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#000000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical-a= lign:top">1<= /p></td><td style=3D"width:171.95pt; border-right:0.75pt solid #000000; bor= der-left:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding:= 0pt 5.03pt; vertical-align:top">Johanna Mariuxi Gonz=C3= =A1lez Arias</td><td style=3D"width:10.5pt; border-right:0.75pt = solid #000000; border-left:0.75pt solid #000000; border-bottom:0.75pt solid= #000000; padding:0pt 5.03pt; vertical-align:top"><i= mg src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0= NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAA2lJREFUOI21lEtoXVUUhr= +197n3nNzbJN6omdTWRmwbikVq4iMxk0ZU2oGgA1FnQcQiUhUnFqo4cGIRQUGd+KDY+Jp25ouiG= COxElqjhba0URPzsPQmuY9zzj1nLwcnuU1iQ0f+mzXZe///Wv/aiy1cBV/83NNOPr/VWHunqO5O= VNuNlQVjmZCGjCVhcPHR3q8XrsaVNUITu/KSlu7LFeSpNNZ+57RTjDTvqFM1IvNiGXWJvD/9x8y= XB/efizYU/Hy8/0W/aA/Xq43r1P3nOCNIFsW2XLleTd648Z/863v3nkjWCL59dp/fWSsfzOXNkS= 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bsolute" /> </spa= n></td><td style=3D"width:168.45pt; border-left:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">= <a href=3D"https://orcid.o= rg/0009-0004-2938-7111" style=3D"text-decoration:none">= https://orcid.org/0009-0004-2938-7111</a></td></tr><tr><td style= =3D"width:3.25pt; border-top:0.75pt solid #000000; border-right:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical= -align:top"> </= span></td><td colspan=3D"3" style=3D"width:372.5pt; border-top:0.75pt s= olid #000000; border-left:0.75pt solid #000000; border-bottom:0.75pt solid = #000000; padding:0pt 5.03pt; vertical-align:top">Universidad Bolivariana del Ecuador (UBE), Du= ran, EcuadorMaestr=C3=ADa en Gesti=C3=B3n y An=C3=A1lisis de Datos con menci=C3=B3n= en Inteligencia de Negocios.<a href=3D"mailto:jmgonzaleza@ube.edu.ec" style=3D"text-decor= ation:none">jmgonzaleza@ube.edu.ec</a><span= style=3D"font-family:'Times New Roman'"> </td></tr><tr><td styl= e=3D"width:3.25pt; border-top:0.75pt solid #000000; border-right:0.75pt sol= id #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertica= l-align:top">2</spa= n></td><td style=3D"width:171.95pt; border:0.75pt solid #000000; paddin= g:0pt 5.03pt; vertical-align:top">Carlos Hern=C3=A1n Su= =C3=A1rez Barrag=C3=A1n</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;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUC= AYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAA2lJREFUOI21= lEtoXVUUhr+197n3nNzbJN6omdTWRmwbikVq4iMxk0ZU2oGgA1FnQcQiUhUnFqo4cGIRQUGd+KD= Y+Jp25ouiGCOxElqjhba0URPzsPQmuY9zzj1nLwcnuU1iQ0f+mzXZe///Wv/aiy1cBV/83NNOPr= /VWHunqO5OVNuNlQVjmZCGjCVhcPHR3q8XrsaVNUITu/KSlu7LFeSpNNZ+57RTjDTvqFM1IvNiG= XWJvD/9x8yXB/efizYU/Hy8/0W/aA/Xq43r1P3nOCNIFsW2XLleTd648Z/863v3nkjWCL59dp/f= WSsfzOXNkShKacvfgnMRlXjqqqIrwr5vkihyhxbT+K2ne082AAzADZXyPj+wL8ehQ1NH700v0N3= 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border-top:0.75pt solid #0= 00000; border-left:0.75pt solid #000000; border-bottom:0.75pt solid #000000= ; padding:0pt 5.03pt; vertical-align:top"><a href=3D"https://orcid.org/0009-0000-7779-3297" style=3D"= text-decoration:none">https://orcid.org/0009-0000-7779-= 3297</a></td></tr><tr><td style=3D"width:3.25pt; border-top:0.75= pt solid #000000; border-right:0.75pt solid #000000; border-bottom:0.75pt s= olid #000000; padding:0pt 5.03pt; vertical-align:top"> </td><td colspan=3D"3" = style=3D"width:372.5pt; border-top:0.75pt solid #000000; border-left:0.75pt= solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; ver= tical-align:top">Un= iversidad Bolivariana del Ecuador (UBE), Duran, Ecuador.Maestr=C3=ADa en Gesti=C3= =B3n y An=C3=A1lisis de Datos con menci=C3=B3n en Inteligencia de Negocios.= <a href=3D"mailto:chsuarezb@ube= .edu.ec" style=3D"text-decoration:none">chsuarezb@= ube.edu.ec</a></td></tr><tr><td style=3D"width:3.25pt; border-to= p:0.75pt solid #000000; border-right:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">3</td><td style=3D"widt= h:171.95pt; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align= :top">Jorge Luis Charco Aguirre </td><td style=3D"width:10.5pt; border:0.75pt solid= #000000; padding:0pt 5.03pt; vertical-align:top"><i= mg src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0= NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAA2lJREFUOI21lEtoXVUUhr= +197n3nNzbJN6omdTWRmwbikVq4iMxk0ZU2oGgA1FnQcQiUhUnFqo4cGIRQUGd+KDY+Jp25ouiG= COxElqjhba0URPzsPQmuY9zzj1nLwcnuU1iQ0f+mzXZe///Wv/aiy1cBV/83NNOPr/VWHunqO5O= VNuNlQVjmZCGjCVhcPHR3q8XrsaVNUITu/KSlu7LFeSpNNZ+57RTjDTvqFM1IvNiGXWJvD/9x8y= XB/efizYU/Hy8/0W/aA/Xq43r1P3nOCNIFsW2XLleTd648Z/863v3nkjWCL59dp/fWSsfzOXNkS= hKacvfgnMRlXjqqqIrwr5vkihyhxbT+K2ne082AAzADZXyPj+wL8ehQ1NH700v0N35OAroutW0r= xDF6rUUvddaJfdwM9HwqYFSa0GO1SrpfucUVUXEZCQcqQtZ0VEU1QTPtmAkDygiEBTsTxq6hx65= 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#000000; padding= :0pt 5.03pt; vertical-align:top"><a href=3D"https://orcid.org/0000-0002-0099-0345" style=3D"text-deco= ration:none">https://orcid.org/0000-0002-0099-0345= </a></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"> </td><td colspan=3D"3" style= =3D"width:372.5pt; border-top:0.75pt solid #000000; border-left:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical= -align:top">Univers= idad Bolivariana del Ecuador (UBE), Duran, Ecuador.<a href=3D"mailto:jlcharcoa@ube.edu.ec" style=3D"text-d= ecoration:none">jlcharcoa@ube.edu.ec</a></p= >Universidad de Guayaquil (UG), Guayaquil, Ecuador.</sp= an><a href=3D"mailto:jorge.charcoa@ug.= edu.ec" style=3D"text-decoration:none">jorge.charc= oa@ug.edu.ec</a> </td></tr><tr><td style=3D"width:3.= 25pt; border-top:0.75pt solid #000000; border-right:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">= 4</td><t= d style=3D"width:171.95pt; border:0.75pt solid #000000; padding:0pt 5.03pt;= vertical-align:top"><span= style=3D"font-family:'Times New Roman'">Dayron Rumbaut Rangel</= td><td style=3D"width:10.5pt; border:0.75pt solid #000000; padding:0pt 5.03= pt; vertical-align:top"><img src=3D"data:image/png;b= ase64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAA= AlwSFlzAAAOxAAADsQBlSsOGwAAA2lJREFUOI21lEtoXVUUhr+197n3nNzbJN6omdTWRmwbikVq= 4iMxk0ZU2oGgA1FnQcQiUhUnFqo4cGIRQUGd+KDY+Jp25ouiGCOxElqjhba0URPzsPQmuY9zzj1= nLwcnuU1iQ0f+mzXZe///Wv/aiy1cBV/83NNOPr/VWHunqO5OVNuNlQVjmZCGjCVhcPHR3q8Xrs= aVNUITu/KSlu7LFeSpNNZ+57RTjDTvqFM1IvNiGXWJvD/9x8yXB/efizYU/Hy8/0W/aA/Xq43r1= P3nOCNIFsW2XLleTd648Z/863v3nkjWCL59dp/fWSsfzOXNkShKacvfgnMRlXjqqqIrwr5vkihy= hxbT+K2ne082AAzADZXyPj+wL8ehQ1NH700v0N35OAroutW0rxDF6rUUvddaJfdwM9HwqYFSa0G= 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#000= 000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align= :top"><a href=3D"https://o= rcid.org/0009-0001-9087-0979" style=3D"text-decoration:none">https://orcid.org/0009-0001-9087-0979</a></td></tr><t= r style=3D"height:18pt"><td style=3D"width:3.25pt; border-top:0.75pt solid = #000000; border-right:0.75pt solid #000000; padding:0pt 5.03pt; vertical-al= ign:top"> </spa= n></td><td colspan=3D"3" style=3D"width:372.5pt; border-top:0.75pt soli= d #000000; border-left:0.75pt solid #000000; padding:0pt 5.03pt; vertical-a= lign:top">Universid= ad Bolivariana del Ecuador (UBE), Duran, Ecuador.<a href=3D"mailto:drumbautr@ube.edu.ec" style=3D"text-dec= oration:none">drumbautr@ube.edu.ec</a><span= style=3D"line-height:115%; font-family:'Times New Roman'; font-size:10pt">= </td></tr></table> <table style=3D"width:436.35pt; margin-le= ft:0.25pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr><t= d colspan=3D"3" style=3D"width:156.4pt; padding:0pt 5.4pt; vertical-align:t= op"><a id=3D"_Hlk92186133"> </span= ></a> </span= ></td><td colspan=3D"2" style=3D"width:258.35pt; border-top:0.75pt soli= d #000000; padding:0pt 5.4pt; vertical-align:top">Art=C3=ADculo de Investigaci= =C3=B3n Cient=C3=ADfica y Tecnol=C3=B3gicaEnviado: Revisado: Aceptado: Publicado:DO= I: </td></tr><tr style=3D"height:6.55pt= "><td colspan=3D"3" style=3D"width:156.4pt; padding:0pt 5.4pt; vertical-ali= gn:top"> </td><td colspan=3D"2" style=3D"width:= 258.35pt; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-a= lign:top"> </td></tr><tr><td style=3D"width:37.8pt; padding:0pt= 5.4pt; vertical-align:top"> C=C3=ADtese: </td><td style=3D"width:1.55= pt; padding:0pt 5.4pt; vertical-align:top"> </td><td colspan= =3D"2" style=3D"width:358.55pt; border-bottom:0.75pt solid #000000; padding= :0pt 5.4pt; vertical-align:top"> DATOS REVISTADATOS REVISTADATOS REVISTA</td><td style=3D"border-top:0= .75pt solid #000000; padding:0pt; vertical-align:top"></td></tr><tr 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p4Kb77777qYqvB48ePDgwYMHD38X4eHhTJky5ZbHPQXFHjx48ODBg4f/Kjw1Nx48ePDgwYOH/yo= 8wY0HDx48ePDg4b8KT3DjwYMHDx48ePiv4v8DkrKy3KIORuIAAAAASUVORK5CYII=3D" width= =3D"567" height=3D"140" alt=3D"" /><table style=3D"width:421pt; margin-= left:0.25pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr>= <td style=3D"width:73.75pt; border:0.75pt solid #000000; padding:0pt 5.03pt= ; vertical-align:top">Palabras claves: An=C3=A1lisis del riesgo, dashboard educativos, titulaci= =C3=B3n universitaria, anal=C3=ADtica de datos, an=C3=A1lisis predictivo.</td><td style=3D"w= idth:324.9pt; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-ali= gn:top">Resumen Introducci=C3=B3n: la educaci=C3=B3n superior en el terri= torio ecuatoriano se enfrenta a escenarios de complejidad en cuanto a proce= sos acad=C3=A9micos y administrativos previos a la titulaci=C3=B3n de grado= , influenciado por el cumplimiento de requisitos, las modalidades de estudi= os y la creciente demanda estudiantes para ciertas carreras. Objetivo:<s= pan style=3D"font-family:'Times New Roman'"> analizar el riesgo de la titul= aci=C3=B3n en el nivel de grado mediante la integraci=C3=B3n de factores ac= ad=C3=A9micos y administrativos, utilizando herramientas interactivas de an= =C3=A1lisis de datos, para identificar tempranamente los casos en riesgo y = proponer estrategias institucionales que optimicen el proceso de titulaci= =C3=B3n. Metodolog=C3=ADa:= la metodolog=C3=ADa aplicada fue de enfoque mixto de car=C3=A1cter no expe= rimental y retrospectivo centrado en la recolecci=C3=B3n de informaci=C3=B3= n de dataset anonimizado de datos estudiantiles cuya muestra de fue de 604 = registros. Resultados: se = identific=C3=B3 que los factores administrativos y los factores acad=C3=A9m= icos que influyen significativamente en la aptitud del estudiante para acce= der al proceso de titulaci=C3=B3n de grado. El an=C3=A1lisis cualitativo y = cuantitativo permiti=C3=B3 establecer el nivel del riesgo potencialmente al= to en estudiantes que presentan retrasos. A trav=C3=A9s del dashboard dise= =C3=B1ado para este estudio, fue posible visualizar los riesgos y facilitar= la toma de decisiones institucionales de manera oportuna y preventiva. </s= pan>Conclus= i=C3=B3n: se determina= que aplicando un an=C3=A1lisis de riesgo anticipado dentro de la gesti=C3= =B3n acad=C3=A9mica en carreras con alto n=C3=BAmero de estudiantes matricu= lados permite identificar aquellos con problemas previo a la titulaci=C3=B3= n para aplicar una correcta decisi=C3=B3n a nivel de direcci=C3=B3n acad=C3= =A9mica. =C3=81rea de estudio general: Anal=C3=ADtica de datos. =C3=81rea de estudio espec=C3=ADfica:</sp= an> An=C3=A1lisis del Riesgo.= Tipo de estudio: Art=C3=ADculo original.<= /p></td></tr><tr><td style=3D"width:73.75pt; border:0.75pt solid #000000; p= adding:0pt 5.03pt; vertical-align:top">Keywords: <a id=3D"_Hlk212297625">Risk analysis, educational dashboards, university d= egrees, </a>da= ta analytics, predictive analytics. </td><td style=3D"width:324.= 9pt; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top"><= p style=3D"margin-bottom:0pt; text-align:justify; line-height:115%; font-si= ze:12pt">AbstractIntroduction: higher education in Ecuador faces complex scenarios = regarding academic and administrative processes prior to undergraduate grad= uation, influenced by the fulfillment of requirements, study modalities, an= d the growing student demand for certain programs. Objective: to analyze the risk of undergraduate gr= aduation by integrating academic and administrative factors and using inter= active data analysis tools to identify at-risk cases early and propose inst= itutional strategies to optimize the graduation process. Methodology:<sp= an style=3D"font-family:'Times New Roman'"> the methodology applied was a m= ixed approach, non-experimental and retrospective, focused on the collectio= n of information from an anonymized dataset of student data, with a sample = of 604 records. Results: t= he administrative and academic factors that significantly influence student= suitability to access the undergraduate graduation process were identified= . Qualitative and quantitative analysis made it possible to establish the l= evel of potentially substantial risk in students who present delays. Using = the dashboard designed for this study, it was possible to visualize risks a= nd facilitate timely and initiative-taking institutional decision-making. <= /span>Concl= usion: it was determin= ed that applying early risk analysis within academic management in programs= with a high number of enrolled students allows students with problems prio= r to graduation to be identified, enabling appropriate decisions to be made= at the academic management level. General Area of =E2=80=8B=E2=80=8BStudy:</sp= an> Data Analytics. <s= pan style=3D"font-family:'Times New Roman'; font-weight:bold">Specific Area= of =E2=80=8B=E2=80=8BStudy: Risk Analysis. Type of Study: Original article.