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https://doi.org/10.37815/rte.v37nE1.1353

<= span lang=3DES>Artículos originales

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Metodología p= ara la Identificación de Buenas Prácticas Docentes a Partir de Comentarios Estudiantiles Utilizando Análisis de Sentimientos y Modelado Temático

Methodology for Identifying Good Teaching Practices Through Student Comments Using Sentiment Analysis and Structural Top= ic Modeling

 

= Diego Moscoso Lozano1<= span style=3D'letter-spacing:2.0pt'> https://orcid.org= /0009-0008-0073-5687, Jorge Maldonado-Mahauad1 https://orc= id.org/0000-0003-1953-390X

 

 

1Universidad= de Cuenca, Cuenca, Ecuador<= o:p>

diego.moscosol@ucuenca.edu.ec jorge.maldonado@ucuen= ca.edu.ec

Enviado:<= /o:p>

2025/06/28<= /o:p>

Aceptado:

2025/08/21<= /o:p>

Publicado:<= /o:p>

2025/10/15<= /o:p>

 
 
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Esta o= bra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0

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Resumen

El presente estudio propone una metodología empírica para identificar las dimensiones más valoradas de la práctica docente en educación superior, mediante un enfoque mixto que combina análisis cualitativo y cuantitativo. Se analizaron más= de 89000 comentarios estudiantiles recopilados durante las evaluaciones docent= es en la Universidad de Cuenca (periodo 2024– 2025). Utilizando técnicas avanzadas de procesamiento del lenguaje natural, específicamente análisis= de sentimiento y modelado estructural de temas (Structural Topic Modeling,= STM), se procesó un amplio corpus textual. El análisis se centró en los comentarios positivos diri= gidos a docentes destacados seg&u= acute;n una evaluación integral previa, con el objetivo de detectar patrones recurrentes en la percepción estudiantil. Como resultado, se identificaron cuatro dimensiones clave de valoración estudiantil: conocimiento disciplina= r, gestión comunicativa, habilidades socioemocionales y metodología de enseñanza. Estas dimensiones reflejan fortalezas docentes percibidas por los estudiantes y presentan variabilidad significativa según contextos disciplinares y niveles académicos. Por ejemplo, mientras el conocimiento disciplinar es prioritario= en áreas técnicas, las habilidades socioemocionales predominan en ciencias sociales y salud. Estos hallazgos destacan que las valoraciones estudiantil= es constituyen una fuente de información cualitativa esencial, que supera las limitaciones de las métricas numéricas tradicionales al ofrecer

un análisis más profundo y contextualiz= ado. Finalmente, la metodología propuesta aporta a las instituciones de educación superior una herramien= ta práctica para identificar fortalezas específicas del profesor= ado y para diseñar programas formativos personalizados basados en eviden= cia, que facilitan una mejora continua y contextualizada de la calidad educativa= .

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Palabras clave: evaluación docente, análisis de comentarios, modela= do temático estructural, educación superior.

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Abstract

This study proposes an empirical methodology to ident= ify the most valued dimensions of teaching practices in higher education throug= h a mixed-method approach that integrates qualitative and quantitative analyses. Over 89,000 student comments collected during teacher evaluations at the University of Cuenca (2024–2025) were analyzed. Advanced natural language processing techniques, specifically sentiment analysis and Structural Topic Modeling (STM), were used to process an extensive textual corpus. The analysis focused on positive co= mments aimed at teachers recognized through a prior comprehensive evaluation, aimi= ng to uncover recurring patterns in student perceptions. Four key dimensions of student evaluation emerged from= the analysis: disciplinary knowledge, communication management, socio-emotional skills, and teaching methodology. These dimensions reflect perceived teaching strengths and exhibit significant variability across disciplinary contexts and academic levels. For example, disciplinary knowledge is prioritized in technical areas, while socio-emotional skills are more value= d in social sciences and health disciplines. These findings underscore that stud= ent evaluations are an essential source of qualitative information, overcoming limitations of traditional numeric metrics by offering deeper, contextual insights. Ultimately, th= e proposed methodology provides higher education institutions with a practical tool to identify specific faculty strengths and design evidence-based, personalized professional development programs, thus fostering continuous and contextualized improvements in educational quality= .

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Keywords: teaching evaluation, comment analysis, structural topic modeling, higher education.

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Introducción

La evaluación docente constituye un pilar fundamental en la búsqueda de la excelencia académica y la mej= ora continua de la calidad educativa en las instituciones de educación superior. A través de ella, las universidades rinden cuentas sobr= e el desempeño de su personal académico y ademá= s obtienen información cruc= ial para fomentar el desarrollo profesional y la innovación pedagó= ;gica (Spooren et al., 2013). En este sentido, la perspectiva del estudiante se ha consolidado como una fuente invaluable de información para estas evaluaciones, ofreciendo una v= isión única sobre el proceso de enseñanza-apre= ndizaje y el impacto directo del desempeño docente. Así, estudios previos han demostrado cómo las prácticas docentes universitarias impactan directa= mente el interés, el compromiso y el rendimiento académico de los estudiantes (Álvarez-Álvarez y Falcon, 2023).

