</td><td style=3D"padding:0.75pt">Juego de identificaci=C3=B3n</td><td style= =3D"padding:0.75pt">Los estudiantes i= dentifican elementos geogr=C3=A1ficos importantes del continente europeo.</= span></td></tr><tr><td style=3D"padding:0.75pt">Asia</td><td style=3D"padding:0.75pt">Cuestionario</td><td style=3D"padding= :0.75pt">Evaluaci=C3=B3n sobre ubicac= i=C3=B3n, extensi=C3=B3n territorial y caracter=C3=ADsticas del continente = asi=C3=A1tico.</td></tr><tr><td style=3D"padding:0.75pt">Continente asi=C3=A1tico</td><td = style=3D"padding:0.75pt">Juego de pre= guntas</td><td style=3D"padding:0.75pt">Actividad que refuerza conocimientos b=C3=A1sicos sobre el co= ntinente m=C3=A1s grande del planeta.</td></tr><tr><td style=3D"= padding:0.75pt">Continente asi=C3=A1t= ico (actividad adicional)</td><td style=3D"padding:0.75pt">Relaci=C3=B3n de conceptos</t= d><td style=3D"padding:0.75pt">Asocia= ci=C3=B3n entre pa=C3=ADses, cultura y caracter=C3=ADsticas geogr=C3=A1fica= s de Asia.</td></tr><tr><td style=3D"padding:0.75pt">Continente de Ocean=C3=ADa</td><td st= yle=3D"padding:0.75pt">Cuestionario</= span></td><td style=3D"padding:0.75pt">Identificaci=C3=B3n de pa=C3=ADses y caracter=C3=ADsticas del contine= nte oce=C3=A1nico.</td></tr><tr><td style=3D"padding:0.75pt">Poblaci=C3=B3n mundial</td><t= d style=3D"padding:0.75pt">Juego de c= onocimiento general</td><td style=3D"padding:0.75pt">Actividad para comprender la distribuci=C3=B3n d= e la poblaci=C3=B3n en los continentes del mundo.</td></tr></tbo= dy></table>Nota: Informaci=C3=B3n reco= pilada de la plataforma Educaplay.</div> <div class=3D"Section_5"><div style=3D"cle= ar:both"><= span style=3D"height:0pt; display:block; position:absolute; z-index:-65536"= ><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAUIAAABPCAYAAACe= RMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAADrlJREFUeJztnX+sV/V= 5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMMcYY48o6Qlc2M6KNq1kXBbuprdYOWjsnRarir4= l2jNZag6jMabV0astgprMtiqICVVEQ2BW4XMTX/nieL/dzP9/zPT++98KF8bwScr/n8+P5POccv= s/3nM/nnOctBUEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBNUAaxjNkJefCawFdgN7= gB8AFwG3Zv17tgO+lNjdA3wq63tnNvaSBv6eAawD9gKDwIoe7SYBi4FngZ3Aw0ndSuAt9/mhfux= nfWYDz7i9fcAm4MqsTdWYTfpvopvL63wLgqABwADwj/7FehKY7OXPAi8B5wNnAdcDrwO3Z/1r23= kdwAvAQNb/POBN4JyG/j5bEhDuyNqcC7zodT8F/hmY5nXf8/JFwC3+eUUb+9lYU4DtwH7gH4BPA= 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mundo; en particular, el uso de mapas i= nteractivos facilit=C3=B3 el desarrollo de las habilidades de orientaci=C3= =B3n espacial, ya que los estudiantes deb=C3=ADan ubicar pa=C3=ADses, regio= nes o continentes en representaciones cartogr=C3=A1ficas. Asimismo, los cue= stionarios y juegos de preguntas contribuyeron a reforzar la comprensi=C3= =B3n de conceptos b=C3=A1sicos mediante la retroalimentaci=C3=B3n inmediata= que proporciona la plataforma; este tipo de actividades no solo permiti=C3= =B3 evaluar el nivel de conocimiento de los estudiantes, sino que tambi=C3= =A9n fortalece el aprendizaje significativo al incentivar la reflexi=C3=B3n= sobre las respuestas correctas e incorrectas. La Tabla 4= presenta las activida= des relacionadas con la tem=C3=A1tica del clima, las cuales fueron dise=C3= =B1adas para que los estudiantes comprendan conceptos fundamentales como la= diferencia entre el clima y tiempo atmosf=C3=A9rico, lo biomas del mundo y= los factores que influyen en las condiciones clim=C3=A1ticas. Estas activi= dades se desarrollaron mediante cuestionarios, ejercicios de asociaci=C3=B3= n y juegos de preguntas que permitieron reforzar los contenidos te=C3=B3ric= os de forma interactiva<div style=3D"clear:both"><img src=3D"data:imag= e/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAABCAYAAACWsrt9AAAABHNCSVQICAgIfA= hkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSIntwTEBAAAIwKDZP7TG8AGm2gAAgA9zm= TsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740" height=3D"1" alt=3D"" style=3D= "margin-top:-37.82pt; margin-left:-63.3pt; position:absolute" /><spa= n style=3D"letter-spacing:3pt; color:#ffffff"> = ; = Nombre = = 0; 1<spa= n> </div></div> <div class=3D"Section_6"><spa= n style=3D"font-family:'Times New Roman'; font-weight:bold; font-style:norm= al; color:#000000">Tabla 4<span= style=3D"font-family:'Times New Roman'; color:#000000">Tem=C3=A1tica: El c= lima<table style=3D"margin-bottom:0pt; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:0pt; border-spacing:1.5= pt"><thead><tr><td style=3D"padding:0.75pt">Juego</td><td style=3D"padding:0.75pt"= >Actividad<= /p></td><td style=3D"padding:0.75pt">Funcionamiento</td></tr></thead><tbody><tr><t= d style=3D"border-top:0.75pt solid #000000; padding:0.75pt">El clima y tiempo atmosf=C3=A9rico</td><t= d style=3D"border-top:0.75pt solid #000000; padding:0.75pt">Cuestionario</td><td style=3D"border-top:= 0.75pt solid #000000; padding:0.75pt">Los estudiantes responden preguntas sobre la diferencia entre clima y tiem= po atmosf=C3=A9rico.</td></tr><tr><td style=3D"padding:0.75pt"><= p style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">Biomas del mundo</td><td st= yle=3D"padding:0.75pt">Relaci=C3=B3n = de conceptos</td><td style=3D"padding:0.75pt">Se relacionan tipos de biomas con sus caracter=C3=ADsti= cas clim=C3=A1ticas.</td></tr><tr><td style=3D"padding:0.75pt"><= p style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">El clima</td><td style=3D"p= adding:0.75pt">Juego de preguntas</sp= an></td><td style=3D"padding:0.75pt">Evaluaci=C3=B3n sobre los diferentes tipos de clima presentes en el pla= neta.</td></tr><tr><td style=3D"padding:0.75pt">Factores que cambian el clima</td><td styl= e=3D"padding:0.75pt">Asociaci=C3=B3n<= /span></td><td style=3D"padding:0.75pt">Los estudiantes relacionan factores naturales con cambios en el clim= a.</td></tr><tr><td style=3D"padding:0.75pt">Elementos y factores del clima</td><td style= =3D"padding:0.75pt">Cuestionario</spa= n></td><td style=3D"padding:0.75pt">Identificaci=C3=B3n de elementos como temperatura, presi=C3=B3n y humeda= d.</td></tr></tbody></table>Nota: Informaci=C3=B3n recopilada de la plataforma Educaplay.</= div> <div class=3D"= Section_7"><div style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoA= AAANSUhEUgAAAUIAAABPCAYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQ= BlSsOGwAADrlJREFUeJztnX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMMcYY48o6Ql= 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"825" height=3D"1127" alt=3D"Imagen que contiene Interfaz de usuario gr=C3= =A1fica Descripci=C3=B3n generada autom=C3=A1ticamente" style=3D"m= argin-top:-34.85pt; margin-left:-88.65pt; position:absolute" /><span= style=3D"font-family:'Times New Roman'; color:#ffffff"> <p= class=3D"Header"> </div>El uso de actividades de relaci=C3=B3n de conce= ptos result=C3=B3 especialmente =C3=BAtil para que los estudiantes comprend= ieran la relaci=C3=B3n entre los diferentes biomas y las condiciones clim= =C3=A1ticas que los caracterizan; a trav=C3=A9s de este tipo de ejercicios,= los estudiantes deb=C3=ADan vincular conceptos como temperatura, precipita= ci=C3=B3n y tipos de vegetaci=C3=B3n con los diferentes ambientes naturales= del planeta. Este proceso favorece el aprendizaje significativo, debido a = que permite que los estudiantes establezcan conexiones entre distintos elem= entos del sistema clim=C3=A1tico. Por otra parte, los cuestionarios y juegos de pre= guntas permitieron evaluar la comprensi=C3=B3n de los estudiantes respecto = a los elementos y factores que influyen en el clima, como la temperatura, l= a presi=C3=B3n atmosf=C3=A9rica y la humedad. La din=C3=A1mica de juego inc= orporada en estas actividades, debido a que deb=C3=ADan responder preguntas= en un tiempo determinado y obtener una puntuaci=C3=B3n seg=C3=BAn sus acie= rtos. La Tabla 5 muestra las actividades relacionadas con el estudio de los mar= es y oc=C3=A9anos del mundo, un contenido fundamental dentro de la geograf= =C3=ADa f=C3=ADsica; estas actividades incluyeron cuestionarios, juegos de = identificaci=C3=B3n, mapas interactivos y ejercicios de relaci=C3=B3n conce= ptual que permitieron a los estudiantes comprender la importancia de los cu= erpos de agua en el equilibrio del planeta. <div style=3D"clear:= both"><s= pan style=3D"height:0pt; display:block; position:absolute; z-index:-65535">= <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAABCAYAAACWs= rt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSIntwTEBAAAI= wKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740" height=3D= "1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-63.3pt; position:ab= solute" /> &#= xa0; = ; Nomb= re </sp= an>1 </div></div> <div class=3D"Section_8">Tabla 5Tem=C3=A1tica: mares y oc=C3=A9anos del mundo<table style=3D"ma= rgin-bottom:0pt; border-top:0.75pt solid #000000; border-bottom:0.75pt soli= d #000000; padding:0pt; border-spacing:1.5pt"><thead><tr><td style=3D"paddi= ng:0.75pt">Juego</= span></td><td style=3D"padding:0.75pt">Actividad</td><td style=3D"padding:0.75= pt">Funcionamiento= </td></tr></thead><tbody><tr><td style=3D"border-top:0.75pt soli= d #000000; padding:0.75pt">Los mares<= /span></td><td style=3D"border-top:0.75pt solid #000000; padding:0.75pt= ">Cuestionario</td><td sty= le=3D"border-top:0.75pt solid #000000; padding:0.75pt">Preguntas sobre caracter=C3=ADsticas y ubicaci=C3=B3n de = los mares del mundo.</td></tr><tr><td style=3D"padding:0.75pt"><= p style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">Los climas</td><td style=3D= "padding:0.75pt">Relaci=C3=B3n concep= tual</td><td style=3D"padding:0.75pt">Asociaci=C3=B3n entre climas y la influencia de los oc=C3=A9ano= s.</td></tr><tr><td style=3D"padding:0.75pt">Oc=C3=A9anos y mares del mundo</td><td style= =3D"padding:0.75pt">Mapa interactivo<= /span></td><td style=3D"padding:0.75pt">Los estudiantes deben ubicar los principales oc=C3=A9anos del planet= a en el mapa.</td></tr><tr><td style=3D"padding:0.75pt">Continentes, oc=C3=A9anos y mares<= /td><td style=3D"padding:0.75pt">Jueg= o de identificaci=C3=B3n</td><td style=3D"padding:0.75pt">Reconocimiento de la relaci=C3=B3n entre co= ntinentes y cuerpos de agua.</td></tr><tr><td style=3D"padding:0= .75pt"><= span style=3D"font-family:'Times New Roman'">Impacto de corrientes marinas<= /span></td><td style=3D"padding:0.75pt">Cuestionario</td><td style=3D"padding:0.75pt">Comprensi=C3=B3n de c=C3=B3mo las corrientes oce= =C3=A1nicas influyen en el clima.</td></tr></tbody></table>Nota: Informaci=C3=B3n recopilada de la pla= taforma Educaplay.</div> <div class=3D"Section_9">Uno de los aspectos m=C3=A1s relevantes de esta tem= =C3=A1tica fue el uso de mapas interactivos para la identificaci=C3=B3n de = los principales oc=C3=A9anos y mares del mundo; mediante este tipo de activ= idades, los estudiantes deb=C3=ADan ubicar correctamente los oc=C3=A9anos e= n el mapa, lo que contribuye a fortalecer sus habilidades de localizaci=C3= =B3n geogr=C3=A1fica, adem=C3=A1s, este tipo de recurso facilita la compren= si=C3=B3n de la distribuci=C3=B3n de los cuerpos de agua en el planeta y su= relaci=C3=B3n con los continentes.Igualmente, las actividades relacionadas con las= corrientes marinas permitieron analizar la influencia que tienen los oc=C3= =A9anos en el clima del mundo; a trav=C3=A9s de cuestionarios y ejercicios = conceptuales, los estudiantes pudieron comprender c=C3=B3mo las corrientes = marinas influyen en la temperatura de diferentes regiones del planeta. En g= eneral, estas actividades contribuyeron a desarrollar una visi=C3=B3n m=C3= =A1s amplia del funcionamiento del sistema natural de la tierra y de la int= eracci=C3=B3n entre oc=C3=A9anos, clima y continentes. La Tabla 6 muestra las actividades enfocadas en el estudio de las region= es del Ecuador, espec=C3=ADficamente en la regi=C3=B3n costa; estas activid= ades se desarrollaron mediante juegos de asociaci=C3=B3n, mapas interactivo= s, cuestionarios e identificaci=C3=B3n geogr=C3=A1fica, con el prop=C3=B3si= to de fortalecer el conocimiento del territorio nacional y sus caracter=C3= =ADsticas culturales y clim=C3=A1ticas. </div> <div class=3D"Section_10"><div style= =3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAUIAAABPC= AYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAADrlJREFUeJzt= nX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMMcYY48o6Qlc2M6KNq1kXBbuprdYOWjs= nRarir4l2jNZag6jMabV0astgprMtiqICVVEQ2BW4XMTX/nieL/dzP9/zPT++98KF8bwScr/n8+= P5POccvs/3nM/nnOctBUEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBNUAaxjNkJefC= awFdgN7gB8AFwG3Zv17tgO+lNjdA3wq63tnNvaSBv6eAawD9gKDwIoe7SYBi4FngZ3Aw0ndSuAt= 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regiones del Ecu= ador<table style=3D"margin-bottom:0pt; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:0pt; border-spacing:1.5= pt"><thead><tr><td style=3D"padding:0.75pt">Juego</td><td style=3D"padding:0.75pt"= >Actividad<= /p></td><td style=3D"padding:0.75pt">Funcionamiento</td></tr></thead><tbody><tr><t= d style=3D"border-top:0.75pt solid #000000; padding:0.75pt">Etnias del Ecuador (Costa)</td><td style= =3D"border-top:0.75pt solid #000000; padding:0.75pt">Asociaci=C3=B3n</td><td style=3D"border-top:0.75= pt solid #000000; padding:0.75pt">Los= estudiantes relacionan grupos =C3=A9tnicos con su ubicaci=C3=B3n geogr=C3= =A1fica.</td></tr><tr><td style=3D"padding:0.75pt">Provincias de la costa del Ecuador</td>= <td style=3D"padding:0.75pt">Mapa int= eractivo</td><td style=3D"padding:0.75pt">Identificaci=C3=B3n de provincias dentro de la regi=C3=B3n = Costa.</td></tr><tr><td style=3D"padding:0.75pt">Culturas y etnias del Ecuador</td><td sty= le=3D"padding:0.75pt">Cuestionario</s= pan></td><td style=3D"padding:0.75pt">Preguntas sobre diversidad cultural del pa=C3=ADs.</td></tr= ><tr><td style=3D"padding:0.75pt">Cli= ma de la regi=C3=B3n Costa</td><td style=3D"padding:0.75pt">Juego conceptual</td><td styl= e=3D"padding:0.75pt">Relaci=C3=B3n en= tre clima y caracter=C3=ADsticas de la regi=C3=B3n.</td></tr><tr= ><td style=3D"padding:0.75pt">Provinc= ias de la costa</td><td style=3D"padding:0.75pt">Identificaci=C3=B3n geogr=C3=A1fica</td><= td style=3D"padding:0.75pt">Ubicaci= =C3=B3n de provincias en el mapa del Ecuador.</td></tr></tbody><= /table><s= pan style=3D"font-family:'Times New Roman'">Nota: Informaci=C3=B3n recopila= da de la plataforma Educaplay.<div style=3D"clear:both"><img src=3D"da= ta:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAABCAYAAACWsrt9AAAABHNCSVQ= ICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSIntwTEBAAAIwKDZP7TG8AGm2g= AAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740" height=3D"1" alt=3D"" s= tyle=3D"margin-top:-37.82pt; margin-left:-63.3pt; position:absolute" /></sp= an> = 0; = Nombre<span= style=3D"width:141.26pt; letter-spacing:3pt; display:inline-block"> <= /span> 1 </div></div> <div class=3D"Section_11">El uso de mapas interactivos permiti=C3=B3 que lo= s estudiantes identifiquen las provincias que conforman la regi=C3=B3n cost= a del Ecuador, facilitando la comprensi=C3=B3n de la organizaci=C3=B3n terr= itorial del pa=C3=ADs. Este tipo de actividad es fundamental para el desarr= ollo de habilidades geogr=C3=A1ficas, debido a que permite relacionar los c= ontenidos te=C3=B3ricos con representaciones visuales del espacio geogr=C3= =A1fico. Adem=C3=A1s, las actividades relacionadas con las culturas y etnia= s del Ecuador contribuyeron a reforzar el conocimiento sobre la diversidad = cultural del pa=C3=ADs; mediante cuestionarios y ejercicios de asociaci=C3= =B3n, los estudiantes pudieron reconocer la presencia de diferentes grupos = =C3=A9tnicos en la regi=C3=B3n costa y comprender la importancia de la dive= rsidad cultural dentro de la sociedad ecuatoriana. En conjunto, estas activ= idades permitieron integrar conocimientos geogr=C3=A1ficos, culturales y so= ciales dentro del proceso de aprendizaje.La Tabla 7 i= ndica las actividades relacionadas con el estudio de las etnias del Ecuador= , las cuales fueron dise=C3=B1adas para promover el conocimiento y la valor= aci=C3=B3n de la diversidad cultural del pa=C3=ADs. Estas actividades inclu= yeron cuestionarios, juegos de preguntas, ejercicios de asociaci=C3=B3n y a= ctividades de relaci=C3=B3n conceptual que permitieron a los estudiantes id= entificar los diferentes pueblos y nacionalidades que conforman la sociedad= ecuatoriana. Tabla 7Tem=C3=A1tica: etnias del Ecuado= r<table style=3D"margin-bottom:0pt; border-top:0.75pt solid #000= 000; border-bottom:0.75pt solid #000000; padding:0pt; border-spacing:1.5pt"= ><thead><tr><td style=3D"padding:0.75pt">Juego</td><td style=3D"padding:0.75pt"><p= style=3D"margin-bottom:0pt; text-align:center; line-height:normal; font-si= ze:10pt">Actividad= </td><td style=3D"padding:0.75pt">Funcionamiento</td></tr></thead><tbody><tr><td s= tyle=3D"border-top:0.75pt solid #000000; padding:0.75pt">Culturas y etnias del Ecuador</td><td style= =3D"border-top:0.75pt solid #000000; padding:0.75pt">Cuestionario</td><td style=3D"border-top:0.75pt = solid #000000; padding:0.75pt">Recono= cimiento de los diferentes pueblos y culturas del pa=C3=ADs.</td= ></tr><tr><td style=3D"padding:0.75pt">Etnias del Ecuador (Costa)</td><td style=3D"padding:0.75pt"><p= style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">Asociaci=C3=B3n</td><td styl= e=3D"padding:0.75pt">Relaci=C3=B3n en= tre grupos =C3=A9tnicos y regi=C3=B3n geogr=C3=A1fica.</td></tr>= <tr><td style=3D"padding:0.75pt">Etni= as del Ecuador</td><td style=3D"padding:0.75pt">Juego de preguntas</td><td style=3D"paddin= g:0.75pt">Evaluaci=C3=B3n sobre carac= ter=C3=ADsticas culturales de cada grupo =C3=A9tnico.</td></tr><= tr><td style=3D"padding:0.75pt">Diver= sidad del Ecuador</td><td style=3D"padding:0.75pt">Relaci=C3=B3n conceptual</td><td style= =3D"padding:0.75pt">Asociaci=C3=B3n e= ntre diversidad cultural y regiones del pa=C3=ADs.</td></tr><tr>= <td style=3D"padding:0.75pt">Etnias d= el Ecuador (actividad adicional)</td><td style=3D"padding:0.75pt= ">Cuestionario</td><td sty= le=3D"padding:0.75pt">Identificaci=C3= =B3n de costumbres y tradiciones de los pueblos ecuatorianos.</t= d></tr><tr><td style=3D"padding:0.75pt">Ecuador multi=C3=A9tnico y cultural</td><td style=3D"padding:= 0.75pt">= Juego de comprensi=C3=B3n</sp= an></td><td style=3D"padding:0.75pt">Preguntas sobre el car=C3=A1cter multicultural del pa=C3=ADs.</p= ></td></tr><tr><td style=3D"padding:0.75pt">Ecuador intercultural</td><td style=3D"padding:0.75pt"><p= style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">Asociaci=C3=B3n conceptual</= td><td style=3D"padding:0.75pt">Relac= i=C3=B3n entre cultura, identidad y diversidad del Ecuador.</td>= </tr></tbody></table>Nota: Informaci= =C3=B3n recopilada de la plataforma Educaplay.