</td></tr></table> <div style=3D"clear:both"> = Nombre </span= >&#x= a0; </spa= n>&= #xa0; </div></div> <div class=3D"Section_= 2">1. = 0; Introducci=C3=B3n</div> <div class=3D"Section_3"><d= iv style=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABg= AAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0= dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh= 4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhEBAxEB/8QAHQABAQACA= wEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBAkJBgcAAAABAgMEBREGByESFzET= QVFVkQgUFSJUVmFxkpTR0jeBobIWIzI2UnR1scEkMzRCYnKCs+EYQ0RGZKIlJzU4ZZPi/8QAGwE= BAQADAQEBAAAAAAAAAAAAAAECAwQFBgf/xAA0EQEAAQQBBAEDAAcIAwAAAAAAAQIDBBFSBRIUIT= ETQVEiMjRhcYGRFSU1QqGxwdEz4fD/2gAMAwEAAhEDEQA/AOgSiyj9Lh8VAAAAgAC/YAFgAFSUW= UEkAFgRUQnX3BaKK65imimqqfREPt7zzNv9Ev8A/wCuUmqIn3LKKZn7PgPpdsXrMRVes3bcT03q= 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La gesti=C3=B3n adecuada de los procesos garantiza qu= e se cumplan con los requisitos favoreciendo desarrollo acad=C3=A9mico flui= do dentro de los plazos establecidos, adem=C3=A1s que integra el fortalecim= iento institucional al mantener la tasa de titulaci=C3=B3n elevada requerim= iento importante dentro de los procesos de acreditaci=C3=B3n de las carrera= s ante el Consejo de Asegu= ramiento de la Calidad de la Educaci=C3=B3n Superior (CACES) e= n Ecuador para garantizar la mejora continua de los procesos acad=C3=A9mico= s.La literatura reciente evidencia una brecha en el sistema ed= ucativo en la aplicaci=C3=B3n de herramientas tecnol=C3=B3gicas integradas,= capaces de combinar datos acad=C3=A9micos y administrativos para predecir,= mitigar o minimizar el riesgo de titulaci=C3=B3n (Acosta et al., 2024), si= n embargo ante la existencia de sobre c=C3=B3mo predecir, hay una brecha en herramientas que logren = mit= igar mediante acciones= automatizadas basadas en datos administrativos. El proceso de= titulaci=C3=B3n universitaria emerge como el hito culminante de la trayect= oria acad=C3=A9mica, funcionando como un mecanismo de validaci=C3=B3n de lo= s conocimientos, habilidades y competencias adquiridas por el estudiante a = lo largo de su carrera (Mendoza et al., 2024). La falta de mecan= ismos predictivos en etapas tempranas perpetua un ciclo donde los problemas= acad=C3=A9micos o administrativos se identifican tard=C3=ADamente dando co= mo resultados estudiantes que abandonan los estudios al llegar a la titulac= i=C3=B3n. Seg=C3=BAn los autores Morales et al. (2025) uno de lo= s factores entre los estudiantes que incrementan la brecha es la falta de m= anejo de plataformas educativas y de gesti=C3=B3n acad=C3=A9mica la falta d= e dispositivos adecuados y la conectividad limitada dificultan la participa= ci=C3=B3n de los estudiantes en el proceso de aprendizaje.Por otra parte, es v=C3=A1lido mencionar que las universidades y= otras instituciones de educaci=C3=B3n superior en Ecuador se expandieron r= =C3=A1pidamente. Sin embargo, este crecimiento, provocado por el aumento de= la poblaci=C3=B3n y la demanda, no estuvo acompa=C3=B1ado de una supervisi= =C3=B3n efectiva ni de est=C3=A1ndares de calidad (Mac=C3=ADas et al., 2025= ).Se pue= de se=C3=B1alar que la utilizaci=C3=B3n de herramientas interactivas que ap= oyan la toma de decisiones estrat=C3=A9gicas anticipando y prediciendo posi= bles riesgos que sirven como modelo de control preventivo (Zamora et al., 2= 019). A fin de que permita tratar la informaci=C3=B3n de manera adecuada y = acorde a las necesidades institucionales que este requiera.Para el autor un dashboa= rd es una herramienta de representaci=C3=B3n visual de la informaci=C3=B3n = m=C3=A1s importante y necesaria para lograr los objetivos de forma consolid= ada y organizada en una sola pantalla cada vez m=C3=A1s utilizados en la in= teligencia de negocios (Quiguanas & Arias, 2024).El an=C3=A1lisis de<= span style=3D"letter-spacing:-0.05pt"> datos educativos con ap= rendizaje autom= =C3=A1tico permite identificar estudiantes en riesgo desde </= span>etapas <spa= n>tempranas, fac= ilitando interve= nciones dirigida= s que<spa= n style=3D"letter-spacing:-0.75pt"> mejoran la retenci=C3=B3n y= el <span= >=C3=A9xito en la titulaci=C3=B3n de pregrado (Villalobos-Murillo et al., 2= 025). <span= >El uso de Dashboard en el =C3=A1mbito educativo permite integrar informaci= =C3=B3n acad=C3=A9mica y administrativa para interpretar de manera clara y = oportuna los niveles de riesgo estudiantil. La visualizaci=C3=B3n consolida= da en una sola interfaz facilita la identificaci=C3=B3n de patrones, alerta= s tempranas y brechas de desempe=C3=B1o asociadas al proceso de titulaci=C3= =B3n, lo que resulta clave para instituciones que enfrentan problem=C3=A1ti= cas de rezago acad=C3=A9mico y baja eficiencia terminal. Asimismo, estos en= tornos visuales posibilitan el seguimiento continuo de cohortes y carreras,= orientando la priorizaci=C3=B3n de intervenciones y la asignaci=C3=B3n de = recursos institucionales con un enfoque preventivo y trazable.<p= class=3D"BodyText" style=3D"margin-top:10.1pt; margin-right:7.25pt; margin= -left:7.1pt; text-align:justify; line-height:115%">El desarrollo de m= odelos matem=C3=A1ticos aplicados a la anal=C3=ADtica educativa requiere un= a definici=C3=B3n rigurosa del fen=C3=B3meno de estudio, la selecci=C3=B3n = de variables relevantes y un tratamiento de datos coherente con los supuest= os estad=C3=ADsticos. La construcci=C3=B3n del modelo debe complementarse c= on procesos de entrenamiento, validaci=C3=B3n cruzada y evaluaci=C3=B3n med= iante m=C3=A9tricas como precisi=C3=B3n, sensibilidad, =C3=81rea Bajo la Curva (AUC) y= error, garantizando as=C3=AD su capacidad predictiva. En el marco de la educac= i=C3=B3n basada en resultados o en sus siglas en ingles OBE (Outcome-Based Education), los r= esultados de aprendizaje pueden definirse y evaluarse tanto a nivel de curs= o como de programa. En esta perspectiva, los resultados de los cursos contr= ibuyen al logro de los resultados del programa y su integraci=C3=B3n se rea= liza mediante una actividad clave denominada mapeo curricular. Para apoyar la= implementaci=C3=B3n de OBE y documentar eficazmente las actividades de eva= luaci=C3=B3n educativa, se desarrollaron diversas herramientas computarizad= as (Namoun & Alshanqiti, 2021).La reproducibilidad del modelo se asegura a= trav=C3=A9s de protocolos metodol=C3=B3gicos, documentaci=C3=B3n adecuada = y pruebas en distintos subconjuntos de datos y periodos temporales.<= /p>En este conte= xto, la toma de decisiones basada en informaci=C3=B3n visual analizada medi= ante Dashboard constituye un soporte estrat=C3=A9gico para la gesti=C3=B3n = acad=C3=A9mica institucional. Al integrar alertas, tendencias y comparativo= s por carrera, periodo y cohorte, se facilita la implementaci=C3=B3n de acc= iones oportunas como tutor=C3=ADas focalizadas, ajustes en la carga acad=C3= =A9mica y optimizaci=C3=B3n de tr=C3=A1mites administrativos. De esta maner= a, la instituci=C3=B3n puede asignar recursos de forma eficiente, monitorea= r el impacto de las intervenciones y retroalimentar sus pol=C3=ADticas acad= =C3=A9micas con el objetivo de mejorar los indicadores de titulaci=C3=B3n y= retenci=C3=B3n estudiantil.Respecto a esto el autor menciona que el <spa= n style=3D"text-decoration:underline">Sistema de Detecci=C3=B3n Temprana (E= DS) desarrollado en este trabajo emplea diversas t=C3=A9cnicas= de aprendizaje autom=C3=A1tico, incluyendo redes neuronales, =C3=A1rboles = de decisi=C3=B3n y los algoritmos, para identificar caracter=C3=ADsticas qu= e distinguen a los estudiantes en riesgo de abandonar. La combinaci=C3=B3n = de estos m=C3=A9todos permite mejorar significativamente la precisi=C3=B3n = de las predicciones y adaptarse a diferentes contextos universitarios (Bere= ns et al., 2018).En cuanto a la tasa de titulaci=C3=B3n y retenci=C3=B3n estudia= ntil en las <a id=3D"_Hlk221653372">Instituciones de Educaci=C3=B3n Superior</a> (IES) (Mendoza et al., 2024), rep= resenta uno de los indicadores sustanciales que miden los procesos de acred= itaci=C3=B3n de las instituciones, aunque actualmente ellas cuentan con su = propio sistema de gesti=C3=B3n acad=C3=A9mico carecen com=C3=BAnmente de si= stemas predictivos de informaci=C3=B3n que son ideales para complementar y = mejorar la tasa de =C3=A9xito en la titulaci=C3=B3n de grado. <p= class=3D"BodyText" style=3D"text-align:justify; line-height:115%">Fi= nalmente comprender c=C3=B3mo los estudiantes perciben las pol=C3=ADticas i= nstitucionales y los apoyos disponibles a lo largo de su trayectoria acad= =C3=A9mica es un aspecto ampliamente reconocido por la literatura contempor= =C3=A1nea en educaci=C3=B3n. Este tipo de percepciones influye de manera si= gnificativa en la equidad del aprendizaje, la permanencia estudiantil y el = bienestar en los entornos formativos. Adem=C3=A1s, atender las particularid= ades de cada recorrido acad=C3=A9mico favorece entornos m=C3=A1s inclusivos= y fortalece la gesti=C3=B3n del conocimiento. <s= pan>2. Metodolog=C3= =ADa<div style=3D"clear:both"> &= #xa0; Nombre </div></div> <div class=3D"Section_4">La presente investigaci=C3=B3n se ajust=C3=B3 con un enfoque mixt= o, combinando m=C3=A9todos cuantitativos y cualitativos para abordar de man= era integral el problema estudiado. Desde la perspectiva cuantitativa, se r= ealiz=C3=B3 la recolecci=C3=B3n y an=C3=A1lisis de datos num=C3=A9ricos y e= structurados provenientes de expedientes estudiantiles, con el prop=C3=B3si= to de identificar patrones, probar relaciones y cuantificar la capacidad pr= edictiva de los modelos aplicados. En el componente cualitativo, se explora= ron datos de otro programa similar y de otras carreras sobre el estado del = proceso de titulaci=C3=B3n. El estudio fue de tipo descriptivo-explicativo y de car=C3=A1cter anal=C3= =ADtico, al buscar no solo caracterizar la poblaci=C3=B3n, las variables ac= ad=C3=A9micas y administrativas relevantes, sino tambi=C3=A9n explicar su r= elaci=C3=B3n con el =C3=A9xito en la titulaci=C3=B3n y desarrollar el an=C3= =A1lisis adecuado que permitan detectar tempranamente el riesgo acad=C3=A9m= ico. El dise=C3=B1o de investigaci=C3=B3n fue no experimental y retrospecti= vo, pues no se manipularon variables, sino que se analizaron datos hist=C3= =B3ricos de estudiantes egresados. La poblaci=C3=B3n estuvo constituida por los expedientes de estudia= ntes de grado del periodo octubre 2024 =E2=80=93 abril 2025. Se incluyeron = los casos con expedientes completos y registros acad=C3=A9micos actualizado= s, y se excluyeron aquellos con informaci=C3=B3n incompleta o en estado de = abandono. Como t=C3=A9cnicas de recolecci=C3=B3n de datos se utilizaron la = revisi=C3=B3n documental y la extracci=C3=B3n de informaci=C3=B3n instituci= onal bajo estrictos criterios de anonimizaci=C3=B3n y confidencialidad corr= espondiente y el cumplimiento de las normas =C3=A9ticas para el tratamiento= de datos personales y acad=C3=A9micos de los estudiantes.</div>= <div class=3D"Sect= ion_5"><div style=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJRgA= BAQEAYABgAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFR= ERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eH= h4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhEBAxEB/8QA= HQABAQACAwEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBAkJBgcAAAABAgMEBRE= GByESFzETQVFVkQgUFSJUVmFxkpTR0jeBobIWIzI2UnR1scEkMzRCYnKCs+EYQ0RGZKIlJzU4ZZ= Pi/8QAGwEBAQADAQEBAAAAAAAAAAAAAAECAwQFBgf/xAA0EQEAAQQBBAEDAAcIAwAAAAAAAQIDB= 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basado en regresi=C3=B3n log=C3=ADstica en Python <span= style=3D"font-family:'Times New Roman'">e) &= #xa0; Comparaci=C3=B3n de valores obtenidos en el dashboard con el modelo de ma= chine learning para sustento matem=C3=A1tico de resultados. </spa= n><div style=3D"clear:both">&= #xa0; = 0; Nombre <spa= n> </div></div> <div class=3D"Section_6">Tabla 1</p= >Descripci=C3=B3n de los campos del dataset= <table style=3D"width:100%; margin-bottom:0pt; padding:0pt; border-collapse= :collapse"><tr style=3D"height:15pt"><td style=3D"width:25.4%; border-top:1= pt solid #7e7e7e; border-bottom:1pt solid #7e7e7e; padding:0pt 5.4pt; verti= cal-align:top">Variable</td= ><td style=3D"width:38.38%; border-top:1pt solid #7e7e7e; border-bottom:1pt= solid #7e7e7e; padding:0pt 5.4pt; vertical-align:top">Descripci=C3=B3n</td><td style=3D"width:36.22%;= border-top:1pt solid #7e7e7e; border-bottom:1pt solid #7e7e7e; padding:0pt= 5.4pt; vertical-align:top">Estado</s= pan></td></tr><tr style=3D"height:55.5pt"><td style=3D"width:25.4%; bor= der-top:1pt solid #7e7e7e; padding:0pt 5.4pt; vertical-align:top">Estado de la titulaci=C3=B3= n</td><td style=3D"width:38.38%; border-top:1pt solid #7e7e7e; p= adding:0pt 5.4pt; vertical-align:top">Esta es la variable principal, indi= ca el estado final del proceso de titulaci=C3=B3n del estudiante.</p= ></td><td style=3D"width:36.22%; border-top:1pt solid #7e7e7e; padding:0pt = 5.4pt; vertical-align:top">Sustentado, no ingresa a titulaci=C3=B3n pendiente de sustentaci=C3= =B3n</td></tr><tr style=3D"height:83.25pt"><td style=3D"width:25= .4%; padding:0pt 5.4pt; vertical-align:top">Estado de Pago</td><td style=3D"width:38.38%; padding:0pt = 5.4pt; vertical-align:top">Variable administrativa que describe la situaci=C3=B3n financiera= del estudiante en relaci=C3=B3n con sus obligaciones con la instituci=C3= =B3n.</td><td style=3D"width:36.22%; padding:0pt 5.4pt; vertical= -align:top">Al d=C3=ADa y pendiente de = cuotas</td></tr><tr style=3D"height:48pt"><td style=3D"width:25.= 4%; padding:0pt 5.4pt; vertical-align:top">Total del monto adeudado</td><td styl= e=3D"width:38.38%; padding:0pt 5.4pt; vertical-align:top">Variable num= =C3=A9rica que representa el total de dinero adeudado por el estudiante.</s= pan></td><td style=3D"width:36.22%; padding:0pt 5.4pt; vertical-align:t= op">Se elimina el campo al ser irreleva= nte para el an=C3=A1lisis. </td></tr><tr style=3D"height:62.25pt= "><td style=3D"width:25.4%; padding:0pt 5.4pt; vertical-align:top">Mater= ias vistas</td><td style=3D"width:38.38%; padding:0pt 5.4pt; ver= tical-align:top">Cantidad de asignaturas que el estudiante curso en el per= iodo acad=C3=A9mico.</td><td style=3D"width:36.22%; padding:0pt = 5.4pt; vertical-align:top">Valores entre 1 y 21 depende del programa oscila entre 18 a 21 m=C3= =A1ximo.</td></tr><tr style=3D"height:48pt"><td style=3D"width:2= 5.4%; padding:0pt 5.4pt; vertical-align:top">Materias aprobadas</td><td style=3D"= width:38.38%; padding:0pt 5.4pt; vertical-align:top">N=C3=BAmero de asignaturas que el estudiante aprob=C3=B3.</= span></td><td style=3D"width:36.22%; padding:0pt 5.4pt; vertical-align:= top"><span= style=3D"font-family:'Times New Roman'">Valores entre 1 y 21 depende del p= rograma.</td></tr><tr style=3D"height:48pt"><td style=3D"width:2= 5.4%; border-bottom:1pt solid #7e7e7e; padding:0pt 5.4pt; vertical-align:to= p">Materias reproba= das</td><td style=3D"width:38.38%; border-bottom:1pt solid #7e7e= 7e; padding:0pt 5.4pt; vertical-align:top">N=C3=BAmero de asignaturas que el estudiante reprob= =C3=B3.</td><td style=3D"width:36.22%; border-bottom:1pt solid #= 7e7e7e; padding:0pt 5.4pt; vertical-align:top">Valores entre 1 y 21 los fa= ctores por financieros, o descuido de estudios.</td></tr></table= > Nota: Dataset extra=C3=ADdo del repositorio acad=C3=A9mico = de la Instrucci=C3=B3n de Educaci=C3=B3n Superior IES. </div><= br style=3D"clear:both; mso-break-type:section-break" /><div class=3D"Secti= 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el an=C3=A1li= sis de los datos para esta investigaci=C3=B3n. Durante el proceso de depura= ci=C3=B3n, se determin=C3=B3 que la variable Total del monto adeudado no er= a relevante para los objetivos del estudio, debido a que se centr=C3=B3 en = la informaci=C3=B3n relacionada con las materias cursadas, aprobadas y repr= obadas y estado de pago de los estudiantes.Para definir las varia= bles asociadas al riesgo de titulaci=C3=B3n, en este estudio se establecier= on niveles espec=C3=ADficos que permiten categorizar dicho riesgo en alto, = medio y bajo. Estos umbrales fueron definidos cuidadosamente por los autore= s a partir del an=C3=A1lisis de los factores acad=C3=A9micos y administrati= vos relevantes, tal como se detalla en la Tabla 2. Esta clasificaci=C3=B3n facilita una eva= luaci=C3=B3n m=C3=A1s precisa y contextualizada del riesgo para la toma de = decisiones mediante paneles interactivos.