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Tra= dicionalmente, la evaluación del desempeño docente se ha apoyado en gran medida en métricas cuantitativas,= como promedios de calificación obtenidos de instrumentos de evaluación como los cuestionarios. Si = bien las valoraciones numéricas ofrecen una visión general, suelen resultar insuficientes para capturar la complejidad de las prácticas pedagógicas efectivas en la enseñanza y el aprendizaje. De he= cho, la mayoría de los estudios anteriores en este campo se han centrado en estas valoraciones numéricas, ignorando el valioso contenido cualitativo de= los

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comentarios abiertos de los estudiantes (Sun y Yan, 2023). Las evaluaciones basadas en escalas Likert limitan la capacidad de los estudiantes para expresar libremente sus percepciones= sobre el desempeño docente= (Gencoglu et al., 2023). Gran parte de la información que los estudiantes comparten en sus comentarios se pierde cuando todo se resume en promedios numéricos, lo que impide entender con claridad lo que los estudiantes realmente valoran y consideran como buenas prácticas docentes. Esta limitación muestra la importa= ncia de agregar datos cualitativos para apr= ovechar los comentarios abiertos de los estudiantes en la evaluación para tener una visión más transparente y completa del trabajo que realiza el docente en el aul= a de clase (Hu et al., 2022).=

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En respuesta a esta limitaci&oacut= e;n, el presente estudio propone una metodología empírica que integra enfoques cuantitativos y cualitativos para el análisis de la evaluación docente. Para es= to, se utilizan herramientas de procesamiento= de lenguaje natural (PLN), como el análisis de sentimiento y el modelado temático, aplica= das a un corpus de comentarios cualitativos emitidos por estudiantes. El análisis de sentimiento permite clasificar la polaridad de los comentarios para captar la intensidad emocional (Muh= ammad et al., 2016; Wankmüller y Heumann, 2021) e identificar especí= ;ficamente las valoraciones positivas. Por su parte, el Structural Topic Model (ST= M) se emplea para identificar patrones temáticos latentes en los comentari= os positivos de los estudiantes (Roberts et al., 2014), con la ventaja de poder explorar cómo varía la prevalencia= de estos temas según características contextuales, como la facul= tad y el nivel académico. Si bien = la mayoría de los estudios previos han aplicado técnicas de análisis de sentimiento= o topic modeling de forma aislada, la integración de ambos enfoques en esta investigación representa una contribución significativa al proporcionar<= span style=3D'letter-spacing:-.6pt'> una comprensión más completa de la percepción estudiantil (Hayat et al., 2024).

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Como resultado de aplicar esta metodología, se logró identificar las dimensiones más valoradas de la práctica docente por los estudiantes de la Universid= ad de Cuenca y entender cómo esas valoraciones cambian seg&ua= cute;n la facultad y el nivel académico, a partir del análisis de los comentarios positivos emitid= os hacia docentes con mejor desempeño. Este enfoque busca ofrecer una comprensión más matizada de las expectativas estudiantiles, reconociendo q= ue las percepciones de los estudiantes pueden variar<= span style=3D'letter-spacing:-.15pt'> significativamente según e= l contexto disciplinar (Marsh, 2007) y el momento formativo (Chen y Hoshower, 2003). La metodología propuesta es replicable y puede servir como una herramienta útil para que las universidades diseñen estrategi= as de formación docente más efectivas y ajustadas a su contexto. Poder identificar fortalezas y planificar capacitaciones con base en los comentarios de los estudiantes abre nuevas posibilidades para la mejora continua de la calidad de la docencia.<= /span>

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El presente artículo se estructura de la siguiente manera: en la sección 2 se detalla la metodología empírica, que se compone de 3 etapas: (1) preparación y segmentación de los datos; (2) modelado temático; y (3) análisis de prevalencia temática. La sección 3 presenta los principales resultados y la discusión. Finalmente, la sección= 5 presenta las principales conclusiones del estudio y sus implicaciones para = la evaluación y el desarrollo docente en la educación superior.<= /span>

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Metodología

Esta investigación adopta un enfoque mixto que combina métodos cuant= itativos (segmentación y modelado temático) y cualitativos (interpretación de los comentarios de los estudiantes), dentro de un diseño exploratorio que busca descubrir patrones temáticos en= los comentarios positivos emitidos por estudiantes hacia docentes con desempeño destacado. Para esto, se desarrolló una metodolog&= iacute;a empírica de tres etapas: (1) la preparación y segmentación

de los datos, (2) el modelado temático y (3) el análisis de prevalencia temática e interpretaci&o= acute;n contextual. En la Figura 1 se presenta visualmente este proceso metodológico.