</div> <div class=3D"Section_12"><div= style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAUI= AAABPCAYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAADrlJRE= FUeJztnX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMMcYY48o6Qlc2M6KNq1kXBbupr= dYOWjsnRarir4l2jNZag6jMabV0astgprMtiqICVVEQ2BW4XMTX/nieL/dzP9/zPT++98KF8bwS= cr/n8+P5POccvs/3nM/nnOctBUEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBNUAaxj= NkJefCawFdgN7gB8AFwG3Zv17tgO+lNjdA3wq63tnNvaSBv6eAawD9gKDwIoe7SYBi4FngZ3Aw0= ndSuAt9/mhfuxnfWYDz7i9fcAm4MqsTdWYTfpvopvL63wLgqABwADwj/7FehKY7OXPAi8B5wNnA= 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asociaci=C3=B3n y relaci=C3=B3n conceptual p= ermitieron que los estudiantes vinculen los diferentes grupos =C3=A9tnicos = con las regiones del pa=C3=ADs donde se encuentran; este tipo de ejercicio = facilita la comprensi=C3=B3n de la diversidad cultural del Ecuador y su dis= tribuci=C3=B3n territorial, lo cual es un aspecto fundamental dentro de los= estudios sociales.Por otra parte, los cuestionarios y juegos de comprensi=C3=B3n p= ermitieron evaluar el conocimiento de los estudiantes sobre las costumbres,= tradiciones y caracter=C3=ADsticas culturales de los diferentes pueblos de= l Ecuador. Estas actividades no solo contribuyen al aprendizaje de contenid= os acad=C3=A9micos, sino que tambi=C3=A9n fomentan valores como el respeto,= la interculturalidad y la valoraci=C3=B3n de la identidad. De esta manera,= el uso de herramientas digitales como Educaplay favorece el desarrollo de = un aprendizaje significativo que integra conocimientos, habilidades y valor= es. 3.3 Motivaci=C3=B3n hacia el aprendizaje significativo</span= >La <spa= n style=3D"font-family:'Times New Roman'; font-weight:bold">Figura 1= muestra los resultados compa= rativos entre pretest y postest en relaci=C3=B3n con los niveles de motivac= i=C3=B3n hacia el aprendizaje significativo en la asignatura de estudios so= ciales, luego de la aplicaci=C3=B3n de estrategias de gamificaci=C3=B3n med= iante la plataforma Educaplay. En t=C3=A9rminos generales, se observa un in= cremento significativo en todas las dimensiones evaluadas, lo que evidencia= que la incorporaci=C3=B3n de herramientas digitales y actividades l=C3=BAd= icas contribuy=C3=B3 a fortalecer la motivaci=C3=B3n de los estudiantes y m= ejorar su disposici=C3=B3n hacia el aprendizaje. Cabe destacar que, para el an=C3=A1lisis de los resul= tados del pretest y postest se utiliz=C3=B3 la metodolog=C3=ADa de medias, = la cual consiste en el c=C3=A1lculo de promedio de las respuestas obtenidas= en cada dimensi=C3=B3n evaluada. Este procedimiento permite identificar el= nivel general de percepci=C3=B3n o valoraci=C3=B3n de los participantes re= specto a un determinado aspecto del estudio; adem=C3=A1s, se debe considera= r los niveles de escala de Likert planteada representada con un valor num= =C3=A9rico del 1 al 5, correspondiendo a la escala de nunca a siempre. De e= sta manera, los valores m=C3=A1s cercanos a 1 indican una baja frecuencia o= presencia del fen=C3=B3meno analizado, mientras que los valores cercanos a= 5 reflejan una mayor frecuencia o percepci=C3=B3n positiva. El uso de la m= edia aritm=C3=A9tica permite sintetizar la informaci=C3=B3n recolectada y c= omparar los resultados obtenidos entre el pretest y el postest, facilitando= as=C3=AD la identificaci=C3=B3n de cambios o mejoras en las variables anal= izadas durante el proceso de intervenci=C3=B3n educativa. <div s= tyle=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQ= AAAABCAYAAACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJRE= FUSIntwTEBAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"= 740" height=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-63.3p= t; position:absolute" /> &#x= a0; Nombre 1<= /span> </div></div> <div class=3D"Section_13"><= div style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA= AUIAAABPCAYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAADrl= JREFUeJztnX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMMcYY48o6Qlc2M6KNq1kXBb= 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La utilizaci=C3=B3n de= juegos interactivos, retos y actividades din=C3=A1micas permiti=C3=B3 tran= sformar a experiencia educativa en un proceso m=C3=A1s atractivo, favorecie= ndo la participaci=C3=B3n activa y el compromiso con los contenidos abordad= os. En c= uanto a las metodolog=C3=ADas y clases tradicionales, los resultados muestr= an un incremento de 3,88 a 4,28, lo que indica que los estudiantes percibie= ron una mejora en la forma en que se desarrollaron las clases despu=C3=A9s = de implementar estrategias innovadoras. Este resultado sugiere que la integ= raci=C3=B3n de metodolog=C3=ADas activas, combinadas con recursos digitales= , permiti=C3=B3 superar significativamente las limitaciones de los m=C3=A9t= odos tradicionales centrados =C3=BAnicamente en la exposici=C3=B3n del doce= nte, promoviendo un aprendizaje m=C3=A1s din=C3=A1mico y participativo.</sp= an>De igual man= era, las dimensiones gamificaci=C3=B3n y uso de juegos, aprendizaje signifi= cativo y uso de herramientas digitales muestran incrementos importantes; la= gamificaci=C3=B3n pas=C3=B3 de 4,15 a 4,66, el aprendizaje significativo d= e 3,36 a 4,53, y el uso de herramientas digitales de 4,09 a 4,86, siendo es= ta =C3=BAltima una de las puntuaciones m=C3=A1s altas en el postest. Estos = resultados evidencian que la incorporaci=C3=B3n de recursos tecnol=C3=B3gic= os interactivos favorece la comprensi=C3=B3n de los contenidos y fortalece = el aprendizaje significativo, debido a que los estudiantes no solo reciben = informaci=C3=B3n, sino que participan activamente en la construcci=C3=B3n d= e su conocimiento a trav=C3=A9s de experiencias educativas din=C3=A1micas y= motivadoras. Por otra parte la Tabla 8 muestra la prueba de hip=C3=B3tesis que se realiz=C3=B3 mediante el c= oeficiente de correlaci=C3=B3n Rho de Spearman, con el prop=C3=B3sito de id= entificar la relaci=C3=B3n existente entre las variables de estudio; para e= ste caso se tom=C3=B3 en consideraci=C3=B3n las dimensiones del pretest y p= ostest por lo cual se pudo medir a la variable gamificaci=C3=B3n, motivaci= =C3=B3n y aprendizaje significativo por separado sin perder el prop=C3=B3si= to de la investigaci=C3=B3n. <div style=3D"clear:both"><img src=3D"da= ta:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAABCAYAAACWsrt9AAAABHNCSVQ= ICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSIntwTEBAAAIwKDZP7TG8AGm2g= AAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740" height=3D"1" alt=3D"" s= tyle=3D"margin-top:-37.82pt; margin-left:-63.3pt; position:absolute" /></sp= an> = 0; = Nombre<span= style=3D"width:141.26pt; letter-spacing:3pt; display:inline-block"> <= /span> 1 </div></div> <div class=3D"Section_15">Tabla 8Prueba de hip=C3=B3tesis<table style=3D"width:93.16%; margi= n-bottom:0pt; padding:0pt; border-collapse:collapse"><tr style=3D"height:20= .8pt"><td style=3D"width:16.48%; border-top:0.75pt solid #000000; border-bo= ttom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"> </td><td style=3D"width:35.2= 2%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pa= dding:0pt 5.4pt; vertical-align:middle"> </td><td style=3D"width:16.52%; border-top:0.75pt solid= #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-a= lign:middle">Gamificaci=C3=B3n= </td><td style=3D"width:15.68%; border-top:0.75pt solid #000000; border= -bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:middle">Motivaci=C3=B3n</span= ></td><td style=3D"width:16.06%; border-top:0.75pt solid #000000; borde= r-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:middle"><p= style=3D"margin-bottom:0pt; text-align:center; line-height:normal; font-si= ze:10pt">Aprendizaje signific= ativo</td></tr><tr style=3D"height:34.2pt"><td rowspan=3D"3" sty= le=3D"width:16.48%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; ver= tical-align:top">Gamificaci=C3=B3n</s= pan>= </td><td sty= le=3D"width:35.22%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; ver= tical-align:middle">Correlaci=C3=B3n = Rho de Spearman</td><td style=3D"width:16.52%; border-top:0.75pt= solid #000000; padding:0pt 5.4pt; vertical-align:middle">1</td><td style=3D"width:= 15.68%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:= middle">,785= **</td><td style=3D"width:16.06%; border-top:0.75pt s= olid #000000; padding:0pt 5.4pt; vertical-align:middle">,759**</t= d></tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; padding:0pt 5= .4pt; vertical-align:middle">Sig. (un= ilateral) </td><td style=3D"width:16.52%; padding:0pt 5.4pt; ver= tical-align:middle"> </td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertic= al-align:middle">,0= 00</td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-al= ign:middle">,000</s= pan></td></tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; pa= dding:0pt 5.4pt; vertical-align:middle">N</td><td style=3D"width:16.52%; padding:0pt 5.4pt; vertical-= align:middle">48</s= pan></td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-align:m= iddle">48</p= ></td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align:middle">= 48</td><= /tr><tr style=3D"height:22.8pt"><td rowspan=3D"3" style=3D"width:16.48%; pa= dding:0pt 5.4pt; vertical-align:top">= Motivaci=C3=B3n </spa= n><s= pan style=3D"font-family:'Times New Roman'"> </td><td style= =3D"width:35.22%; padding:0pt 5.4pt; vertical-align:middle">Correlaci=C3=B3n Rho de Spearman</td><td = style=3D"width:16.52%; padding:0pt 5.4pt; vertical-align:middle">,785**</s= pan></td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-align:m= iddle">1= </td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align:middle"><= p style=3D"margin-bottom:0pt; text-align:right; line-height:normal; font-si= ze:10pt">,804</td>= </tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; padding:0pt 5.4= pt; vertical-align:middle">Sig. (unil= ateral)</td><td style=3D"width:16.52%; padding:0pt 5.4pt; vertic= al-align:middle">,0= 00</td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-al= ign:middle"> <= /span></td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align= :middle">,000</span= ></td></tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; paddi= ng:0pt 5.4pt; vertical-align:middle">= N</td><td style=3D"width:16.52%; padding:0pt 5.4pt; vertical-ali= gn:middle">48</span= ></td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-align:midd= le">48</= td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align:middle">48</td></tr= ><tr style=3D"height:22.8pt"><td rowspan=3D"3" style=3D"width:16.48%; paddi= ng:0pt 5.4pt; vertical-align:top">Apr= endizaje significativo = 0; </td><t= d style=3D"width:35.22%; padding:0pt 5.4pt; vertical-align:middle">Correlaci=C3=B3n Rho de Spearman</= td><td style=3D"width:16.52%; padding:0pt 5.4pt; vertical-align:middle">,759= **</td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-al= ign:middle">,804</s= pan></td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align:m= iddle">1= </td></tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; padding:0p= t 5.4pt; vertical-align:middle">Sig. = (unilateral)</td><td style=3D"width:16.52%; padding:0pt 5.4pt; v= ertical-align:middle">,000</td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertic= al-align:middle">,0= 00</td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-al= ign:middle"> <= /span></td></tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; = padding:0pt 5.4pt; vertical-align:middle">N</td><td style=3D"width:16.52%; padding:0pt 5.4pt; vertica= l-align:middle">48<= /span></td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-align= :middle">48<= /p></td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align:middle= ">48</td= ></tr><tr style=3D"height:18.6pt"><td colspan=3D"4" style=3D"border-bottom:= 0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">**. La correlaci=C3=B3n es significativa en el nive= l 0,01 (unilateral).</td><td style=3D"width:16.06%; border-botto= m:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:middle"> </td></tr= ></table>= Nota: datos obtenidos de la i= nvestigaci=C3=B3n en la Unidad Educativa San Agust=C3=ADn de Guayaquil</spa= n></div> <div c= lass=3D"Section_16"><div style=3D"clear:both"><img src=3D"data:image/png;base64,iVB= ORw0KGgoAAAANSUhEUgAAAUIAAABPCAYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAA= AOxAAADsQBlSsOGwAADrlJREFUeJztnX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMM= cYY48o6Qlc2M6KNq1kXBbuprdYOWjsnRarir4l2jNZag6jMabV0astgprMtiqICVVEQ2BW4XMTX= /nieL/dzP9/zPT++98KF8bwScr/n8+P5POccvs/3nM/nnOctBUEQBEEQBEEQBEEQBEEQBEEQBEE= QBEEQBEEQBEEQBEEQBNUAaxjNkJefCawFdgN7gB8AFwG3Zv17tgO+lNjdA3wq63tnNvaSBv6eAa= wD9gKDwIoe7SYBi4FngZ3Aw0ndSuAt9/mhfuxnfWYDz7i9fcAm4MqsTdWYTfpvopvL63wLgqABw= ADwj/7FehKY7OXPAi8B5wNnAdcDrwO3Z/1r23kdwAvAQNb/POBN4JyG/j5bEhDuyNqcC7zodT8F= 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valor con el nivel de significancia asumida d= e 0,05 el p =E2=80=93 valor de la correlaci=C3=B3n es menor (0,000 < 0,0= 5). Esto significa que existe una probabilidad muy baja de que estas relaci= ones se deban al azar, lo que permite afirmar que las variables analizadas = presentan una asociaci=C3=B3n importante dentro del proceso educativo. </sp= an>En primer lu= gar, se observa una correlaci=C3=B3n alta entre la gamificaci=C3=B3n y la m= otivaci=C3=B3n, con un coeficiente de Rho =3D 0,735; este resultado evidenc= ia que, a mayor uso de estrategias de gamificaci=C3=B3n dentro del proceso = de ense=C3=B1anza, mayor es el nivel de motivaci=C3=B3n de los estudiantes = hacia el aprendizaje. La incorporaci=C3=B3n de actividades l=C3=BAdicas, di= n=C3=A1micas interactivas y herramientas digitales favorece la participaci= =C3=B3n activa del estudiante, generando mayor inter=C3=A9s y compromiso co= n las actividades acad=C3=A9micas. En este sentido, la gamificaci=C3=B3n se= convierte en un recurso pedag=C3=B3gico relevante para transformar las cla= ses tradicionales en experiencias m=C3=A1s atractivas y participativas. </s= pan><= span style=3D"font-family:'Times New Roman'">Por otro lado, tambi=C3=A9n se= identifican relaciones significativas entre gamificaci=C3=B3n y aprendizaj= e significativo Rho =3D 0,759, as=C3=AD como entre la motivaci=C3=B3n y apr= endizaje significativo Rho =3D 0,804, siendo esta =C3=BAltima la correlaci= =C3=B3n m=C3=A1s alta; indicando que cuando los estudiantes presentan mayor= es niveles de motivaci=C3=B3n, tienden a desarrollar un aprendizaje m=C3=A1= s profundo y comprensivo de los contenidos. Es as=C3=AD como, el uso de est= rategias gamificadas contribuye a fortalecer el proceso de aprendizaje sign= ificativo, debido a que promueve la interacci=C3=B3n, la resoluci=C3=B3n de= retos y la participaci=C3=B3n activa, elementos que facilitan la construcc= i=C3=B3n del conocimiento. En consecuencia, los resultados permiten aceptar= la hip=C3=B3tesis de investigaci=C3=B3n, confirmando que la gamificaci=C3= =B3n se relaciona positivamente con la motivaci=C3=B3n hacia el aprendizaje= significativo en el contexto educativo analizado. 4. = Discusi=C3=B3nLos resultados obtenidos en la investigaci=C3=B3n evidenc= ian que la incorporaci=C3=B3n de estrategias de gamificaci=C3=B3n basadas e= n la plataforma Educaplay contribuye de manera significativa al fortalecimi= ento de la motivaci=C3=B3n y del aprendizaje significativo en estudiantes d= e educaci=C3=B3n b=C3=A1sica. Los resultados del pretest y postest muestran= un incremento notable en todas las dimensiones analizadas, especialmente e= n la motivaci=C3=B3n hacia el aprendizaje y el uso de herramientas digitale= s. Estos hallazgos coinciden con los planteamientos te=C3=B3ricos de autore= s como P=C3=A9rez & Barrio (2021) y S=C3=A1nchez (2023) quienes se=C3= =B1alan que el juego constituye un recurso pedag=C3=B3gico natural que favo= rece la participaci=C3=B3n activa del estudiante, estimula la curiosidad y = facilita la construcci=C3=B3n del conocimiento. En este sentido, la gamific= aci=C3=B3n permiti=C3=B3 transformar las clases tradicionales de estudios s= ociales en experiencias m=C3=A1s din=C3=A1micas, participativas y centradas= en el estudiante. De igual forma, los resultados de an=C3=A1lisis correlacional ev= idencian relaciones positivas y significativas entre las variables de gamif= icaci=C3=B3n, motivaci=C3=B3n y aprendizaje significativo; esto confirma qu= e el inter=C3=A9s y la disposici=C3=B3n del estudiante influyen directament= e en la profundidad con la que se asimilan los contenidos. Estos resultados= se relacionan con los postulados de la teor=C3=ADa del aprendizaje signifi= cativo de David Ausubel (1980) quien plantea que el aprendizaje se produce = de forma m=C3=A1s efectiva cuando el estudiante posee una actitud favorable= para aprender y puede relacionar los nuevos conocimientos con sus experien= cias previas. De esta manera, la motivaci=C3=B3n act=C3=BAa como un element= o clave que permite la integraci=C3=B3n cognitiva de los contenidos y el de= sarrollo de aprendizajes duraderos. Por otra parte, los resultados tambi=C3=A9n coinciden con investig= aciones previas desarrolladas en el =C3=A1mbito educativo latinoamericano; = estudios como los de Prieto et al. (2022) y Sep=C3=BAlveda & Mayoral (2= 025) destacan que la gamificaci=C3=B3n incrementa la motivaci=C3=B3n intr= =C3=ADnseca y mejora el rendimiento acad=C3=A9mico al incorporar din=C3=A1m= icas de juego, retos y sistemas de retroalimentaci=C3=B3n inmediata. De igu= al forma, Buestan et al. (2025) se=C3=B1alan que estas estrategias contribu= yen al desarrollo de habilidades sociales, emocionales y cognitivas. En con= cordancia con estas investigaciones, el presente estudio demuestra que la u= tilizaci=C3=B3n de herramientas digitales gamificadas no solo favorece la c= omprensi=C3=B3n de los contenidos de estudios sociales, sino que tambi=C3= =A9n promueve un aprendizaje activo, participativo y contextualizado, capaz= de responder a las necesidades educativas actuales. 5. Con= clusionesLa implementaci=C3=B3n de una estrategia de gamificaci=C3=B3n basada en la= plataforma Educaplay permiti=C3=B3 incrementar significativamente la motiv= aci=C3=B3n de los estudiantes hacia el aprendizaje en la asignatura de estu= dios sociales, evidenciado en el aumento de las medias obtenidas entre el p= retest y el postest en todas las dimensiones evaluadas. Los resultados del an=C3=A1= lisis correlacional demostraron que existe una relaci=C3=B3n positiva y est= ad=C3=ADsticamente significativa entre la gamificaci=C3=B3n, la motivaci=C3= =B3n y el aprendizaje significativo, lo que confirma que el inter=C3=A9s de= l estudiante es un factor determinante en la construcci=C3=B3n del conocimi= ento. La gamificaci=C3=B3n = mediada por herramientas digitales se consolida como una estrategia pedag= =C3=B3gica innovadora y eficaz que permite transformar las metodolog=C3=ADa= s tradicionales en experiencias de aprendizaje m=C3=A1s din=C3=A1micas, par= ticipativas y contextualizadas, favoreciendo el desarrollo de habilidades c= ognitivas, sociales y actitudinales en los estudiantes. 6. = ; Conflicto de interesesLos autores declaran que no existe conflicto de intereses en = relaci=C3=B3n con el estudio presentado. 7. = 0; Derechos de autor (copyrigth)Los autores son los titulares de los derechos de autor</span= > <s= pan style=3D"font-family:'Times New Roman'">(patrimoniales y/o de explotaci= =C3=B3n) de los contenidos de la revista (copyright).8. </s= pan>Declaraci=C3=B3n de contribuci=C3=B3n de los autoresLos autores declaran ha= ber participado de manera equitativa y sustancial en la realizaci=C3=B3n de= esta investigaci=C3=B3n, bajo las responsabilidades seg=C3=BAn la <= a href=3D"https://credit.niso.org/" style=3D"text-decoration:none">Taxonom=C3=ADa CR= ediT</a> para describi= r las contribuciones individuales de cada autor al trabajo. Cindy Karina Chug=C3=A1= Fuentes y Esmelinger Thais Magallanes Tabares realizaron la conceptualizac= i=C3=B3n de la investigaci=C3=B3n, organizaci=C3=B3n y ejecuci=C3=B3n del e= nsayo, recopilaci=C3=B3n y curaci=C3=B3n de los datos, revisi=C3=B3n del an= =C3=A1lisis estad=C3=ADstico, interpretaci=C3=B3n de los resultados, elabor= aci=C3=B3n del borrador original, preparaci=C3=B3n de tablas y figuras, rev= isi=C3=B3n cr=C3=ADtica del contenido y aprobaci=C3=B3n de la versi=C3=B3n = final del manuscrito.Johana del Carmen Parre=C3=B1o S=C3=A1nchez y Hendy Maier P=C3= =A9rez Barrera, realizaron la supervisi=C3=B3n acad=C3=A9mica y metodol=C3= =B3gica de la investigaci=C3=B3n, brind=C3=B3 orientaci=C3=B3n durante el d= esarrollo del ensayo, revis=C3=B3 el an=C3=A1lisis y la interpretaci=C3=B3n= de los resultados, efectu=C3=B3 la revisi=C3=B3n cr=C3=ADtica del manuscri= to, realiz=C3=B3 la correcci=C3=B3n cient=C3=ADfica y acad=C3=A9mica y apro= b=C3=B3 la versi=C3=B3n final para su publicaci=C3=B3n.Divulgac= i=C3=B3n de la delegaci=C3=B3n a la IA generativa<span= style=3D"font-family:'Times New Roman'">Los autores declaran que emplearon= ChatGPT (OpenAI, GPT-5.5) como herramienta de apoyo para reorganizar y ref= ormular editorialmente el manuscrito original, corregir el estilo, traducir= el resumen al ingl=C3=A9s y comprobar el cumplimiento formal del formato d= e la revista. Los datos experimentales, el dise=C3=B1o, los resultados esta= d=C3=ADsticos y las decisiones cient=C3=ADficas proceden de la investigaci= =C3=B3n de las autoras y no fueron generados ni modificados por la herramie= nta. Los autores revisaron, contrastaron y aprobaron cada secci=C3=B3n y as= umen la responsabilidad =C3=ADntegra del contenido final. La herramienta no= figura como autora ni asume responsabilidad por los resultados.