<div style=3D"clear:bot= h"><span= style=3D"letter-spacing:3pt; color:#ffffff"> = <= span style=3D"letter-spacing:3pt; color:#ffffff">Nombre = = 0; </div></= div> <div class=3D"= Section_8">Tabla 2</s= pan><s= pan style=3D"font-family:'Times New Roman'; font-style:italic">Declaraci=C3= =B3n de las variables de riesgos<table style=3D"width:100%; marg= in-bottom:0pt; padding:0pt; border-collapse:collapse"><tr><td style=3D"widt= h:17.8%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #00000= 0; padding:0pt 5.4pt; vertical-align:top">Nivel del riesgo</td><td style=3D"width:17.72%; border-top:0.= 75pt solid #7f7f7f; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; = vertical-align:top">Estado de finanzas<= /span></td><td style=3D"width:25.92%; border-top:0.75pt solid #7f7f7f; = border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">= Materias aprobadas</td><td s= tyle=3D"width:20.96%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt= solid #000000; padding:0pt 5.4pt; vertical-align:top">Materias reprobadas</td><td style=3D"width:17.6%= ; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #000000; padd= ing:0pt 5.4pt; vertical-align:top">Requ= isitos adicionales</td></tr><tr><td style=3D"width:17.8%; border= -top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Bajo</td><td style=3D"width:17.72%; = border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Al d=C3=ADa</td><td style=3D"= width:25.92%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-= align:top">Todas las cursadas</p= ></td><td style=3D"width:20.96%; border-top:0.75pt solid #000000; padding:0= pt 5.4pt; vertical-align:top">0 = </td><td style=3D"width:17.6%; border-top:0.75pt solid #000000; padding= :0pt 5.4pt; vertical-align:top">Nivel d= e Idiomas completo.</td></tr><tr><td style=3D"width:17.8%; paddi= ng:0pt 5.4pt; vertical-align:top">Medio= </td><td style=3D"width:17.72%; padding:0pt 5.4pt; vertical-alig= n:top"><sp= an style=3D"font-family:'Times New Roman'">Desde 2 pagos pendientes en adel= ante</td><td style=3D"width:25.92%; padding:0pt 5.4pt; vertical-= align:top">Depende si el factor es soci= oecon=C3=B3mico, bajo rendimiento acad=C3=A9mico.</td><td style= =3D"width:20.96%; padding:0pt 5.4pt; vertical-align:top">Entre 1 a 3 materias </td><td style=3D"width:1= 7.6%; padding:0pt 5.4pt; vertical-align:top">2 nivel de Idiomas completo.</td></tr><tr><td style=3D"wid= th:17.8%; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-a= lign:top">= Alto</td><td style= =3D"width:17.72%; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; ve= rtical-align:top">Desde 3 pagos pendien= te en adelante</td><td style=3D"width:25.92%; border-bottom:0.75= pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Depende si el factor es socioecon=C3=B3mico, bajo rendimi= ento acad=C3=A9mico.</td><td style=3D"width:20.96%; border-botto= m:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Entre 4 materias en adelante</td><td sty= le=3D"width:17.6%; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; v= ertical-align:top">2 o todos los nivele= s de idiomas incompletos.</td></tr></table></div> <div class=3D"Section_9"><div sty= le=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2w= BDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fE= xciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4e= Hh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhEBAxEB/8QAHQABAQACAwEBAQA= AAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBAkJBgcAAAABAgMEBREGByESFzETQVFVkQ= gUFSJUVmFxkpTR0jeBobIWIzI2UnR1scEkMzRCYnKCs+EYQ0RGZKIlJzU4ZZPi/8QAGwEBAQADA= QEBAAAAAAAAAAAAAAECAwQFBgf/xAA0EQEAAQQBBAEDAAcIAwAAAAAAAQIDBBFSBRIUITETQVEi= 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Estas permitieron automatizar el an=C3=A1lisis de r= iesgo frente a una posible no titulaci=C3=B3n, garantizar coherencia en los= indicadores clave generados en los reportes administrativos y mejorar la t= razabilidad de los estudiantes en riesgo.Adem=C3=A1s, facilitaron la comparaci=C3= =B3n din=C3=A1mica entre periodos, con otras carreras o programas de simila= res caracter=C3=ADsticas, para llegar a una conclusi=C3=B3n sobre la mitiga= ci=C3=B3n o minimizaci=C3=B3n el riesgo fortaleciendo la toma de decisiones= basada en un apoyo visual de inteligencia de negocios y experiencia profes= ional de la direcci=C3=B3n de carreras.Se implement=C3=B3 un modelo de regresi=C3= =B3n log=C3=ADstica para la clasificaci=C3=B3n binaria, utilizando un enfoq= ue supervisado. El conjunto de datos fue dividido en 80% para entrenamiento= y 20% para prueba, garantizando que la partici=C3=B3n se realizara de mane= ra aleatoria para evitar sesgos. Durante la fase de entrenamiento, se ajust= aron los coeficientes del modelo mediante la maximizaci=C3=B3n de la verosi= militud, con el objetivo de estimar la probabilidad de pertenencia a cada c= lase. Posteriormente, el modelo entrenado se evalu=C3=B3 sobre el conjunto = de prueba utilizando m=C3=A9tricas est=C3=A1ndar como precisi=C3=B3n (Accur= acy) y ROC AUC, lo que permiti=C3=B3 medir tanto la exactitud global como l= a capacidad discriminativa del modelo. Este procedimiento asegura una valid= aci=C3=B3n adecuada del rendimiento y la generalizaci=C3=B3n del modelo.</s= pan> 3. &= #xa0; Resultados <div style=3D"clea= r:both">= &= #xa0; </s= pan>Nombre </sp= an>&= #xa0; </div= ></div> <div class= =3D"Section_10"><div style=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4A= AQSkZJRgABAQEAYABgAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETF= hwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4e= Hh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhE= BAxEB/8QAHQABAQACAwEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBAkJBgcAAA= ABAgMEBREGByESFzETQVFVkQgUFSJUVmFxkpTR0jeBobIWIzI2UnR1scEkMzRCYnKCs+EYQ0RGZ= KIlJzU4ZZPi/8QAGwEBAQADAQEBAAAAAAAAAAAAAAECAwQFBgf/xAA0EQEAAQQBBAEDAAcIAwAA= AAAAAQIDBBFSBRIUITETQVEiMjRhcYGRFSU1QqGxwdEz4fD/2gAMAwEAAhEDEQA/AOgSiyj9Lh8= VAAAAgAC/YAFgAFSUWUEkAFgRUQnX3BaKK65imimqqfREPt7zzNv9Ev8A/wCuUmqIn3LKKZn7Pg= 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La clasi= ficaci=C3=B3n para el an=C3=A1lisis con la herramienta Power Bi visualiza l= os resultados para determinar los valores que arroja los indicadores que se= rvir=C3=A1n para la toma de decisiones adecuadas e implementar medidas ante= la necesidad de identificar que procesos causan los retrasos en la fluidez= al cursar la malla acad=C3=A9mica normalmente.El an=C3=A1lisis de los datos revel= a que los programas de 15 meses de profundizaci=C3=B3n presentan un mayor n= =C3=BAmero de asignaturas que deber cursar los estudiantes en periodos de c= orta duraci=C3=B3n de un a=C3=B1o y una mayor matr=C3=ADcula estudiantil de= bido a la amplitud del campo de estudios que tienen como requisitos para se= r admitidos en el programa causando que los procesos se ralenticen como la = titulaci=C3=B3n increment=C3=A1ndose m=C3=A1s en referente a los programas = de 12 meses de profundizaci=C3=B3n tomado de ejemplo para la comparaci=C3= =B3n as=C3=AD como otros programas de profundizaci=C3=B3n de similares cara= cter=C3=ADsticas de duraci=C3=B3n en la formaci=C3=B3n de otras carreras de= ntro de la IES. Tabla 3Resultados de los indicadores del riesgo<table style=3D"widt= h:100%; margin-right:auto; margin-left:auto; margin-bottom:0pt; padding:0pt= ; border-collapse:collapse"><tr style=3D"height:15pt"><td style=3D"width:42= .24%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f; = padding:0pt 5.4pt; vertical-align:top"> </td><td colspan=3D"2" style=3D"border-t= op:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f; padding:0pt 5.= 4pt; vertical-align:top">12 meses</span= ></td><td style=3D"width:31.1%; border-top:0.75pt solid #7f7f7f; border= -bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">15 meses</td></tr><tr style=3D"hei= ght:15pt"><td style=3D"width:42.24%; border-top:0.75pt solid #7f7f7f; borde= r-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">Factor acad=C3=A9mico</td><td sty= le=3D"width:24.44%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt s= olid #7f7f7f; padding:0pt 5.4pt; vertical-align:top"> 13.47</td><td colspan=3D"2" st= yle=3D"border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f;= padding:0pt 5.4pt; vertical-align:top"> 12.24</td></tr><tr style=3D"height:15pt"><t= d style=3D"width:42.24%; padding:0pt 5.4pt; vertical-align:top">Factor Socioecon=C3=B3mico</td><td styl= e=3D"width:24.44%; padding:0pt 5.4pt; vertical-align:top"> $1207</td><td colspan=3D"= 2" style=3D"padding:0pt 5.4pt; vertical-align:top"> $1511</td></tr><tr style=3D"heig= ht:27.75pt"><td style=3D"width:42.24%; border-top:0.75pt solid #7f7f7f; bor= der-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">Valor de Riesgo </td><td styl= e=3D"width:24.44%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt so= lid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">45.9% </td><td colspan=3D"2" = style=3D"border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #7f7f7= f; padding:0pt 5.4pt; vertical-align:top"><span= style=3D"font-family:'Times New Roman'">41.78% </td></tr><tr st= yle=3D"height:0pt"><td style=3D"width:179.6pt"></td><td style=3D"width:103.= 9pt"></td><td style=3D"width:9.45pt"></td><td style=3D"width:132.25pt"></td= ></tr></table> Junto con la recolecci=C3=B3n de la informaci= =C3=B3n se considera evaluar la situaci=C3=B3n a otras carreras, el tema so= cioecon=C3=B3mico en el programa de 15 meses el riesgo se elev=C3=B3 consid= erablemente emergi=C3=B3 como el primer filtro cr=C3=ADtico, se=C3=B1alando= que la situaci=C3=B3n financiera del estudiante es un predictor temprano c= lave para el =C3=A9xito en la titulaci=C3=B3n en las IES privadas, cabe se= =C3=B1alar que establecen sus procesos internos institucionales que influye= n en el mismo. Un segundo filtro importante es el n=C3=BAmero de materias a= probadas por parte del estudiante el factor acad=C3=A9mico demuestra ser re= levante incluso cuando las condiciones financieras son desfavorables puesto= que implica que un alto rendimiento acad=C3=A9mico puede mitigar el riesgo= de no titularse, incluso si el estudiante arrastra valores financieros, au= nque tambi=C3=A9n se considera un riesgo de titulaci=C3=B3n sobre aquellos = que no logran puntajes requeridos por falta de desempe=C3=B1o en las asigna= turas de la malla como se observa en la Tabla 4. Tabla 4An=C3=A1lisis comparativo de los indicadores del riesgo mediano = a alto riesgo<table style=3D"width:100%; margin-right:auto; marg= in-left:auto; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr= style=3D"height:15pt"><td style=3D"width:42.24%; border-top:0.75pt solid #= 7f7f7f; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-ali= gn:top"><s= pan style=3D"font-family:'Times New Roman'; font-weight:bold"> = </td><td colspan=3D"2" style=3D"border-top:0.75pt solid #7f7f7f; border= -bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">12 meses</td><td style=3D"width:31= .1%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #000000; p= adding:0pt 5.4pt; vertical-align:top">1= 5 meses</td></tr><tr style=3D"height:15pt"><td style=3D"width:42= .24%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:to= p">Factor acad=C3=A9mico</td= ><td style=3D"width:24.44%; border-top:0.75pt solid #000000; padding:0pt 5.= 4pt; vertical-align:top"> 17.62</td><td colspan=3D"2" style=3D"border-top:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">14.79 </td></tr><tr style=3D"heigh= t:15pt"><td style=3D"width:42.24%; padding:0pt 5.4pt; vertical-align:top"><= p style=3D"margin-bottom:0pt; line-height:115%; font-size:10pt">Factor Socioecon=C3=B3mico</t= d><td style=3D"width:24.44%; padding:0pt 5.4pt; vertical-align:top">1660 </td><td col= span=3D"2" style=3D"padding:0pt 5.4pt; vertical-align:top"> 1830</td></tr><tr style= =3D"height:27.75pt"><td style=3D"width:42.24%; border-bottom:0.75pt solid #= 000000; padding:0pt 5.4pt; vertical-align:top">Valor de Riesgo </td><td style=3D"width:24.44%; border-b= ottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">63.1% </td><td = colspan=3D"2" style=3D"border-bottom:0.75pt solid #000000; padding:0pt 5.4p= t; vertical-align:top">50.6% </td></tr><tr style=3D"height:0pt"><= td style=3D"width:179.6pt"></td><td style=3D"width:103.9pt"></td><td style= =3D"width:9.45pt"></td><td style=3D"width:132.25pt"></td></tr></table> Con el fin de validar los resultados obtenidos mediante el Dashboa= rd en Power BI, se implement=C3=B3 un modelo de regresi=C3=B3n log=C3=ADsti= ca como herramienta de contraste. Este modelo permiti=C3=B3 evaluar de form= a cuantitativa la capacidad predictiva del sistema desarrollado, empleando = las mismas variables acad=C3=A9micas y socioecon=C3=B3micas del dataset ins= titucional. Las m=C3=A9tricas de desempe=C3=B1o obtenidas confirman la soli= dez del enfoque metodol=C3=B3gico adoptado, como se muestra en la <s= pan style=3D"font-family:'Times New Roman'; font-weight:bold">Tabla 5.</spa= n><spa= n style=3D"font-family:'Times New Roman'; font-weight:bold">Tabla 5<= /p>An=C3=A1lisis com= parativo de modelos <table style=3D"width:100%; margin-right:aut= o; margin-left:auto; margin-bottom:0pt; padding:0pt; border-collapse:collap= se"><tr style=3D"height:15pt"><td style=3D"width:40.44%; border-top:0.75pt = solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; verti= cal-align:top">M=C3= =A9trica</td><td style=3D"width:29.78%; border-top:0.75pt solid = #7f7f7f; border-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-al= ign:top"><= span style=3D"font-family:'Times New Roman'">Valor</td><td style= =3D"width:29.78%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt sol= id #7f7f7f; padding:0pt 5.4pt; vertical-align:top">Descripci=C3=B3n</td></tr><tr style=3D"height:15pt">= <td style=3D"width:40.44%; border-top:0.75pt solid #7f7f7f; border-bottom:0= .75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">Accuracy</td><td style=3D"width:29.78%; bor= der-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f; padding:0= pt 5.4pt; vertical-align:top">0.967</td><td style=3D"width:29.78%; border-top:0.75pt = solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; verti= cal-align:top">Indica el porcentaje de = predicciones correctas sobre el total de casos.</td></tr><tr sty= le=3D"height:15pt"><td style=3D"width:40.44%; border-bottom:0.75pt solid #7= f7f7f; padding:0pt 5.4pt; vertical-align:top">ROC AUC</td><td style=3D"width:29.78%; border-bottom:0.75= pt solid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">0.954</td><td style=3D"width= :29.78%; border-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-al= ign:top"><= span style=3D"font-family:'Times New Roman'">Mide la capacidad del modelo p= ara distinguir entre clases</td></tr></table> <a id=3D"_Hlk221654946">Para complementar la evaluaci=C3=B3n del modelo, se an= aliz=C3=B3 la distribuci=C3=B3n de las predicciones a trav=C3=A9s de una ma= triz de confusi=C3=B3n, la cual permite identificar con precisi=C3=B3n los = aciertos y errores de clasificaci=C3=B3n en cada categor=C3=ADa de riesgo. = Este an=C3=A1lisis detallado evidencia que el modelo logra una alta tasa de= verdaderos positivos y verdaderos negativos, con m=C3=ADnimos errores de c= lasificaci=C3=B3n entre las clases, lo que ratifica su idoneidad para su im= plementaci=C3=B3n en entornos institucionales, como se muestra en la </span= >Figura 1</= span></a>.