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Figura 1

Etapas de la metodología empírica


 

Etapa 1: Preparación y segmentación de los datos

Recolección de datos

Los datos utilizados para este tra= bajo corresponden al proceso de evaluación docente realizado en las 12 facultades de la Universidad de Cuenca, específicamente los datos del componente de heteroevaluación (evaluación que realizan los estudiantes a sus docentes). El conjunto de datos utilizado pertenece al periodo lectivo septiembre 2024-febrero 2025 y está conformado por 1891 registros de evaluación; cada uno de estos corresponde a una asignatura evaluada por los estudian= tes. Esto implica que un mismo docente puede estar representado en más de un registro en el caso de que imparta distintas asignaturas. Esta decisión metodológica permite considerar el desempeño docente de forma contextualizada por asignatura, ya que un docente puede mostrar fortalezas en determinadas materias o niveles, pero no necesariamen= te en todas.

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Tabla 1

Resumen de las variables utilizadas en el estudio

 

VARIABLE

TIPO DE DATO

DESCRIPCIÓN

= docente_id=

Cualitativa nominal

Identificad= or anónimo único = asignado a cada docente evaluado en una o más

asignaturas.=

= facultad=

Cualitativa nominal

Facultad a = la que pertenece la asignatura evaluada.

carrera

Cualitativa nominal

Carrera en = la que se imparte la asignatura evaluada.

= asignatura=

Cualitativa nominal

Nombre de la materia evaluada por los estudiantes. Un docente puede tener

varias asignaturas.

= nivel=

Cualitativa ordinal

Nivel académico correspondiente al ciclo = en el que se dicta la asignatura, codificado del 1 al 10 (de primer a décimo ciclo).

= numero_evaluadores=

Cuantitativa discreta

Total de estudiantes que completaron la evaluación docente para= un registro

determinado.=

= nota_heteroevaluacion=

Cuantitativa continua

Promedio obtenido por el docente en la evaluación estudiantil = (sobre 40

puntos).=

 

comentarios_destaca= =

 

Cualitativa textual

Texto libre escrito por los estudiantes en una pregunta abierta del instrumento de evaluac= ión. Contiene los comentari= os por asignatura sobre lo que destaca del docente.<= o:p>

 

comentarios_mejorar= =

 

Cualitativa textual

Texto libre escrito por los estudiantes en una pregunta abierta del instrumento de evaluación. Contiene= los comentar= ios por asignatu= ra sobre lo que el docente<= /span> debería mejorar.

 

Dentro de este conjunto de datos, = se recopilaron 89281 comentarios abiertos emitidos por los estudiante= s en respuesta al apartado cualitativo del instrumento= de evaluación aplicado. Estos comentarios conforma= n el corpus sobre el cual se aplicaron las técnicas de análisis de= sentimiento y modelado temático. Para este estudio se consideraron variables de tipo cualitativo y cuantitativo, que se describen en la Tabla 1.

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Todos los datos fueron anonimizados antes del análisis y provienen del proceso regular de evaluación docente de la Universidad de Cuenca. No se utilizó información personal de los docentes ni de los estudiantes, por lo que no fue necesario obtener consentimiento adicional para su uso con fines de este trabajo.

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Preprocesamiento básico del dataset

Se realizó un proceso de li= mpieza y estructuración del dataset con el fin de garantizar la integridad y coherencia de los datos. Se filtraron los registros que no contenían comentarios en = ninguna de las dos categorías cualitativas (“lo que<= span style=3D'letter-spacing:-.35pt'> destaca del docente” y “lo que el docente debería mejorar”), conservando solamente los registros con contenido suficiente para el análisis.

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En cuanto al tratamiento de los comentarios, estos fueron obtenidos originalmente como cadenas concatenadas separadas por el carácter “|”. Cada comentario fue desagregado y etiquetado con los prefijos "Destaca:" o "Debe= mejorar:", según la sección del formulario de donde proviene. Fina= lmente, se integraron en una sola columna ambos tipos de comentarios que se utilizaron para= el siguiente paso del análisis de sentimientos. La incorporación= de estos prefijos responde<= span style=3D'letter-spacing:-.65pt'> a la necesidad de mantener la intención de los comentarios emitidos por los estudiantes, ya que es frecuente que se utilicen términos idénticos= con sentidos opuestos según la sección donde se respondan. Esta decisión se basa en el enfoque de análisis de sentimiento contextual planteado por Muhammad et al. (2016), donde se demuestra que identificar correctamente la polaridad de un texto es más preciso cu= ando se incluye información adicional que ayude a entender el contexto en= el que se escribió.

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Cálculo del indicador de desempeño compuesto

En el contexto de la evaluaci&oacu= te;n docente, suele ser insuficiente considerar solamente las calificaciones numéricas que resultan de las valoraciones estudiantiles para captur= ar el desempeño docente. Estas valoraciones de los estudiantes comprenden dim= ensiones tanto cuantitativas como cualitativas, que deben integrarse para lograr una visión más completa y significativa (Hu et al., 2022). En este sentido, como parte de la metodología, se planteó la construcció= ;n de un índice de desempeño compuesto, que combine la calificación promedio otorgada= por los estudiantes, la polaridad semántica de los comentarios abiertos y el número de evaluadores por asignatura. Este enfoque responde a la necesidad de interpretar la evaluación m&aacut= e;s allá de indicadores unidimensionales, permitiendo capturar aspectos = no visibles en los promedios numéricos, como la percepción emoci= onal del estudiante o la participación en el proceso evaluativo.