= Declaraci=C3=B3n presentada por: Cindy= Karina Chug=C3=A1 Fuentes.9. </sp= an>Costos de financiamiento La presente investigaci=C3=B3n fue financiada en su= totalidad con fondos propios de los autores.10. <s= pan style=3D"font-family:'Times New Roman'; font-weight:bold; color:#767171= ">Referencias bibliogr=C3=A1ficas <h1 style=3D"margin-top:0pt; m= argin-left:35.4pt; text-indent:-35.4pt; line-height:115%">Abarca Zaquinaula, A. (2025). Metodolog=C3=ADas Activas en Ecuador: apro= ximaci=C3=B3n a la revisi=C3=B3n de literatura de aprendizaje basado en pro= yectos, aprendizaje basado en problemas y aula invertida. MLS Educational Research, 9(1), 175-192. <a href=3D"https://doi.org/10.29314/mlser.v9i1= .2429" style=3D"text-decoration:none">https://doi.= org/10.29314/mlser.v9i1.2429</a> </h1>Ausubel, David P. (1= 980). Psicolog=C3=ADa educativa. Trillas. <a href=3D"https://es.scribd.com/document/461254772= /Ausubel-1980-Psicologia-educativa-pdf" style=3D"text-decoration:none"><spa= n class=3D"Hyperlink" style=3D"font-family:'Times New Roman'">https://es.sc= ribd.com/document/461254772/Ausubel-1980-Psicologia-educativa-pdf</a= ><= span style=3D"font-family:'Times New Roman'">Bastidas Gonz=C3=A1lez, L. D. = (2024). Estrategias de gamificaci=C3=B3n en la educaci=C3=B3n: herramientas= innovadoras para promover aprendizajes significativos y transformar proces= os pedag=C3=B3gicos tradicionales. Sapiens in Education, 1(3), 1-16. <a href=3D"https://doi.o= rg/10.71068/gsscqw59" style=3D"text-decoration:none">https://doi.org/10.71068/gsscqw= 59</a>Bertossi, V., Rome= ro, L., & Guti=C3=A9rrez, M. (2022). Revisi=C3=B3n sistem=C3=A1tica de = instrumentos de evaluaci=C3=B3n de calidad de objetos de aprendizaje. </spa= n>Revista = Ib=C3=A9rica de Sistemas e Tecnologias de Informa=C3=A7=C3=A3o, 46<s= pan style=3D"font-family:'Times New Roman'">(1), 34-53. <a href=3D"h= ttps://scielo.pt/pdf/rist/n46/1646-9895-rist-46-34.pdf" style=3D"text-decor= ation:none">https://scielo.pt/pdf/rist/n46/1646-9895-rist-46-34.pdf</a> Buestan Tigua, G. J., Godoy Salazar, G. T., Ponce Ram=C3= =ADrez, N. D., & Reyes Barreiro, A. N. (2024). Gamificaci=C3=B3n para e= l desarrollo de la inteligencia emocional en la resoluci=C3=B3n de problema= s. Revista Acad=C3=A9mica YACHAKUNA, 2(1), 79=E2=80=93101. <a href=3D"https://doi.org/10.7055= 7/2025.ychkn.2.1.p79-101" style=3D"text-decoration:none">https://doi.org/10.70557/20= 25.ychkn.2.1.p79-101</a>= Chavez Paredes, R. C., Aquije Ramirez, K., & Michuy Rodas, F. D. M. (20= 25). Plataformas digitales interactivas y comprensi=C3=B3n lectora: revisi= =C3=B3n sistem=C3=A1tica de estrategias aplicadas en educaci=C3=B3n primari= a. = Revista InveCom, 6(2),= 1-12. <a href=3D"https://www.revistainvecom.org/index.php/invecom/a= rticle/view/4113" style=3D"text-decoration:none">https://www.revistainvecom.org/inde= x.php/invecom/article/view/4113</a> Gonz=C3=A1= lez Calleros, C. B., & Torres Gastel=C3=BA, C. A. (2024). Transformando= el aprendizaje de Metodolog=C3=ADa de Investigaci=C3=B3n: una revisi=C3=B3= n sistem=C3=A1tica de gamificaci=C3=B3n y otras estrategias de aprendizaje = activo. RIDE - Revista Iberoamericana para la Investigaci=C3=B3n y el Desarrol= lo Educativo, 14(28), = e663. <a href=3D"https://www.scielo.org.mx/scielo.php?pid=3DS2007-74= 672024000100663&script=3Dsci_arttext" style=3D"text-decoration:none"><s= pan class=3D"Hyperlink" style=3D"font-family:'Times New Roman'">https://www= .scielo.org.mx/scielo.php?pid=3DS2007-74672024000100663&script=3Dsci_ar= ttext</a> <= p class=3D"Bibliography" style=3D"line-height:115%; font-size:12pt">=E2=80=AA Lorenzo-Lled=C3=B3, A., P=C3=A9rez V=C3=A1zqu= ez, E., Cabrera, E. A., Lled=C3=B3, G. L., & Lorenzo, A. (2023). Aplica= ci=C3=B3n de la gamificaci=C3=B3n en Educaci=C3=B3n Infantil y Educaci=C3= =B3n Primaria: an=C3=A1lisis tem=C3=A1tico. Retos, 50(1), 858-875. <a href=3D"https://www.pro= quest.com/openview/337ad963bae67465704c5a059ab48718/1?pq-origsite=3Dgschola= r&cbl=3D5517081" style=3D"text-decoration:none">https://www.proquest.com/openvie= w/337ad963bae67465704c5a059ab48718/1?pq-origsite=3Dgscholar&cbl=3D55170= 81</a>=E2=80=AC=E2= =80=AC=E2=80=AC=E2=80=AC=E2=80=ACMiranda Baja=C3=B1a, R. S., & Choez Calder=C3=B3n, C. J. (2024= ). Impacto de las metodolog=C3=ADas activas en el rendimiento acad=C3=A9mic= o y la motivaci=C3=B3n de los estudiantes: Una revisi=C3=B3n sistem=C3=A1ti= ca de la literatura. Revista Cient=C3=ADfica Multidisciplinar G-Nerando, 5</sp= an>(2), 1141=E2=80=931154. </= span><a href=3D"https://revista.gnerando.org/revista/index.php/RCMG/article= /view/305" style=3D"text-decoration:none">https://revista.gnerando.org/revista/ind= ex.php/RCMG/article/view/305</a> Moreno Pedraz= a, C. A., Quintero Vagas, O. A., Gafaro Sierra, A. S., Robayo, H. A., Maza,= J. M., & Jaimes Beltran , C. E. (2025). Juegos agon=C3=ADsticos y moti= vaci=C3=B3n estudiantil en educaci=C3=B3n superior: una revisi=C3=B3n desde= la teor=C3=ADa del aprendizaje social. GADE: Revista Cient=C3=ADfica, 5</span= >(3), 398-409. <a href= =3D"https://revista.redgade.com/index.php/Gade/article/view/719" style=3D"t= ext-decoration:none">https://revista.redgade.com/index.php/Gade/article/view/719</sp= an></a> N=C3=A1poles N=C3=A1poles, K. (2023). Desmo= tivaci=C3=B3n escolar de los alumnos como resultado del fen=C3=B3meno educa= tivo postpandemia: aproximaci=C3=B3n desde una revisi=C3=B3n sistem=C3=A1ti= ca. Revista Ecos de La Academia, 9(17), 57-81. <a href=3D"https://revistasojs.utn.edu.ec/inde= x.php/ecosacademia/article/view/881" style=3D"text-decoration:none">https://revistas= ojs.utn.edu.ec/index.php/ecosacademia/article/view/881</a> P=C3=A9rez Gallardo, E., & G=C3=A9rtrudix-Barrio, F. (2021= ). Ventajas de la gamificaci=C3=B3n en el =C3=A1mbito de la educaci=C3=B3n = formal en Espa=C3=B1a. Una revisi=C3=B3n bibliogr=C3=A1fica en el periodo d= e 2015-2020. Contextos Educativos - Revista de Educaci=C3=B3n, (28), 203-227. <a href=3D"http= s://publicaciones.unirioja.es/ojs/index.php/contextos/article/view/4741" st= yle=3D"text-decoration:none">https://publicaciones.unirioja.es/ojs/index.php/context= os/article/view/4741</a> Prieto Andreu, J. M.,= G=C3=B3mez Escalonilla-Torrijos, J. D., & Said-Hung, E. (2022). Gamifi= caci=C3=B3n, motivaci=C3=B3n y rendimiento en educaci=C3=B3n: una revisi=C3= =B3n sistem=C3=A1tica. Revista Electr=C3=B3nica Educare, 26(1), 251-273. <a href=3D"https://= www.scielo.sa.cr/scielo.php?script=3Dsci_arttext&pid=3DS1409-4258202200= 0100251" style=3D"text-decoration:none">https://www.scielo.sa.cr/scielo.php?script= =3Dsci_arttext&pid=3DS1409-42582022000100251</a> Revelo Manosalvas, S. L., & Y=C3=A1nez Ronquillo, N. del P. (2= 023). Material concreto y su importancia en el fortalecimiento de la matem= =C3=A1tica: una revisi=C3=B3n documental. MENTOR Revista de Investigaci=C3=B3n= Educativa y Deportiva, 2(4), 69-87. <a href=3D"https://revistamentor.ec/index.php/mentor/= article/view/5304" style=3D"text-decoration:none">https://revistamentor.ec/index.php= /mentor/article/view/5304</a> Rosado Rosado, M= . P., Quiroz Vera, L. G., Andrade P=C3=A1rraga, M. E., & Yumisaca Sag= =C3=B1ay, L. H. (2024). Herramientas Tics de gamificaci=C3=B3n para fomenta= r el inter=C3=A9s de los estudiantes en el aprendizaje. RELIGACI=C3=93N - Rev= ista de Ciencias Sociales y Humanidades, 9(40), e2401199. <a href=3D"https://pdfs.semantic= scholar.org/4bca/8034210802c828d53a48d8f9d20d8b00e062.pdf" style=3D"text-de= coration:none">https://pdfs.semanticscholar.org/4bca/8034210802c828d53a48d8f9d20d8b0= 0e062.pdf</a> <= /p><sp= an style=3D"font-family:'Times New Roman'">S=C3=A1nchez, C. G. (2023). El e= fecto de la gamificaci=C3=B3n sobre el alumnado en Educaci=C3=B3n Primaria = y Educaci=C3=B3n Secundaria. Educaci=C3=B3n y Futuro: Revista de Investigaci= =C3=B3n Aplicada y Experiencias Educativas, (49), 181-208. <a href=3D"https://educacionyfu= turo.com/article/view/7575" style=3D"text-decoration:none">https://educacionyfuturo.= com/article/view/7575</a> Sepulveda Pe=C3=B1ar= anda, F. A., & Mayoral Hinestroza, L (2025). La gamificaci=C3=B3n en el= aula: estrategias, beneficios, retos e impacto. Saber Ser, 2(1), 35-52. <a href=3D"https://d= oi.org/10.35997/saberser.v2i1.38" style=3D"text-decoration:none">https://doi.org/10.= 35997/saberser.v2i1.38</a> Vasconez Le=C3=B3n,= L. D. C. (2025). Pedagog=C3=ADa interactiva y aprendizaje significativo en= estudiantes con dificultades socioemocionales: revisi=C3=B3n sistem=C3=A1t= ica de la literatura. Revista InveCom, 6(2), e602117. <a href=3D"https://ve.scielo.org/scielo= .php?script=3Dsci_arttext&pid=3DS2739-00632026000202117" style=3D"text-= decoration:none">https://ve.scielo.org/scielo.php?script=3Dsci_arttext&pid=3DS27= 39-00632026000202117</a> V=C3=A1squez Tafur, L= . A., & Reynoso L=C3=A1zaro, M. (2025). Aprendizaje significativo y su = impacto en la transformaci=C3=B3n educativa: una revisi=C3=B3n sistem=C3=A1= tica. Horizontes Revista de Investigaci=C3=B3n en Ciencias de la Educaci=C3=B3= n, 9(39), 3024-3036. <= /span><a href=3D"http://www.scielo.org.bo/scielo.php?pid=3DS2616-7964202500= 0403024&script=3Dsci_arttext" style=3D"text-decoration:none">http://www.scielo.o= rg.bo/scielo.php?pid=3DS2616-79642025000403024&script=3Dsci_arttext</sp= an></a> Villadiego Almentero, K= . L. (2025). Pr=C3=A1cticas pedag=C3=B3gicas y aprendizaje significativo en= educaci=C3=B3n b=C3=A1sica primaria: una revisi=C3=B3n sistem=C3=A1tica. <= /span>Revi= sta Ciencia & Sociedad, 5(2), 239-255. <a href=3D"https://cienciaysociedaduatf.com/ind= ex.php/ciesocieuatf/article/view/200" style=3D"text-decoration:none">https://ciencia= ysociedaduatf.com/index.php/ciesocieuatf/article/view/200</a> <= /span> </sp= an><div style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAA= ANSUhEUgAAAuQAAAABCAYAAACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBl= SsOGwAAABxJREFUSIntwTEBAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII= =3D" width=3D"740" height=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; marg= in-left:-63.3pt; position:absolute" /> &= #xa0; Nombre = 1 </div></= div></body></html>

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border-left:0.75pt s= olid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; verti= cal-align:top"><a href=3D"= https://orcid.org/0009-0008-6626-4486" style=3D"text-decoration:none"><span= class=3D"Hyperlink" style=3D"line-height:150%; font-family:'Times New Roma= n'; font-size:10pt">https://orcid.org/0009-0008-6626-4486</a> <= /span></td></tr><tr><td style=3D"width:3.25pt; border-top:0.75pt solid = #000000; border-right:0.75pt solid #000000; border-bottom:0.75pt solid #000= 000; padding:0pt 5.03pt; vertical-align:top"> </td><td colspan=3D"3" style=3D"w= idth:407.25pt; border-top:0.75pt solid #000000; border-left:0.75pt solid #0= 00000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical-ali= gn:top">Universid= ad Bolivariana del Ecuador (UBE), Guayaquil, Ecuador.Maestr=C3=ADa en Educaci=C3= =B3n Entornos Digitalesckchugaf@ube.ed= u.ec</td></tr><tr><td style=3D"width:3.25pt; border-top:0.75pt s= olid #000000; border-right:0.75pt solid #000000; 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vertical-align:top"><a href=3D"https://orcid.org/0= 009-0004-9405-3564" style=3D"text-decoration:none">https://orcid.org/0009-0004-9405-3564</a> </td></tr= ><tr><td style=3D"width:3.25pt; border-top:0.75pt solid #000000; border-rig= ht:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.= 03pt; vertical-align:top"> </td><td colspan=3D"3" style=3D"width:407.25pt; bord= er-top:0.75pt solid #000000; border-left:0.75pt solid #000000; border-botto= m:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">Universidad Bolivariana del = Ecuador (UBE), Guayaquil, Ecuador.Maestr=C3=ADa en Educaci=C3=B3n Entornos Digita= lesetmagallanest@ube.edu.ec= </td></tr><tr><td style=3D"width:3.25pt; border-top:0.75pt solid #000000; b= order-right:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:0pt 5.03pt; vertical-align:top">3</td><td style=3D"width:171.95pt; border:0.75pt= solid #000000; padding:0pt 5.03pt; vertical-align:top">Johana Del Carmen Parre=C3=B1o S=C3=A1nchez</td><td style=3D"= width:10.5pt; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-ali= gn:top"><img src=3D"data:image/png;base64,iVBORw0KGg= oAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAAD= sQBlSsOGwAAA2lJREFUOI21lEtoXVUUhr+197n3nNzbJN6omdTWRmwbikVq4iMxk0ZU2oGgA1Fn= QcQiUhUnFqo4cGIRQUGd+KDY+Jp25ouiGCOxElqjhba0URPzsPQmuY9zzj1nLwcnuU1iQ0f+mzX= Ze///Wv/aiy1cBV/83NNOPr/VWHunqO5OVNuNlQVjmZCGjCVhcPHR3q8XrsaVNUITu/KSlu7LFe= SpNNZ+57RTjDTvqFM1IvNiGXWJvD/9x8yXB/efizYU/Hy8/0W/aA/Xq43r1P3nOCNIFsW2XLleT= d648Z/863v3nkjWCL59dp/fWSsfzOXNkShKacvfgnMRlXjqqqIrwr5vkihyhxbT+K2ne082AAzA= DZXyPj+wL8ehQ1NH700v0N35OAroutW0rxDF6rUUvddaJfdwM9HwqYFSa0GO1SrpfucUVUXEZCQ= cqQtZ0VEU1QTPtmAkDygiEBTsTxq6hx65fWTOy7l0exjKgGrGcjTo2/oKS9EUZ2aPcf/Oo1iTI9= UGqFJrzDF5+Sv+Wvg+y+IEVXaGibsbOO5hGBSkbUUQdRT9zaQuBhE6Ct2MT7/D9OIIVnw6Ct3cs= fk52oNtTMwcBbE0YteeC7we4LinSI9Z03dBNUVxTdu1eI7FcBIjHvPVU1Tjv7nn5sNMXv6WSjyF= 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style=3D"width:3.25pt; border-top:0.75pt solid #000000; border-right:0.75= pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; v= ertical-align:top">&= #xa0;</td><td colspan=3D"3" style=3D"width:407.25pt; border-top:= 0.75pt solid #000000; border-left:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:0pt 5.03pt; vertical-align:top">Universidad Bolivariana del Ecuador= (UBE), Guayaquil, Ecuador.<s= pan class=3D"Hyperlink" style=3D"font-family:'Times New Roman'">jdparre=C3= =B1os@ube.edu.ec</td></tr><tr><td style=3D"width:3.25pt; border-= top:0.75pt solid #000000; border-right:0.75pt solid #000000; border-bottom:= 0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top"><spa= n style=3D"line-height:150%; font-family:'Times New Roman'; font-size:6.67p= t; font-weight:bold; vertical-align:super">4</td><td style=3D"wi= dth:171.95pt; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-ali= gn:top">Hendy Maier P=C3=A9rez Barrera</td= ><td style=3D"width:10.5pt; border:0.75pt solid #000000; padding:0pt 5.03pt= ; vertical-align:top"><img src=3D"data:image/png;bas= e64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAl= wSFlzAAAOxAAADsQBlSsOGwAAA2lJREFUOI21lEtoXVUUhr+197n3nNzbJN6omdTWRmwbikVq4i= Mxk0ZU2oGgA1FnQcQiUhUnFqo4cGIRQUGd+KDY+Jp25ouiGCOxElqjhba0URPzsPQmuY9zzj1nL= wcnuU1iQ0f+mzXZe///Wv/aiy1cBV/83NNOPr/VWHunqO5OVNuNlQVjmZCGjCVhcPHR3q8XrsaV= NUITu/KSlu7LFeSpNNZ+57RTjDTvqFM1IvNiGXWJvD/9x8yXB/efizYU/Hy8/0W/aA/Xq43r1P3= nOCNIFsW2XLleTd648Z/863v3nkjWCL59dp/fWSsfzOXNkShKacvfgnMRlXjqqqIrwr5vkihyhx= bT+K2ne082AAzADZXyPj+wL8ehQ1NH700v0N35OAroutW0rxDF6rUUvddaJfdwM9HwqYFSa0GO1= SrpfucUVUXEZCQcqQtZ0VEU1QTPtmAkDygiEBTsTxq6hx65fWTOy7l0exjKgGrGcjTo2/oKS9EU= Z2aPcf/Oo1iTI9UGqFJrzDF5+Sv+Wvg+y+IEVXaGibsbOO5hGBSkbUUQdRT9zaQuBhE6Ct2MT7/= D9OIIVnw6Ct3csfk52oNtTMwcBbE0YteeC7we4LinSI9Z03dBNUVxTdu1eI7FcBIjHvPVU1Tjv7= nn5sNMXv6WSjyFiIg17AAwqpRW9fqayNkiM0tjqDrag67mDCi0ARiB6gaTsSEEg4htusj2qGeCI= 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border-right:0.75pt solid #000000; padding:0pt 5.03pt; verti= cal-align:top"> = ;</td><td colspan=3D"3" style=3D"width:407.25pt; border-top:0.75= pt solid #000000; border-left:0.75pt solid #000000; padding:0pt 5.03pt; ver= tical-align:top">= Universidad Bolivariana del Ecuador (UBE), Guayaquil, Ecuador.<p= style=3D"margin-left:7.1pt; margin-bottom:0pt; text-indent:-7.1pt; text-al= ign:justify; line-height:normal">hmperezb@ube.edu.ec</td></tr></table> <table style=3D"width:436.35pt; margin= -left:0.25pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr= ><td colspan=3D"3" style=3D"width:156.4pt; padding:0pt 5.4pt; vertical-alig= n:top"><a id=3D"_Hlk92186133"> <= /span></a> = ;</td><td colspan=3D"2" style=3D"width:258.35pt; border-top:0.75= pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Art=C3=ADculo de In= vestigaci=C3=B3n Cient=C3=ADfica y Tecnol=C3=B3gica<sp= an style=3D"font-family:'Times New Roman'">Enviado: <s= pan style=3D"font-family:'Times New 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PQXFHjx48ODBg4f/Kjw1Nx48ePDgwYOH/yo8wY0HDx48ePDg4b8KT3DjwYMHDx48ePiv4v8DkrK= y3KIORuIAAAAASUVORK5CYII=3D" width=3D"567" height=3D"140" alt=3D"" /><t= able style=3D"width:437.1pt; margin-left:0.25pt; margin-bottom:0pt; padding= :0pt; border-collapse:collapse"><tr><td colspan=3D"2" style=3D"width:74.75p= t; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">Palabras claves: Gamif= icaci=C3=B3n, motivaci=C3=B3n, aprendizaje significativo, herramienta Educa= play, estrategias pedag=C3=B3gicas.</td><td style=3D"width:4.3pt= ; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top"> </td><td style=3D"width:324.9p= t; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">Resumen Introducci=C3=B3n: La gamificaci=C3=B3n se ha consolidado como una e= strategia innovadora en el =C3=A1mbito educativo, permitiendo mejorar la mo= tivaci=C3=B3n y el aprendizaje significativo en los estudiantes. En context= os donde predominan metodolog=C3=ADas tradicionales, se evidencian bajos ni= veles de inter=C3=A9s y participaci=C3=B3n, lo que afecta el rendimiento ac= ad=C3=A9mico. Objetivo: El= aborar una estrategia de gamificaci=C3=B3n basada en la herramienta Educapl= ay que contribuya a incrementar la motivaci=C3=B3n hacia el aprendizaje sig= nificativo en la asignatura de estudios sociales. Metodolog=C3=ADa: <spa= n style=3D"font-family:'Times New Roman'">Se emple=C3=B3 un enfoque mixto, = y de tipo cuasiexperimental, permitiendo analizar tanto resultados cuantita= tivos como percepciones cualitativas mediante el uso de instrumentos como p= retest, postest, encuesta diagn=C3=B3stica y entrevistas. Los resultados se= evaluaron por medio de la escala de Likert y c=C3=A1lculo de medias, adem= =C3=A1s, se aplic=C3=B3 una prueba de hip=C3=B3tesis por medio del estad=C3= =ADstico Rho de Spearman. Resultados: Se evidencio que la implementaci=C3=B3n de actividades gamifica= das increment=C3=B3 significativamente la motivaci=C3=B3n, la participaci= =C3=B3n activa y la comprensi=C3=B3n de los contenidos, reflej=C3=A1ndose e= n mejoras en todas las dimensiones evaluadas. Se evidenci=C3=B3 una alta co= rrelaci=C3=B3n entre la gamificaci=C3=B3n y la motivaci=C3=B3n (Rho =3D 0,7= 35), as=C3=AD como relaciones significativas entre la motivaci=C3=B3n y el = aprendizaje significativo (Rho =3D 0,804). Estos resultados indican que el = uso de estrategias gamificadas incrementa la participaci=C3=B3n, el inter= =C3=A9s y el compromiso de los estudiantes, favoreciendo una comprensi=C3= =B3n m=C3=A1s profunda de los contenidos. Conclusi=C3=B3n: La propuesta resulta eficaz como estrate= gia innovadora para fortalecer el aprendizaje significativo, siempre que se= consideren aspectos como la adecuada planificaci=C3=B3n pedag=C3=B3gica, l= a capacitaci=C3=B3n docente y el acceso a recursos tecnol=C3=B3gicos que ga= ranticen su correcta implementaci=C3=B3n. =C3=81rea de estudio general: = Educaci=C3=B3n. =C3=81rea de estu= dio espec=C3=ADfica: T= ecnolog=C3=ADa educativa y did=C3=A1ctica. Tipo de estudio: Art=C3=AD= culos original.</td></tr><tr><td style=3D"width:73.75pt; border-= top:0.75pt solid #000000; border-right:0.75pt solid #5b9bd5; border-left:0.