<= /span> Figura 1Matriz de Confusi=C3=B3n </p= ><img src=3D"data:image/pn= g;base64,iVBORw0KGgoAAAANSUhEUgAAATwAAAEdCAYAAACR9duVAAAABHNCSVQICAgIfAhkiA= AAAAlwSFlzAAAOxAAADsQBlSsOGwAAIABJREFUeJzt3XeclOW9///Xdd/TtrCNjqvUBEKJwMkig= 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pocos e= rrores en las clases. <div style=3D"clear:both"> = ; Nombre <s= pan style=3D"width:27.7pt; letter-spacing:3pt; display:inline-block"> = <= span style=3D"width:32.69pt; letter-spacing:3pt; display:inline-block"> = 0; </div></div> <div class=3D"Section_11"= >En la herramienta = seleccionada, Power BI, se construy=C3=B3 un dashboard orientado a la visua= lizaci=C3=B3n integral de los resultados del an=C3=A1lisis. Para su dise=C3= =B1o se seleccionaron elementos gr=C3=A1ficos adecuados, tales como gr=C3= =A1ficos de barras, tarjetas de indicadores y tablas din=C3=A1micas, priori= zando aquellos que facilitan la comparaci=C3=B3n de variables y la identifi= caci=C3=B3n r=C3=A1pida de valores cr=C3=ADticos. Al igual que, se incorpor= aron segmentadores para filtrar la informaci=C3=B3n por per=C3=ADodo acad= =C3=A9mico, carrera, paralelo y nivel de riesgo, permitiendo un an=C3=A1lis= is din=C3=A1mico y adaptado a las necesidades como se muestra en la = Figura 2.</= span></div> <di= v class=3D"Section_12">Figura 2Presentaci=C3=B3n final del Dashboard </spa= n></div> <div c= lass=3D"Section_13"><div style=3D"clear:both"><img src=3D"data:im= age/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwc= HBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw= 4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eH= h7/wAARCARfAzMDASIAAhEBAxEB/8QAHQABAQACAwEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQ= AQABAwIDAwUMBAkJBgcAAAABAgMEBREGByESFzETQVFVkQgUFSJUVmFxkpTR0jeBobIWIzI2UnR= 1scEkMzRCYnKCs+EYQ0RGZKIlJzU4ZZPi/8QAGwEBAQADAQEBAAAAAAAAAAAAAAECAwQFBgf/xA= A0EQEAAQQBBAEDAAcIAwAAAAAAAQIDBBFSBRIUITETQVEiMjRhcYGRFSU1QqGxwdEz4fD/2gAMA= 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El panel muestra el porcentaje de= riesgo acad=C3=A9mico obtenido mediante la f=C3=B3rmula definida para el e= studio, as=C3=AD como los valores asociados a cada variable.El= valor de riesgo se obtiene al multiplicar dos veces el valor de materias r= eprobadas por una unidad mayor a la cantidad de cuotas pendientes para evit= ar que un estudiante que no tenga materias reprobadas tenga un valor nulo d= e riesgo y tiene un rango de 0 a 672 puntos los cuales luego ser=C3=A1n nor= malizados para obtener el porcentaje de riesgo que va en una escala de 0 a = 1.Esta configuraci=C3=B3n fue seleccionada con el objetivo de = proporcionar a interesados acad=C3=A9micos una herramienta intuitiva y func= ional que permita identificar oportunamente a los estudiantes con mayor vul= nerabilidad, facilitando la toma de decisiones informada y la planificaci= =C3=B3n de intervenciones preventivas.El estudio demostr=C3=B3 que el peso de las materias reprobadas = es aproximadamente doble el impacto al factor socioecon=C3=B3mico que manti= ene el estudiante, para un correcto an=C3=A1lisis en la herramienta se suma= una unidad para evitar que la multiplicaci=C3=B3n no devuelva valores nulo= s (cero) justificando por no tener valores pendientes, permite representar = correctamente los casos en los que el estudiante no tiene valores socioecon= =C3=B3micos cr=C3=ADticos, pero s=C3=AD registra materias reprobadas, sin e= mbargo el nivel del riesgo se alto cae en la incidencia lo cual favorece un= a toma de decisiones m=C3=A1s precisa por parte de los directivos acad=C3= =A9micos.Se realizo la ejec= uci=C3=B3n con el modelo Machine Learning para diferenciar cercan=C3=ADa de valores y si estos se ap= egan a la realidad del resultado obtenido como la regresi=C3=B3n log=C3=ADs= tica junto con Python para las comparativas num=C3=A9ricas del dataset obte= nido de Power Bi a manera verificar la precisi=C3=B3n de la probabilidad de= l riesgo de no titularse que muestra el Dashboard finalmente sea confiable.= Como se evidencia en la Figura 1.Los valores promedi= o del porcentaje de riesgo para el grupo alto para el modelo de regresi=C3= =B3n log=C3=ADstica y el campo calculado en Power BI es de 62.43% y 60.61% = respectivamente es decir una diferencia porcentual a 2 puntos, para el grup= o medio con 18.56% 19.05% y grupo bajo con 0.92% y 0.87% es menor a un punt= o porcentual.4.<s= pan style=3D"width:9pt; font:7pt 'Times New Roman'; display:inline-block">&= #xa0; Discusi=C3=B3n<div styl= e=3D"clear:both"> = 0; &= #xa0; Nombre</span= >&= #xa0; = </div></div> <d= iv class=3D"Section_15"><div style=3D"clear:both"><img src=3D"data:image/jpeg;base6= 4,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SE= hEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4e= Hh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzM= DASIAAhEBAxEB/8QAHQABAQACAwEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBA= 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+WZJX5K0VdKC7Ph39iH14RmZCh/YA9ZvyK4kjwdOlwlTlZ4DSR/xvrEqHRy90P1mydak7u68nB5= aG0mf64EHsrJc9+JWbOL/BWyFcz1wZtL+Fp8bW0uNBkbJ/vSjUVKleVKmwZL611bTo+2x6NIS6T= FmT42Ukn1Ym21vSz6/nNUPUXEOKNFYCYIgmBAIjZQg6CKEsY8hsFv64yX9BqGREgTBsYbfFqeER= koQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQ= BEEQBEFwzPJ/mFD7KUN7Uq0AAAAASUVORK5CYII=3D" width=3D"322" height=3D"79" alt= =3D"" style=3D"margin-top:-17.45pt; margin-left:239.7pt; position:absolute"= /> = 0; </div>Los procesos internos que conllevan las IES referente a la titul= aci=C3=B3n son cruciales para garantizar el =C3=A9xito acad=C3=A9mico </spa= n><a id=3D"_Hlk221654858">(Ma= chado & Castillo, 2017). </a>En estos no solo se eval=C3=BAa la metodolog=C3=ADa, sino tambi= =C3=A9n el cumplir con los lineamientos acad=C3=A9micos de manera estricta = (Mendoza et al., 2024). La titulaci=C3=B3n representa una etapa cr=C3=ADtica; sin e= mbargo, el proceso suele verse obstaculizado por la carencia de herramienta= s predictivas que permitan anticipar el =C3=A9xito del estudiante (Rabelo &= amp; Z=C3=A1rate, 2025). Esta deficiencia deriva en la identificaci=C3=B3n = tard=C3=ADa de requisitos pendientes, problema que se acent=C3=BAa por la g= esti=C3=B3n manual y los tiempos de revisi=C3=B3n prolongados. Asimismo, la= gesti=C3=B3n ineficiente de los recursos genera cuellos de botella y una a= dministraci=C3=B3n reactiva que interrumpe la progresi=C3=B3n fluida del eg= resado (Mac=C3=ADas, 2025).Ante la falta de an=C3=A1lisis de datos, el uso de herra= mientas anal=C3=ADticas se convierte en un aliado estrat=C3=A9gico (Ramaswa= mi et al., 2022). Estas permiten asociar los niveles de riesgo de los estud= iantes con los inconvenientes previos al ingreso a la unidad de titulaci=C3= =B3n, mitigando los desaf=C3=ADos que enfrentan las instituciones (Quiguana= s & Arias, 2024).El incremento en la matr=C3=ADcula y la diversificaci=C3=B3n d= e programas ponen a prueba los est=C3=A1ndares de los procesos actuales (Ma= c=C3=ADas et al., 2025). El principal conflicto surge al finalizar la malla= curricular: el incumplimiento de requisitos obliga al estudiante a una reg= ularizaci=C3=B3n forzada o, en casos m=C3=A1s cr=C3=ADticos, ocasiona la de= serci=C3=B3n estudiantil al no contar con acompa=C3=B1amiento sobre el proc= eso (Morales et al., 2025).Los resultados del modelo de regresi=C3=B3n log=C3=ADsti= ca demuestran un desempe=C3=B1o consistente con los reportados en el dashbo= ard, lo cual valida el enfoque metodol=C3=B3gico (Berens et al., 2018). La = equivalencia entre las m=C3=A9tricas alcanzadas y los indicadores visuales = confirma que el proceso de entrenamiento es reproducible (Namoun & Alsh= anqiti, 2021). Esta convergencia entre el an=C3=A1lisis estad=C3=ADstico y = la representaci=C3=B3n gr=C3=A1fica aporta robustez a los hallazgos y confi= rma la estabilidad del modelo como herramienta de toma de decisiones.</span= >Las m=C3=A9tri= cas obtenidas fueron consistentes con los indicadores visualizados en la he= rramienta, lo que evidencia que el proceso de entrenamiento y evaluaci=C3= =B3n fue reproducible y confiable (Jimbo-Santana et al., 2023). Esta cohere= ncia sugiere la ausencia de discrepancias significativas entre el an=C3=A1l= isis estad=C3=ADstico y la representaci=C3=B3n gr=C3=A1fica; por lo tanto, = se aporta robustez a la interpretaci=C3=B3n de los hallazgos y se confirma = la estabilidad del modelo frente a distintos m=C3=A9todos de presentaci=C3= =B3n de resultados (Ngulube & Ncube, 2025).= 5.</spa= n> ConclusionesEl presente estudio examin= =C3=B3 la efectividad de los procesos de titulaci=C3=B3n, demostrando que l= a optimizaci=C3=B3n y prevenci=C3=B3n de nudos cr=C3=ADticos dependen direc= tamente de un seguimiento acad=C3=A9mico riguroso. En particular, se determ= in=C3=B3 que los programas de corta duraci=C3=B3n requieren un monitoreo pe= rmanente para asegurar su eficiencia operativa.A trav=C3=A9s de la aplicaci=C3=B3n del algoritmo de ri= esgo, se logr=C3=B3 segmentar a la poblaci=C3=B3n estudiantil en niveles ba= jo, medio y alto. Esta categorizaci=C3=B3n facilita que el departamento de = titulaci=C3=B3n ejecute acciones prioritarias y personalizadas. Asimismo, l= a integraci=C3=B3n de herramientas de visualizaci=C3=B3n interactiva, como = Power BI, transform=C3=B3 datos complejos en informaci=C3=B3n estrat=C3=A9g= ica, permitiendo a los directivos una toma de decisiones basada en evidenci= a.En definitiva, es esencia= l que las instituciones superiores adopten la ciencia de datos y la anal=C3= =ADtica educativa como ejes transversales para fortalecer sus procesos admi= nistrativos y asegurar est=C3=A1ndares de calidad internacional.= 5.1. Recomendaciones para ser consideradas</s= pan><= span style=3D"font-family:'Times New Roman'">Para garantizar la efectividad= del dashboard desarrollado, se proponen las siguientes medidas de interven= ci=C3=B3n:<ul style=3D"margin:0pt; padding-left:0pt"><li class= =3D"ListParagraph" style=3D"margin-left:17.63pt; margin-bottom:0pt; text-al= ign:justify; line-height:115%; padding-left:3.67pt; font-family:serif; font= -size:12pt">Riesgo Bajo: delegar = el monitoreo a tutores personales mediante revisiones mensuales. El objetiv= o es actuar de forma preventiva para evitar el escalamiento hacia niveles d= e riesgo superiores.</li><li class=3D"ListParagraph" style=3D"margin= -left:17.63pt; margin-bottom:0pt; text-align:justify; line-height:115%; pad= ding-left:3.67pt; font-family:serif; font-size:12pt">Riesgo Medio: implementar tutor=C3=ADas acad=C3=A9mica= s focalizadas en casos de bajo rendimiento. Si el origen es financiero o pe= rsonal, se debe coordinar el acompa=C3=B1amiento con Bienestar Estudiantil = y las direcciones acad=C3=A9micas con una frecuencia quincenal.</li>= <li class=3D"ListParagraph" style=3D"margin-left:17.63pt; text-align:justif= y; line-height:115%; padding-left:3.67pt; font-family:serif; font-size:12pt= ">Riesgo Al= to: ejecutar intervenc= iones inmediatas con el apoyo del Vicerrectorado Acad=C3=A9mico y las facul= tades. Se recomienda un seguimiento semanal mediante un plan personalizado = de casos especiales, el cual debe activarse al menos tres meses antes de fi= nalizar el ciclo de formaci=C3=B3n acad=C3=A9mica.</li></ul> <sp= an style=3D"font-weight:bold; color:#767171">6. &= #xa0; Conflicto de interesesLos autores declaran que no existe conflicto de int= ereses en relaci=C3=B3n con el art=C3=ADculo presentado.7. = 0; Declaraci=C3=B3n de contribuci=C3=B3n de los autores</s= pan><= span style=3D"font-family:'Times New Roman'">Todos autores contribuyeron si= gnificativamente en la elaboraci=C3=B3n del art=C3=ADculo.8. = 0; Costos de financiamiento La presente investigaci=C3=B3n fue financiada = en su totalidad con fondos propios de los autores.<div style=3D"= clear:both"> &#x= a0; = Nombre<spa= n style=3D"width:141.26pt; letter-spacing:3pt; display:inline-block"> = <= /div></div> <div cl= ass=3D"Section_16"> <= span style=3D"font-weight:bold; color:#767171">9. = 0; Referencias Bibliogr=C3=A1ficas<span= style=3D"font-family:'Times New Roman'"> </div= > <div class=3D"Sec= tion_17"><div style=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJR= gABAQEAYABgAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQa= FRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4= eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhEBAxEB/8= QAHQABAQACAwEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBAkJBgcAAAABAgMEB= REGByESFzETQVFVkQgUFSJUVmFxkpTR0jeBobIWIzI2UnR1scEkMzRCYnKCs+EYQ0RGZKIlJzU4= ZZPi/8QAGwEBAQADAQEBAAAAAAAAAAAAAAECAwQFBgf/xAA0EQEAAQQBBAEDAAcIAwAAAAAAAQI= DBBFSBRIUITETQVEiMjRhcYGRFSU1QqGxwdEz4fD/2gAMAwEAAhEDEQA/AOgSiyj9Lh8VAAAAgA= C/YAFgAFSUWUEkAFgRUQnX3BaKK65imimqqfREPt7zzNv9Ev8A/wCuUmqIn3LKKZn7PgPpdsXrM= RVes3bcT03qoml89pWKok1P9BJVJVigAEosoACRv5xY+N/hUlUkVAAAASUWUUAECUWUIAAkEnwV= J8AQBQARUk+slaaZqqimPGVhJn7vtiWvKV7zHxYfvYWqIt0RTH62aw47le5AFYgCSqSiyiAAASE= 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style=3D"font-family:'Times New Roman'; color:#ffffff"> <p= class=3D"Header"> </= span></div>Ac= osta Guanoquiza , C. 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MaELy42O7R39fz/1f8C/hZ5YvjpSKGwAAAABJRU5ErkJggg=3D=3D" width=3D"20" height= =3D"20" alt=3D"" style=3D"margin-top:0.45pt; margin-left:-0.3pt; position:a= bsolute" /> </spa= n></td><td style=3D"width:168.45pt; border-left:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">= <a href=3D"https://orcid.o= rg/0009-0004-2938-7111" style=3D"text-decoration:none">= https://orcid.org/0009-0004-2938-7111</a></td></tr><tr><td style= =3D"width:3.25pt; border-top:0.75pt solid #000000; border-right:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical= -align:top"> </= span></td><td colspan=3D"3" style=3D"width:372.5pt; border-top:0.75pt s= olid #000000; border-left:0.75pt solid #000000; border-bottom:0.75pt solid = #000000; padding:0pt 5.03pt; vertical-align:top">Universidad Bolivariana del Ecuador (UBE), Du= ran, EcuadorMaestr=C3=ADa en Gesti=C3=B3n y An=C3=A1lisis de Datos con menci=C3=B3n= en Inteligencia de Negocios.