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El propósito de este índice es identificar a los docentes que presentan un desempeñ= ;o sobresaliente desde la percepción estudiantil. Esta identificación permite seleccionar un grupo de referencia sobre el cual aplicar el análisis para reconocer= patrones asociados a buenas prácticas docentes que son valoradas por los estudiantes. Este puntaje de desempeño compuesto considera tres variables fundamentales:

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•      =    Nota de heteroevaluación= (Nc): Corresponde a la calificación promedio otorgada por los estudiantes a cada docente en= una asignatura específica. Dado que la nota

original se encuentra en una escala de 0 a 40, se transformó a una escala de 0 a 100

mediante una regla de tres simple.

•      =    Calificación de sentimie= nto (Sc): Se obtuvo mediante un modelo de tipo BERT (RoBERTuito) previamente ajustado (fine-tuned) con un co= rpus de comentarios etiquetados con los prefijos “Destaca:” y “Debe mejorar:”. La elección de este modelo se justifica= por su capacidad para capturar la semántica de las palabras en su contexto, lo que es crucial para el análisis de sentimientos<= span style=3D'letter-spacing:-.3pt'> en textos cortos como los comentarios de estudiantes (Prottasha et al., 2022). El modelo clasifica cada comentario en una de cuatro categorías: posi= tivo, neutral, negativo o alerta, y asigna ponderaciones específicas a cada una. Para entrenar y validar el rendimiento del modelo de clasificación = utilizado para la obtención del S_c, se empleó un conjunto de 3244 comentarios etiquetados manualmente. A fin de garantizar la robustez del modelo, se aplicó una estrategia de validación cruzada con k-fold (k=3D5), dividiendo aleatoriamente los datos en cinco subconjuntos para entrenamiento y prueba.= El modelo alcanzó un accuracy del 85 % y un F1-score del 83 %, lo cual evidencia un buen desempeño en la clasificación de sentimientos en textos cortos. En la Figura 2 se presenta la matriz de confusión del modelo, que permite visualizar el nivel de acierto en cada una de las categorías: positivo, neutr= al, negativo y alerta. A partir de esta clasificación se= construyó un score ponderado de sentimiento= , que también fue transformado a una escala de 0 = a 100. Esta transformación de una variable textual en una variable continua= es especialmente útil para captar la intensidad emocional expresada en textos cortos, como los comentarios que dejan los estudiantes en las evaluaciones docentes (Wankmüller et al., 2021).

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Figura 2

Matriz de confusión del modelo RoBERTuito para clasificac= ión de sentimientos

 

•      =    Número de evaluadores (E= n): como medida indirecta del grado de consenso, esta variable fue transformada mediante logaritmo base 10 y posteriormente normalizada a una escala de 0 a 100 utilizando la técnica de min-max scaling, con el fin de reducir la asimetría propia de su distribución, de forma que su influencia en el índice fuera comparable con las demás dimensiones.

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La integración de estas tres variables se resume en la Figura 3, donde se presenta gráficamente la estructura del puntaje de desempeño compuesto (PDdocente) y sus componentes. La formulación matemática del puntaje es la siguiente:

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PDdocente =3D 0,4 ∙ Nc + 0,4 ͨ= 9; Sc + 0,2 W= 29; En<= /p>

 

Figura 3

Componentes y fórmula del puntaje de desempeño compuesto


 

La forma en que se distribuyen los= pesos en la fórmula responde a dos criterios principales. Por un lado, se buscó dar la misma importancia a la calificación numér= ica y al análisis de sentimiento, ya que ambos reflejan dimensiones complementarias del desempeño docente. Por otro lado, se decidi&oacu= te; incluir el número de evaluadores con un peso menor, pero significati= vo, reconociendo que un mayor número de estudiantes aporta mayor confiabilidad a los resultados. Esta decisión está respaldada por estudios que destacan la necesidad de considerar la cantidad de respuestas para asegurar que las valoraciones sean representativas y estables (Spooren et al., 2013).

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Segmentación de docentes con desempeño sobresaliente

Una vez calculado el puntaje compu= esto de desempeño, se procedió a segmentar a los docentes considerando el cuartil superior de este indicador. De esta manera, se pudo identificar un subconjunto de 455 registros, correspondientes a 334 docentes únicos que presentan un desempeño sobresaliente= . La elección del cuartil superior permite trabajar con un subconjunto representativo que conc= entra a los perfiles mejor valorados desde la percepción estudiantil, y so= bre el cual se aplicará el análisis temático posterior.

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La distribución de estos registros por facultad se presenta en la Figura 4. En ella se observa, por = un lado, la cantidad de docentes destacados en cada unidad académica, y= por otro, el promedio del índice de desempeño compuesto para los casos identificados. De forma complementaria, la Figura 5 muestra la misma información desagregada por nivel académico, que re= sulta útil para contextualizar el análisis posterior sobre patrones temáticos.