= 75pt solid #5b9bd5; border-bottom:0.75pt solid #5b9bd5; padding:0pt 5.03pt;= vertical-align:top">Keyword= s: </sp= an>Gamification, motivation, = meaningful learning, Educaplay tool, pedagogical strategies.</td= ><td colspan=3D"2" style=3D"width:5.3pt; border-top:0.75pt solid #000000; b= order-right:0.75pt solid #5b9bd5; border-left:0.75pt solid #5b9bd5; border-= bottom:0.75pt solid #5b9bd5; padding:0pt 5.03pt; vertical-align:top"> </td><td style=3D"width:324.9pt;= border-top:0.75pt solid #000000; border-right:0.75pt solid #5b9bd5; border= -left:0.75pt solid #5b9bd5; border-bottom:0.75pt solid #5b9bd5; padding:0pt= 5.03pt; vertical-align:top">Abstract<= span style=3D"font-family:'Times New Roman'; font-weight:bold">Introduction= : gamification has bee= n consolidated as an innovative strategy in the educational field, allowing= the improvement of student motivation and meaningful learning. In contexts= where traditional methodologies predominate, low levels of interest and pa= rticipation are evident, which negatively affect academic performance.</spa= n> Objectiv= e: to design a gamific= ation strategy based on the Educaplay tool to increase motivation toward me= aningful learning in the subject of social studies. Methodology: a mixed-method and quasi-experimenta= l approach was used, allowing the analysis of both quantitative results and= qualitative perceptions through instruments such as pretest, postest, diag= nostic survey, and interviews. The results were evaluated using the Likert = scale and mean calculation; additionally, a hypothesis test was conducted u= sing Spearman=E2=80=99s Rho statistic. Results: it was evidenced that the implementation of gamified = activities significantly increased motivation, active participation, and co= ntent comprehension, reflected in improvements across all evaluated dimensi= ons. A high correlation between gamification and motivation was found (Rho = =3D 0.735), as well as significant relationships between motivation and mea= ningful learning (Rho =3D 0.804). These results indicate that the use of ga= mified strategies increases student participation, interest, and engagement= , promoting a deeper understanding of the content. Conclusion: the proposal proves to be effective as= an innovative strategy to strengthen meaningful learning, if aspects such = as proper pedagogical planning, teacher training, and access to technologic= al resources are considered to ensure its correct implementation. <s= pan style=3D"font-family:'Times New Roman'; font-weight:bold">General Area = of Study: Education. <= /span>Speci= fic area of study: Edu= cational Technology and Didactics. Typ= e of study: Original article.</td></tr><tr st= yle=3D"height:0pt"><td style=3D"width:84.55pt"></td><td style=3D"width:1pt"= ></td><td style=3D"width:15.1pt"></td><td style=3D"width:335.7pt"></td></tr= ></table>1. &#= xa0; Introducci=C3=B3n<sp= an style=3D"font-family:'Times New Roman'">Hist=C3=B3ricamente el juego ha = formado parte fundamental de la vida infantil a lo largo del tiempo, convir= ti=C3=A9ndose en uno de los componentes clave del aprendizaje temprano, rep= resenta uno de los recursos m=C3=A1s relevantes para adquirir conocimientos= , debido a que permite al infante explorar, descubrir y comprender su entor= no (P=C3=A9rez & Barrio, 2021). Es una actividad natural del ser humano= que es especialmente importante en la ni=C3=B1ez debido a que se estimula = un acercamiento a la comprensi=C3=B3n de la realidad y la resoluci=C3=B3n d= e problemas. La gamificaci=C3=B3n se ha consolidado como una estrategia inn= ovadora en el =C3=A1mbito educativo, esta t=C3=A9cnica permite fomentar la = participaci=C3=B3n activa y despertar emociones positivas y reforzar su mot= ivaci=C3=B3n intr=C3=ADnseca (S=C3=A1nchez, 2023). Por ello se considera im= portante comprender los impactos en las din=C3=A1micas motivacionales y de = aprendizaje significativo resultantes de la gamificaci=C3=B3n al desarrolla= r nuevas habilidades.Uno de los desaf=C3=ADos m=C3=A1s importantes en la educaci=C3= =B3n infantil radica en mantener la atenci=C3=B3n, inter=C3=A9s y motivaci= =C3=B3n de los ni=C3=B1os; en el contexto educativo mundial persiste esta p= roblem=C3=A1tica adem=C3=A1s de promover aprendizajes verdaderamente signif= icativos (Miranda & Choez, 2024). A nivel latinoamericano el problema s= e acent=C3=BAa debido a brechas pedag=C3=B3gicas, tecnol=C3=B3gicas y forma= tivas; muchos sistemas educativos de la regi=C3=B3n enfrentan limitaciones = relacionadas con el acceso a recursos digitales, capacitaci=C3=B3n docente = y la resistencia al cambio metodol=C3=B3gico (N=C3=A1poles, 2023). En Ecuad= or estas dificultades se evidencian de forma concreta en el aula, en muchas= instituciones educativas, los estudiantes muestran desinter=C3=A9s, baja p= articipaci=C3=B3n y dificultades para relacionar los contenidos con situaci= ones reales (Abarca, 2025). <a id=3D"_Hlk223773627"><s= pan style=3D"font-family:'Times New Roman'">En la Unidad Educativa San= Agust=C3=ADn de Guayaquil</a>, se evidencia que las metodolog=C3=ADas tradicionales utilizadas, = basadas principalmente en la repetici=C3=B3n, la memorizaci=C3=B3n y la ins= trucci=C3=B3n directa, no logran captar la atenci=C3=B3n de los estudiantes= ni mantener su inter=C3=A9s durante las clases. Esta falta de motivaci=C3= =B3n se refleja en una participaci=C3=B3n limitada, desinter=C3=A9s por las= actividades acad=C3=A9micas y dificultades para comprender y relacionar lo= s contenidos con sus experiencias previas. Adem=C3=A1s, se observa que los = estudiantes presentan bajos niveles de compromiso y escasa disposici=C3=B3n= para involucrarse activamente en el aprendizaje, lo que afecta el desarrol= lo de procesos significativos. Aunque se han implementado algunas estrategi= as, a=C3=BAn no se cuenta con m=C3=A9todos que respondan de manera efectiva= a las necesidades motivacionales de los ni=C3=B1os, lo cual mantiene la pr= oblem=C3=A1tica vigente y afecta el rendimiento y la calidad del aprendizaj= e. Es por eso por lo que se plantea el siguiente problema de investigaci=C3= =B3n =C2=BFDe qu=C3=A9 manera se puede incrementar la motivaci=C3=B3n en lo= s estudiantes y favorecer el aprendizaje significativo?Esta investigaci=C3=B3n se = justifica debido a la necesidad de b=C3=BAsqueda de estrategias pedag=C3=B3= gicas innovadoras y eficientes que puedan fortalecer el aprendizaje en la p= rimera infancia. De esta forma la gamificaci=C3=B3n se plantea como una alt= ernativa capaz de transformar las din=C3=A1micas de ense=C3=B1anza tradicio= nales en una promoci=C3=B3n de experiencias m=C3=A1s significativas. Por el= lo, la creaci=C3=B3n de una estrategia de gamificaci=C3=B3n es necesaria pa= ra contribuir en la motivaci=C3=B3n hacia el aprendizaje significativo y pa= ra aportar evidencia que permitan a los docentes la implementaci=C3=B3n de = pr=C3=A1cticas educativas m=C3=A1s din=C3=A1micas. <= span style=3D"font-family:'Times New Roman'">En un contexto general se pued= e mencionar a la investigaci=C3=B3n de Prieto et al. (2022) quienes analiza= n la relaci=C3=B3n entre la gamificaci=C3=B3n, la motivaci=C3=B3n y el apre= ndizaje, con la finalidad de proporcionar estrategias pedag=C3=B3gicas y di= d=C3=A1cticas que se puedan aplicar con efectividad; es as=C3=AD como en ba= se a un exhaustivo estudio y revisi=C3=B3n bibliogr=C3=A1fica se constat=C3= =B3 que la gamificaci=C3=B3n tiene un efecto directo y positivo en cuanto a= la motivaci=C3=B3n y rendimiento del alumnado. Sep=C3=BAlveda & Mayora= l (2025) en su investigaci=C3=B3n encontraron que la gamificaci=C3=B3n esti= mula la motivaci=C3=B3n intr=C3=ADnseca, favorece el desarrollo de las habi= lidades sociales y mejora la calidad del aprendizaje; adem=C3=A1s mencionan= que esta estrategia ofrece un recurso valioso en cuanto al proceso de ense= =C3=B1anza =E2=80=93 aprendizaje siempre que se apoye en una planificaci=C3= =B3n cuidadosa y sobre todo en un profesorado con una formaci=C3=B3n adecua= da. Por = otro lado, en la investigaci=C3=B3n de Buestan et al. (2025) se muestra que= la gamificaci=C3=B3n, mediante aspectos clave como desaf=C3=ADos, recompen= sas y retroalimentaci=C3=B3n, puede fomentar habilidades emocionales clave = como la empat=C3=ADa, la toma de decisiones y la autorregulaci=C3=B3n. Por = medio de combinaci=C3=B3n de ecuaciones booleanas y operadores l=C3=B3gicos= se analiz=C3=B3 la relaci=C3=B3n entre gamificaci=C3=B3n, inteligencia emo= cional y resoluci=C3=B3n de problemas y se obtuvo que la gamificaci=C3=B3n = se presenta como una herramienta eficaz para la integraci=C3=B3n de la inte= ligencia emocional y contribuir en el desarrollo de habilidades sociales y = de resoluci=C3=B3n de problemas en contextos de alta presi=C3=B3n. <= /p>Pues bien, en t= =C3=A9rminos te=C3=B3ricos se explica que la gamificaci=C3=B3n pretende est= imular el pensamiento del alumnado por medio de t=C3=A9cnicas de juego que = plantean la resoluci=C3=B3n de problemas y que es aplicable en diversas =C3= =A1reas de estudio, adem=C3=A1s, como estrategia did=C3=A1ctica ha aportado= mejoras significativas del rendimiento acad=C3=A9mico, motivaci=C3=B3n y e= mocionales positivas (=E2=80=AALled=C3=B3= et al., 2023). La gamificaci=C3=B3n es una estrategia educativa que hace u= so de elementos y din=C3=A1micas de los juegos con la intenci=C3=B3n de inc= entivar la motivaci=C3=B3n, el compromiso y la mejora en el desempe=C3=B1o = acad=C3=A9mico; es as=C3=AD como, esta estrategia concentra diversos compon= entes como desaf=C3=ADos, recompensas y competencia amistosa, convirtiendo = experiencias tradicionales en procesos m=C3=A1s interactivos y atractivos (= Bastidas, 2024). =E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2= =80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80= =AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC=E2=80=AC= =E2=80=AC=E2=80=ACAdem=C3=A1s, se examina el concepto de =E2=80=9CGamificaci=C3=B3n= =E2=80=9D como una estrategia que incorpora principios, din=C3=A1micas y co= mponentes propios de los juegos en entornos que no son de car=C3=A1cter l= =C3=BAdico, con la finalidad de estimular la motivaci=C3=B3n, favorecer el = aprendizaje y promover la participaci=C3=B3n activa (Prieto et al., 2022). = Consiste en integrar mec=C3=A1nicas, elementos y din=C3=A1micas propias del= juego en contextos diversos, tales como la educaci=C3=B3n, los negocios o = la salud, con el fin de incrementar el compromiso y el rendimiento en difer= entes actividades (Gonz=C3=A1lez & Torres, 2024). Esta metodolog=C3=ADa= recurre a recursos como retos, sistemas de recompensas, niveles, retroalim= entaci=C3=B3n inmediata y din=C3=A1micas de competencia positiva, logrando = transformar actividades convencionales en experiencias din=C3=A1micas (Bast= idas, 2024). En cuanto al espacio educativo, la gamificaci=C3=B3n se ha con= solidado como una estrategia eficaz para fortalecer el aprendizaje signific= ativo, estimular la participaci=C3=B3n del estudiante y favorecer el desarr= ollo de sus habilidades cognitivas y sociales. En cuanto a la gamificaci=C3=B3n bas= ada en herramientas digitales consiste en la incorporaci=C3=B3n de elemento= s propios de los videojuegos como desaf=C3=ADos, recompensas, niveles, insi= gnias y retroalimentaci=C3=B3n inmediata, dentro de las plataformas tecnol= =C3=B3gicas dise=C3=B1adas para el aprendizaje (Rosado et al., 2024). Estas= herramientas permiten transformar actividades tradicionales en experiencia= s din=C3=A1micas, interactivas y motivadoras, facilitando la participaci=C3= =B3n activa del estudiante. Aplicaciones como Educaplay no solo ofrecen ent= ornos visualmente atractivos, sino que tambi=C3=A9n permite adaptar conteni= dos, monitorear el progreso y reforzar habilidades cognitivas mediante din= =C3=A1micas de juego (Chavez et al., 2025). De esta forma, la gamificaci=C3= =B3n digital se convierte en una estrategia pedag=C3=B3gica que potencia la= motivaci=C3=B3n intr=C3=ADnseca, favorece el aprendizaje significativo y p= romueve un rol m=C3=A1s atractivo y aut=C3=B3nomo por parte del estudiante.= Adem=C3= =A1s, se muestra a la motivaci=C3=B3n hacia el aprendizaje significativo pa= rtiendo desde la teor=C3=ADa de David Ausubel <a id=3D"_Hlk231821023= ">(1980) </a>quien plantea que el aprendizaje ocurr= e de manera profunda y duradera cuando los nuevos conocimientos se relacion= an de forma sustancial con las ideas previas del estudiante (V=C3=A1squez &= amp; Reynoso, 2025). A diferencia del aprendizaje memor=C3=ADstico o repeti= tivo, que se limita a almacenar informaci=C3=B3n sin una comprensi=C3=B3n r= eal, el aprendizaje significativo implica una integraci=C3=B3n cognitiva qu= e reorganiza y ampl=C3=ADa la estructura mental existente (Vasconez, 2025).= Su teor=C3=ADa se sostiene en tres componentes fundamentales: la significa= tividad l=C3=B3gica del material, la significatividad psicol=C3=B3gica del = aprendizaje y la actitud y disposici=C3=B3n para aprender (motivaci=C3=B3n)= , que interact=C3=BAan para garantizar un proceso educativo eficaz <= /p>La significativi= dad l=C3=B3gica del material se refiere a la coherencia interna, claridad c= onceptual y estructura organizada que posee el contenido que se ense=C3=B1a= ; para que un material sea l=C3=B3gicamente significativo, debe estar elabo= rado de manera secuencial, precisa y comprensible, facilitando conexiones c= laras entre conceptos generales espec=C3=ADficos (Bertossi et al., 2022). E= sto implica que el dise=C3=B1o curricular y los recursos pedag=C3=B3gicos d= eben organizarse intencionalmente para permitir que el estudiante identifiq= ue patrones, relaciones y jerarqu=C3=ADas conceptuales; la estructura del m= aterial no solo facilita la comprensi=C3=B3n, sino que tambi=C3=A9n posibil= ita la asimilaci=C3=B3n de ideas nuevas dentro de los esquemas cognitivos p= reexistentes, favoreciendo as=C3=AD la construcci=C3=B3n de aprendizajes du= raderos (Revelo & Y=C3=A1nez, 2023). Por otra parte, la significatividad psic= ol=C3=B3gica del aprendizaje se relaciona con la capacidad del individuo pa= ra vincular los nuevos conocimientos con su estructura cognitiva previa, se= destaca la importancia de evaluar y activar los saberes que posee el estud= iante, ya que la comprensi=C3=B3n depende de la disponibilidad de conceptos= clave o ideas previas relevantes (V=C3=A1squez & Reynoso, 2025). Cuand= o el estudiante puede relacionar el nuevo contenido con experiencias, conoc= imientos o representaciones ya interiorizadas, se genera un proceso de asim= ilaci=C3=B3n en el que la informaci=C3=B3n adquiere sentido personal (Moren= o et al., 2025). En este aspecto el rol del docente es fundamental pues deb= e identificar el punto de partida cognitivo del estudiante y promover estra= tegias que faciliten el enlace entre lo nuevo y lo conocido, garantizando u= n aprendizaje aut=C3=A9nticamente significativo. <span= style=3D"font-family:'Times New Roman'">Tambi=C3=A9n, se toma en cuenta a = la actitud favorable o motivaci=C3=B3n constituye el tercer componente esen= cial del aprendizaje significativo y se refiere a la disposici=C3=B3n inter= na del estudiante para aprender (Prieto et al., 2022). Ausubel (1980) se=C3= =B1ala que aun cuando el material sea claro exista conocimiento previo pert= inente, el aprendizaje no ocurrir=C3=A1 se el alumno no est=C3=A1 motivado = o no muestra inter=C3=A9s (V=C3=A1squez & Reynoso, 2025). La actitud po= sitiva hacia la tarea acad=C3=A9mica, la curiosidad, la expectativa de =C3= =A9xito y la valoraci=C3=B3n del contenido son factores clave que potencian= la participaci=C3=B3n activa y la integraci=C3=B3n cognitiva (Miranda &= ; Choez, 2024). Adem=C3=A1s, la motivaci=C3=B3n puede fortalecerse mediante= ambientes de aprendizaje estimulantes, actitudes retadoras, metodolog=C3= =ADas activas y una relaci=C3=B3n pedag=C3=B3gica que genere confianza (N= =C3=A1poles, 2023). De esta manera, la disposici=C3=B3n para aprender act= =C3=BAa como un catalizador que permite que los otros componentes se articu= len eficazmente en el proceso educativo. Finalmente se considera importante mencionar a una dimensi=C3= =B3n propuesta por D=C3=ADaz y Hern=C3=A1ndez sobre el aprendizaje signific= ativo desde su enfoque pedag=C3=B3gico, se centra en la capacidad del estud= iante para aplicar de manera efectiva los aprendizajes y situaciones reales= o simuladas, demostrando que el conocimiento adquirido tiene utilidad m=C3= =A1s all=C3=A1 de aula (Villadiego, 2025). Esto es importante al evidenciar= que el estudiante utiliza los contenidos para resolver problemas pr=C3=A1c= ticos, transferir conceptos a otros contextos o =C3=A1reas del conocimiento= y participa activamente en actividades como proyectos, juegos educativos o= tareas funcionales que requieren poner en acci=C3=B3n lo aprendido. Es as= =C3=AD como el objetivo de esta investigaci=C3=B3n es: Elaborar una estrate= gia de gamificaci=C3=B3n basada en la herramienta Educaplay, que contribuya= a incrementar la motivaci=C3=B3n hacia el aprendizaje significativo en la = materia de estudios sociales.2.</= span> Metodolog=C3=ADa= La presente investi= gaci=C3=B3n se desarroll=C3=B3 bajo un enfoque mixto, que integra procedimi= entos cuantitativos y cualitativos para obtener una compresi=C3=B3n amplia = y profunda del fen=C3=B3meno estudiado. El enfoque cuantitativo permite med= ir el efecto de la estrategia de gamificaci=C3=B3n basada en Educaplay sobr= e la motivaci=C3=B3n y el aprendizaje significativo; mientras que el enfoqu= e cualitativo posibilitar=C3=A1 analizar las percepciones, experiencias y a= ctitudes de los estudiantes durante la implementaci=C3=B3n de la estrategia= . El dis= e=C3=B1o metodol=C3=B3gico es de tipo cuasiexperimental, debido a que se tr= abaj=C3=B3 con dos grupos, el grupo control al cual se le aplic=C3=B3 la me= todolog=C3=ADa pedag=C3=B3gica de forma tradicional y al grupo experimento = que se le aplic=C3=B3 la propuesta pedag=C3=B3gica de esta investigaci=C3= =B3n, comparando los resultados obtenidos por medio de pretest y un postest= . Este dise=C3=B1o permiti=C3=B3 evaluar los cambios generados por la estra= tegia sin necesidad de conformar grupos completamente aleatorizados, lo cua= l se ajusta a la realidad escolar de la Unidad Educativa San Agust=C3= =ADn de Guayaquil.</sp= an>Por su alcan= ce, la investigaci=C3=B3n se considera exploratoria, descriptiva y correlac= ional. Es exploratoria porque aborda un campo poco investigado en el contex= to espec=C3=ADfico de la instituci=C3=B3n, como es la gamificaci=C3=B3n med= iante herramientas digitales para el fortalecimiento del aprendizaje signif= icativo. A su vez, es descriptiva porque busca detallar las caracter=C3=ADs= ticas del proceso de motivaci=C3=B3n, las conductas observadas y los nivele= s de aprendizaje antes y despu=C3=A9s de la intervenci=C3=B3n basada en gam= ificaci=C3=B3n. Finalmente, es correlacional debido a que permite identific= ar, analizar y predecir relaciones entre variables sin manipularlas, propor= cionando informaci=C3=B3n =C3=BAtil para la toma de decisiones. Es as=C3=AD= como se plantea la siguiente hip=C3=B3tesis: La estrategia de gamificaci= =C3=B3n basada en Educaplay aplicada en la Unidad Educativa San Agust= =C3=ADn de Guayaquil e= n estudiantes de 8vo a=C3=B1o permiti=C3=B3 incrementar la motivaci=C3=B3n = hacia el aprendizaje significativo. Para el desarrollo de este estudio hubo la nece= sidad de implementar m=C3=A9todos te=C3=B3ricos como en an=C3=A1lisis y la = s=C3=ADntesis; por un lado, el an=C3=A1lisis permiti=C3=B3 descomponer los = conceptos centrales como la gamificaci=C3=B3n, motivaci=C3=B3n y aprendizaj= e significativo para comprender sus caracter=C3=ADsticas y relaciones. Por = su parte, la s=C3=ADntesis facilit=C3=B3 la integraci=C3=B3n de dicha infor= maci=C3=B3n para fundamentar y estructurar la propuesta pedag=C3=B3gica.