<a href=3D"mailto:jmgonzaleza@ube.edu.ec" style=3D"text-decor= ation:none">jmgonzaleza@ube.edu.ec</a><span= style=3D"font-family:'Times New Roman'"> </td></tr><tr><td styl= e=3D"width:3.25pt; border-top:0.75pt solid #000000; border-right:0.75pt sol= id #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertica= l-align:top">2</spa= n></td><td style=3D"width:171.95pt; border:0.75pt solid #000000; paddin= g:0pt 5.03pt; vertical-align:top">Carlos Hern=C3=A1n Su= =C3=A1rez Barrag=C3=A1n</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;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUC= AYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAA2lJREFUOI21= lEtoXVUUhr+197n3nNzbJN6omdTWRmwbikVq4iMxk0ZU2oGgA1FnQcQiUhUnFqo4cGIRQUGd+KD= Y+Jp25ouiGCOxElqjhba0URPzsPQmuY9zzj1nLwcnuU1iQ0f+mzXZe///Wv/aiy1cBV/83NNOPr= /VWHunqO5OVNuNlQVjmZCGjCVhcPHR3q8XrsaVNUITu/KSlu7LFeSpNNZ+57RTjDTvqFM1IvNiG= 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0" height=3D"20" alt=3D"" style=3D"margin-top:0.45pt; margin-left:-0.3pt; p= osition:absolute" />&#= xa0;</td><td style=3D"width:168.45pt; border-top:0.75pt solid #0= 00000; border-left:0.75pt solid #000000; border-bottom:0.75pt solid #000000= ; padding:0pt 5.03pt; vertical-align:top"><a href=3D"https://orcid.org/0009-0000-7779-3297" style=3D"= text-decoration:none">https://orcid.org/0009-0000-7779-= 3297</a></td></tr><tr><td style=3D"width:3.25pt; border-top:0.75= pt solid #000000; border-right:0.75pt solid #000000; border-bottom:0.75pt s= olid #000000; padding:0pt 5.03pt; vertical-align:top"> </td><td colspan=3D"3" = style=3D"width:372.5pt; border-top:0.75pt solid #000000; border-left:0.75pt= solid #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; ver= tical-align:top">Un= iversidad Bolivariana del Ecuador (UBE), Duran, Ecuador.Maestr=C3=ADa en Gesti=C3= =B3n y An=C3=A1lisis de Datos con menci=C3=B3n en Inteligencia de Negocios.= <a href=3D"mailto:chsuarezb@ube= .edu.ec" style=3D"text-decoration:none">chsuarezb@= ube.edu.ec</a></td></tr><tr><td style=3D"width:3.25pt; border-to= p:0.75pt solid #000000; border-right:0.75pt solid #000000; border-bottom:0.= 75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">3</td><td style=3D"widt= h:171.95pt; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align= :top">Jorge Luis Charco Aguirre </td><td style=3D"width:10.5pt; border:0.75pt solid= #000000; padding:0pt 5.03pt; vertical-align:top"><i= mg src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0= NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAA2lJREFUOI21lEtoXVUUhr= +197n3nNzbJN6omdTWRmwbikVq4iMxk0ZU2oGgA1FnQcQiUhUnFqo4cGIRQUGd+KDY+Jp25ouiG= COxElqjhba0URPzsPQmuY9zzj1nLwcnuU1iQ0f+mzXZe///Wv/aiy1cBV/83NNOPr/VWHunqO5O= VNuNlQVjmZCGjCVhcPHR3q8XrsaVNUITu/KSlu7LFeSpNNZ+57RTjDTvqFM1IvNiGXWJvD/9x8y= XB/efizYU/Hy8/0W/aA/Xq43r1P3nOCNIFsW2XLleTd648Z/863v3nkjWCL59dp/fWSsfzOXNkS= hKacvfgnMRlXjqqqIrwr5vkihyhxbT+K2ne082AAzADZXyPj+wL8ehQ1NH700v0N35OAroutW0r= xDF6rUUvddaJfdwM9HwqYFSa0GO1SrpfucUVUXEZCQcqQtZ0VEU1QTPtmAkDygiEBTsTxq6hx65= fWTOy7l0exjKgGrGcjTo2/oKS9EUZ2aPcf/Oo1iTI9UGqFJrzDF5+Sv+Wvg+y+IEVXaGibsbOO5= hGBSkbUUQdRT9zaQuBhE6Ct2MT7/D9OIIVnw6Ct3csfk52oNtTMwcBbE0YteeC7we4LinSI9Z03= dBNUVxTdu1eI7FcBIjHvPVU1Tjv7nn5sNMXv6WSjyFiIg17AAwqpRW9fqayNkiM0tjqDrag67mD= Ci0ARiB6gaTsSEEg4htusj2qGeCIr9dW0JR3PKrR2zreIDURZTrZ7NTBYdeBPAU+cY59zxCkFlX= jHiI2GY1pZbtRMkC1gZcX9hFV+lBTs98SDWexYjFGqkldT0J4EkY/ZovBb+klbQ/G1zDYvQntXg= WFC5VJ9hSGmRLaRCnCZeqE3x34SXK9XMY8bK+BvZ8JUzGAEQV+Wy87xm/xbwZ1lwewGmCIIhYnC= ZXjKvDaQNrfIzkAKXYZl11wR16omfkyHIP0RB/2CV8GrRkNldbNmKbYU2enC0uV6bkfUNUd8P1u= ry3ktQADO05UV6azx1IE/dRULDomjGSVXEFhVbrXMoncTV99smBH5ZW327igzP3trY2ONCyyQ6F= tbRLUw2yZ8p+FwARifyCvRDV048rcfXdoT3j5fXp1+BVxdx2um+HGnuX9egX4dY01U0iVK2R80n= MaELy42O7R39fz/1f8C/hZ5YvjpSKGwAAAABJRU5ErkJggg=3D=3D" width=3D"20" height= =3D"20" alt=3D"" style=3D"margin-top:0.45pt; margin-left:-0.3pt; position:a= bsolute" /> </spa= n></td><td style=3D"width:168.45pt; border-top:0.75pt solid #000000; bo= rder-left:0.75pt solid #000000; border-bottom:0.75pt solid #000000; padding= :0pt 5.03pt; vertical-align:top"><a href=3D"https://orcid.org/0000-0002-0099-0345" style=3D"text-deco= ration:none">https://orcid.org/0000-0002-0099-0345= </a></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"> </td><td colspan=3D"3" style= =3D"width:372.5pt; border-top:0.75pt solid #000000; border-left:0.75pt soli= d #000000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical= -align:top">Univers= idad Bolivariana del Ecuador (UBE), Duran, Ecuador.<a href=3D"mailto:jlcharcoa@ube.edu.ec" style=3D"text-d= ecoration:none">jlcharcoa@ube.edu.ec</a></p= >Universidad de Guayaquil (UG), Guayaquil, Ecuador.</sp= an><a href=3D"mailto:jorge.charcoa@ug.= edu.ec" style=3D"text-decoration:none">jorge.charc= oa@ug.edu.ec</a> </td></tr><tr><td style=3D"width:3.= 25pt; border-top:0.75pt solid #000000; border-right:0.75pt solid #000000; b= order-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top">= 4</td><t= d style=3D"width:171.95pt; border:0.75pt solid #000000; padding:0pt 5.03pt;= vertical-align:top"><span= style=3D"font-family:'Times New Roman'">Dayron Rumbaut Rangel</= td><td style=3D"width:10.5pt; border:0.75pt solid #000000; padding:0pt 5.03= pt; vertical-align:top"><img src=3D"data:image/png;b= ase64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAABHNCSVQICAgIfAhkiAAAA= AlwSFlzAAAOxAAADsQBlSsOGwAAA2lJREFUOI21lEtoXVUUhr+197n3nNzbJN6omdTWRmwbikVq= 4iMxk0ZU2oGgA1FnQcQiUhUnFqo4cGIRQUGd+KDY+Jp25ouiGCOxElqjhba0URPzsPQmuY9zzj1= nLwcnuU1iQ0f+mzXZe///Wv/aiy1cBV/83NNOPr/VWHunqO5OVNuNlQVjmZCGjCVhcPHR3q8Xrs= aVNUITu/KSlu7LFeSpNNZ+57RTjDTvqFM1IvNiGXWJvD/9x8yXB/efizYU/Hy8/0W/aA/Xq43r1= 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argin-top:0.45pt; margin-left:-0.3pt; position:absolute" /> </td><td style=3D"wid= th:168.45pt; border-top:0.75pt solid #000000; border-left:0.75pt solid #000= 000; border-bottom:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align= :top"><a href=3D"https://o= rcid.org/0009-0001-9087-0979" style=3D"text-decoration:none">https://orcid.org/0009-0001-9087-0979</a></td></tr><t= r style=3D"height:18pt"><td style=3D"width:3.25pt; border-top:0.75pt solid = #000000; border-right:0.75pt solid #000000; padding:0pt 5.03pt; vertical-al= ign:top"> </spa= n></td><td colspan=3D"3" style=3D"width:372.5pt; border-top:0.75pt soli= d #000000; border-left:0.75pt solid #000000; padding:0pt 5.03pt; vertical-a= lign:top">Universid= ad Bolivariana del Ecuador (UBE), Duran, Ecuador.<a href=3D"mailto:drumbautr@ube.edu.ec" style=3D"text-dec= oration:none">drumbautr@ube.edu.ec</a><span= style=3D"line-height:115%; font-family:'Times New Roman'; font-size:10pt">= </td></tr></table> <table style=3D"width:436.35pt; margin-le= ft:0.25pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr><t= d colspan=3D"3" style=3D"width:156.4pt; padding:0pt 5.4pt; vertical-align:t= op"><a id=3D"_Hlk92186133"> </span= ></a> </span= ></td><td colspan=3D"2" style=3D"width:258.35pt; border-top:0.75pt soli= d #000000; padding:0pt 5.4pt; vertical-align:top">Art=C3=ADculo de Investigaci= =C3=B3n Cient=C3=ADfica y Tecnol=C3=B3gicaEnviado: Revisado: Aceptado: Publicado:DO= I: </td></tr><tr style=3D"height:6.55pt= "><td colspan=3D"3" style=3D"width:156.4pt; padding:0pt 5.4pt; vertical-ali= gn:top"> </td><td colspan=3D"2" style=3D"width:= 258.35pt; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-a= lign:top"> </td></tr><tr><td style=3D"width:37.8pt; padding:0pt= 5.4pt; vertical-align:top"> C=C3=ADtese: </td><td style=3D"width:1.55= pt; padding:0pt 5.4pt; vertical-align:top"> </td><td colspan= =3D"2" style=3D"width:358.55pt; border-bottom:0.75pt solid #000000; padding= :0pt 5.4pt; vertical-align:top"> DATOS REVISTADATOS REVISTADATOS REVISTA</td><td style=3D"border-top:0= .75pt solid #000000; padding:0pt; vertical-align:top"></td></tr><tr style= =3D"height:0pt"><td style=3D"width:48.6pt"></td><td style=3D"width:12.35pt"= ></td><td style=3D"width:106.25pt"></td><td style=3D"width:263.1pt"></td><t= d style=3D"width:6.05pt"></td></tr></table><img src=3D"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA= jcAAACMCAYAAACAuogxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAOxAAADsQBlSsOGwAAIABJ= REFUeJzsnXd4VFXe+D93ZpKZZNIrIRB6TQhdEOngUgSkClIUEGUJUlR4URcVVBTQVRBFRBSR3gO= hJjQDIQXSJ5n0RnqfJJM6M/f3R8hds7q7rovu6/ubz/PAA7ecc+655575nm87giiKImbMmDFjxo= wZM/9HkP23G2DGjBkzZsyYMfMoMQs3ZsyYMWPGjJn/U5iFGzNmzJgxY8bM/ynMwo0ZM2bMmDFj5= v8UZuHGjBkzZsyYMfN/CsVvX4XY6l8CSH8jiiCA2HJUFBGFh8dbjgnCj27+23EzZsyYMWPGjJmf= 4/fR3IjQLLVI/wHAJDT/U0QEUcQkCpgQQBAwic3XiaKx+fzf3WvGjBkzZsyYMfNz/A7CjYCkhRE= FQIYoglEEEwKiICBDRMCEIEBWSRXHbyeTW1GLIAiYBBETBhCamssRzZobM2bMmDFjxsw/5vfR3A= 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6cTifl5eVYLJZbD/QXBje3i79ijjKZjICAAHQ6nbuA04MHDx48ePDw13FbghsPHjx48ODBg4f/F= DxLCR48ePDgwYOH/yo8wY0HDx48ePDg4b+KP7UVPDs72+3C7cGDBw8ePHjw8L+BRqOhRYsWtzz+= p4Kb77777qYqvB48ePDgwYMHD38X4eHhTJky5ZbHPQXFHjx48ODBg4f/Kjw1Nx48ePDgwYOH/yo= 8wY0HDx48ePDg4b8KT3DjwYMHDx48ePiv4v8DkrKy3KIORuIAAAAASUVORK5CYII=3D" width= =3D"567" height=3D"140" alt=3D"" /><table style=3D"width:421pt; margin-= left:0.25pt; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr>= <td style=3D"width:73.75pt; border:0.75pt solid #000000; padding:0pt 5.03pt= ; vertical-align:top">Palabras claves: An=C3=A1lisis del riesgo, dashboard educativos, titulaci= =C3=B3n universitaria, anal=C3=ADtica de datos, an=C3=A1lisis predictivo.</td><td style=3D"w= idth:324.9pt; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-ali= gn:top">Resumen Introducci=C3=B3n: la educaci=C3=B3n superior en el terri= torio ecuatoriano se enfrenta a escenarios de complejidad en cuanto a proce= sos acad=C3=A9micos y administrativos previos a la titulaci=C3=B3n de grado= , influenciado por el cumplimiento de requisitos, las modalidades de estudi= os y la creciente demanda estudiantes para ciertas carreras. Objetivo:<s= pan style=3D"font-family:'Times New Roman'"> analizar el riesgo de la titul= aci=C3=B3n en el nivel de grado mediante la integraci=C3=B3n de factores ac= ad=C3=A9micos y administrativos, utilizando herramientas interactivas de an= =C3=A1lisis de datos, para identificar tempranamente los casos en riesgo y = proponer estrategias institucionales que optimicen el proceso de titulaci= =C3=B3n. Metodolog=C3=ADa:= la metodolog=C3=ADa aplicada fue de enfoque mixto de car=C3=A1cter no expe= rimental y retrospectivo centrado en la recolecci=C3=B3n de informaci=C3=B3= n de dataset anonimizado de datos estudiantiles cuya muestra de fue de 604 = registros. Resultados: se = identific=C3=B3 que los factores administrativos y los factores acad=C3=A9m= icos que influyen significativamente en la aptitud del estudiante para acce= der al proceso de titulaci=C3=B3n de grado. El an=C3=A1lisis cualitativo y = cuantitativo permiti=C3=B3 establecer el nivel del riesgo potencialmente al= to en estudiantes que presentan retrasos. A trav=C3=A9s del dashboard dise= =C3=B1ado para este estudio, fue posible visualizar los riesgos y facilitar= la toma de decisiones institucionales de manera oportuna y preventiva. </s= pan>Conclus= i=C3=B3n: se determina= que aplicando un an=C3=A1lisis de riesgo anticipado dentro de la gesti=C3= =B3n acad=C3=A9mica en carreras con alto n=C3=BAmero de estudiantes matricu= lados permite identificar aquellos con problemas previo a la titulaci=C3=B3= n para aplicar una correcta decisi=C3=B3n a nivel de direcci=C3=B3n acad=C3= =A9mica. =C3=81rea de estudio general: Anal=C3=ADtica de datos. =C3=81rea de estudio espec=C3=ADfica:</sp= an> An=C3=A1lisis del Riesgo.= Tipo de estudio: Art=C3=ADculo original.<= /p></td></tr><tr><td style=3D"width:73.75pt; border:0.75pt solid #000000; p= adding:0pt 5.03pt; vertical-align:top">Keywords: <a id=3D"_Hlk212297625">Risk analysis, educational dashboards, university d= egrees, </a>da= ta analytics, predictive analytics. </td><td style=3D"width:324.= 9pt; border:0.75pt solid #000000; padding:0pt 5.03pt; vertical-align:top"><= p style=3D"margin-bottom:0pt; text-align:justify; line-height:115%; font-si= ze:12pt">AbstractIntroduction: higher education in Ecuador faces complex scenarios = regarding academic and administrative processes prior to undergraduate grad= uation, influenced by the fulfillment of requirements, study modalities, an= d the growing student demand for certain programs. Objective: to analyze the risk of undergraduate gr= aduation by integrating academic and administrative factors and using inter= active data analysis tools to identify at-risk cases early and propose inst= itutional strategies to optimize the graduation process. Methodology:<sp= an style=3D"font-family:'Times New Roman'"> the methodology applied was a m= ixed approach, non-experimental and retrospective, focused on the collectio= n of information from an anonymized dataset of student data, with a sample = of 604 records. Results: t= he administrative and academic factors that significantly influence student= suitability to access the undergraduate graduation process were identified= . Qualitative and quantitative analysis made it possible to establish the l= evel of potentially substantial risk in students who present delays. Using = the dashboard designed for this study, it was possible to visualize risks a= nd facilitate timely and initiative-taking institutional decision-making. <= /span>Concl= usion: it was determin= ed that applying early risk analysis within academic management in programs= with a high number of enrolled students allows students with problems prio= r to graduation to be identified, enabling appropriate decisions to be made= at the academic management level. General Area of =E2=80=8B=E2=80=8BStudy:</sp= an> Data Analytics. <s= pan style=3D"font-family:'Times New Roman'; font-weight:bold">Specific Area= of =E2=80=8B=E2=80=8BStudy: Risk Analysis. Type of Study: Original article.