Figura 4

Distribución de docentes destacados y promedio del índice de desempeño por facultad


 

Figura 5

Distribución de docentes destacados y promedio del índice de desempeño por nivel


 

Etapa = 2: Modelado temático=

Análisis de sentimiento y filtrado del corpus

En esta etapa, se definió el subconjunto de comentarios sobre el que se aplicaría el modelado temático. Considerando que el objetivo del estudio es identificar bu= enas prácticas docentes reconocidas por los estudiantes, se optó por trabajar exclusivamente con los comentarios clasificados como positivos por el modelo de análisis de sentimiento, es decir, aquellos que expresan valoraciones favorables hacia = el desempeño del docente.

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Este filtro se aplicó a los registros pertenecientes al cuartil superior del índice de desempeño compuesto, lo que permitió obtener un total de 19240 comentarios positivos que conforman el corpus final de análisis. Esta selección permite enfocar el modelado en expresiones directamente vinculadas con aspectos reconocidos como buenas prácticas por parte de los estudiantes, reduciendo así la presencia de ruido derivado de comentarios neutros o negativos. El conjunto resultante constituye una muestra textual coherente con los objetivos del estudio, sobre la cual se aplicarán los procesos de preprocesamiento lingüístico y modelado temático descritos en los pasos siguientes.

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Preprocesamiento l= ingüístico

Antes de aplicar el modelado temático, se llevó a cabo un proceso de limpieza y normalización de los comentarios positivos, con el objetivo de reducir el ruido textual y asegurar la coherencia léxica en el corpus. Este procesamiento se implementó en Python, utilizando expresiones regulares y la bibliote= ca spaCy con el modelo en español es_core_news_md. Las transformaciones aplicadas se resumen en la Figura 6.

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Figura 6

Etapas del preprocesamiento lingüístico del corpus


 

Entrenamiento del modelo STM

Para identificar los patrones temáticos presentes en los comentarios positivos emitidos por los estudiantes, se utiliz&oacu= te; el modelo Structural Topic Model (STM) propuesto por Roberts et al. (2014). Este enfoque permite identificar temas latentes en textos, tomando en cuenta información adicional de los documentos como variables explicativas,= lo que facilita explorar cómo varía la prevalencia de los t&oacu= te;picos según características contextuales. A diferencia de otros métodos como LDA (Latent Dirichlet Allocation),<= span style=3D'letter-spacing:-.05pt'> el STM permite estimar directamente los efectos de variables externas sobre la distribución temática = de cada documento, lo que lo hace especialmente útil para estudios en c= iencias sociales y educación.

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El modelo se entrenó en R utilizando la librería stm (Roberts et al., 2019), con el corpus previamente procesado y representado mediante los objetos documents, vocab y meta. El preprocesamiento del texto, que incluyó la tokenizaci&oacut= e;n y la aplicación de umbrales de frecuencia para palabras raras, se realizó de manera automática por las funciones integradas en e= ste paquete. Este proceso asegura la rigurosidad y la calidad de los datos de entrada al modelo, y concentra el análisis<= span style=3D'letter-spacing:-.3pt'> en palabras con suficiente representatividad. La estimación se realizó con el método de inicialización Spectral, recomendado por los autores del modelo para facilitar la convergencia y mejorar la estabilidad de los temas generados. En la fórmula de prevalencia se incluyeron dos covariables: facu= ltad y nivel.

Se evaluaron modelos con 4, 5 y 6 = temas (K) con base en métricas cuantitativas como la Held-Out Likel= ihood, residuos y convergencia. Si bien los resultados (ilustrados en la Figura 7) mostraron diferencias marginales entre las configuraciones, la literatura reciente señala<= span style=3D'letter-spacing:4.0pt'> que la selección del nú= ;mero óptimo de tópicos no puede basarse exclusivamente en métricas cuantitativas (Hayat et al., 2024). Un modelo puede mostrar métricas ligeramente superiores, pero su interpretabilidad y relevan= cia contextual pueden ser bajas. Por ello, se consideró necesario complementar este análisis con una validación cualitativa más detallada, abordada en la siguiente sección.

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Figura 7

Métricas de diagnóstico para la selección del núme= ro óptimo de temas (K) en el modelo STM


 

 

Validación= cualitativa

Una vez entrenados los modelos con diferentes cantidades de temas, se procedió a realizar una validación cualitativa para determinar cuál de las configuraciones generadas ofrecía una mayor coherencia semántica y claridad interpretativa, un criterio considerado fundamental= en el modelado de temas (Hayat et al., 2024). Esta revisión se centró en analizar las palabras clave asociadas a cada tema, así como ejemplos representativos de comentarios asignado= s a cada uno.

 = ;

La validación fue realizada= por una experta en educación superior, con experiencia en evaluaci&oacut= e;n docente y análisis cualitativo. Su revisión consideró = tres criterios principales: qué tan bien se entendía cada tema a partir de sus palabras clave, si los temas estaban claramente diferenciados= entre sí, y si los comentarios asignados a cada tópico re= almente reflejaban lo que suger&iac= ute;an esas palabras.