</s= pan>En cuanto a= la recopilaci=C3=B3n de datos primarios se tomar=C3=A1 en cuenta a la pobl= aci=C3=B3n de estudiantes conformada por 33 hombres y 15 <a id=3D"_H= lk224048309">mujeres de 8vo a= =C3=B1o, paralelos a y b de la asignatura de estudios sociales. Para la mue= stra se aplic=C3=B3 una muestra censal tomando a toda la poblaci=C3=B3n com= o parte del estudio dando un total de 48 participantes</a>. La estrategia para aplicarse se enfoc= a en la implementaci=C3=B3n de juegos con Educaplay orientados a temas de e= studios sociales, se llev=C3=B3 a cabo la evaluaci=C3=B3n por medio de inst= rumentos como pruebas pretest y postest para conocer el nivel de motivaci= =C3=B3n y el rendimiento sobre el aprendizaje significativo, adem=C3=A1s, s= e hizo un registro de interacci=C3=B3n por medio de una ficha de observaci= =C3=B3n, se aplic=C3=B3 una entrevista a los docentes para establecer la ap= licaci=C3=B3n de la estrategia. Por otra parte, para el tratamiento de datos primarios se hizo uso del= programa estad=C3=ADstico SPSS =E2=80=93 Statics para la evaluaci=C3=B3n de instrumentos y la prese= ntaci=C3=B3n de datos. Finalmente se desarroll=C3=B3 una operacionalizaci= =C3=B3n de variables para entender la aplicaci=C3=B3n de la estrategia tal = y como se muestra en la Tabla 1.Ta= bla 1Operacionalizaci=C3=B3n de variables</sp= an><table style=3D"width:101.68%; margin-bottom:0pt; padding:0pt; borde= r-collapse:collapse"><tr style=3D"height:14.4pt"><td style=3D"width:14.22%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:0pt 3.5pt; vertical-align:bottom">Variable</td><td style=3D"width:21.66%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt= 3.5pt; vertical-align:bottom">Dimensi=C3=B3n</td><td style=3D"width:64.12%; borde= r-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:0pt= 3.5pt; vertical-align:bottom">Indicador</td></tr><tr style=3D"height:14.4pt"><td = rowspan=3D"12" style=3D"width:14.22%; border-top:0.75pt solid #000000; bord= er-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertical-align:middle"><= p style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">VI: Gamificaci=C3=B3n basada en la her= ramienta Educaplay</td><td rowspan=3D"4" style=3D"width:21.66%; = border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddin= g:0pt 3.5pt; vertical-align:middle">U= so de elementos de gamificaci=C3=B3n</td><td style=3D"width:64.1= 2%; border-top:0.75pt solid #000000; padding:0pt 3.5pt; vertical-align:bott= om"><spa= n style=3D"font-family:'Times New Roman'">Puntuaciones</td></tr>= <tr style=3D"height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; v= ertical-align:bottom">Retroalimentaci= =C3=B3n inmediata</td></tr><tr style=3D"height:14.4pt"><td style= =3D"width:64.12%; padding:0pt 3.5pt; vertical-align:bottom">Recompensas visuales (insignias, niveles)= </td></tr><tr style=3D"height:14.4pt"><td style=3D"width:64.12%; border-bot= tom:0.75pt solid #000000; padding:0pt 3.5pt; vertical-align:bottom">Competencias entre estudiantes</t= d></tr><tr style=3D"height:14.4pt"><td rowspan=3D"3" style=3D"width:21.66%;= border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; paddi= ng:0pt 3.5pt; vertical-align:middle">= Dise=C3=B1o de actividades gamificadas</td><td style=3D"width:64= .12%; border-top:0.75pt solid #000000; padding:0pt 3.5pt; vertical-align:bo= ttom"><s= pan style=3D"font-family:'Times New Roman'">Actividades creadas en Educapla= y</td></tr><tr style=3D"height:14.4pt"><td style=3D"width:64.12%= ; padding:0pt 3.5pt; vertical-align:bottom">Adecuaci=C3=B3n al curr=C3=ADculo de estudios</td></tr><t= r style=3D"height:14.4pt"><td style=3D"width:64.12%; border-bottom:0.75pt s= olid #000000; padding:0pt 3.5pt; vertical-align:bottom">Nivel de interacci=C3=B3n del estudiante con la activida= d</td></tr><tr style=3D"height:14.4pt"><td rowspan=3D"2" style= =3D"width:21.66%; border-top:0.75pt solid #000000; border-bottom:0.75pt sol= id #000000; padding:0pt 3.5pt; vertical-align:middle">Interactividad y usabilidad</td><td style=3D"wi= dth:64.12%; border-top:0.75pt solid #000000; padding:0pt 3.5pt; vertical-al= ign:bottom">Facilidad de uso por part= e del estudiante </td></tr><tr style=3D"height:14.4pt"><td style= =3D"width:64.12%; border-bottom:0.75pt solid #000000; padding:0pt 3.5pt; ve= rtical-align:bottom">Tiempo de intera= cci=C3=B3n por actividad</td></tr><tr style=3D"height:14.4pt"><t= d rowspan=3D"3" style=3D"width:21.66%; border-top:0.75pt solid #000000; bor= der-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertical-align:middle">= Integraci=C3=B3n pedag=C3=B3gica</spa= n></td><td style=3D"width:64.12%; border-top:0.75pt solid #000000; padd= ing:0pt 3.5pt; vertical-align:bottom">Pertinencia pedag=C3=B3gica de las actividades</td></tr><tr sty= le=3D"height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; vertical= -align:bottom">Relaci=C3=B3n con los = aprendizajes significativos</td></tr><tr style=3D"height:14.4pt"= ><td style=3D"width:64.12%; border-bottom:0.75pt solid #000000; padding:0pt= 3.5pt; vertical-align:bottom">Adecua= ci=C3=B3n al nivel de los estudiantes</td></tr><tr style=3D"heig= ht:14.4pt"><td rowspan=3D"15" style=3D"width:14.22%; border-top:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertical-= align:middle">VD: Motivaci=C3=B3n hac= ia el aprendizaje significativo</td><td rowspan=3D"5" style=3D"w= idth:21.66%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #0= 00000; padding:0pt 3.5pt; vertical-align:middle">Actitud favorable/Motivaci=C3=B3n hacia el aprendizaje</= p></td><td style=3D"width:64.12%; border-top:0.75pt solid #000000; padding:= 0pt 3.5pt; vertical-align:bottom">Int= er=C3=A9s y curiosidad por los contenidos</td></tr><tr style=3D"= height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; vertical-align= :bottom">Participaci=C3=B3n activa en= las actividades</td></tr><tr style=3D"height:14.4pt"><td style= =3D"width:64.12%; padding:0pt 3.5pt; vertical-align:bottom">Atenci=C3=B3n sostenida durante el proceso de aprend= izaje</td></tr><tr style=3D"height:14.4pt"><td style=3D"width:64= .12%; padding:0pt 3.5pt; vertical-align:bottom">Persistencia frente a tareas o dificultades </td></tr= ><tr style=3D"height:14.4pt"><td style=3D"width:64.12%; border-bottom:0.75p= t solid #000000; padding:0pt 3.5pt; vertical-align:bottom">Disposici=C3=B3n positiva para aprender significativa= mente</td></tr><tr style=3D"height:14.4pt"><td rowspan=3D"5" sty= le=3D"width:21.66%; border-top:0.75pt solid #000000; border-bottom:0.75pt s= olid #000000; padding:0pt 3.5pt; vertical-align:middle">Significatividad psicol=C3=B3gica del aprendizaje= </td><td style=3D"width:64.12%; border-top:0.75pt solid #000000; paddin= g:0pt 3.5pt; vertical-align:bottom">A= ctivaci=C3=B3n de conocimientos previos del estudiante</td></tr>= <tr style=3D"height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; v= ertical-align:bottom">Relaci=C3=B3n e= ntre el saber previo y los nuevos conceptos</td></tr><tr style= =3D"height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; vertical-a= lign:bottom">Capacidad del estudiante= para integrar, comparar y diferenciar ideas</td></tr><tr style= =3D"height:14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; vertical-a= lign:bottom">Elaboraci=C3=B3n propia = (explicar con sus palabras, producir ideas)</td></tr><tr style= =3D"height:14.4pt"><td style=3D"width:64.12%; border-bottom:0.75pt solid #0= 00000; padding:0pt 3.5pt; vertical-align:bottom">Comprensi=C3=B3n profunda m=C3=A1s all=C3=A1 de la memorizaci= =C3=B3n</td></tr><tr style=3D"height:14.4pt"><td rowspan=3D"5" s= tyle=3D"width:21.66%; border-top:0.75pt solid #000000; border-bottom:0.75pt= solid #000000; padding:0pt 3.5pt; vertical-align:middle">Pr=C3=A1ctico - funcional</td><td style=3D"= width:64.12%; border-top:0.75pt solid #000000; padding:0pt 3.5pt; vertical-= align:bottom">Aplica los aprendizajes= en situaciones reales o simuladas</td></tr><tr style=3D"height:= 14.4pt"><td style=3D"width:64.12%; padding:0pt 3.5pt; vertical-align:bottom= ">Utiliza conocimientos para resolver= problemas pr=C3=A1cticos</td></tr><tr style=3D"height:14.4pt"><= td style=3D"width:64.12%; padding:0pt 3.5pt; vertical-align:bottom">Transfiere conceptos a otras =C3=A1reas o co= ntextos </td></tr><tr style=3D"height:14.4pt"><td style=3D"width= :64.12%; padding:0pt 3.5pt; vertical-align:bottom">Participa en actividades activas (Proyectos, juegos o tareas = funcionales)</td></tr><tr style=3D"height:14.4pt"><td style=3D"w= idth:64.12%; border-bottom:0.75pt solid #000000; padding:0pt 3.5pt; vertica= l-align:bottom">Demuestra autonom=C3= =ADa para usar lo aprendido</td></tr></table>3. </spa= n>ResultadosLa fiabilidad del instrumento se demostr=C3=B3 con el uzo del coefi= ciente Alfa de Cronbach, que alcanz=C3=B3 un valor de 0.851 en el cuestiona= rio de diagn=C3=B3stico y 0.816 en el cuestionario de pretest y postest apl= icados a los estudiantes. Estos resultados indican niveles de consistencia = elevado y significa que los =C3=ADtems del cuestionario miden de forma cohe= rente las variables de estudio. Por ello, se garantiza la solidez y confian= za de los datos recogidos de los estudiantes que se muestran a continuaci= =C3=B3n. <s= pan style=3D"font-family:'Times New Roman'; font-style:italic">3.1.</= span> Diagn=C3=B3stico inicial Los resultados del diagn=C3=B3stico inicial evidencian = caracter=C3=ADsticas generales de la poblaci=C3=B3n estudiantil encuestada.= En cuanto a la edad, los estudiantes se encuentran en una etapa de desarro= llo cognitivo y social en la que las estrategias din=C3=A1micas y participa= tivas suelen tener mayor impacto en el aprendizaje. En relaci=C3=B3n con la= motivaci=C3=B3n hacia las clases de estudios sociales la Tabla 2 muestra que la mayor=C3=ADa de estu= diantes presenta niveles de motivaci=C3=B3n moderados o bajos, lo que sugie= re que existe la necesidad de fortalecer las estrategias pedag=C3=B3gicas q= ue promuevan mayor inter=C3=A9s y participaci=C3=B3n en la asignatura. </sp= an><a id=3D"_Hlk223960946"></a><di= v style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA= AuQAAAABCAYAAACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABx= JREFUSIntwTEBAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width= =3D"740" height=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-6= 3.3pt; position:absolute" /> = ; Nombre 1 </div></div> <div class=3D"Section_2"= >T= abla 2 Diagn=C3=B3stico inicial<tabl= e style=3D"width:97.84%; margin-bottom:0pt; padding:0pt; border-collapse:co= llapse"><tr><td style=3D"width:52.82%; border-top:0.75pt solid #000000; bor= der-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Pregunta</= td><td style=3D"width:32.6%; border-top:0.75pt solid #000000; border-bottom= :0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"><s= pan style=3D"font-family:'Times New Roman'">Categor=C3=ADa</td><= td style=3D"width:14.58%; border-top:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"><span= style=3D"font-family:'Times New Roman'">Porcentaje</td></tr><tr= ><td rowspan=3D"2" style=3D"width:52.82%; border-top:0.75pt solid #000000; = padding:0pt 5.4pt; vertical-align:middle">Edad</td><td style=3D"width:32.6%; border-top:0.75pt solid = #000000; padding:0pt 5.4pt; vertical-align:top">12</td><td style=3D"width:14.58%; border-top:0.75pt s= olid #000000; padding:0pt 5.4pt; vertical-align:top">39,6%</td></tr><tr><td style= =3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top">13</td><td style=3D"width:14.58%; padding:0pt= 5.4pt; vertical-align:top">60,4%</td></tr><tr><td style=3D"width:52.82%; padding:= 0pt 5.4pt; vertical-align:middle">&#x= a0;</td><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-al= ign:top"> </td><td st= yle=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top"><spa= n style=3D"font-family:'Times New Roman'"> </td></tr><tr><t= d rowspan=3D"2" style=3D"width:52.82%; padding:0pt 5.4pt; vertical-align:mi= ddle"><s= pan style=3D"font-family:'Times New Roman'">G=C3=A9nero</td><td = style=3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top">Masculino</td><td style=3D"width:14.58%;= padding:0pt 5.4pt; vertical-align:top">68,8%</td></tr><tr><td style=3D"width:32.6= %; padding:0pt 5.4pt; vertical-align:top">Femenino</td><td style=3D"width:14.58%; padding:0pt 5.4pt; = vertical-align:top">31,2%</td></tr><tr><td style=3D"width:52.82%; padding:0pt 5.4p= t; vertical-align:middle"> </spa= n></td><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top"= > </td><td style=3D"w= idth:14.58%; padding:0pt 5.4pt; vertical-align:top"> </td></tr><tr><td rowsp= an=3D"5" style=3D"width:52.82%; padding:0pt 5.4pt; vertical-align:middle"><= p style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">=C2=BFTe encuentras motivado al estar = en clases de estudios sociales?</td><td style=3D"width:32.6%; pa= dding:0pt 5.4pt; vertical-align:top">= Nunca</td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical= -align:top">0%</sp= an></td></tr><tr><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-= align:top">Casi nunca</td>= <td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top">12,5%</td></tr>= <tr><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top">A veces</td><td style=3D"width:1= 4.58%; padding:0pt 5.4pt; vertical-align:top">56,3%</td></tr><tr><td style=3D"widt= h:32.6%; padding:0pt 5.4pt; vertical-align:top">Casi siempre</td><td style=3D"width:14.58%; padding:0= pt 5.4pt; vertical-align:top">25,0%</td></tr><tr><td style=3D"width:32.6%; padding= :0pt 5.4pt; vertical-align:top">Siemp= re</td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-al= ign:top">6,3%</spa= n></td></tr><tr><td style=3D"width:52.82%; padding:0pt 5.4pt; vertical-= align:middle"> </td><= td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top"> </td><td style=3D"width:14.58%;= padding:0pt 5.4pt; vertical-align:top"> </td></tr><tr><td rowspan=3D"3" styl= e=3D"width:52.82%; padding:0pt 5.4pt; vertical-align:middle">=C2=BFEl docente de estudios sociales ense=C3=B1a a= trav=C3=A9s de qu=C3=A9 m=C3=A9todo?</td><td style=3D"width:32.= 6%; padding:0pt 5.4pt; vertical-align:top">Memorizaci=C3=B3n mec=C3=A1nica</td><td style=3D"width:14.= 58%; padding:0pt 5.4pt; vertical-align:top">14,6%</td></tr><tr><td style=3D"width:= 32.6%; padding:0pt 5.4pt; vertical-align:top">Construcci=C3=B3n de conocimiento</td><td style=3D"widt= h:14.58%; padding:0pt 5.4pt; vertical-align:top">68,8%</td></tr><tr><td style=3D"w= idth:32.6%; padding:0pt 5.4pt; vertical-align:top">Juegos din=C3=A1micos</td><td style=3D"width:14.58= %; padding:0pt 5.4pt; vertical-align:top">16,7%</td></tr><tr><td style=3D"width:52= .82%; padding:0pt 5.4pt; vertical-align:middle"> </td><td style=3D"width:32.6%; padding:0pt 5.4p= t; vertical-align:top"> <= /p></td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top"><= p style=3D"margin-bottom:0pt; text-align:center; line-height:normal; font-s= ize:10pt"> </= td></tr><tr><td rowspan=3D"5" style=3D"width:52.82%; padding:0pt 5.4pt; ver= tical-align:middle">=C2=BFEl docente = de estudios sociales utiliza herramientas tecnol=C3=B3gicas para impartir l= as clases?</td><td style=3D"width:32.6%; padding:0pt 5.4pt; vert= ical-align:top">Nunca</td>= <td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top">0%</td></tr><tr= ><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-align:top">Casi nunca</td><td style=3D"width:= 14.58%; padding:0pt 5.4pt; vertical-align:top">10,4%</td></tr><tr><td style=3D"wid= th:32.6%; padding:0pt 5.4pt; vertical-align:top">A veces</td><td style=3D"width:14.58%; padding:0pt 5= .4pt; vertical-align:top">56,3%</td></tr><tr><td style=3D"width:32.6%; padding:0pt= 5.4pt; vertical-align:top">Casi siem= pre</td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-a= lign:top">22,9%</s= pan></td></tr><tr><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical= -align:top">Siempre</td><t= d style=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top">= 10,4%</td></tr><tr= ><td style=3D"width:52.82%; padding:0pt 5.4pt; vertical-align:middle"> </td><td style=3D"width= :32.6%; padding:0pt 5.4pt; vertical-align:top"> </td><td style=3D"width:14.58%; padding:0pt 5.4p= t; vertical-align:top"> </td></tr><tr><td rowspan=3D"2" style=3D"width:52.82%= ; padding:0pt 5.4pt; vertical-align:middle">=C2=BFTe gustar=C3=ADa aprender por medio de juegos interactivos en = plataformas digitales?</td><td style=3D"width:32.6%; padding:0pt= 5.4pt; vertical-align:top">S=C3=AD</= span></td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-align:= top">100,0%= </td></tr><tr><td style=3D"width:32.6%; padding:0pt 5.4pt; vertical-ali= gn:top">= No</td><td style= =3D"width:14.58%; padding:0pt 5.4pt; vertical-align:top">0%</td></tr><tr><td style= =3D"width:52.82%; padding:0pt 5.4pt; vertical-align:middle"> </td><td style=3D"width:32.6%; padd= ing:0pt 5.4pt; vertical-align:top">&#= xa0;</td><td style=3D"width:14.58%; padding:0pt 5.4pt; vertical-= align:top"> <= /span></td></tr><tr><td rowspan=3D"2" style=3D"width:52.82%; border-bot= tom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:middle">=C2=BFEl docente ha utilizado la plataforma = Educaplay u otras similares en sus clases?</td><td style=3D"widt= h:32.6%; padding:0pt 5.4pt; vertical-align:top">S=C3=AD</td><td style=3D"width:14.58%; padding:0pt 5.= 4pt; vertical-align:top">47,9%</td></tr><tr><td style=3D"width:32.6%; border-botto= m:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">No</td><td style=3D"width:14.58%; bord= er-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">52,1%</td><= /tr></table>Nota: datos obtenidos de l= a investigaci=C3=B3n en <a id=3D"_Hlk224050000">la Unidad Educativa San Agust=C3=ADn de Guayaquil= </a></div> <div class=3D"Section_3"><div style=3D"clear:both"><img src=3D"data:image/png;ba= se64,iVBORw0KGgoAAAANSUhEUgAAAUIAAABPCAYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAA= lwSFlzAAAOxAAADsQBlSsOGwAADrlJREFUeJztnX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNz= KRxWdwMMcYY48o6Qlc2M6KNq1kXBbuprdYOWjsnRarir4l2jNZag6jMabV0astgprMtiqICVVEQ= 2BW4XMTX/nieL/dzP9/zPT++98KF8bwScr/n8+P5POccvs/3nM/nnOctBUEQBEEQBEEQBEEQBEE= QBEEQBEEQBEEQBEEQBEEQBEEQBNUAaxjNkJefCawFdgN7gB8AFwG3Zv17tgO+lNjdA3wq63tnNv= aSBv6eAawD9gKDwIoe7SYBi4FngZ3Aw0ndSuAt9/mhfuxnfWYDz7i9fcAm4MqsTdWYTfpvopvL6= 3wLgqABwADwj/7FehKY7OXPAi8B5wNnAdcDrwO3Z/1r23kdwAvAQNb/POBN4JyG/j5bEhDuyNqc= C7zodT8F/hmY5nXf8/JFwC3+eUUb+9lYU4DtwH7gH4BPA/+LBdIz6sZs2H9OiU9bmxyvIAgaApz= 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de conocimien= to; no obstante, aunque predomina un enfoque orientado a la construcci=C3= =B3n del conocimiento, los resultados muestran que el uso de metodolog=C3= =ADas l=C3=BAdicas o gamificadas a=C3=BAn es limitado dentro del aula. Resp= ecto al uso de herramientas tecnol=C3=B3gicas en las clases de estudios soc= iales se evidencia que la integraci=C3=B3n de recursos tecnol=C3=B3gicos en= el proceso educativo a=C3=BAn no es constante ni sistem=C3=A1tica. Un aspe= cto relevante del diagn=C3=B3stico es la percepci=C3=B3n positiva de los es= tudiantes hacia el aprendizaje mediado por tecnolog=C3=ADas interactivas, l= os estudiantes manifestaron que les gustar=C3=ADa aprender mediante juegos = interactivos en plataformas digitales, lo cual demuestra una alta aceptaci= =C3=B3n hacia estrategias pedag=C3=B3gicas innovadoras basadas en la gamifi= caci=C3=B3n. No obstante, aunque se ha empezado a hacer uso de la plataform= a Educaplay no est=C3=A1 completamente integrada en el proceso de ense=C3= =B1anza. Por otra parte, los resultados obtenidos en las entrevistas aplicadas a lo= s docentes refuerzan y complementan la informaci=C3=B3n del diagn=C3=B3stic= o cuantitativo; en general, los docentes coinciden en que las metodolog=C3= =ADas tradicionales, basadas principalmente en la exposici=C3=B3n del docen= te y la memorizaci=C3=B3n de contenidos, tienden a generar bajos niveles de= motivaci=C3=B3n en los estudiantes. Igualmente, se=C3=B1alan que uno de lo= s principales desaf=C3=ADos para mantener la atenci=C3=B3n en el aula es la= presencia de distractores como el uso del celular, el exceso de contenido = te=C3=B3rico y la falta de conexi=C3=B3n entre los temas abordados y la rea= lidad de los estudiantes. Los docentes tambi=C3=A9n destacan que las activi= dades din=C3=A1micas y participativas generan mayor inter=C3=A9s, participa= ci=C3=B3n y comprensi=C3=B3n por parte de los estudiantes, en comparaci=C3= =B3n con las actividades memor=C3=ADsticas. En este sentido, todos coincide= n en que el aprendizaje significativo se produce cuando el estudiante logra= comprender los contenidos, relacionarlos con su entorno y aplicarlos en si= tuaciones reales, m=C3=A1s all=C3=A1 de la simple memorizaci=C3=B3n de info= rmaci=C3=B3n. En relaci=C3=B3n con la gamificaci=C3=B3n, los entrevistados reconoce= n que la incorporaci=C3=B3n de elementos de juego como retos, recompensas, = niveles o competencias sanas puede aumentar significativamente la motivaci= =C3=B3n, el compromiso y la participaci=C3=B3n estudiantil. Algunos docente= s manifestaron haber utilizado herramientas digitales como cuestionarios in= teractivos, juegos de rol o plataformas como Educaplay, obteniendo resultad= os positivos en cuanto a la participaci=C3=B3n y comprensi=C3=B3n de los co= ntenidos. No obstante, tambi=C3=A9n se identifican algunas limitaciones par= a la implementaci=C3=B3n de estas estrategias, entre las que se mencionan l= a falta de conocimiento o capacitaci=C3=B3n en el uso de herramientas digit= ales, problemas de conectividad a internet y el riesgo de distracciones se = no se utilizan de manera pedag=C3=B3gicamente planificada.En s=C3=ADntesis, tanto e= l diagn=C3=B3stico inicial como las entrevistas docentes evidencian la nece= sidad de fortalecer las estrategias did=C3=A1cticas utilizadas en la ense= =C3=B1anza de estudios sociales mediante la incorporaci=C3=B3n de metodolog= =C3=ADas innovadoras que incrementen la motivaci=C3=B3n y favorezcan el apr= endizaje significativo. En este contexto, la gamificaci=C3=B3n apoyada en h= erramientas digitales como Educaplay se presenta como una alternativa pedag= =C3=B3gica viable, ya que permite transformar el proceso de aprendizaje en = una experiencia m=C3=A1s interactiva, participativa y atractiva para los es= tudiantes. = 3.2.