</td></tr></table> <div style=3D"clear:both"> = Nombre </span= >&#x= a0; </spa= n>&= #xa0; </div></div> <div class=3D"Section_= 2">1. = 0; Introducci=C3=B3n</div> <div class=3D"Section_3"><d= iv style=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABg= AAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0= dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh= 4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhEBAxEB/8QAHQABAQACA= wEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBAkJBgcAAAABAgMEBREGByESFzET= QVFVkQgUFSJUVmFxkpTR0jeBobIWIzI2UnR1scEkMzRCYnKCs+EYQ0RGZKIlJzU4ZZPi/8QAGwE= BAQADAQEBAAAAAAAAAAAAAAECAwQFBgf/xAA0EQEAAQQBBAEDAAcIAwAAAAAAAQIDBBFSBRIUIT= ETQVEiMjRhcYGRFSU1QqGxwdEz4fD/2gAMAwEAAhEDEQA/AOgSiyj9Lh8VAAAAgAC/YAFgAFSUW= UEkAFgRUQnX3BaKK65imimqqfREPt7zzNv9Ev8A/wCuUmqIn3LKKZn7PgPpdsXrMRVes3bcT03q= 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AASUVORK5CYII=3D" width=3D"322" height=3D"79" alt=3D"" style=3D"margin-top:= -17.45pt; margin-left:239.7pt; position:absolute" /> </= div>La educaci= =C3=B3n superior en el territorio ecuatoriano se enfrenta a escenarios de c= omplejidad en procesos acad=C3=A9micos y administrativos previos a la titul= aci=C3=B3n de grado, con el cumplimiento de requisitos, las modalidades de = estudios y la creciente demanda estudiantes para ciertas carreras constituy= e un elemento decisivo que marcan un precedente para anticipar el =C3=A9xit= o o riesgo acad=C3=A9mico. La gesti=C3=B3n adecuada de los procesos garantiza qu= e se cumplan con los requisitos favoreciendo desarrollo acad=C3=A9mico flui= do dentro de los plazos establecidos, adem=C3=A1s que integra el fortalecim= iento institucional al mantener la tasa de titulaci=C3=B3n elevada requerim= iento importante dentro de los procesos de acreditaci=C3=B3n de las carrera= s ante el Consejo de Asegu= ramiento de la Calidad de la Educaci=C3=B3n Superior (CACES) e= n Ecuador para garantizar la mejora continua de los procesos acad=C3=A9mico= s.La literatura reciente evidencia una brecha en el sistema ed= ucativo en la aplicaci=C3=B3n de herramientas tecnol=C3=B3gicas integradas,= capaces de combinar datos acad=C3=A9micos y administrativos para predecir,= mitigar o minimizar el riesgo de titulaci=C3=B3n (Acosta et al., 2024), si= n embargo ante la existencia de sobre c=C3=B3mo predecir, hay una brecha en herramientas que logren = mit= igar mediante acciones= automatizadas basadas en datos administrativos. El proceso de= titulaci=C3=B3n universitaria emerge como el hito culminante de la trayect= oria acad=C3=A9mica, funcionando como un mecanismo de validaci=C3=B3n de lo= s conocimientos, habilidades y competencias adquiridas por el estudiante a = lo largo de su carrera (Mendoza et al., 2024). La falta de mecan= ismos predictivos en etapas tempranas perpetua un ciclo donde los problemas= acad=C3=A9micos o administrativos se identifican tard=C3=ADamente dando co= mo resultados estudiantes que abandonan los estudios al llegar a la titulac= i=C3=B3n. Seg=C3=BAn los autores Morales et al. (2025) uno de lo= s factores entre los estudiantes que incrementan la brecha es la falta de m= anejo de plataformas educativas y de gesti=C3=B3n acad=C3=A9mica la falta d= e dispositivos adecuados y la conectividad limitada dificultan la participa= ci=C3=B3n de los estudiantes en el proceso de aprendizaje.Por otra parte, es v=C3=A1lido mencionar que las universidades y= otras instituciones de educaci=C3=B3n superior en Ecuador se expandieron r= =C3=A1pidamente. Sin embargo, este crecimiento, provocado por el aumento de= la poblaci=C3=B3n y la demanda, no estuvo acompa=C3=B1ado de una supervisi= =C3=B3n efectiva ni de est=C3=A1ndares de calidad (Mac=C3=ADas et al., 2025= ).Se pue= de se=C3=B1alar que la utilizaci=C3=B3n de herramientas interactivas que ap= oyan la toma de decisiones estrat=C3=A9gicas anticipando y prediciendo posi= bles riesgos que sirven como modelo de control preventivo (Zamora et al., 2= 019). A fin de que permita tratar la informaci=C3=B3n de manera adecuada y = acorde a las necesidades institucionales que este requiera.Para el autor un dashboa= rd es una herramienta de representaci=C3=B3n visual de la informaci=C3=B3n = m=C3=A1s importante y necesaria para lograr los objetivos de forma consolid= ada y organizada en una sola pantalla cada vez m=C3=A1s utilizados en la in= teligencia de negocios (Quiguanas & Arias, 2024).El an=C3=A1lisis de<= span style=3D"letter-spacing:-0.05pt"> datos educativos con ap= rendizaje autom= =C3=A1tico permite identificar estudiantes en riesgo desde </= span>etapas <spa= n>tempranas, fac= ilitando interve= nciones dirigida= s que<spa= n style=3D"letter-spacing:-0.75pt"> mejoran la retenci=C3=B3n y= el <span= >=C3=A9xito en la titulaci=C3=B3n de pregrado (Villalobos-Murillo et al., 2= 025). <span= >El uso de Dashboard en el =C3=A1mbito educativo permite integrar informaci= =C3=B3n acad=C3=A9mica y administrativa para interpretar de manera clara y = oportuna los niveles de riesgo estudiantil. La visualizaci=C3=B3n consolida= da en una sola interfaz facilita la identificaci=C3=B3n de patrones, alerta= s tempranas y brechas de desempe=C3=B1o asociadas al proceso de titulaci=C3= =B3n, lo que resulta clave para instituciones que enfrentan problem=C3=A1ti= cas de rezago acad=C3=A9mico y baja eficiencia terminal. Asimismo, estos en= tornos visuales posibilitan el seguimiento continuo de cohortes y carreras,= orientando la priorizaci=C3=B3n de intervenciones y la asignaci=C3=B3n de = recursos institucionales con un enfoque preventivo y trazable.<p= class=3D"BodyText" style=3D"margin-top:10.1pt; margin-right:7.25pt; margin= -left:7.1pt; text-align:justify; line-height:115%">El desarrollo de m= odelos matem=C3=A1ticos aplicados a la anal=C3=ADtica educativa requiere un= a definici=C3=B3n rigurosa del fen=C3=B3meno de estudio, la selecci=C3=B3n = de variables relevantes y un tratamiento de datos coherente con los supuest= os estad=C3=ADsticos. La construcci=C3=B3n del modelo debe complementarse c= on procesos de entrenamiento, validaci=C3=B3n cruzada y evaluaci=C3=B3n med= iante m=C3=A9tricas como precisi=C3=B3n, sensibilidad, =C3=81rea Bajo la Curva (AUC) y= error, garantizando as=C3=AD su capacidad predictiva. En el marco de la educac= i=C3=B3n basada en resultados o en sus siglas en ingles OBE (Outcome-Based Education), los r= esultados de aprendizaje pueden definirse y evaluarse tanto a nivel de curs= o como de programa. En esta perspectiva, los resultados de los cursos contr= ibuyen al logro de los resultados del programa y su integraci=C3=B3n se rea= liza mediante una actividad clave denominada mapeo curricular. Para apoyar la= implementaci=C3=B3n de OBE y documentar eficazmente las actividades de eva= luaci=C3=B3n educativa, se desarrollaron diversas herramientas computarizad= as (Namoun & Alshanqiti, 2021).La reproducibilidad del modelo se asegura a= trav=C3=A9s de protocolos metodol=C3=B3gicos, documentaci=C3=B3n adecuada = y pruebas en distintos subconjuntos de datos y periodos temporales.<= /p>En este conte= xto, la toma de decisiones basada en informaci=C3=B3n visual analizada medi= ante Dashboard constituye un soporte estrat=C3=A9gico para la gesti=C3=B3n = acad=C3=A9mica institucional. Al integrar alertas, tendencias y comparativo= s por carrera, periodo y cohorte, se facilita la implementaci=C3=B3n de acc= iones oportunas como tutor=C3=ADas focalizadas, ajustes en la carga acad=C3= =A9mica y optimizaci=C3=B3n de tr=C3=A1mites administrativos. De esta maner= a, la instituci=C3=B3n puede asignar recursos de forma eficiente, monitorea= r el impacto de las intervenciones y retroalimentar sus pol=C3=ADticas acad= =C3=A9micas con el objetivo de mejorar los indicadores de titulaci=C3=B3n y= retenci=C3=B3n estudiantil.Respecto a esto el autor menciona que el <spa= n style=3D"text-decoration:underline">Sistema de Detecci=C3=B3n Temprana (E= DS) desarrollado en este trabajo emplea diversas t=C3=A9cnicas= de aprendizaje autom=C3=A1tico, incluyendo redes neuronales, =C3=A1rboles = de decisi=C3=B3n y los algoritmos, para identificar caracter=C3=ADsticas qu= e distinguen a los estudiantes en riesgo de abandonar. La combinaci=C3=B3n = de estos m=C3=A9todos permite mejorar significativamente la precisi=C3=B3n = de las predicciones y adaptarse a diferentes contextos universitarios (Bere= ns et al., 2018).En cuanto a la tasa de titulaci=C3=B3n y retenci=C3=B3n estudia= ntil en las <a id=3D"_Hlk221653372">Instituciones de Educaci=C3=B3n Superior</a> (IES) (Mendoza et al., 2024), rep= resenta uno de los indicadores sustanciales que miden los procesos de acred= itaci=C3=B3n de las instituciones, aunque actualmente ellas cuentan con su = propio sistema de gesti=C3=B3n acad=C3=A9mico carecen com=C3=BAnmente de si= stemas predictivos de informaci=C3=B3n que son ideales para complementar y = mejorar la tasa de =C3=A9xito en la titulaci=C3=B3n de grado. <p= class=3D"BodyText" style=3D"text-align:justify; line-height:115%">Fi= nalmente comprender c=C3=B3mo los estudiantes perciben las pol=C3=ADticas i= nstitucionales y los apoyos disponibles a lo largo de su trayectoria acad= =C3=A9mica es un aspecto ampliamente reconocido por la literatura contempor= =C3=A1nea en educaci=C3=B3n. Este tipo de percepciones influye de manera si= gnificativa en la equidad del aprendizaje, la permanencia estudiantil y el = bienestar en los entornos formativos. Adem=C3=A1s, atender las particularid= ades de cada recorrido acad=C3=A9mico favorece entornos m=C3=A1s inclusivos= y fortalece la gesti=C3=B3n del conocimiento. <s= pan>2. Metodolog=C3= =ADa<div style=3D"clear:both"> &= #xa0; Nombre </div></div> <div class=3D"Section_4">La presente investigaci=C3=B3n se ajust=C3=B3 con un enfoque mixt= o, combinando m=C3=A9todos cuantitativos y cualitativos para abordar de man= era integral el problema estudiado. Desde la perspectiva cuantitativa, se r= ealiz=C3=B3 la recolecci=C3=B3n y an=C3=A1lisis de datos num=C3=A9ricos y e= structurados provenientes de expedientes estudiantiles, con el prop=C3=B3si= to de identificar patrones, probar relaciones y cuantificar la capacidad pr= edictiva de los modelos aplicados. En el componente cualitativo, se explora= ron datos de otro programa similar y de otras carreras sobre el estado del = proceso de titulaci=C3=B3n. El estudio fue de tipo descriptivo-explicativo y de car=C3=A1cter anal=C3= =ADtico, al buscar no solo caracterizar la poblaci=C3=B3n, las variables ac= ad=C3=A9micas y administrativas relevantes, sino tambi=C3=A9n explicar su r= elaci=C3=B3n con el =C3=A9xito en la titulaci=C3=B3n y desarrollar el an=C3= =A1lisis adecuado que permitan detectar tempranamente el riesgo acad=C3=A9m= ico. El dise=C3=B1o de investigaci=C3=B3n fue no experimental y retrospecti= vo, pues no se manipularon variables, sino que se analizaron datos hist=C3= =B3ricos de estudiantes egresados. La poblaci=C3=B3n estuvo constituida por los expedientes de estudia= ntes de grado del periodo octubre 2024 =E2=80=93 abril 2025. Se incluyeron = los casos con expedientes completos y registros acad=C3=A9micos actualizado= s, y se excluyeron aquellos con informaci=C3=B3n incompleta o en estado de = abandono. Como t=C3=A9cnicas de recolecci=C3=B3n de datos se utilizaron la = revisi=C3=B3n documental y la extracci=C3=B3n de informaci=C3=B3n instituci= onal bajo estrictos criterios de anonimizaci=C3=B3n y confidencialidad corr= espondiente y el cumplimiento de las normas =C3=A9ticas para el tratamiento= de datos personales y acad=C3=A9micos de los estudiantes.</div>= <div class=3D"Sect= ion_5"><div style=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJRgA= BAQEAYABgAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFR= ERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eH= h4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhEBAxEB/8QA= HQABAQACAwEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBAkJBgcAAAABAgMEBRE= GByESFzETQVFVkQgUFSJUVmFxkpTR0jeBobIWIzI2UnR1scEkMzRCYnKCs+EYQ0RGZKIlJzU4ZZ= Pi/8QAGwEBAQADAQEBAAAAAAAAAAAAAAECAwQFBgf/xAA0EQEAAQQBBAEDAAcIAwAAAAAAAQIDB= 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/nNUPUXEOKNFYCYIgmBAIjZQg6CKEsY8hsFv64yX9BqGREgTBsYbfFqeERkoQBEEQBEEQBEEQBE= EQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEEQBEFwzPJ/mFD7K= UN7Uq0AAAAASUVORK5CYII=3D" width=3D"322" height=3D"79" alt=3D"" style=3D"ma= rgin-top:-17.45pt; margin-left:239.7pt; position:absolute" /> </sp= an></div>Para el an=C3=A1lisis de = datos de los dataset escogidos se llev=C3=B3 en tres fases:a) &= #xa0; Extracci=C3=B3n, limpieza y anonimizaci=C3=B3n de datos sensibles de estu= diantes, adem=C3=A1s de la eliminaci=C3=B3n de campos innecesarios para el = estudio.b)</s= pan> Validaci=C3=B3n y declaraci=C3=B3n de variables para identif= icar los niveles de riesgos estudiantiles.c) &#= xa0; Integraci=C3=B3n = de todos los dataset a un solo conjunto de datos para utilizarlo en la herr= amienta interactiva que generar=C3=A1 el dashboard que servir=C3=A1 para pa= ra visualizar la informaci=C3=B3n.d) <span= style=3D"font-family:'Times New Roman'">Creaci=C3=B3n de modelo de machine= learning basado en regresi=C3=B3n log=C3=ADstica en Python <span= style=3D"font-family:'Times New Roman'">e) &= #xa0; Comparaci=C3=B3n de valores obtenidos en el dashboard con el modelo de ma= chine learning para sustento matem=C3=A1tico de resultados. </spa= n><div style=3D"clear:both">&= #xa0; = 0; Nombre <spa= n> </div></div> <div class=3D"Section_6">Tabla 1</p= >Descripci=C3=B3n de los campos del dataset= <table style=3D"width:100%; margin-bottom:0pt; padding:0pt; border-collapse= :collapse"><tr style=3D"height:15pt"><td style=3D"width:25.4%; border-top:1= pt solid #7e7e7e; border-bottom:1pt solid #7e7e7e; padding:0pt 5.4pt; verti= cal-align:top">Variable</td= ><td style=3D"width:38.38%; border-top:1pt solid #7e7e7e; border-bottom:1pt= solid #7e7e7e; padding:0pt 5.4pt; vertical-align:top">Descripci=C3=B3n</td><td style=3D"width:36.22%;= border-top:1pt solid #7e7e7e; border-bottom:1pt solid #7e7e7e; padding:0pt= 5.4pt; vertical-align:top">Estado</s= pan></td></tr><tr style=3D"height:55.5pt"><td style=3D"width:25.4%; bor= der-top:1pt solid #7e7e7e; padding:0pt 5.4pt; vertical-align:top">Estado de la titulaci=C3=B3= n</td><td style=3D"width:38.38%; border-top:1pt solid #7e7e7e; p= adding:0pt 5.4pt; vertical-align:top">Esta es la variable principal, indi= ca el estado final del proceso de titulaci=C3=B3n del estudiante.</p= ></td><td style=3D"width:36.22%; border-top:1pt solid #7e7e7e; padding:0pt = 5.4pt; vertical-align:top">Sustentado, no ingresa a titulaci=C3=B3n pendiente de sustentaci=C3= =B3n</td></tr><tr style=3D"height:83.25pt"><td style=3D"width:25= .4%; padding:0pt 5.4pt; vertical-align:top">Estado de Pago</td><td style=3D"width:38.38%; padding:0pt = 5.4pt; vertical-align:top">Variable administrativa que describe la situaci=C3=B3n financiera= del estudiante en relaci=C3=B3n con sus obligaciones con la instituci=C3= =B3n.</td><td style=3D"width:36.22%; padding:0pt 5.4pt; vertical= -align:top">Al d=C3=ADa y pendiente de = cuotas</td></tr><tr style=3D"height:48pt"><td style=3D"width:25.= 4%; padding:0pt 5.4pt; vertical-align:top">Total del monto adeudado</td><td styl= e=3D"width:38.38%; padding:0pt 5.4pt; vertical-align:top">Variable num= =C3=A9rica que representa el total de dinero adeudado por el estudiante.</s= pan></td><td style=3D"width:36.22%; padding:0pt 5.4pt; vertical-align:t= op">Se elimina el campo al ser irreleva= nte para el an=C3=A1lisis. </td></tr><tr style=3D"height:62.25pt= "><td style=3D"width:25.4%; padding:0pt 5.4pt; vertical-align:top">Mater= ias vistas</td><td style=3D"width:38.38%; padding:0pt 5.4pt; ver= tical-align:top">Cantidad de asignaturas que el estudiante curso en el per= iodo acad=C3=A9mico.</td><td style=3D"width:36.22%; padding:0pt = 5.4pt; vertical-align:top">Valores entre 1 y 21 depende del programa oscila entre 18 a 21 m=C3= =A1ximo.</td></tr><tr style=3D"height:48pt"><td style=3D"width:2= 5.4%; padding:0pt 5.4pt; vertical-align:top">Materias aprobadas</td><td style=3D"= width:38.38%; padding:0pt 5.4pt; vertical-align:top">N=C3=BAmero de asignaturas que el estudiante aprob=C3=B3.</= span></td><td style=3D"width:36.22%; padding:0pt 5.4pt; vertical-align:= top"><span= style=3D"font-family:'Times New Roman'">Valores entre 1 y 21 depende del p= rograma.</td></tr><tr style=3D"height:48pt"><td style=3D"width:2= 5.4%; border-bottom:1pt solid #7e7e7e; padding:0pt 5.4pt; vertical-align:to= p">Materias reproba= das</td><td style=3D"width:38.38%; border-bottom:1pt solid #7e7e= 7e; padding:0pt 5.4pt; vertical-align:top">N=C3=BAmero de asignaturas que el estudiante reprob= =C3=B3.</td><td style=3D"width:36.22%; border-bottom:1pt solid #= 7e7e7e; padding:0pt 5.4pt; vertical-align:top">Valores entre 1 y 21 los fa= ctores por financieros, o descuido de estudios.