&nbs= p;

Aunque los modelos con 5 y 6 temas presentaban ventajas cuantitativas, la revisión cualitativa reveló que esos modelos generaban temas con mayor solapamiento, menor claridad semántica y una división forzada de tópicos que dificultaba su interpretación. Por el contrario, la configuración <= /span>con K =3D 4 ofrecía una mejor coherencia conceptual, dif= erenciación clara entre

&nb= sp;

tópicos<= span lang=3DES style=3D'letter-spacing:-.5pt'> y relevancia directa respecto al objetivo del estudio. Esta validación sustentó la adopción del modelo con K =3D 4 como base para el posterior proceso de etiquetado e interpretación de los temas.

&nbs= p;

Etiquetado de tema= s

Una vez seleccionado el modelo STM= con cuatro tópicos, se realizó el proceso de interpretació= n de los resultados. Para cada tema, se revisaron las palabras clave con mayor <= span style=3D'letter-spacing:-.1pt'>exclusividad (FREX) y se leyeron comentarios representativos asignados por el modelo. A partir de esta revisión, se asign&oacut= e; un nombre que resume de forma clara la idea principal de cada grupo de comentarios, tal como se s= ugiere en el enfoque propuesto por Roberts et al. (2014).

&nbs= p;

En la Tabla 2 se presentan los cuatro temas identificados, junto con sus palabras clave más relev= antes y un ejemplo de comentario real para cada uno. Estos temas servirán como base para l= os análisis posteriores por facultad y nivel.

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Tabla 2

Temas identificados mediante STM

 

TÓPICO

NOMBRE ASIGNADO

PALABRAS= CLAVE (FREX)

COMENTARIO REPRESENTATIVO=

 

T1=

= Gestión=

= Comunicativa=

lenguaje, actividad, organización, dinámica, creatividad, impartir, = interactivo

“El = docente destaca en su expresión corporal y su lenguaje, a la hora de impartir su clase y su manera llamativa de interactu= ar con el grupo”.

 

T2=

Metodología de

= Enseñanza=

método, contenido, explicación, c l a r i d a d , t e ó r i c o , d i n á m i c o , metodología

“Destaca la aplicación de metodologías dinámicas y participativas que fomentan el aprendizaje significativo, el pensamiento crítico y la resolución de problemas”.

 

T3=

Co= nocimiento Discipli= nar

amplio, explicar, comprensión, claro, dedicación, excelente, tema, área, retroalimentación

“El docente demuestra un alto nivel de conocimiento en la

asignatura y = explica los = temas de = manera clara = y organizada”.

T4=

Habilida= des Socioemo= cionales

manejo, ambiente, = ejemplo, despertar,

profesor, momento, forma, fácil

“Compresniva y pacien= te, siempre con críticas constructivas que me ayudan a mejorar”.

 

Etapa<= span style=3D'letter-spacing:-.3pt'> 3: Análisis de prevalencia temática

Estimación de efectos marginales

Después de etiquetar los te= mas obtenidos con el modelo STM, se estimó cómo varía la presencia de cada tópico en función de dos variables contextuales: la facultad y el nivel académico de la asignatura evaluada. Esto permitió analizar si las prácticas valoradas por los estudiantes, que fueron identificada= s en el modelo tienden a aparecer con más frecuencia en algunas facultade= s o en determinados ciclos formativos.

&nbs= p;

Par= a este análisis se utilizó= la funció= n estimateEffect() del paquete stm en R, que permite mod= elar la relación entre los temas generados y las covariables incluidas en la fórmula de prevalencia. = En este caso, se trabajó con facultad y nivel, variables que representan el contexto institucional y formativo en el que se imparten las clases. La elecci&oacut= e;n de estas covariables se basa e= n evidencia que muestra que las percepciones estudiantiles = pueden variar según la unidad académica, por difere= ncias disciplinares (Marsh, 2007), así como por el momento formativo del estudiante, ya que sus expectativas y valoraciones tienden a cambiar conforme avanza en la carrera (Chen y Hoshower, 2003).=

&n= bsp;

= Resultados y Discusión

Esta sección presenta los resultados obtenidos a partir del análisis temático de los comentarios positivos emitidos por los estudiantes a los docentes con mejor desempeño, según

el índice compuesto calculado. El objetivo es identificar patrones discursivos que revelen buenas pr&a= acute;cticas docentes valoradas por los estudiantes. A continuación, se describe = la distribución general de<= span style=3D'letter-spacing:-.4pt'> los temas extraídos por el modelo STM, y luego se analiza su prevalencia según dos variables contextuales: facultad y nivel académi= co.