= Actividades de gamificaci=C3=B3n basada en la herramienta Educaplay</s= pan>Para el des= arrollo de las actividades did=C3=A1cticas se utiliz=C3=B3 la plataforma Ed= ucaplay, la cual permite crear juegos educativos multimedia como cuestionar= ios, crucigramas, sopas de letras, mapas interactivos y ejercicios de asoci= aci=C3=B3n. Esta herramienta digital facilit=C3=B3 la gamificaci=C3=B3n del= aprendizaje al incorporar elementos propios del juego como puntuaciones, t= iempo limitado, retos y retroalimentaci=C3=B3n inmediata, lo que contribuye= a aumentar la motivaci=C3=B3n y participaci=C3=B3n de los estudiantes en e= l proceso educativo. Las actividades aplicadas en el aula se estructuraron = en varias tem=C3=A1ticas del =C3=A1rea de estudios sociales: los continente= s, el clima, mares y oc=C3=A9anos del mundo, regiones y etnias del Ecuador.= En cada tem=C3=A1tica se utilizaron diferentes tipos de juegos interactivo= s que permitieron reforzar los contenidos te=C3=B3ricos mediante el aprendi= zaje activo. Uno de los recursos m=C3=A1s utilizados fue el mapa interactivo, en el= cual los estudiantes deben identificar ubicaciones o elementos se=C3=B1ala= dos sobre una imagen o mapa geogr=C3=A1fico; en este tipo de actividad el e= studiante observa un mapa y debe seleccionar la ubicaci=C3=B3n correcta de = un pa=C3=ADs, regios o continente, lo que fortalece la orientaci=C3=B3n esp= acial y comprensi=C3=B3n geogr=C3=A1fica; adem=C3=A1s, estos juegos pueden = incluir informaci=C3=B3n adicional, explicaciones o im=C3=A1genes que apare= cen despu=C3=A9s de cada respuesta. La Tabla <= span style=3D"font-family:'Times New Roman'; font-weight:bold">3<spa= n style=3D"font-family:'Times New Roman'"> presenta un conjunto de activida= des gamificadas orientadas al aprendizaje de los continentes del mundo, des= arrolladas mediante la plataforma Educaplay, estas actividades incluyen cue= stionarios, juegos de identificaci=C3=B3n, mapas interactivos y ejercicios = de relaci=C3=B3n de conceptos, los cuales permiten reforzar los conocimient= os geogr=C3=A1ficos de los estudiantes de forma din=C3=A1mica. <= div style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUg= AAAuQAAAABCAYAAACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAA= BxJREFUSIntwTEBAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" widt= h=3D"740" height=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-= 63.3pt; position:absolute" /> = 0; Nombre 1 </div></div> <div class=3D"Section_4= ">Tabla 3</p= >Tem=C3=A1tica: Los continentes<table style= =3D"margin-bottom:0pt; border-top:0.75pt solid #000000; border-bottom:0.75p= t solid #000000; padding:0pt; border-spacing:1.5pt"><thead><tr><td style=3D= "padding:0.75pt">J= uego</td><td style=3D"padding:0.75pt">Actividad</td><td style=3D"paddin= g:0.75pt">Funciona= miento</td></tr></thead><tbody><tr><td style=3D"border-top:0.75p= t solid #000000; padding:0.75pt">Am= =C3=A9rica: Generalidades</td><td style=3D"border-top:0.75pt sol= id #000000; padding:0.75pt">Cuestiona= rio interactivo</td><td style=3D"border-top:0.75pt solid #000000= ; padding:0.75pt">Los estudiantes res= ponden preguntas sobre caracter=C3=ADsticas generales del continente americ= ano, como ubicaci=C3=B3n, pa=C3=ADses y caracter=C3=ADsticas geogr=C3=A1fic= as. Cada respuesta correcta suma puntos.</td></tr><tr><td style= =3D"padding:0.75pt">El continente ame= ricano</td><td style=3D"padding:0.75pt">Relaci=C3=B3n de conceptos</td><td style=3D"paddin= g:0.75pt">Se presentan conceptos o ca= racter=C3=ADsticas y los estudiantes deben asociarlos con el continente cor= respondiente.</td></tr><tr><td style=3D"padding:0.75pt">Continente africano</td><td style= =3D"padding:0.75pt">Cuestionario</spa= n></td><td style=3D"padding:0.75pt">Los estudiantes identifican datos importantes de =C3=81frica como pa=C3= =ADses, caracter=C3=ADsticas geogr=C3=A1ficas y cultura.</td></t= r><tr><td style=3D"padding:0.75pt">Co= ntinente africano (actividad adicional)</td><td style=3D"padding= :0.75pt">Juego de preguntas</p= ></td><td style=3D"padding:0.75pt">Se= refuerzan conocimientos mediante preguntas r=C3=A1pidas sobre ubicaci=C3= =B3n y caracter=C3=ADsticas del continente.</td></tr><tr><td sty= le=3D"padding:0.75pt">Mapa interactiv= o de Europa</td><td style=3D"padding:0.75pt">Mapa interactivo</td><td style=3D"padding:0.7= 5pt"><sp= an style=3D"font-family:'Times New Roman'">Los estudiantes observan el mapa= del continente europeo y deben seleccionar correctamente pa=C3=ADses o reg= iones se=C3=B1aladas en el mapa.</td></tr><tr><td style=3D"paddi= ng:0.75pt">Generalidades de Europa</s= pan></td><td style=3D"padding:0.75pt">Cuestionario</td><td style=3D"padding:0.75pt">Preguntas sobre caracter=C3=ADsticas f=C3=ADsicas,= culturales y geogr=C3=A1ficas del continente europeo.</td></tr>= <tr><td style=3D"padding:0.75pt">Cont= inente europeo</td><td style=3D"padding:0.75pt">Juego de identificaci=C3=B3n</td><td style= =3D"padding:0.75pt">Los estudiantes i= dentifican elementos geogr=C3=A1ficos importantes del continente europeo.</= span></td></tr><tr><td style=3D"padding:0.75pt">Asia</td><td style=3D"padding:0.75pt">Cuestionario</td><td style=3D"padding= :0.75pt">Evaluaci=C3=B3n sobre ubicac= i=C3=B3n, extensi=C3=B3n territorial y caracter=C3=ADsticas del continente = asi=C3=A1tico.</td></tr><tr><td style=3D"padding:0.75pt">Continente asi=C3=A1tico</td><td = style=3D"padding:0.75pt">Juego de pre= guntas</td><td style=3D"padding:0.75pt">Actividad que refuerza conocimientos b=C3=A1sicos sobre el co= ntinente m=C3=A1s grande del planeta.</td></tr><tr><td style=3D"= padding:0.75pt">Continente asi=C3=A1t= ico (actividad adicional)</td><td style=3D"padding:0.75pt">Relaci=C3=B3n de conceptos</t= d><td style=3D"padding:0.75pt">Asocia= ci=C3=B3n entre pa=C3=ADses, cultura y caracter=C3=ADsticas geogr=C3=A1fica= s de Asia.</td></tr><tr><td style=3D"padding:0.75pt">Continente de Ocean=C3=ADa</td><td st= yle=3D"padding:0.75pt">Cuestionario</= span></td><td style=3D"padding:0.75pt">Identificaci=C3=B3n de pa=C3=ADses y caracter=C3=ADsticas del contine= nte oce=C3=A1nico.</td></tr><tr><td style=3D"padding:0.75pt">Poblaci=C3=B3n mundial</td><t= d style=3D"padding:0.75pt">Juego de c= onocimiento general</td><td style=3D"padding:0.75pt">Actividad para comprender la distribuci=C3=B3n d= e la poblaci=C3=B3n en los continentes del mundo.</td></tr></tbo= dy></table>Nota: Informaci=C3=B3n reco= pilada de la plataforma Educaplay.</div> <div class=3D"Section_5"><div style=3D"cle= ar:both"><= span style=3D"height:0pt; display:block; position:absolute; z-index:-65536"= ><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAUIAAABPCAYAAACe= RMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAADrlJREFUeJztnX+sV/V= 5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMMcYY48o6Qlc2M6KNq1kXBbuprdYOWjsnRarir4= l2jNZag6jMabV0astgprMtiqICVVEQ2BW4XMTX/nieL/dzP9/zPT++98KF8bwScr/n8+P5POccv= s/3nM/nnOctBUEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBNUAaxjNkJefCawFdgN7= gB8AFwG3Zv17tgO+lNjdA3wq63tnNvaSBv6eAawD9gKDwIoe7SYBi4FngZ3Aw0ndSuAt9/mhfux= nfWYDz7i9fcAm4MqsTdWYTfpvopvL63wLgqABwADwj/7FehKY7OXPAi8B5wNnAdcDrwO3Z/1r23= kdwAvAQNb/POBN4JyG/j5bEhDuyNqcC7zodT8F/hmY5nXf8/JFwC3+eUUb+9lYU4DtwH7gH4BPA= 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mundo; en particular, el uso de mapas i= nteractivos facilit=C3=B3 el desarrollo de las habilidades de orientaci=C3= =B3n espacial, ya que los estudiantes deb=C3=ADan ubicar pa=C3=ADses, regio= nes o continentes en representaciones cartogr=C3=A1ficas. Asimismo, los cue= stionarios y juegos de preguntas contribuyeron a reforzar la comprensi=C3= =B3n de conceptos b=C3=A1sicos mediante la retroalimentaci=C3=B3n inmediata= que proporciona la plataforma; este tipo de actividades no solo permiti=C3= =B3 evaluar el nivel de conocimiento de los estudiantes, sino que tambi=C3= =A9n fortalece el aprendizaje significativo al incentivar la reflexi=C3=B3n= sobre las respuestas correctas e incorrectas. La Tabla 4= presenta las activida= des relacionadas con la tem=C3=A1tica del clima, las cuales fueron dise=C3= =B1adas para que los estudiantes comprendan conceptos fundamentales como la= diferencia entre el clima y tiempo atmosf=C3=A9rico, lo biomas del mundo y= los factores que influyen en las condiciones clim=C3=A1ticas. Estas activi= dades se desarrollaron mediante cuestionarios, ejercicios de asociaci=C3=B3= n y juegos de preguntas que permitieron reforzar los contenidos te=C3=B3ric= os de forma interactiva<div style=3D"clear:both"><img src=3D"data:imag= e/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAABCAYAAACWsrt9AAAABHNCSVQICAgIfA= hkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSIntwTEBAAAIwKDZP7TG8AGm2gAAgA9zm= TsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740" height=3D"1" alt=3D"" style=3D= "margin-top:-37.82pt; margin-left:-63.3pt; position:absolute" /><spa= n style=3D"letter-spacing:3pt; color:#ffffff"> = ; = Nombre = = 0; 1<spa= n> </div></div> <div class=3D"Section_6"><spa= n style=3D"font-family:'Times New Roman'; font-weight:bold; font-style:norm= al; color:#000000">Tabla 4<span= style=3D"font-family:'Times New Roman'; color:#000000">Tem=C3=A1tica: El c= lima<table style=3D"margin-bottom:0pt; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:0pt; border-spacing:1.5= pt"><thead><tr><td style=3D"padding:0.75pt">Juego</td><td style=3D"padding:0.75pt"= >Actividad<= /p></td><td style=3D"padding:0.75pt">Funcionamiento</td></tr></thead><tbody><tr><t= d style=3D"border-top:0.75pt solid #000000; padding:0.75pt">El clima y tiempo atmosf=C3=A9rico</td><t= d style=3D"border-top:0.75pt solid #000000; padding:0.75pt">Cuestionario</td><td style=3D"border-top:= 0.75pt solid #000000; padding:0.75pt">Los estudiantes responden preguntas sobre la diferencia entre clima y tiem= po atmosf=C3=A9rico.</td></tr><tr><td style=3D"padding:0.75pt"><= p style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">Biomas del mundo</td><td st= yle=3D"padding:0.75pt">Relaci=C3=B3n = de conceptos</td><td style=3D"padding:0.75pt">Se relacionan tipos de biomas con sus caracter=C3=ADsti= cas clim=C3=A1ticas.</td></tr><tr><td style=3D"padding:0.75pt"><= p style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">El clima</td><td style=3D"p= adding:0.75pt">Juego de preguntas</sp= an></td><td style=3D"padding:0.75pt">Evaluaci=C3=B3n sobre los diferentes tipos de clima presentes en el pla= neta.</td></tr><tr><td style=3D"padding:0.75pt">Factores que cambian el clima</td><td styl= e=3D"padding:0.75pt">Asociaci=C3=B3n<= /span></td><td style=3D"padding:0.75pt">Los estudiantes relacionan factores naturales con cambios en el clim= a.</td></tr><tr><td style=3D"padding:0.75pt">Elementos y factores del clima</td><td style= =3D"padding:0.75pt">Cuestionario</spa= n></td><td style=3D"padding:0.75pt">Identificaci=C3=B3n de elementos como temperatura, presi=C3=B3n y humeda= d.</td></tr></tbody></table>Nota: Informaci=C3=B3n recopilada de la plataforma Educaplay.</= div> <div class=3D"= Section_7"><div style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoA= AAANSUhEUgAAAUIAAABPCAYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQ= BlSsOGwAADrlJREFUeJztnX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMMcYY48o6Ql= 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"825" height=3D"1127" alt=3D"Imagen que contiene Interfaz de usuario gr=C3= =A1fica Descripci=C3=B3n generada autom=C3=A1ticamente" style=3D"m= argin-top:-34.85pt; margin-left:-88.65pt; position:absolute" /><span= style=3D"font-family:'Times New Roman'; color:#ffffff"> <p= class=3D"Header"> </div>El uso de actividades de relaci=C3=B3n de conce= ptos result=C3=B3 especialmente =C3=BAtil para que los estudiantes comprend= ieran la relaci=C3=B3n entre los diferentes biomas y las condiciones clim= =C3=A1ticas que los caracterizan; a trav=C3=A9s de este tipo de ejercicios,= los estudiantes deb=C3=ADan vincular conceptos como temperatura, precipita= ci=C3=B3n y tipos de vegetaci=C3=B3n con los diferentes ambientes naturales= del planeta. Este proceso favorece el aprendizaje significativo, debido a = que permite que los estudiantes establezcan conexiones entre distintos elem= entos del sistema clim=C3=A1tico. Por otra parte, los cuestionarios y juegos de pre= guntas permitieron evaluar la comprensi=C3=B3n de los estudiantes respecto = a los elementos y factores que influyen en el clima, como la temperatura, l= a presi=C3=B3n atmosf=C3=A9rica y la humedad. La din=C3=A1mica de juego inc= orporada en estas actividades, debido a que deb=C3=ADan responder preguntas= en un tiempo determinado y obtener una puntuaci=C3=B3n seg=C3=BAn sus acie= rtos. La Tabla 5 muestra las actividades relacionadas con el estudio de los mar= es y oc=C3=A9anos del mundo, un contenido fundamental dentro de la geograf= =C3=ADa f=C3=ADsica; estas actividades incluyeron cuestionarios, juegos de = identificaci=C3=B3n, mapas interactivos y ejercicios de relaci=C3=B3n conce= ptual que permitieron a los estudiantes comprender la importancia de los cu= erpos de agua en el equilibrio del planeta. <div style=3D"clear:= both"><s= pan style=3D"height:0pt; display:block; position:absolute; z-index:-65535">= <img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAABCAYAAACWs= rt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSIntwTEBAAAI= wKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740" height=3D= "1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-63.3pt; position:ab= solute" /> &#= xa0; = ; Nomb= re </sp= an>1 </div></div> <div class=3D"Section_8">Tabla 5Tem=C3=A1tica: mares y oc=C3=A9anos del mundo<table style=3D"ma= rgin-bottom:0pt; border-top:0.75pt solid #000000; border-bottom:0.75pt soli= d #000000; padding:0pt; border-spacing:1.5pt"><thead><tr><td style=3D"paddi= ng:0.75pt">Juego</= span></td><td style=3D"padding:0.75pt">Actividad</td><td style=3D"padding:0.75= pt">Funcionamiento= </td></tr></thead><tbody><tr><td style=3D"border-top:0.75pt soli= d #000000; padding:0.75pt">Los mares<= /span></td><td style=3D"border-top:0.75pt solid #000000; padding:0.75pt= ">Cuestionario</td><td sty= le=3D"border-top:0.75pt solid #000000; padding:0.75pt">Preguntas sobre caracter=C3=ADsticas y ubicaci=C3=B3n de = los mares del mundo.</td></tr><tr><td style=3D"padding:0.75pt"><= p style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">Los climas</td><td style=3D= "padding:0.75pt">Relaci=C3=B3n concep= tual</td><td style=3D"padding:0.75pt">Asociaci=C3=B3n entre climas y la influencia de los oc=C3=A9ano= s.</td></tr><tr><td style=3D"padding:0.75pt">Oc=C3=A9anos y mares del mundo</td><td style= =3D"padding:0.75pt">Mapa interactivo<= /span></td><td style=3D"padding:0.75pt">Los estudiantes deben ubicar los principales oc=C3=A9anos del planet= a en el mapa.</td></tr><tr><td style=3D"padding:0.75pt">Continentes, oc=C3=A9anos y mares<= /td><td style=3D"padding:0.75pt">Jueg= o de identificaci=C3=B3n</td><td style=3D"padding:0.75pt">Reconocimiento de la relaci=C3=B3n entre co= ntinentes y cuerpos de agua.</td></tr><tr><td style=3D"padding:0= .75pt"><= span style=3D"font-family:'Times New Roman'">Impacto de corrientes marinas<= /span></td><td style=3D"padding:0.75pt">Cuestionario</td><td style=3D"padding:0.75pt">Comprensi=C3=B3n de c=C3=B3mo las corrientes oce= =C3=A1nicas influyen en el clima.</td></tr></tbody></table>Nota: Informaci=C3=B3n recopilada de la pla= taforma Educaplay.</div> <div class=3D"Section_9">Uno de los aspectos m=C3=A1s relevantes de esta tem= =C3=A1tica fue el uso de mapas interactivos para la identificaci=C3=B3n de = los principales oc=C3=A9anos y mares del mundo; mediante este tipo de activ= idades, los estudiantes deb=C3=ADan ubicar correctamente los oc=C3=A9anos e= n el mapa, lo que contribuye a fortalecer sus habilidades de localizaci=C3= =B3n geogr=C3=A1fica, adem=C3=A1s, este tipo de recurso facilita la compren= si=C3=B3n de la distribuci=C3=B3n de los cuerpos de agua en el planeta y su= relaci=C3=B3n con los continentes.Igualmente, las actividades relacionadas con las= corrientes marinas permitieron analizar la influencia que tienen los oc=C3= =A9anos en el clima del mundo; a trav=C3=A9s de cuestionarios y ejercicios = conceptuales, los estudiantes pudieron comprender c=C3=B3mo las corrientes = marinas influyen en la temperatura de diferentes regiones del planeta. En g= eneral, estas actividades contribuyeron a desarrollar una visi=C3=B3n m=C3= =A1s amplia del funcionamiento del sistema natural de la tierra y de la int= eracci=C3=B3n entre oc=C3=A9anos, clima y continentes. La Tabla 6 muestra las actividades enfocadas en el estudio de las region= es del Ecuador, espec=C3=ADficamente en la regi=C3=B3n costa; estas activid= ades se desarrollaron mediante juegos de asociaci=C3=B3n, mapas interactivo= s, cuestionarios e identificaci=C3=B3n geogr=C3=A1fica, con el prop=C3=B3si= to de fortalecer el conocimiento del territorio nacional y sus caracter=C3= =ADsticas culturales y clim=C3=A1ticas. </div> <div class=3D"Section_10"><div style= =3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAUIAAABPC= AYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAADrlJREFUeJzt= nX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMMcYY48o6Qlc2M6KNq1kXBbuprdYOWjs= nRarir4l2jNZag6jMabV0astgprMtiqICVVEQ2BW4XMTX/nieL/dzP9/zPT++98KF8bwScr/n8+= P5POccvs/3nM/nnOctBUEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBNUAaxjNkJefC= awFdgN7gB8AFwG3Zv17tgO+lNjdA3wq63tnNvaSBv6eAawD9gKDwIoe7SYBi4FngZ3Aw0ndSuAt= 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regiones del Ecu= ador<table style=3D"margin-bottom:0pt; border-top:0.75pt solid #= 000000; border-bottom:0.75pt solid #000000; padding:0pt; border-spacing:1.5= pt"><thead><tr><td style=3D"padding:0.75pt">Juego</td><td style=3D"padding:0.75pt"= >Actividad<= /p></td><td style=3D"padding:0.75pt">Funcionamiento</td></tr></thead><tbody><tr><t= d style=3D"border-top:0.75pt solid #000000; padding:0.75pt">Etnias del Ecuador (Costa)</td><td style= =3D"border-top:0.75pt solid #000000; padding:0.75pt">Asociaci=C3=B3n</td><td style=3D"border-top:0.75= pt solid #000000; padding:0.75pt">Los= estudiantes relacionan grupos =C3=A9tnicos con su ubicaci=C3=B3n geogr=C3= =A1fica.