</td></tr></table= > Nota: Dataset extra=C3=ADdo del repositorio acad=C3=A9mico = de la Instrucci=C3=B3n de Educaci=C3=B3n Superior IES. </div><= br style=3D"clear:both; mso-break-type:section-break" /><div class=3D"Secti= 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el an=C3=A1li= sis de los datos para esta investigaci=C3=B3n. Durante el proceso de depura= ci=C3=B3n, se determin=C3=B3 que la variable Total del monto adeudado no er= a relevante para los objetivos del estudio, debido a que se centr=C3=B3 en = la informaci=C3=B3n relacionada con las materias cursadas, aprobadas y repr= obadas y estado de pago de los estudiantes.Para definir las varia= bles asociadas al riesgo de titulaci=C3=B3n, en este estudio se establecier= on niveles espec=C3=ADficos que permiten categorizar dicho riesgo en alto, = medio y bajo. Estos umbrales fueron definidos cuidadosamente por los autore= s a partir del an=C3=A1lisis de los factores acad=C3=A9micos y administrati= vos relevantes, tal como se detalla en la Tabla 2. Esta clasificaci=C3=B3n facilita una eva= luaci=C3=B3n m=C3=A1s precisa y contextualizada del riesgo para la toma de = decisiones mediante paneles interactivos.<div style=3D"clear:bot= h"><span= style=3D"letter-spacing:3pt; color:#ffffff"> = <= span style=3D"letter-spacing:3pt; color:#ffffff">Nombre = = 0; </div></= div> <div class=3D"= Section_8">Tabla 2</s= pan><s= pan style=3D"font-family:'Times New Roman'; font-style:italic">Declaraci=C3= =B3n de las variables de riesgos<table style=3D"width:100%; marg= in-bottom:0pt; padding:0pt; border-collapse:collapse"><tr><td style=3D"widt= h:17.8%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #00000= 0; padding:0pt 5.4pt; vertical-align:top">Nivel del riesgo</td><td style=3D"width:17.72%; border-top:0.= 75pt solid #7f7f7f; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; = vertical-align:top">Estado de finanzas<= /span></td><td style=3D"width:25.92%; border-top:0.75pt solid #7f7f7f; = border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">= Materias aprobadas</td><td s= tyle=3D"width:20.96%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt= solid #000000; padding:0pt 5.4pt; vertical-align:top">Materias reprobadas</td><td style=3D"width:17.6%= ; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #000000; padd= ing:0pt 5.4pt; vertical-align:top">Requ= isitos adicionales</td></tr><tr><td style=3D"width:17.8%; border= -top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Bajo</td><td style=3D"width:17.72%; = border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Al d=C3=ADa</td><td style=3D"= width:25.92%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-= align:top">Todas las cursadas</p= ></td><td style=3D"width:20.96%; border-top:0.75pt solid #000000; padding:0= pt 5.4pt; vertical-align:top">0 = </td><td style=3D"width:17.6%; border-top:0.75pt solid #000000; padding= :0pt 5.4pt; vertical-align:top">Nivel d= e Idiomas completo.</td></tr><tr><td style=3D"width:17.8%; paddi= ng:0pt 5.4pt; vertical-align:top">Medio= </td><td style=3D"width:17.72%; padding:0pt 5.4pt; vertical-alig= n:top"><sp= an style=3D"font-family:'Times New Roman'">Desde 2 pagos pendientes en adel= ante</td><td style=3D"width:25.92%; padding:0pt 5.4pt; vertical-= align:top">Depende si el factor es soci= oecon=C3=B3mico, bajo rendimiento acad=C3=A9mico.</td><td style= =3D"width:20.96%; padding:0pt 5.4pt; vertical-align:top">Entre 1 a 3 materias </td><td style=3D"width:1= 7.6%; padding:0pt 5.4pt; vertical-align:top">2 nivel de Idiomas completo.</td></tr><tr><td style=3D"wid= th:17.8%; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-a= lign:top">= Alto</td><td style= =3D"width:17.72%; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; ve= rtical-align:top">Desde 3 pagos pendien= te en adelante</td><td style=3D"width:25.92%; border-bottom:0.75= pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Depende si el factor es socioecon=C3=B3mico, bajo rendimi= ento acad=C3=A9mico.</td><td style=3D"width:20.96%; border-botto= m:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">Entre 4 materias en adelante</td><td sty= le=3D"width:17.6%; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; v= ertical-align:top">2 o todos los nivele= s de idiomas incompletos.</td></tr></table></div> <div class=3D"Section_9"><div sty= le=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2w= BDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fE= xciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4e= Hh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhEBAxEB/8QAHQABAQACAwEBAQA= AAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBAkJBgcAAAABAgMEBREGByESFzETQVFVkQ= gUFSJUVmFxkpTR0jeBobIWIzI2UnR1scEkMzRCYnKCs+EYQ0RGZKIlJzU4ZZPi/8QAGwEBAQADA= QEBAAAAAAAAAAAAAAECAwQFBgf/xAA0EQEAAQQBBAEDAAcIAwAAAAAAAQIDBBFSBRIUITETQVEi= 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Estas permitieron automatizar el an=C3=A1lisis de r= iesgo frente a una posible no titulaci=C3=B3n, garantizar coherencia en los= indicadores clave generados en los reportes administrativos y mejorar la t= razabilidad de los estudiantes en riesgo.Adem=C3=A1s, facilitaron la comparaci=C3= =B3n din=C3=A1mica entre periodos, con otras carreras o programas de simila= res caracter=C3=ADsticas, para llegar a una conclusi=C3=B3n sobre la mitiga= ci=C3=B3n o minimizaci=C3=B3n el riesgo fortaleciendo la toma de decisiones= basada en un apoyo visual de inteligencia de negocios y experiencia profes= ional de la direcci=C3=B3n de carreras.Se implement=C3=B3 un modelo de regresi=C3= =B3n log=C3=ADstica para la clasificaci=C3=B3n binaria, utilizando un enfoq= ue supervisado. El conjunto de datos fue dividido en 80% para entrenamiento= y 20% para prueba, garantizando que la partici=C3=B3n se realizara de mane= ra aleatoria para evitar sesgos. Durante la fase de entrenamiento, se ajust= aron los coeficientes del modelo mediante la maximizaci=C3=B3n de la verosi= militud, con el objetivo de estimar la probabilidad de pertenencia a cada c= lase. Posteriormente, el modelo entrenado se evalu=C3=B3 sobre el conjunto = de prueba utilizando m=C3=A9tricas est=C3=A1ndar como precisi=C3=B3n (Accur= acy) y ROC AUC, lo que permiti=C3=B3 medir tanto la exactitud global como l= a capacidad discriminativa del modelo. Este procedimiento asegura una valid= aci=C3=B3n adecuada del rendimiento y la generalizaci=C3=B3n del modelo.</s= pan> 3. &= #xa0; Resultados <div style=3D"clea= r:both">= &= #xa0; </s= pan>Nombre </sp= an>&= #xa0; </div= ></div> <div class= =3D"Section_10"><div style=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4A= AQSkZJRgABAQEAYABgAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETF= hwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4e= Hh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhE= BAxEB/8QAHQABAQACAwEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBAkJBgcAAA= ABAgMEBREGByESFzETQVFVkQgUFSJUVmFxkpTR0jeBobIWIzI2UnR1scEkMzRCYnKCs+EYQ0RGZ= KIlJzU4ZZPi/8QAGwEBAQADAQEBAAAAAAAAAAAAAAECAwQFBgf/xAA0EQEAAQQBBAEDAAcIAwAA= AAAAAQIDBBFSBRIUITETQVEiMjRhcYGRFSU1QqGxwdEz4fD/2gAMAwEAAhEDEQA/AOgSiyj9Lh8= VAAAAgAC/YAFgAFSUWUEkAFgRUQnX3BaKK65imimqqfREPt7zzNv9Ev8A/wCuUmqIn3LKKZn7Pg= 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zPJ/mFD7KUN7Uq0AAAAASUVORK5CYII=3D" width=3D"322" height=3D"79" alt=3D"" st= yle=3D"margin-top:-17.45pt; margin-left:239.7pt; position:absolute" /></spa= n> </span= > &= #xa0;</div>Los resultados d= e los indicadores permiten observar el riesgo basado de los dataset escogid= os de 604 registros estudi= antiles; el programa de estudio evaluado para esta investigaci=C3=B3n se di= vidi=C3=B3 en dos como se muestra en la Tabla 3. La clasi= ficaci=C3=B3n para el an=C3=A1lisis con la herramienta Power Bi visualiza l= os resultados para determinar los valores que arroja los indicadores que se= rvir=C3=A1n para la toma de decisiones adecuadas e implementar medidas ante= la necesidad de identificar que procesos causan los retrasos en la fluidez= al cursar la malla acad=C3=A9mica normalmente.El an=C3=A1lisis de los datos revel= a que los programas de 15 meses de profundizaci=C3=B3n presentan un mayor n= =C3=BAmero de asignaturas que deber cursar los estudiantes en periodos de c= orta duraci=C3=B3n de un a=C3=B1o y una mayor matr=C3=ADcula estudiantil de= bido a la amplitud del campo de estudios que tienen como requisitos para se= r admitidos en el programa causando que los procesos se ralenticen como la = titulaci=C3=B3n increment=C3=A1ndose m=C3=A1s en referente a los programas = de 12 meses de profundizaci=C3=B3n tomado de ejemplo para la comparaci=C3= =B3n as=C3=AD como otros programas de profundizaci=C3=B3n de similares cara= cter=C3=ADsticas de duraci=C3=B3n en la formaci=C3=B3n de otras carreras de= ntro de la IES. Tabla 3Resultados de los indicadores del riesgo<table style=3D"widt= h:100%; margin-right:auto; margin-left:auto; margin-bottom:0pt; padding:0pt= ; border-collapse:collapse"><tr style=3D"height:15pt"><td style=3D"width:42= .24%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f; = padding:0pt 5.4pt; vertical-align:top"> </td><td colspan=3D"2" style=3D"border-t= op:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f; padding:0pt 5.= 4pt; vertical-align:top">12 meses</span= ></td><td style=3D"width:31.1%; border-top:0.75pt solid #7f7f7f; border= -bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">15 meses</td></tr><tr style=3D"hei= ght:15pt"><td style=3D"width:42.24%; border-top:0.75pt solid #7f7f7f; borde= r-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">Factor acad=C3=A9mico</td><td sty= le=3D"width:24.44%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt s= olid #7f7f7f; padding:0pt 5.4pt; vertical-align:top"> 13.47</td><td colspan=3D"2" st= yle=3D"border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f;= padding:0pt 5.4pt; vertical-align:top"> 12.24</td></tr><tr style=3D"height:15pt"><t= d style=3D"width:42.24%; padding:0pt 5.4pt; vertical-align:top">Factor Socioecon=C3=B3mico</td><td styl= e=3D"width:24.44%; padding:0pt 5.4pt; vertical-align:top"> $1207</td><td colspan=3D"= 2" style=3D"padding:0pt 5.4pt; vertical-align:top"> $1511</td></tr><tr style=3D"heig= ht:27.75pt"><td style=3D"width:42.24%; border-top:0.75pt solid #7f7f7f; bor= der-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">Valor de Riesgo </td><td styl= e=3D"width:24.44%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt so= lid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">45.9% </td><td colspan=3D"2" = style=3D"border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #7f7f7= f; padding:0pt 5.4pt; vertical-align:top"><span= style=3D"font-family:'Times New Roman'">41.78% </td></tr><tr st= yle=3D"height:0pt"><td style=3D"width:179.6pt"></td><td style=3D"width:103.= 9pt"></td><td style=3D"width:9.45pt"></td><td style=3D"width:132.25pt"></td= ></tr></table> Junto con la recolecci=C3=B3n de la informaci= =C3=B3n se considera evaluar la situaci=C3=B3n a otras carreras, el tema so= cioecon=C3=B3mico en el programa de 15 meses el riesgo se elev=C3=B3 consid= erablemente emergi=C3=B3 como el primer filtro cr=C3=ADtico, se=C3=B1alando= que la situaci=C3=B3n financiera del estudiante es un predictor temprano c= lave para el =C3=A9xito en la titulaci=C3=B3n en las IES privadas, cabe se= =C3=B1alar que establecen sus procesos internos institucionales que influye= n en el mismo. Un segundo filtro importante es el n=C3=BAmero de materias a= probadas por parte del estudiante el factor acad=C3=A9mico demuestra ser re= levante incluso cuando las condiciones financieras son desfavorables puesto= que implica que un alto rendimiento acad=C3=A9mico puede mitigar el riesgo= de no titularse, incluso si el estudiante arrastra valores financieros, au= nque tambi=C3=A9n se considera un riesgo de titulaci=C3=B3n sobre aquellos = que no logran puntajes requeridos por falta de desempe=C3=B1o en las asigna= turas de la malla como se observa en la Tabla 4. Tabla 4An=C3=A1lisis comparativo de los indicadores del riesgo mediano = a alto riesgo<table style=3D"width:100%; margin-right:auto; marg= in-left:auto; margin-bottom:0pt; padding:0pt; border-collapse:collapse"><tr= style=3D"height:15pt"><td style=3D"width:42.24%; border-top:0.75pt solid #= 7f7f7f; border-bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-ali= gn:top"><s= pan style=3D"font-family:'Times New Roman'; font-weight:bold"> = </td><td colspan=3D"2" style=3D"border-top:0.75pt solid #7f7f7f; border= -bottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">12 meses</td><td style=3D"width:31= .1%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #000000; p= adding:0pt 5.4pt; vertical-align:top">1= 5 meses</td></tr><tr style=3D"height:15pt"><td style=3D"width:42= .24%; border-top:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:to= p">Factor acad=C3=A9mico</td= ><td style=3D"width:24.44%; border-top:0.75pt solid #000000; padding:0pt 5.= 4pt; vertical-align:top"> 17.62</td><td colspan=3D"2" style=3D"border-top:0.75pt sol= id #000000; padding:0pt 5.4pt; vertical-align:top">14.79 </td></tr><tr style=3D"heigh= t:15pt"><td style=3D"width:42.24%; padding:0pt 5.4pt; vertical-align:top"><= p style=3D"margin-bottom:0pt; line-height:115%; font-size:10pt">Factor Socioecon=C3=B3mico</t= d><td style=3D"width:24.44%; padding:0pt 5.4pt; vertical-align:top">1660 </td><td col= span=3D"2" style=3D"padding:0pt 5.4pt; vertical-align:top"> 1830</td></tr><tr style= =3D"height:27.75pt"><td style=3D"width:42.24%; border-bottom:0.75pt solid #= 000000; padding:0pt 5.4pt; vertical-align:top">Valor de Riesgo </td><td style=3D"width:24.44%; border-b= ottom:0.75pt solid #000000; padding:0pt 5.4pt; vertical-align:top">63.1% </td><td = colspan=3D"2" style=3D"border-bottom:0.75pt solid #000000; padding:0pt 5.4p= t; vertical-align:top">50.6% </td></tr><tr style=3D"height:0pt"><= td style=3D"width:179.6pt"></td><td style=3D"width:103.9pt"></td><td style= =3D"width:9.45pt"></td><td style=3D"width:132.25pt"></td></tr></table> Con el fin de validar los resultados obtenidos mediante el Dashboa= rd en Power BI, se implement=C3=B3 un modelo de regresi=C3=B3n log=C3=ADsti= ca como herramienta de contraste. Este modelo permiti=C3=B3 evaluar de form= a cuantitativa la capacidad predictiva del sistema desarrollado, empleando = las mismas variables acad=C3=A9micas y socioecon=C3=B3micas del dataset ins= titucional. Las m=C3=A9tricas de desempe=C3=B1o obtenidas confirman la soli= dez del enfoque metodol=C3=B3gico adoptado, como se muestra en la <s= pan style=3D"font-family:'Times New Roman'; font-weight:bold">Tabla 5.</spa= n><spa= n style=3D"font-family:'Times New Roman'; font-weight:bold">Tabla 5<= /p>An=C3=A1lisis com= parativo de modelos <table style=3D"width:100%; margin-right:aut= o; margin-left:auto; margin-bottom:0pt; padding:0pt; border-collapse:collap= se"><tr style=3D"height:15pt"><td style=3D"width:40.44%; border-top:0.75pt = solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; verti= cal-align:top">M=C3= =A9trica</td><td style=3D"width:29.78%; border-top:0.75pt solid = #7f7f7f; border-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-al= ign:top"><= span style=3D"font-family:'Times New Roman'">Valor</td><td style= =3D"width:29.78%; border-top:0.75pt solid #7f7f7f; border-bottom:0.75pt sol= id #7f7f7f; padding:0pt 5.4pt; vertical-align:top">Descripci=C3=B3n</td></tr><tr style=3D"height:15pt">= <td style=3D"width:40.44%; border-top:0.75pt solid #7f7f7f; border-bottom:0= .75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">Accuracy</td><td style=3D"width:29.78%; bor= der-top:0.75pt solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f; padding:0= pt 5.4pt; vertical-align:top">0.967</td><td style=3D"width:29.78%; border-top:0.75pt = solid #7f7f7f; border-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; verti= cal-align:top">Indica el porcentaje de = predicciones correctas sobre el total de casos.