 = ;

El análisis temático permitió identificar cuatro grandes categorías recurrentes en= los comentarios positivos de los estudiantes hacia docentes destacados. La Figu= ra 8 presenta la distribución de estos temas según su frecuencia media en el corpus. El tema más representado fue conocimiento disci= plinar (35,2 %), seguido de habilidades socioemocionales (24,3 %) y gestión comunicativa (24,1 %). En menor proporción, aunque aún releva= nte, se identificó la metodología de enseñanza, con un 16,3= % de aparición promedio en los documentos. Esta primera aproximación muestra que los estudiantes tienden a valorar con mayor frecuencia el dominio del contenido por parte del docente, pero tambi&eacut= e;n reconocen aspectos actitudinales y comunicativos en sus valoraciones positi= vas.

&nb= sp;

Figura 8

Frecuencia= de aparición de los temas identificados


 

La Figura 9 muestra cómo varían los temas identificados en los comentarios positivos según la facultad de la Universidad de Cuenca. En términos generales, el conocimiento disciplinar predomina notablemente y alcanza su máxima presencia en la Facultad de Odontología (38,9

%), seguida por Ciencias de la Hospitalidad (38,4 %) e Ingeniería (37,7 %), indicando una fuerte apreciación estudiantil por el dominio conceptual y técnico en estas áreas. Por otro lado, la facultad con menor valoración en este aspecto es = Artes (27,0 %).

&nbs= p;

En contra= ste, las habilidades socioemocionales muestran una presencia significativa en faculta= des de carácter más humanístico y social, destacando especialmente= en Psicología (23,9

%) y Arquitectura y Urbanismo (22,1 %), mientras que su representaci&= oacute;n más baja aparece en

Ciencias Químicas (13,9 %).

&nbs= p;

Respecto a la gestión comunicativa, esta dimensión destaca particularmente en Artes (26,5 = %), Arquitectura y Urbanismo (24,1 %) y Ciencias Médicas (22,6 %), lo que refleja una alta valoración hacia la claridad, interacción y empatía comunicativa po= r parte de los docentes en di= chas áreas. Su representac= ión más limitada se observa en Ciencias Agropecuarias (15,3 %) y Cie= ncias Económicas y Administrativas (15,6 %).

&nb= sp;

Finalmente, la dimensión de metodología de enseñanza, aunque presenta una menor prevalencia en general, alcanza un valor máximo en Filosofía, Letras y Ciencias de la Educación (27,3 %), resaltando la importancia que adquiere la estructura y dinámica pedagógica en esta facultad. En contraste, Arquitectura y Urbanismo muestra el mínimo valor para esta dimensión (17,1 %).

&nbs= p;

Estas diferencias ilustran claramente cómo la valoración estudiantil sobr= e las prácticas docentes varía según el contexto disciplinar, subrayando la importancia de considerar estas particularidades en la interpretació= n y aplicación de resultados de evaluación docente.

&nbs= p;

Figura 9

Frecuencia= media estimada de los temas por facultad


 

La Figura 10 presenta la evolución porcentual de los temas identificados en los comentarios positivos a lo largo de los diferentes niveles académicos. El conocimiento disciplinar se mantiene consistentemente como el más frecuente en todos l= os niveles, registrando valores cercanos<= /span> al 35 %, aunque muestra una leve disminución hacia los niveles superiores. Esto podría indicar que, aunque la solidez conceptual y técnica sigue siendo prioritaria, otros aspectos cobran más relevancia conforme avanzan los estudiantes en su formación.

&nbs= p;

En contraste, la gestión comunicativa y las habilidades socioemocionales muestran una tendencia ligeramente ascendente a medida que aumentan los niveles académicos. En particular, la gestión comunicativa se consolida claramente por encima de= l 25 % a partir del nivel 5, destacando la importancia que adquieren las capacidades de interacción y comunicación efectiva del profesorado en etapas académicas avanzadas. Por su parte, las habilidades socioemocionales presentan un leve repunte en los niveles superiores, sugiriendo que = los estudiantes valoran crecientemente la empatía y el apoyo emocional conforme avanzan en sus estudios.

Finalmente, la metodología = de enseñanza se mantiene relativamente estable, con porcentajes en torn= o al 15-18 % en todos los ciclos académicos, constituyendo el tema menos frecuente a lo largo del recorrido formativo analizado. Esto podría reflejar que, aunque la metodología didáctica es apreciada, l= os estudiantes asignan consistentemente mayor peso a otros aspectos relaci= onados directamente con el contenido y la interacción docente-estudiante.

&nbs= p;

Estos resultados sugieren que, conforme avanzan en sus estudios, las<= span style=3D'letter-spacing:-.45pt'> prioridades de los estudiantes experimentan cambios sutiles pero relevantes, destacando progresivamente aspectos relacionales y comunicativos del proceso formativo, además = del continuo énfasis en la claridad conceptual.