</td></tr><tr><td style=3D"padding:0.75pt">Provincias de la costa del Ecuador</td>= <td style=3D"padding:0.75pt">Mapa int= eractivo</td><td style=3D"padding:0.75pt">Identificaci=C3=B3n de provincias dentro de la regi=C3=B3n = Costa.</td></tr><tr><td style=3D"padding:0.75pt">Culturas y etnias del Ecuador</td><td sty= le=3D"padding:0.75pt">Cuestionario</s= pan></td><td style=3D"padding:0.75pt">Preguntas sobre diversidad cultural del pa=C3=ADs.</td></tr= ><tr><td style=3D"padding:0.75pt">Cli= ma de la regi=C3=B3n Costa</td><td style=3D"padding:0.75pt">Juego conceptual</td><td styl= e=3D"padding:0.75pt">Relaci=C3=B3n en= tre clima y caracter=C3=ADsticas de la regi=C3=B3n.</td></tr><tr= ><td style=3D"padding:0.75pt">Provinc= ias de la costa</td><td style=3D"padding:0.75pt">Identificaci=C3=B3n geogr=C3=A1fica</td><= td style=3D"padding:0.75pt">Ubicaci= =C3=B3n de provincias en el mapa del Ecuador.</td></tr></tbody><= /table><s= pan style=3D"font-family:'Times New Roman'">Nota: Informaci=C3=B3n recopila= da de la plataforma Educaplay.<div style=3D"clear:both"><img src=3D"da= ta:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAABCAYAAACWsrt9AAAABHNCSVQ= ICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSIntwTEBAAAIwKDZP7TG8AGm2g= AAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740" height=3D"1" alt=3D"" s= tyle=3D"margin-top:-37.82pt; margin-left:-63.3pt; position:absolute" /></sp= an> = 0; = Nombre<span= style=3D"width:141.26pt; letter-spacing:3pt; display:inline-block"> <= /span> 1 </div></div> <div class=3D"Section_11">El uso de mapas interactivos permiti=C3=B3 que lo= s estudiantes identifiquen las provincias que conforman la regi=C3=B3n cost= a del Ecuador, facilitando la comprensi=C3=B3n de la organizaci=C3=B3n terr= itorial del pa=C3=ADs. Este tipo de actividad es fundamental para el desarr= ollo de habilidades geogr=C3=A1ficas, debido a que permite relacionar los c= ontenidos te=C3=B3ricos con representaciones visuales del espacio geogr=C3= =A1fico. Adem=C3=A1s, las actividades relacionadas con las culturas y etnia= s del Ecuador contribuyeron a reforzar el conocimiento sobre la diversidad = cultural del pa=C3=ADs; mediante cuestionarios y ejercicios de asociaci=C3= =B3n, los estudiantes pudieron reconocer la presencia de diferentes grupos = =C3=A9tnicos en la regi=C3=B3n costa y comprender la importancia de la dive= rsidad cultural dentro de la sociedad ecuatoriana. En conjunto, estas activ= idades permitieron integrar conocimientos geogr=C3=A1ficos, culturales y so= ciales dentro del proceso de aprendizaje.La Tabla 7 i= ndica las actividades relacionadas con el estudio de las etnias del Ecuador= , las cuales fueron dise=C3=B1adas para promover el conocimiento y la valor= aci=C3=B3n de la diversidad cultural del pa=C3=ADs. Estas actividades inclu= yeron cuestionarios, juegos de preguntas, ejercicios de asociaci=C3=B3n y a= ctividades de relaci=C3=B3n conceptual que permitieron a los estudiantes id= entificar los diferentes pueblos y nacionalidades que conforman la sociedad= ecuatoriana. Tabla 7Tem=C3=A1tica: etnias del Ecuado= r<table style=3D"margin-bottom:0pt; border-top:0.75pt solid #000= 000; border-bottom:0.75pt solid #000000; padding:0pt; border-spacing:1.5pt"= ><thead><tr><td style=3D"padding:0.75pt">Juego</td><td style=3D"padding:0.75pt"><p= style=3D"margin-bottom:0pt; text-align:center; line-height:normal; font-si= ze:10pt">Actividad= </td><td style=3D"padding:0.75pt">Funcionamiento</td></tr></thead><tbody><tr><td s= tyle=3D"border-top:0.75pt solid #000000; padding:0.75pt">Culturas y etnias del Ecuador</td><td style= =3D"border-top:0.75pt solid #000000; padding:0.75pt">Cuestionario</td><td style=3D"border-top:0.75pt = solid #000000; padding:0.75pt">Recono= cimiento de los diferentes pueblos y culturas del pa=C3=ADs.</td= ></tr><tr><td style=3D"padding:0.75pt">Etnias del Ecuador (Costa)</td><td style=3D"padding:0.75pt"><p= style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">Asociaci=C3=B3n</td><td styl= e=3D"padding:0.75pt">Relaci=C3=B3n en= tre grupos =C3=A9tnicos y regi=C3=B3n geogr=C3=A1fica.</td></tr>= <tr><td style=3D"padding:0.75pt">Etni= as del Ecuador</td><td style=3D"padding:0.75pt">Juego de preguntas</td><td style=3D"paddin= g:0.75pt">Evaluaci=C3=B3n sobre carac= ter=C3=ADsticas culturales de cada grupo =C3=A9tnico.</td></tr><= tr><td style=3D"padding:0.75pt">Diver= sidad del Ecuador</td><td style=3D"padding:0.75pt">Relaci=C3=B3n conceptual</td><td style= =3D"padding:0.75pt">Asociaci=C3=B3n e= ntre diversidad cultural y regiones del pa=C3=ADs.</td></tr><tr>= <td style=3D"padding:0.75pt">Etnias d= el Ecuador (actividad adicional)</td><td style=3D"padding:0.75pt= ">Cuestionario</td><td sty= le=3D"padding:0.75pt">Identificaci=C3= =B3n de costumbres y tradiciones de los pueblos ecuatorianos.</t= d></tr><tr><td style=3D"padding:0.75pt">Ecuador multi=C3=A9tnico y cultural</td><td style=3D"padding:= 0.75pt">= Juego de comprensi=C3=B3n</sp= an></td><td style=3D"padding:0.75pt">Preguntas sobre el car=C3=A1cter multicultural del pa=C3=ADs.</p= ></td></tr><tr><td style=3D"padding:0.75pt">Ecuador intercultural</td><td style=3D"padding:0.75pt"><p= style=3D"margin-bottom:0pt; line-height:normal; font-size:10pt">Asociaci=C3=B3n conceptual</= td><td style=3D"padding:0.75pt">Relac= i=C3=B3n entre cultura, identidad y diversidad del Ecuador.</td>= </tr></tbody></table>Nota: Informaci= =C3=B3n recopilada de la plataforma Educaplay.</div> <div class=3D"Section_12"><div= style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAUI= AAABPCAYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAADrlJRE= FUeJztnX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMMcYY48o6Qlc2M6KNq1kXBbupr= dYOWjsnRarir4l2jNZag6jMabV0astgprMtiqICVVEQ2BW4XMTX/nieL/dzP9/zPT++98KF8bwS= cr/n8+P5POccvs/3nM/nnOctBUEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBNUAaxj= NkJefCawFdgN7gB8AFwG3Zv17tgO+lNjdA3wq63tnNvaSBv6eAawD9gKDwIoe7SYBi4FngZ3Aw0= ndSuAt9/mhfuxnfWYDz7i9fcAm4MqsTdWYTfpvopvL63wLgqABwADwj/7FehKY7OXPAi8B5wNnA= 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asociaci=C3=B3n y relaci=C3=B3n conceptual p= ermitieron que los estudiantes vinculen los diferentes grupos =C3=A9tnicos = con las regiones del pa=C3=ADs donde se encuentran; este tipo de ejercicio = facilita la comprensi=C3=B3n de la diversidad cultural del Ecuador y su dis= tribuci=C3=B3n territorial, lo cual es un aspecto fundamental dentro de los= estudios sociales.Por otra parte, los cuestionarios y juegos de comprensi=C3=B3n p= ermitieron evaluar el conocimiento de los estudiantes sobre las costumbres,= tradiciones y caracter=C3=ADsticas culturales de los diferentes pueblos de= l Ecuador. Estas actividades no solo contribuyen al aprendizaje de contenid= os acad=C3=A9micos, sino que tambi=C3=A9n fomentan valores como el respeto,= la interculturalidad y la valoraci=C3=B3n de la identidad. De esta manera,= el uso de herramientas digitales como Educaplay favorece el desarrollo de = un aprendizaje significativo que integra conocimientos, habilidades y valor= es. 3.3 Motivaci=C3=B3n hacia el aprendizaje significativo</span= >La <spa= n style=3D"font-family:'Times New Roman'; font-weight:bold">Figura 1= muestra los resultados compa= rativos entre pretest y postest en relaci=C3=B3n con los niveles de motivac= i=C3=B3n hacia el aprendizaje significativo en la asignatura de estudios so= ciales, luego de la aplicaci=C3=B3n de estrategias de gamificaci=C3=B3n med= iante la plataforma Educaplay. En t=C3=A9rminos generales, se observa un in= cremento significativo en todas las dimensiones evaluadas, lo que evidencia= que la incorporaci=C3=B3n de herramientas digitales y actividades l=C3=BAd= icas contribuy=C3=B3 a fortalecer la motivaci=C3=B3n de los estudiantes y m= ejorar su disposici=C3=B3n hacia el aprendizaje. Cabe destacar que, para el an=C3=A1lisis de los resul= tados del pretest y postest se utiliz=C3=B3 la metodolog=C3=ADa de medias, = la cual consiste en el c=C3=A1lculo de promedio de las respuestas obtenidas= en cada dimensi=C3=B3n evaluada. Este procedimiento permite identificar el= nivel general de percepci=C3=B3n o valoraci=C3=B3n de los participantes re= specto a un determinado aspecto del estudio; adem=C3=A1s, se debe considera= r los niveles de escala de Likert planteada representada con un valor num= =C3=A9rico del 1 al 5, correspondiendo a la escala de nunca a siempre. De e= sta manera, los valores m=C3=A1s cercanos a 1 indican una baja frecuencia o= presencia del fen=C3=B3meno analizado, mientras que los valores cercanos a= 5 reflejan una mayor frecuencia o percepci=C3=B3n positiva. El uso de la m= edia aritm=C3=A9tica permite sintetizar la informaci=C3=B3n recolectada y c= omparar los resultados obtenidos entre el pretest y el postest, facilitando= as=C3=AD la identificaci=C3=B3n de cambios o mejoras en las variables anal= izadas durante el proceso de intervenci=C3=B3n educativa. <div s= tyle=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQ= AAAABCAYAAACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJRE= FUSIntwTEBAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"= 740" height=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; margin-left:-63.3p= t; position:absolute" /> &#x= a0; Nombre 1<= /span> </div></div> <div class=3D"Section_13"><= div style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA= AUIAAABPCAYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAADrl= JREFUeJztnX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMMcYY48o6Qlc2M6KNq1kXBb= 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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAH/9k=3D" width=3D"825" heigh= t=3D"1127" alt=3D"Imagen que contiene Interfaz de usuario gr=C3=A1fica = Descripci=C3=B3n generada autom=C3=A1ticamente" style=3D"margin-top:-3= 4.85pt; margin-left:-88.65pt; position:absolute" /> </div>Figura 1Comparativa entre pre= test y postest<img src=3D"data:image/png;base64,iVBORw0= KGgoAAAANSUhEUgAAAaYAAADuCAYAAAByDaO2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxA= AADsQBlSsOGwAAIABJREFUeJzs3Xlc1VX++PHXRVxIEGVxRcVcIAgXLCWdfmk6k1vzNR3T0DQNK= 9OscRm1RTMjaXJqnEgnNbcAbdyX3AhxS3FBFNzQzCQXBJFNxCvL5/cHc+9wuQsXuBcu8H4+Hj7q= 3s927of7Oe97zud8zlt16dIlBSGEEMIGeHt7q+wBvLy8qrosQggharnExEQA7Kq4HEIIIYQOCUx= CCCFsigQmIYQQNkUCkxBCCJsigUkIIYRNkcAkhBDCpkhgEkIIYVMkMAkhhLApEpiEEELYFAlMQg= ghbIoEJiGEEDZFApMQQgibIoFJCCGETZHAJIQQwqZIYBKVLiQkhMDAQObMmYOi6KcDO3XqFIGBg= QQGBmqnwTfXpk2bOHPmjKWKanK/eXl5TJs2jVu3bpV5f9euXWPBggVkZmZavFzlkZmZydq1a4mJ= ianwvoSoKAlMotLNmDGDXr16kZmZyYULF/SWHzt2DIAvvvii1FxhCQkJZGdna18PHz6crl27Wrb= 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hQGQAAAAAAS6X6d9z3LmVTtzH61Nu3EV36u7rT5I+7/MCR1OAxAAAAAAAAAAAAAAAAAAAAAAAAA= AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAH/9k=3D" width=3D"825" hei= ght=3D"1127" alt=3D"Imagen que contiene Interfaz de usuario gr=C3=A1fica&#x= A; Descripci=C3=B3n generada autom=C3=A1ticamente" style=3D"margin-top:= -34.85pt; margin-left:-88.65pt; position:absolute" /> </div>En la dimensi=C3=B3n de motivaci=C3=B3n hacia el aprendiz= aje, se evidencia un aumento notable al pasar de una media de 3,33 en el pr= e =E2=80=93 test a 4,65 en el postest; este incremento refleja que las acti= vidades gamificadas generaron mayor inter=C3=A9s y entusiasmo por parte de = los estudiantes durante el desarrollo de las clases. La utilizaci=C3=B3n de= juegos interactivos, retos y actividades din=C3=A1micas permiti=C3=B3 tran= sformar a experiencia educativa en un proceso m=C3=A1s atractivo, favorecie= ndo la participaci=C3=B3n activa y el compromiso con los contenidos abordad= os. En c= uanto a las metodolog=C3=ADas y clases tradicionales, los resultados muestr= an un incremento de 3,88 a 4,28, lo que indica que los estudiantes percibie= ron una mejora en la forma en que se desarrollaron las clases despu=C3=A9s = de implementar estrategias innovadoras. Este resultado sugiere que la integ= raci=C3=B3n de metodolog=C3=ADas activas, combinadas con recursos digitales= , permiti=C3=B3 superar significativamente las limitaciones de los m=C3=A9t= odos tradicionales centrados =C3=BAnicamente en la exposici=C3=B3n del doce= nte, promoviendo un aprendizaje m=C3=A1s din=C3=A1mico y participativo.</sp= an>De igual man= era, las dimensiones gamificaci=C3=B3n y uso de juegos, aprendizaje signifi= cativo y uso de herramientas digitales muestran incrementos importantes; la= gamificaci=C3=B3n pas=C3=B3 de 4,15 a 4,66, el aprendizaje significativo d= e 3,36 a 4,53, y el uso de herramientas digitales de 4,09 a 4,86, siendo es= ta =C3=BAltima una de las puntuaciones m=C3=A1s altas en el postest. Estos = resultados evidencian que la incorporaci=C3=B3n de recursos tecnol=C3=B3gic= os interactivos favorece la comprensi=C3=B3n de los contenidos y fortalece = el aprendizaje significativo, debido a que los estudiantes no solo reciben = informaci=C3=B3n, sino que participan activamente en la construcci=C3=B3n d= e su conocimiento a trav=C3=A9s de experiencias educativas din=C3=A1micas y= motivadoras. Por otra parte la Tabla 8 muestra la prueba de hip=C3=B3tesis que se realiz=C3=B3 mediante el c= oeficiente de correlaci=C3=B3n Rho de Spearman, con el prop=C3=B3sito de id= entificar la relaci=C3=B3n existente entre las variables de estudio; para e= ste caso se tom=C3=B3 en consideraci=C3=B3n las dimensiones del pretest y p= ostest por lo cual se pudo medir a la variable gamificaci=C3=B3n, motivaci= =C3=B3n y aprendizaje significativo por separado sin perder el prop=C3=B3si= to de la investigaci=C3=B3n. <div style=3D"clear:both"><img src=3D"da= ta:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAuQAAAABCAYAAACWsrt9AAAABHNCSVQ= ICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAABxJREFUSIntwTEBAAAIwKDZP7TG8AGm2g= AAgA9zmTsBAl+AgmQAAAAASUVORK5CYII=3D" width=3D"740" height=3D"1" alt=3D"" s= tyle=3D"margin-top:-37.82pt; margin-left:-63.3pt; position:absolute" /></sp= an> = 0; = Nombre<span= style=3D"width:141.26pt; letter-spacing:3pt; display:inline-block"> <= /span> 1 </div></div> <div class=3D"Section_15">Tabla 8Prueba de hip=C3=B3tesis<table style=3D"width:93.16%; margi= n-bottom:0pt; padding:0pt; border-collapse:collapse"><tr style=3D"height:20= .8pt"><td style=3D"width:16.48%; border-top:0.75pt solid #000000; border-bo= ttom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top"> </td><td style=3D"width:35.2= 2%; border-top:0.75pt solid #000000; border-bottom:0.75pt solid #000000; pa= dding:0pt 5.4pt; vertical-align:middle"> </td><td style=3D"width:16.52%; border-top:0.75pt solid= #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-a= lign:middle">Gamificaci=C3=B3n= </td><td style=3D"width:15.68%; border-top:0.75pt solid #000000; border= -bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:middle">Motivaci=C3=B3n</span= ></td><td style=3D"width:16.06%; border-top:0.75pt solid #000000; borde= r-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:middle"><p= style=3D"margin-bottom:0pt; text-align:center; line-height:normal; font-si= ze:10pt">Aprendizaje signific= ativo</td></tr><tr style=3D"height:34.2pt"><td rowspan=3D"3" sty= le=3D"width:16.48%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; ver= tical-align:top">Gamificaci=C3=B3n</s= pan>= </td><td sty= le=3D"width:35.22%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; ver= tical-align:middle">Correlaci=C3=B3n = Rho de Spearman</td><td style=3D"width:16.52%; border-top:0.75pt= solid #000000; padding:0pt 5.4pt; vertical-align:middle">1</td><td style=3D"width:= 15.68%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:= middle">,785= **</td><td style=3D"width:16.06%; border-top:0.75pt s= olid #000000; padding:0pt 5.4pt; vertical-align:middle">,759**</t= d></tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; padding:0pt 5= .4pt; vertical-align:middle">Sig. (un= ilateral) </td><td style=3D"width:16.52%; padding:0pt 5.4pt; ver= tical-align:middle"> </td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertic= al-align:middle">,0= 00</td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-al= ign:middle">,000</s= pan></td></tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; pa= dding:0pt 5.4pt; vertical-align:middle">N</td><td style=3D"width:16.52%; padding:0pt 5.4pt; vertical-= align:middle">48</s= pan></td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-align:m= iddle">48</p= ></td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align:middle">= 48</td><= /tr><tr style=3D"height:22.8pt"><td rowspan=3D"3" style=3D"width:16.48%; pa= dding:0pt 5.4pt; vertical-align:top">= Motivaci=C3=B3n </spa= n><s= pan style=3D"font-family:'Times New Roman'"> </td><td style= =3D"width:35.22%; padding:0pt 5.4pt; vertical-align:middle">Correlaci=C3=B3n Rho de Spearman</td><td = style=3D"width:16.52%; padding:0pt 5.4pt; vertical-align:middle">,785**</s= pan></td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-align:m= iddle">1= </td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align:middle"><= p style=3D"margin-bottom:0pt; text-align:right; line-height:normal; font-si= ze:10pt">,804</td>= </tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; padding:0pt 5.4= pt; vertical-align:middle">Sig. (unil= ateral)</td><td style=3D"width:16.52%; padding:0pt 5.4pt; vertic= al-align:middle">,0= 00</td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-al= ign:middle"> <= /span></td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align= :middle">,000</span= ></td></tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; paddi= ng:0pt 5.4pt; vertical-align:middle">= N</td><td style=3D"width:16.52%; padding:0pt 5.4pt; vertical-ali= gn:middle">48</span= ></td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-align:midd= le">48</= td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align:middle">48</td></tr= ><tr style=3D"height:22.8pt"><td rowspan=3D"3" style=3D"width:16.48%; paddi= ng:0pt 5.4pt; vertical-align:top">Apr= endizaje significativo = 0; </td><t= d style=3D"width:35.22%; padding:0pt 5.4pt; vertical-align:middle">Correlaci=C3=B3n Rho de Spearman</= td><td style=3D"width:16.52%; padding:0pt 5.4pt; vertical-align:middle">,759= **</td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-al= ign:middle">,804</s= pan></td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align:m= iddle">1= </td></tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; padding:0p= t 5.4pt; vertical-align:middle">Sig. = (unilateral)</td><td style=3D"width:16.52%; padding:0pt 5.4pt; v= ertical-align:middle">,000</td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertic= al-align:middle">,0= 00</td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-al= ign:middle"> <= /span></td></tr><tr style=3D"height:14.4pt"><td style=3D"width:35.22%; = padding:0pt 5.4pt; vertical-align:middle">N</td><td style=3D"width:16.52%; padding:0pt 5.4pt; vertica= l-align:middle">48<= /span></td><td style=3D"width:15.68%; padding:0pt 5.4pt; vertical-align= :middle">48<= /p></td><td style=3D"width:16.06%; padding:0pt 5.4pt; vertical-align:middle= ">48</td= ></tr><tr style=3D"height:18.6pt"><td colspan=3D"4" style=3D"border-bottom:= 0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">**. La correlaci=C3=B3n es significativa en el nive= l 0,01 (unilateral).