</td></tr><tr sty= le=3D"height:15pt"><td style=3D"width:40.44%; border-bottom:0.75pt solid #7= f7f7f; padding:0pt 5.4pt; vertical-align:top">ROC AUC</td><td style=3D"width:29.78%; border-bottom:0.75= pt solid #7f7f7f; padding:0pt 5.4pt; vertical-align:top">0.954</td><td style=3D"width= :29.78%; border-bottom:0.75pt solid #7f7f7f; padding:0pt 5.4pt; vertical-al= ign:top"><= span style=3D"font-family:'Times New Roman'">Mide la capacidad del modelo p= ara distinguir entre clases</td></tr></table> <a id=3D"_Hlk221654946">Para complementar la evaluaci=C3=B3n del modelo, se an= aliz=C3=B3 la distribuci=C3=B3n de las predicciones a trav=C3=A9s de una ma= triz de confusi=C3=B3n, la cual permite identificar con precisi=C3=B3n los = aciertos y errores de clasificaci=C3=B3n en cada categor=C3=ADa de riesgo. = Este an=C3=A1lisis detallado evidencia que el modelo logra una alta tasa de= verdaderos positivos y verdaderos negativos, con m=C3=ADnimos errores de c= lasificaci=C3=B3n entre las clases, lo que ratifica su idoneidad para su im= plementaci=C3=B3n en entornos institucionales, como se muestra en la </span= >Figura 1</= span></a>.<= /span> Figura 1Matriz de Confusi=C3=B3n </p= ><img src=3D"data:image/pn= g;base64,iVBORw0KGgoAAAANSUhEUgAAATwAAAEdCAYAAACR9duVAAAABHNCSVQICAgIfAhkiA= AAAAlwSFlzAAAOxAAADsQBlSsOGwAAIABJREFUeJzt3XeclOW9///Xdd/TtrCNjqvUBEKJwMkig= 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pocos e= rrores en las clases. <div style=3D"clear:both"> = ; Nombre <s= pan style=3D"width:27.7pt; letter-spacing:3pt; display:inline-block"> = <= span style=3D"width:32.69pt; letter-spacing:3pt; display:inline-block"> = 0; </div></div> <div class=3D"Section_11"= >En la herramienta = seleccionada, Power BI, se construy=C3=B3 un dashboard orientado a la visua= lizaci=C3=B3n integral de los resultados del an=C3=A1lisis. Para su dise=C3= =B1o se seleccionaron elementos gr=C3=A1ficos adecuados, tales como gr=C3= =A1ficos de barras, tarjetas de indicadores y tablas din=C3=A1micas, priori= zando aquellos que facilitan la comparaci=C3=B3n de variables y la identifi= caci=C3=B3n r=C3=A1pida de valores cr=C3=ADticos. Al igual que, se incorpor= aron segmentadores para filtrar la informaci=C3=B3n por per=C3=ADodo acad= =C3=A9mico, carrera, paralelo y nivel de riesgo, permitiendo un an=C3=A1lis= is din=C3=A1mico y adaptado a las necesidades como se muestra en la = Figura 2.</= span></div> <di= v class=3D"Section_12">Figura 2Presentaci=C3=B3n final del Dashboard </spa= n></div> <div c= lass=3D"Section_13"><div style=3D"clear:both"><img src=3D"data:im= age/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwc= HBw8LCwkMEQ8SEhEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw= 4ICA4eFBEUHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eH= h7/wAARCARfAzMDASIAAhEBAxEB/8QAHQABAQACAwEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQ= AQABAwIDAwUMBAkJBgcAAAABAgMEBREGByESFzETQVFVkQgUFSJUVmFxkpTR0jeBobIWIzI2UnR= 1scEkMzRCYnKCs+EYQ0RGZKIlJzU4ZZPi/8QAGwEBAQADAQEBAAAAAAAAAAAAAAECAwQFBgf/xA= A0EQEAAQQBBAEDAAcIAwAAAAAAAQIDBBFSBRIUITETQVEiMjRhcYGRFSU1QqGxwdEz4fD/2gAMA= 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El panel muestra el porcentaje de= riesgo acad=C3=A9mico obtenido mediante la f=C3=B3rmula definida para el e= studio, as=C3=AD como los valores asociados a cada variable.El= valor de riesgo se obtiene al multiplicar dos veces el valor de materias r= eprobadas por una unidad mayor a la cantidad de cuotas pendientes para evit= ar que un estudiante que no tenga materias reprobadas tenga un valor nulo d= e riesgo y tiene un rango de 0 a 672 puntos los cuales luego ser=C3=A1n nor= malizados para obtener el porcentaje de riesgo que va en una escala de 0 a = 1.Esta configuraci=C3=B3n fue seleccionada con el objetivo de = proporcionar a interesados acad=C3=A9micos una herramienta intuitiva y func= ional que permita identificar oportunamente a los estudiantes con mayor vul= nerabilidad, facilitando la toma de decisiones informada y la planificaci= =C3=B3n de intervenciones preventivas.El estudio demostr=C3=B3 que el peso de las materias reprobadas = es aproximadamente doble el impacto al factor socioecon=C3=B3mico que manti= ene el estudiante, para un correcto an=C3=A1lisis en la herramienta se suma= una unidad para evitar que la multiplicaci=C3=B3n no devuelva valores nulo= s (cero) justificando por no tener valores pendientes, permite representar = correctamente los casos en los que el estudiante no tiene valores socioecon= =C3=B3micos cr=C3=ADticos, pero s=C3=AD registra materias reprobadas, sin e= mbargo el nivel del riesgo se alto cae en la incidencia lo cual favorece un= a toma de decisiones m=C3=A1s precisa por parte de los directivos acad=C3= =A9micos.Se realizo la ejec= uci=C3=B3n con el modelo Machine Learning para diferenciar cercan=C3=ADa de valores y si estos se ap= egan a la realidad del resultado obtenido como la regresi=C3=B3n log=C3=ADs= tica junto con Python para las comparativas num=C3=A9ricas del dataset obte= nido de Power Bi a manera verificar la precisi=C3=B3n de la probabilidad de= l riesgo de no titularse que muestra el Dashboard finalmente sea confiable.= Como se evidencia en la Figura 1.Los valores promedi= o del porcentaje de riesgo para el grupo alto para el modelo de regresi=C3= =B3n log=C3=ADstica y el campo calculado en Power BI es de 62.43% y 60.61% = respectivamente es decir una diferencia porcentual a 2 puntos, para el grup= o medio con 18.56% 19.05% y grupo bajo con 0.92% y 0.87% es menor a un punt= o porcentual.4.<s= pan style=3D"width:9pt; font:7pt 'Times New Roman'; display:inline-block">&= #xa0; Discusi=C3=B3n<div styl= e=3D"clear:both"> = 0; &= #xa0; Nombre</span= >&= #xa0; = </div></div> <d= iv class=3D"Section_15"><div style=3D"clear:both"><img src=3D"data:image/jpeg;base6= 4,/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SE= hEPERETFhwXExQaFRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4e= Hh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzM= DASIAAhEBAxEB/8QAHQABAQACAwEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBA= 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La titulaci=C3=B3n representa una etapa cr=C3=ADtica; sin e= mbargo, el proceso suele verse obstaculizado por la carencia de herramienta= s predictivas que permitan anticipar el =C3=A9xito del estudiante (Rabelo &= amp; Z=C3=A1rate, 2025). Esta deficiencia deriva en la identificaci=C3=B3n = tard=C3=ADa de requisitos pendientes, problema que se acent=C3=BAa por la g= esti=C3=B3n manual y los tiempos de revisi=C3=B3n prolongados. Asimismo, la= gesti=C3=B3n ineficiente de los recursos genera cuellos de botella y una a= dministraci=C3=B3n reactiva que interrumpe la progresi=C3=B3n fluida del eg= resado (Mac=C3=ADas, 2025).Ante la falta de an=C3=A1lisis de datos, el uso de herra= mientas anal=C3=ADticas se convierte en un aliado estrat=C3=A9gico (Ramaswa= mi et al., 2022). Estas permiten asociar los niveles de riesgo de los estud= iantes con los inconvenientes previos al ingreso a la unidad de titulaci=C3= =B3n, mitigando los desaf=C3=ADos que enfrentan las instituciones (Quiguana= s & Arias, 2024).El incremento en la matr=C3=ADcula y la diversificaci=C3=B3n d= e programas ponen a prueba los est=C3=A1ndares de los procesos actuales (Ma= c=C3=ADas et al., 2025). El principal conflicto surge al finalizar la malla= curricular: el incumplimiento de requisitos obliga al estudiante a una reg= ularizaci=C3=B3n forzada o, en casos m=C3=A1s cr=C3=ADticos, ocasiona la de= serci=C3=B3n estudiantil al no contar con acompa=C3=B1amiento sobre el proc= eso (Morales et al., 2025).Los resultados del modelo de regresi=C3=B3n log=C3=ADsti= ca demuestran un desempe=C3=B1o consistente con los reportados en el dashbo= ard, lo cual valida el enfoque metodol=C3=B3gico (Berens et al., 2018). La = equivalencia entre las m=C3=A9tricas alcanzadas y los indicadores visuales = confirma que el proceso de entrenamiento es reproducible (Namoun & Alsh= anqiti, 2021). Esta convergencia entre el an=C3=A1lisis estad=C3=ADstico y = la representaci=C3=B3n gr=C3=A1fica aporta robustez a los hallazgos y confi= rma la estabilidad del modelo como herramienta de toma de decisiones.</span= >Las m=C3=A9tri= cas obtenidas fueron consistentes con los indicadores visualizados en la he= rramienta, lo que evidencia que el proceso de entrenamiento y evaluaci=C3= =B3n fue reproducible y confiable (Jimbo-Santana et al., 2023). Esta cohere= ncia sugiere la ausencia de discrepancias significativas entre el an=C3=A1l= isis estad=C3=ADstico y la representaci=C3=B3n gr=C3=A1fica; por lo tanto, = se aporta robustez a la interpretaci=C3=B3n de los hallazgos y se confirma = la estabilidad del modelo frente a distintos m=C3=A9todos de presentaci=C3= =B3n de resultados (Ngulube & Ncube, 2025).= 5.</spa= n> ConclusionesEl presente estudio examin= =C3=B3 la efectividad de los procesos de titulaci=C3=B3n, demostrando que l= a optimizaci=C3=B3n y prevenci=C3=B3n de nudos cr=C3=ADticos dependen direc= tamente de un seguimiento acad=C3=A9mico riguroso. En particular, se determ= in=C3=B3 que los programas de corta duraci=C3=B3n requieren un monitoreo pe= rmanente para asegurar su eficiencia operativa.A trav=C3=A9s de la aplicaci=C3=B3n del algoritmo de ri= esgo, se logr=C3=B3 segmentar a la poblaci=C3=B3n estudiantil en niveles ba= jo, medio y alto. Esta categorizaci=C3=B3n facilita que el departamento de = titulaci=C3=B3n ejecute acciones prioritarias y personalizadas. Asimismo, l= a integraci=C3=B3n de herramientas de visualizaci=C3=B3n interactiva, como = Power BI, transform=C3=B3 datos complejos en informaci=C3=B3n estrat=C3=A9g= ica, permitiendo a los directivos una toma de decisiones basada en evidenci= a.En definitiva, es esencia= l que las instituciones superiores adopten la ciencia de datos y la anal=C3= =ADtica educativa como ejes transversales para fortalecer sus procesos admi= nistrativos y asegurar est=C3=A1ndares de calidad internacional.= 5.1. Recomendaciones para ser consideradas</s= pan><= span style=3D"font-family:'Times New Roman'">Para garantizar la efectividad= del dashboard desarrollado, se proponen las siguientes medidas de interven= ci=C3=B3n:<ul style=3D"margin:0pt; padding-left:0pt"><li class= =3D"ListParagraph" style=3D"margin-left:17.63pt; margin-bottom:0pt; text-al= ign:justify; line-height:115%; padding-left:3.67pt; font-family:serif; font= -size:12pt">Riesgo Bajo: delegar = el monitoreo a tutores personales mediante revisiones mensuales. El objetiv= o es actuar de forma preventiva para evitar el escalamiento hacia niveles d= e riesgo superiores.</li><li class=3D"ListParagraph" style=3D"margin= -left:17.63pt; margin-bottom:0pt; text-align:justify; line-height:115%; pad= ding-left:3.67pt; font-family:serif; font-size:12pt">Riesgo Medio: implementar tutor=C3=ADas acad=C3=A9mica= s focalizadas en casos de bajo rendimiento. Si el origen es financiero o pe= rsonal, se debe coordinar el acompa=C3=B1amiento con Bienestar Estudiantil = y las direcciones acad=C3=A9micas con una frecuencia quincenal.</li>= <li class=3D"ListParagraph" style=3D"margin-left:17.63pt; text-align:justif= y; line-height:115%; padding-left:3.67pt; font-family:serif; font-size:12pt= ">Riesgo Al= to: ejecutar intervenc= iones inmediatas con el apoyo del Vicerrectorado Acad=C3=A9mico y las facul= tades. Se recomienda un seguimiento semanal mediante un plan personalizado = de casos especiales, el cual debe activarse al menos tres meses antes de fi= nalizar el ciclo de formaci=C3=B3n acad=C3=A9mica.</li></ul> <sp= an style=3D"font-weight:bold; color:#767171">6. &= #xa0; Conflicto de interesesLos autores declaran que no existe conflicto de int= ereses en relaci=C3=B3n con el art=C3=ADculo presentado.7. = 0; Declaraci=C3=B3n de contribuci=C3=B3n de los autores</s= pan><= span style=3D"font-family:'Times New Roman'">Todos autores contribuyeron si= gnificativamente en la elaboraci=C3=B3n del art=C3=ADculo.8. = 0; Costos de financiamiento La presente investigaci=C3=B3n fue financiada = en su totalidad con fondos propios de los autores.<div style=3D"= clear:both"> &#x= a0; = Nombre<spa= n style=3D"width:141.26pt; letter-spacing:3pt; display:inline-block"> = <= /div></div> <div cl= ass=3D"Section_16"> <= span style=3D"font-weight:bold; color:#767171">9. = 0; Referencias Bibliogr=C3=A1ficas<span= style=3D"font-family:'Times New Roman'"> </div= > <div class=3D"Sec= tion_17"><div style=3D"clear:both"><img src=3D"data:image/jpeg;base64,/9j/4AAQSkZJR= gABAQEAYABgAAD/2wBDAAUDBAQEAwUEBAQFBQUGBwwIBwcHBw8LCwkMEQ8SEhEPERETFhwXExQa= FRERGCEYGh0dHx8fExciJCIeJBweHx7/2wBDAQUFBQcGBw4ICA4eFBEUHh4eHh4eHh4eHh4eHh4= eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh4eHh7/wAARCARfAzMDASIAAhEBAxEB/8= QAHQABAQACAwEBAQAAAAAAAAAAAAECCAUGBwMECf/EAFQQAQABAwIDAwUMBAkJBgcAAAABAgMEB= REGByESFzETQVFVkQgUFSJUVmFxkpTR0jeBobIWIzI2UnR1scEkMzRCYnKCs+EYQ0RGZKIlJzU4= ZZPi/8QAGwEBAQADAQEBAAAAAAAAAAAAAAECAwQFBgf/xAA0EQEAAQQBBAEDAAcIAwAAAAAAAQI= DBBFSBRIUITETQVEiMjRhcYGRFSU1QqGxwdEz4fD/2gAMAwEAAhEDEQA/AOgSiyj9Lh8VAAAAgA= C/YAFgAFSUWUEkAFgRUQnX3BaKK65imimqqfREPt7zzNv9Ev8A/wCuUmqIn3LKKZn7PgPpdsXrM= RVes3bcT03qoml89pWKok1P9BJVJVigAEosoACRv5xY+N/hUlUkVAAAASUWUUAECUWUIAAkEnwV= J8AQBQARUk+slaaZqqimPGVhJn7vtiWvKV7zHxYfvYWqIt0RTH62aw47le5AFYgCSqSiyiAAASE= 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style=3D"font-family:'Times New Roman'; color:#ffffff"> <p= class=3D"Header"> </= span></div>Ac= osta Guanoquiza , C. O., Mej=C3=ADa Alban, G. M., Ram=C3=ADrez Guti=C3=A9rr= ez, C. V., & Reigosa Lara, A. (2024). Herramientas digitales para forta= lecer la metodolog=C3=ADa de ense=C3=B1anza de los docentes. Ciencia Digital</= span>, 8(3), 161-178. = <a href=3D"https://doi.org/10.33262/cienciadigital.v8i3.3144" style=3D"text= -decoration:none">https://doi.org/10.33262/cienciadigital.v8i3.3144</a><p= style=3D"margin-left:35.4pt; text-indent:-35.4pt; line-height:115%; font-s= ize:12pt">Berens, J., Schneid= er, K., G=C3=B6rtz, S., Oster, S., & Burghoff, J. (2018). Early detecti= on of students at risk=E2=80=93predicting student dropouts using administra= tive student data and machine learning methods. Journal of Educational Data Mi= ning, 11(3). https://d= oi.org/10.2139/ssrn.3275433Jimbo-Santana, P., Lanzarini, L. C., Jimbo-Santana, M.= , & Morales-Morales, M. (2023). Inteligencia artificial para analizar e= l rendimiento acad=C3=A9mico en instituciones de educaci=C3=B3n superior. 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