 = ;

Figura 10<= /span>

Proporción media de los temas por nivel


 

Al observar la distribución= de los temas por área académica, se evidencian diferencias asoci= adas al enfoque de formación. En carreras técnicas y aplicadas, predominan valoraciones relacionadas con el conocimiento disciplinar, mientras que en áreas de salud, educación y ciencias sociales emergen con mayor frecuencia comentarios sobre habilidades comunicativas y relacionales. En cua= nto al nivel académico, se observó que la proporción de comentarios asociados a Gestión comunicativa tiende a incrementarse levemente en los ciclos superiores, mientras que las Habilidades soci= oemocionales presentan una ligera disminución. Esto sugiere que, conforme avanzan en su formación, los estudiantes podrían val= orar más la capacidad del docente para sostener una comunicación c= lara y estructurada que aspectos de tipo empático o relacional.

&nb= sp;

Conclusiones

El estudio ha demostrado con éxito la eficacia de la metodología empírica, al combinar análisis cualitativos y cuantitativos mediante técnicas avanzada= s de procesamiento de lenguaje natural para comprender las= percepciones estudiantiles s= obre las prácticas docentes. Las cuatro dimensiones identificadas —conocimiento disciplinar, gestión comunicativa, habilidades socioemocionales y metodología de enseñanza— destacan aspectos críticos de la docencia valorados por los estudiantes. Particularmente, el predominio del conocimien= to disciplinar en la mayor&ia= cute;a de las facultades subraya la importancia continua de un sólido dominio del contenido,

&nb= sp;

mientras que las variaciones en el énfasis sobre aspectos socioemocionales y comunicativos reflejan expectativas diferenciadas según el contexto disciplinar y la progresión académic= a.

&nbs= p;

A pesar = de la solidez de la metodología utilizada, este estudio presenta ciertas limitaciones. = Primero, el análisis se restringió a comentarios<= /span> positivos de estudiantes<= /span> que evaluaron a docentes altamente calificados, lo que podría dejar fuera percepciones valiosas presentes en comentarios neutros o negativos. En segundo lugar, la generalización contextual podría ve= rse limitada dado que los hallazgos corresponden a una única institución y a un periodo temporal específico (2024–2025).

 = ;

Inv= estigaciones futuras deberían ampliar el análisis para incluir comentarios con distintos niveles de sentimiento, permitiendo= una comprensión más integral tanto de fortalezas como de áreas de mejora. Adem&aacu= te;s, estudios comparativos entre diversas instituciones y a lo largo de periodos más extendidos enriquecerían la generalizabilidad y robustez de los resultados obtenidos. Finalmente, explorar análisis longitu= dinales podría revelar tenden= cias a largo plazo, brindando perspectivas valio= sas sobre la evolución de las percepciones estudiantiles y la efectividad institucional de las iniciativas de desarrollo profesional docente.<= /p>

&n= bsp;

Reconocimientos y Declaraciones

Est= e artículo recibió el apoyo de la Vicerrectoría Académica de la Universidad de Cuenca = y la Dirección de Innovación Educativa.

 = ;

Este trabajo forma parte de una investigación realizada por la Dirección de Innovación

Educativa de= la Universidad de Cuenca.

&nbs= p;

Los autores desean expresar su agradecimiento a la Universidad de Cuenca, al Vicerrectorado Académico y a la Dirección de Innovación Educativa por el apoyo instituci= onal brindado para el desarrollo de la presente investigación.

&nbs= p;

En cuanto a la contribución= de los autores, siguiendo la taxonomía CRediT se declara que la participación ha sido equitativa en todas las fases del trabajo.

 = ;

Asi= mismo, los autores manifiestan que, <= span lang=3DES style=3D'letter-spacing:-.1pt'>en la elaboración de este<= span lang=3DES style=3D'letter-spacing:-.65pt'> artículo, se han utilizado herramientas de inteligenci= a artificial como apoyo al proceso de investigación y redacción. En particular:

&nbs= p;

•= ;      =    Consensus,= para la búsqueda y análisis de literatura científica.

•ttps://doi.org/10.1007/ s11423-023-1= 0239-8

 

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Gencoglu, B., Helms-Lorenz, M., Maulana, R., Janse= n, E. P. W. A., & Gencoglu, O. (2023). Machine and expert judgments= of student perceptions of teaching behavior in secondary education: Added value of t= opic modeling with big data. Computers &= amp; Education, 193, 104682. https://doi.org/10.1016/j. compedu.2022= .104682

 

Hayat, F., Shatnawi, S., & Haig, E. (2024). Comparative analysis of topic modelling approaches on student feedback. En Proceedings of the 16th International Joint Conference on Knowledge Discove= ry, Knowledge Engineering and Knowledge Management (KDIR2024) (pp. 226-233). SciTePress. https:// doi.org/10.5= 220/0012890400003838

 

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Knowledge-Ba= sed Systems, 108, 92–101. https://doi.org/10.1016/j.k= nosys.2016.05.032<= /span>

 

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Wankmüller, S., & Heumann, C. (2021). How to estimate continuous sentiments from texts using binary training data. En Proceedings of the Conference on Natural Language Processing (KONVENS 2021) (pp. 131–139). = German Society for Computational Linguistics & Language Technology. https://aclanthology.org/2021.<= span lang=3DES style=3D'font-size:10.0pt;mso-bidi-font-size:11.0pt;line-height:1= 03%'> konvens-1.16= /

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