</td><td style=3D"width:16.06%; border-botto= m:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:middle"> </td></tr= ></table>= Nota: datos obtenidos de la i= nvestigaci=C3=B3n en la Unidad Educativa San Agust=C3=ADn de Guayaquil</spa= n></div> <div c= lass=3D"Section_16"><div style=3D"clear:both"><img src=3D"data:image/png;base64,iVB= ORw0KGgoAAAANSUhEUgAAAUIAAABPCAYAAACeRMz2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAA= AOxAAADsQBlSsOGwAADrlJREFUeJztnX+sV/V5x9+nP8zcdrdm7Ui6uJqSlSY21YWNzKRxWdwMM= cYY48o6Qlc2M6KNq1kXBbuprdYOWjsnRarir4l2jNZag6jMabV0astgprMtiqICVVEQ2BW4XMTX= /nieL/dzP9/zPT++98KF8bwScr/n8+P5POccvs/3nM/nnOctBUEQBEEQBEEQBEEQBEEQBEEQBEE= QBEEQBEEQBEEQBEEQBNUAaxjNkJefCawFdgN7gB8AFwG3Zv17tgO+lNjdA3wq63tnNvaSBv6eAa= wD9gKDwIoe7SYBi4FngZ3Aw0ndSuAt9/mhfuxnfWYDz7i9fcAm4MqsTdWYTfpvopvL63wLgqABw= ADwj/7FehKY7OXPAi8B5wNnAdcDrwO3Z/1r23kdwAvAQNb/POBN4JyG/j5bEhDuyNqcC7zodT8F= 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valor con el nivel de significancia asumida d= e 0,05 el p =E2=80=93 valor de la correlaci=C3=B3n es menor (0,000 < 0,0= 5). Esto significa que existe una probabilidad muy baja de que estas relaci= ones se deban al azar, lo que permite afirmar que las variables analizadas = presentan una asociaci=C3=B3n importante dentro del proceso educativo. </sp= an>En primer lu= gar, se observa una correlaci=C3=B3n alta entre la gamificaci=C3=B3n y la m= otivaci=C3=B3n, con un coeficiente de Rho =3D 0,735; este resultado evidenc= ia que, a mayor uso de estrategias de gamificaci=C3=B3n dentro del proceso = de ense=C3=B1anza, mayor es el nivel de motivaci=C3=B3n de los estudiantes = hacia el aprendizaje. La incorporaci=C3=B3n de actividades l=C3=BAdicas, di= n=C3=A1micas interactivas y herramientas digitales favorece la participaci= =C3=B3n activa del estudiante, generando mayor inter=C3=A9s y compromiso co= n las actividades acad=C3=A9micas. En este sentido, la gamificaci=C3=B3n se= convierte en un recurso pedag=C3=B3gico relevante para transformar las cla= ses tradicionales en experiencias m=C3=A1s atractivas y participativas. </s= pan><= span style=3D"font-family:'Times New Roman'">Por otro lado, tambi=C3=A9n se= identifican relaciones significativas entre gamificaci=C3=B3n y aprendizaj= e significativo Rho =3D 0,759, as=C3=AD como entre la motivaci=C3=B3n y apr= endizaje significativo Rho =3D 0,804, siendo esta =C3=BAltima la correlaci= =C3=B3n m=C3=A1s alta; indicando que cuando los estudiantes presentan mayor= es niveles de motivaci=C3=B3n, tienden a desarrollar un aprendizaje m=C3=A1= s profundo y comprensivo de los contenidos. Es as=C3=AD como, el uso de est= rategias gamificadas contribuye a fortalecer el proceso de aprendizaje sign= ificativo, debido a que promueve la interacci=C3=B3n, la resoluci=C3=B3n de= retos y la participaci=C3=B3n activa, elementos que facilitan la construcc= i=C3=B3n del conocimiento. En consecuencia, los resultados permiten aceptar= la hip=C3=B3tesis de investigaci=C3=B3n, confirmando que la gamificaci=C3= =B3n se relaciona positivamente con la motivaci=C3=B3n hacia el aprendizaje= significativo en el contexto educativo analizado. 4. = Discusi=C3=B3nLos resultados obtenidos en la investigaci=C3=B3n evidenc= ian que la incorporaci=C3=B3n de estrategias de gamificaci=C3=B3n basadas e= n la plataforma Educaplay contribuye de manera significativa al fortalecimi= ento de la motivaci=C3=B3n y del aprendizaje significativo en estudiantes d= e educaci=C3=B3n b=C3=A1sica. Los resultados del pretest y postest muestran= un incremento notable en todas las dimensiones analizadas, especialmente e= n la motivaci=C3=B3n hacia el aprendizaje y el uso de herramientas digitale= s. Estos hallazgos coinciden con los planteamientos te=C3=B3ricos de autore= s como P=C3=A9rez & Barrio (2021) y S=C3=A1nchez (2023) quienes se=C3= =B1alan que el juego constituye un recurso pedag=C3=B3gico natural que favo= rece la participaci=C3=B3n activa del estudiante, estimula la curiosidad y = facilita la construcci=C3=B3n del conocimiento. En este sentido, la gamific= aci=C3=B3n permiti=C3=B3 transformar las clases tradicionales de estudios s= ociales en experiencias m=C3=A1s din=C3=A1micas, participativas y centradas= en el estudiante. De igual forma, los resultados de an=C3=A1lisis correlacional ev= idencian relaciones positivas y significativas entre las variables de gamif= icaci=C3=B3n, motivaci=C3=B3n y aprendizaje significativo; esto confirma qu= e el inter=C3=A9s y la disposici=C3=B3n del estudiante influyen directament= e en la profundidad con la que se asimilan los contenidos. Estos resultados= se relacionan con los postulados de la teor=C3=ADa del aprendizaje signifi= cativo de David Ausubel (1980) quien plantea que el aprendizaje se produce = de forma m=C3=A1s efectiva cuando el estudiante posee una actitud favorable= para aprender y puede relacionar los nuevos conocimientos con sus experien= cias previas. De esta manera, la motivaci=C3=B3n act=C3=BAa como un element= o clave que permite la integraci=C3=B3n cognitiva de los contenidos y el de= sarrollo de aprendizajes duraderos. Por otra parte, los resultados tambi=C3=A9n coinciden con investig= aciones previas desarrolladas en el =C3=A1mbito educativo latinoamericano; = estudios como los de Prieto et al. (2022) y Sep=C3=BAlveda & Mayoral (2= 025) destacan que la gamificaci=C3=B3n incrementa la motivaci=C3=B3n intr= =C3=ADnseca y mejora el rendimiento acad=C3=A9mico al incorporar din=C3=A1m= icas de juego, retos y sistemas de retroalimentaci=C3=B3n inmediata. De igu= al forma, Buestan et al. (2025) se=C3=B1alan que estas estrategias contribu= yen al desarrollo de habilidades sociales, emocionales y cognitivas. En con= cordancia con estas investigaciones, el presente estudio demuestra que la u= tilizaci=C3=B3n de herramientas digitales gamificadas no solo favorece la c= omprensi=C3=B3n de los contenidos de estudios sociales, sino que tambi=C3= =A9n promueve un aprendizaje activo, participativo y contextualizado, capaz= de responder a las necesidades educativas actuales. 5. Con= clusionesLa implementaci=C3=B3n de una estrategia de gamificaci=C3=B3n basada en la= plataforma Educaplay permiti=C3=B3 incrementar significativamente la motiv= aci=C3=B3n de los estudiantes hacia el aprendizaje en la asignatura de estu= dios sociales, evidenciado en el aumento de las medias obtenidas entre el p= retest y el postest en todas las dimensiones evaluadas. Los resultados del an=C3=A1= lisis correlacional demostraron que existe una relaci=C3=B3n positiva y est= ad=C3=ADsticamente significativa entre la gamificaci=C3=B3n, la motivaci=C3= =B3n y el aprendizaje significativo, lo que confirma que el inter=C3=A9s de= l estudiante es un factor determinante en la construcci=C3=B3n del conocimi= ento. La gamificaci=C3=B3n = mediada por herramientas digitales se consolida como una estrategia pedag= =C3=B3gica innovadora y eficaz que permite transformar las metodolog=C3=ADa= s tradicionales en experiencias de aprendizaje m=C3=A1s din=C3=A1micas, par= ticipativas y contextualizadas, favoreciendo el desarrollo de habilidades c= ognitivas, sociales y actitudinales en los estudiantes. 6. = ; Conflicto de interesesLos autores declaran que no existe conflicto de intereses en = relaci=C3=B3n con el estudio presentado. 7. = 0; Derechos de autor (copyrigth)Los autores son los titulares de los derechos de autor</span= > <s= pan style=3D"font-family:'Times New Roman'">(patrimoniales y/o de explotaci= =C3=B3n) de los contenidos de la revista (copyright).8. </s= pan>Declaraci=C3=B3n de contribuci=C3=B3n de los autoresLos autores declaran ha= ber participado de manera equitativa y sustancial en la realizaci=C3=B3n de= esta investigaci=C3=B3n, bajo las responsabilidades seg=C3=BAn la <= a href=3D"https://credit.niso.org/" style=3D"text-decoration:none">Taxonom=C3=ADa CR= ediT</a> para describi= r las contribuciones individuales de cada autor al trabajo. Cindy Karina Chug=C3=A1= Fuentes y Esmelinger Thais Magallanes Tabares realizaron la conceptualizac= i=C3=B3n de la investigaci=C3=B3n, organizaci=C3=B3n y ejecuci=C3=B3n del e= nsayo, recopilaci=C3=B3n y curaci=C3=B3n de los datos, revisi=C3=B3n del an= =C3=A1lisis estad=C3=ADstico, interpretaci=C3=B3n de los resultados, elabor= aci=C3=B3n del borrador original, preparaci=C3=B3n de tablas y figuras, rev= isi=C3=B3n cr=C3=ADtica del contenido y aprobaci=C3=B3n de la versi=C3=B3n = final del manuscrito.Johana del Carmen Parre=C3=B1o S=C3=A1nchez y Hendy Maier P=C3= =A9rez Barrera, realizaron la supervisi=C3=B3n acad=C3=A9mica y metodol=C3= =B3gica de la investigaci=C3=B3n, brind=C3=B3 orientaci=C3=B3n durante el d= esarrollo del ensayo, revis=C3=B3 el an=C3=A1lisis y la interpretaci=C3=B3n= de los resultados, efectu=C3=B3 la revisi=C3=B3n cr=C3=ADtica del manuscri= to, realiz=C3=B3 la correcci=C3=B3n cient=C3=ADfica y acad=C3=A9mica y apro= b=C3=B3 la versi=C3=B3n final para su publicaci=C3=B3n.Divulgac= i=C3=B3n de la delegaci=C3=B3n a la IA generativa<span= style=3D"font-family:'Times New Roman'">Los autores declaran que emplearon= ChatGPT (OpenAI, GPT-5.5) como herramienta de apoyo para reorganizar y ref= ormular editorialmente el manuscrito original, corregir el estilo, traducir= el resumen al ingl=C3=A9s y comprobar el cumplimiento formal del formato d= e la revista. Los datos experimentales, el dise=C3=B1o, los resultados esta= d=C3=ADsticos y las decisiones cient=C3=ADficas proceden de la investigaci= =C3=B3n de las autoras y no fueron generados ni modificados por la herramie= nta. Los autores revisaron, contrastaron y aprobaron cada secci=C3=B3n y as= umen la responsabilidad =C3=ADntegra del contenido final. La herramienta no= figura como autora ni asume responsabilidad por los resultados.= Declaraci=C3=B3n presentada por: Cindy= Karina Chug=C3=A1 Fuentes.9. </sp= an>Costos de financiamiento La presente investigaci=C3=B3n fue financiada en su= totalidad con fondos propios de los autores.10. <s= pan style=3D"font-family:'Times New Roman'; font-weight:bold; color:#767171= ">Referencias bibliogr=C3=A1ficas <h1 style=3D"margin-top:0pt; m= argin-left:35.4pt; text-indent:-35.4pt; line-height:115%">Abarca Zaquinaula, A. (2025). Metodolog=C3=ADas Activas en Ecuador: apro= ximaci=C3=B3n a la revisi=C3=B3n de literatura de aprendizaje basado en pro= yectos, aprendizaje basado en problemas y aula invertida. MLS Educational Research, 9(1), 175-192. <a href=3D"https://doi.org/10.29314/mlser.v9i1= .2429" style=3D"text-decoration:none">https://doi.= org/10.29314/mlser.v9i1.2429</a> </h1>Ausubel, David P. (1= 980). Psicolog=C3=ADa educativa. Trillas. <a href=3D"https://es.scribd.com/document/461254772= /Ausubel-1980-Psicologia-educativa-pdf" style=3D"text-decoration:none"><spa= n class=3D"Hyperlink" style=3D"font-family:'Times New Roman'">https://es.sc= ribd.com/document/461254772/Ausubel-1980-Psicologia-educativa-pdf</a= ><= span style=3D"font-family:'Times New Roman'">Bastidas Gonz=C3=A1lez, L. D. = (2024). Estrategias de gamificaci=C3=B3n en la educaci=C3=B3n: herramientas= innovadoras para promover aprendizajes significativos y transformar proces= os pedag=C3=B3gicos tradicionales. Sapiens in Education, 1(3), 1-16. <a href=3D"https://doi.o= rg/10.71068/gsscqw59" style=3D"text-decoration:none">https://doi.org/10.71068/gsscqw= 59</a>Bertossi, V., Rome= ro, L., & Guti=C3=A9rrez, M. (2022). Revisi=C3=B3n sistem=C3=A1tica de = instrumentos de evaluaci=C3=B3n de calidad de objetos de aprendizaje. </spa= n>Revista = Ib=C3=A9rica de Sistemas e Tecnologias de Informa=C3=A7=C3=A3o, 46<s= pan style=3D"font-family:'Times New Roman'">(1), 34-53. <a href=3D"h= ttps://scielo.pt/pdf/rist/n46/1646-9895-rist-46-34.pdf" style=3D"text-decor= ation:none">https://scielo.pt/pdf/rist/n46/1646-9895-rist-46-34.pdf</a> Buestan Tigua, G. J., Godoy Salazar, G. T., Ponce Ram=C3= =ADrez, N. D., & Reyes Barreiro, A. N. (2024). Gamificaci=C3=B3n para e= l desarrollo de la inteligencia emocional en la resoluci=C3=B3n de problema= s. Revista Acad=C3=A9mica YACHAKUNA, 2(1), 79=E2=80=93101. <a href=3D"https://doi.org/10.7055= 7/2025.ychkn.2.1.p79-101" style=3D"text-decoration:none">https://doi.org/10.70557/20= 25.ychkn.2.1.p79-101</a>= Chavez Paredes, R. C., Aquije Ramirez, K., & Michuy Rodas, F. D. M. (20= 25). Plataformas digitales interactivas y comprensi=C3=B3n lectora: revisi= =C3=B3n sistem=C3=A1tica de estrategias aplicadas en educaci=C3=B3n primari= a. = Revista InveCom, 6(2),= 1-12. <a href=3D"https://www.revistainvecom.org/index.php/invecom/a= rticle/view/4113" style=3D"text-decoration:none">https://www.revistainvecom.org/inde= x.php/invecom/article/view/4113</a> Gonz=C3=A1= lez Calleros, C. B., & Torres Gastel=C3=BA, C. A. (2024). Transformando= el aprendizaje de Metodolog=C3=ADa de Investigaci=C3=B3n: una revisi=C3=B3= n sistem=C3=A1tica de gamificaci=C3=B3n y otras estrategias de aprendizaje = activo. RIDE - Revista Iberoamericana para la Investigaci=C3=B3n y el Desarrol= lo Educativo, 14(28), = e663. <a href=3D"https://www.scielo.org.mx/scielo.php?pid=3DS2007-74= 672024000100663&script=3Dsci_arttext" style=3D"text-decoration:none"><s= pan class=3D"Hyperlink" style=3D"font-family:'Times New Roman'">https://www= .scielo.org.mx/scielo.php?pid=3DS2007-74672024000100663&script=3Dsci_ar= ttext</a> <= p class=3D"Bibliography" style=3D"line-height:115%; font-size:12pt">=E2=80=AA Lorenzo-Lled=C3=B3, A., P=C3=A9rez V=C3=A1zqu= ez, E., Cabrera, E. A., Lled=C3=B3, G. L., & Lorenzo, A. (2023). Aplica= ci=C3=B3n de la gamificaci=C3=B3n en Educaci=C3=B3n Infantil y Educaci=C3= =B3n Primaria: an=C3=A1lisis tem=C3=A1tico. Retos, 50(1), 858-875. <a href=3D"https://www.pro= quest.com/openview/337ad963bae67465704c5a059ab48718/1?pq-origsite=3Dgschola= r&cbl=3D5517081" style=3D"text-decoration:none">https://www.proquest.com/openvie= w/337ad963bae67465704c5a059ab48718/1?pq-origsite=3Dgscholar&cbl=3D55170= 81</a>=E2=80=AC=E2= =80=AC=E2=80=AC=E2=80=AC=E2=80=ACMiranda Baja=C3=B1a, R. S., & Choez Calder=C3=B3n, C. J. (2024= ). Impacto de las metodolog=C3=ADas activas en el rendimiento acad=C3=A9mic= o y la motivaci=C3=B3n de los estudiantes: Una revisi=C3=B3n sistem=C3=A1ti= ca de la literatura. Revista Cient=C3=ADfica Multidisciplinar G-Nerando, 5</sp= an>(2), 1141=E2=80=931154. </= span><a href=3D"https://revista.gnerando.org/revista/index.php/RCMG/article= /view/305" style=3D"text-decoration:none">https://revista.gnerando.org/revista/ind= ex.php/RCMG/article/view/305</a> Moreno Pedraz= a, C. A., Quintero Vagas, O. A., Gafaro Sierra, A. S., Robayo, H. A., Maza,= J. M., & Jaimes Beltran , C. E. (2025). Juegos agon=C3=ADsticos y moti= vaci=C3=B3n estudiantil en educaci=C3=B3n superior: una revisi=C3=B3n desde= la teor=C3=ADa del aprendizaje social. GADE: Revista Cient=C3=ADfica, 5</span= >(3), 398-409. <a href= =3D"https://revista.redgade.com/index.php/Gade/article/view/719" style=3D"t= ext-decoration:none">https://revista.redgade.com/index.php/Gade/article/view/719</sp= an></a> N=C3=A1poles N=C3=A1poles, K. (2023). Desmo= tivaci=C3=B3n escolar de los alumnos como resultado del fen=C3=B3meno educa= tivo postpandemia: aproximaci=C3=B3n desde una revisi=C3=B3n sistem=C3=A1ti= ca. Revista Ecos de La Academia, 9(17), 57-81. <a href=3D"https://revistasojs.utn.edu.ec/inde= x.php/ecosacademia/article/view/881" style=3D"text-decoration:none">https://revistas= ojs.utn.edu.ec/index.php/ecosacademia/article/view/881</a> P=C3=A9rez Gallardo, E., & G=C3=A9rtrudix-Barrio, F. (2021= ). Ventajas de la gamificaci=C3=B3n en el =C3=A1mbito de la educaci=C3=B3n = formal en Espa=C3=B1a. Una revisi=C3=B3n bibliogr=C3=A1fica en el periodo d= e 2015-2020. Contextos Educativos - Revista de Educaci=C3=B3n, (28), 203-227. <a href=3D"http= s://publicaciones.unirioja.es/ojs/index.php/contextos/article/view/4741" st= yle=3D"text-decoration:none">https://publicaciones.unirioja.es/ojs/index.php/context= os/article/view/4741</a> Prieto Andreu, J. M.,= G=C3=B3mez Escalonilla-Torrijos, J. D., & Said-Hung, E. (2022). Gamifi= caci=C3=B3n, motivaci=C3=B3n y rendimiento en educaci=C3=B3n: una revisi=C3= =B3n sistem=C3=A1tica. Revista Electr=C3=B3nica Educare, 26(1), 251-273. <a href=3D"https://= www.scielo.sa.cr/scielo.php?script=3Dsci_arttext&pid=3DS1409-4258202200= 0100251" style=3D"text-decoration:none">https://www.scielo.sa.cr/scielo.php?script= =3Dsci_arttext&pid=3DS1409-42582022000100251</a> Revelo Manosalvas, S. L., & Y=C3=A1nez Ronquillo, N. del P. (2= 023). Material concreto y su importancia en el fortalecimiento de la matem= =C3=A1tica: una revisi=C3=B3n documental. MENTOR Revista de Investigaci=C3=B3n= Educativa y Deportiva, 2(4), 69-87. <a href=3D"https://revistamentor.ec/index.php/mentor/= article/view/5304" style=3D"text-decoration:none">https://revistamentor.ec/index.php= /mentor/article/view/5304</a> Rosado Rosado, M= . P., Quiroz Vera, L. G., Andrade P=C3=A1rraga, M. E., & Yumisaca Sag= =C3=B1ay, L. H. (2024). Herramientas Tics de gamificaci=C3=B3n para fomenta= r el inter=C3=A9s de los estudiantes en el aprendizaje. RELIGACI=C3=93N - Rev= ista de Ciencias Sociales y Humanidades, 9(40), e2401199. <a href=3D"https://pdfs.semantic= scholar.org/4bca/8034210802c828d53a48d8f9d20d8b00e062.pdf" style=3D"text-de= coration:none">https://pdfs.semanticscholar.org/4bca/8034210802c828d53a48d8f9d20d8b0= 0e062.pdf</a> <= /p><sp= an style=3D"font-family:'Times New Roman'">S=C3=A1nchez, C. G. (2023). El e= fecto de la gamificaci=C3=B3n sobre el alumnado en Educaci=C3=B3n Primaria = y Educaci=C3=B3n Secundaria. Educaci=C3=B3n y Futuro: Revista de Investigaci= =C3=B3n Aplicada y Experiencias Educativas, (49), 181-208. <a href=3D"https://educacionyfu= turo.com/article/view/7575" style=3D"text-decoration:none">https://educacionyfuturo.= com/article/view/7575</a> Sepulveda Pe=C3=B1ar= anda, F. A., & Mayoral Hinestroza, L (2025). La gamificaci=C3=B3n en el= aula: estrategias, beneficios, retos e impacto. Saber Ser, 2(1), 35-52. <a href=3D"https://d= oi.org/10.35997/saberser.v2i1.38" style=3D"text-decoration:none">https://doi.org/10.= 35997/saberser.v2i1.38</a> Vasconez Le=C3=B3n,= L. D. C. (2025). Pedagog=C3=ADa interactiva y aprendizaje significativo en= estudiantes con dificultades socioemocionales: revisi=C3=B3n sistem=C3=A1t= ica de la literatura. Revista InveCom, 6(2), e602117. <a href=3D"https://ve.scielo.org/scielo= .php?script=3Dsci_arttext&pid=3DS2739-00632026000202117" style=3D"text-= decoration:none">https://ve.scielo.org/scielo.php?script=3Dsci_arttext&pid=3DS27= 39-00632026000202117</a> V=C3=A1squez Tafur, L= . A., & Reynoso L=C3=A1zaro, M. (2025). Aprendizaje significativo y su = impacto en la transformaci=C3=B3n educativa: una revisi=C3=B3n sistem=C3=A1= tica. Horizontes Revista de Investigaci=C3=B3n en Ciencias de la Educaci=C3=B3= n, 9(39), 3024-3036. <= /span><a href=3D"http://www.scielo.org.bo/scielo.php?pid=3DS2616-7964202500= 0403024&script=3Dsci_arttext" style=3D"text-decoration:none">http://www.scielo.o= rg.bo/scielo.php?pid=3DS2616-79642025000403024&script=3Dsci_arttext</sp= an></a> Villadiego Almentero, K= . L. (2025). Pr=C3=A1cticas pedag=C3=B3gicas y aprendizaje significativo en= educaci=C3=B3n b=C3=A1sica primaria: una revisi=C3=B3n sistem=C3=A1tica. <= /span>Revi= sta Ciencia & Sociedad, 5(2), 239-255. <a href=3D"https://cienciaysociedaduatf.com/ind= ex.php/ciesocieuatf/article/view/200" style=3D"text-decoration:none">https://ciencia= ysociedaduatf.com/index.php/ciesocieuatf/article/view/200</a> <= /span> </sp= an><div style=3D"clear:both"><img src=3D"data:image/png;base64,iVBORw0KGgoAAA= ANSUhEUgAAAuQAAAABCAYAAACWsrt9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBl= SsOGwAAABxJREFUSIntwTEBAAAIwKDZP7TG8AGm2gAAgA9zmTsBAl+AgmQAAAAASUVORK5CYII= =3D" width=3D"740" height=3D"1" alt=3D"" style=3D"margin-top:-37.82pt; marg= in-left:-63.3pt; position:absolute" /> &= #xa0; Nombre = 1 </div></= div></body></html>