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=
.
Esta o=
bra
está bajo una licencia internacional Creative
Commons Atribución-NoComercial 4.0
Enviado: 2025/06/29 Aceptado: 2025=
/08/20
Publicado: 2025/10/15
Resumen La asignación de docentes a materias en
educación superior constituye un desafío crítico para =
la
gestión académica, ya que impacta directamente en la calidad =
del
proceso de enseñanza- aprendizaje. A pesar de su vigencia, los proce=
sos
manuales de asignación enfrentan retos evidentes, como la subjetividad, la falta de estandarización y una elevada
carga administrativa. Fr=
ente a
este escenario, este trabajo plantea un sistema recomendador que combina el
análisis de sentimiento=
s, mediante
modelos de lenguaje
basados en transformers adaptados localmente (RoBERTuito), con técnicas de
optimización matemática, con el propósito de alinear l=
as
competencias de los docentes con los requerimientos académicos
específicos. Para ello, se desarrollaron perfiles docentes enriqueci=
dos
a partir de evaluaciones históricas, comentarios estudiantiles
clasificados automáticamente y competencias definidas institucionalm=
ente
bajo el marco del pentágono de competencias. Además, se
incorporaron pesos dinámicos que permiten ajustar la relevancia de los factores
pedagógicos y técnicos según l=
as particularidades de cada ciclo
académico. Los resultados obtenidos con el sistema recomendador mues=
tran una alta correspondencia entre las
recomendaciones generadas y las asignaciones manuales, especialmente en
carreras de perfil técnico. Adicionalmente, los directores de carrera
que lo utilizaron en un piloto valoraron favorablemente el sistema, indican=
do
que no solo disminuye significativamente la carga operativa, sino que
también se consolida como una herramienta estratégica con alto potencial
de escalabilidad y replicabilidad en diversos contextos educativos.
&nb=
sp;
Palabras clave: an&a=
acute;lisis automático de texto, docentes
universitarios, intelig=
encia artificial,
sistemas de apoyo a la decisión, sistemas de información educativa.
&nb=
sp;
Abstract The assignment of teachers to courses in higher educa=
tion
represents a critical challenge for ac=
ademic
management, as it directly impacts the quality of the teaching-learning
process. Despite their c=
ontinued use, manual assignment processes face evident
challenges, such as subjectivity,
lack of standardization, and a high administrative workload. In response to
this scenario, this study prop=
oses a recommender system
that combines sentiment
analysis, using locally
adapted transformer-based language models (RoBERTuito), with mathematical optimization techniques,
aiming to align teachers' competencies with specific academic
requirements. To achieve this, enriched
teacher profiles were developed based on historical evaluations, automatica=
lly
classified student comments, and institutionally defined competencies
within the framework of the Competency=
Pentagon. Additionally, dynamic weights were incorporated to adjust the relevance
of pedagogical and technical
factors according to the particularities of each academic cycle. The results obtained from the recommender system
demonstrate a high correlation between generated recommendations <=
span
style=3D'letter-spacing:-.1pt'>and manual assignments, particularly in technically oriented degree programs.
Moreover, program directors who participated in a pilot test positiv=
ely
evaluated the system, noting th=
at it not only significantly reduces
the operational workload
but also establishes itself as a strategic tool with high potential for scalability and replicability across
diverse educational contexts.
&nb=
sp;
Keywords: Academic teaching personnel, artificial intelligence, automatic text analysis, decision
support syste=
ms,
educational information systems.
&nb=
sp;
Introducción En la gestión del talento h=
umano,
la tecnología ha transformado <=
/span>los
procesos organizativos, integrando herramientas que=
optimizan
la planificación<=
span
style=3D'letter-spacing:-.3pt'> estratégica y alinean las capacidades
del personal con los objetivos organizacionales (Perdomo-Charry y
Riascos-Erazo, 2008). En=
el ámbito educativo, la asignaci&oacu=
te;n adecuada
de profesores a los cursos constituye un desafío crítico para las Instituciones de Educación Superior (IES), dado que este proceso impacta
directamente en la calidad del aprendizaje y la eficiencia
administrativa (Patfield 2023). En contextos
como el de la Universidad de Cuenca, la asignación aún se realiza de forma subjetiva y depende principalmente =
de
evaluaciones curriculares y criterios poco estandarizados, lo que genera
inconsistencias y limita la disponibilidad del personal administrativo para
actividades estratégicas (Universidad de Cuenca, 2022).
&nbs=
p;
Diversos estudios han abordado la
problemática de la asignación docente mediante sistemas
expertos y técnicas de optimización. Por ejemplo, Calle López et al. (2018) desarrollaron
un sistema basado en minería de datos y programación
lineal entera, que logró mejorar la precisión y reducir el tiempo en la planificación académica. De forma complementaria, Szwarc et al. (2020) propusieron un modelo interactivo de
asignación basado en competencias y programación por restricciones, logrando
un mayor equilibrio en la carga
docente y resiliencia frente a cambios
curriculares. En otro enfoque, Tabares-Ospina et al. (2013) desarrollaron un
sistema experto (ExpertoRH_ITM) que incorporab=
a indicadores como formaci&oa=
cute;n, experiencia y producción académica para mejorar los procesos de selección de personal universitario. Más recientemente, Krugel et al. (2020) presentaron =
el
sistema JACK, enfocado en la evaluación automatizada de competencias en cursos de programación, destacando el papel de los modelos de competencias en la perso=
nalización
del aprendizaje.
&nb=
sp;
En<=
/span> paralelo, el análi=
sis de evaluaciones estudiantiles mediante técnicas de procesamiento del
lenguaje natural (PLN) ha emergido
como una herramienta clave para la toma de decisiones
en
educación. Gonzalez-Gómez et al. (2024) destacan el uso de PLN para generar taxonomías de competencias y detectar patrones
en la retroalimentación de los estudiantes, mientras
que Song et al. (2024) desarrollaron el TeachingSenti=
-lexicon, un recurso diseñado específicamente =
para
identificar la polaridad de comentarios sobre la enseñanza. Modelos
basados en transformers como BERT o
RoBERTa han demostrado un rendimiento sobresaliente en análisis de
sentimientos aplicados a=
contextos
educativos (Shuqin et al., 2024; Id et al., 2024), aunque con limitaciones
en cuanto a requerimientos=
computacionales y adaptabilidad=
a distintos dominios
lingüísticos.
&nb=
sp;
Dentro de esta línea, resul=
ta
relevante destacar a RoBERTuito, un modelo de lenguaje basado en RoBERTa y
entrenado con un corpus de 500 millones de tuits en español, que ha
demostrado superar a otros modelos preentrenados como BETO, RoBERTa-BNE e
incluso al BERT multilingü=
;e ajustado, en tareas de análisis de sentimientos, detección de discurso de odio
e ironía (Pérez, Furman, Alonso Alemany, y Luque, 2022; Sahoo=
, et
al., 2023). Esta ventaja comparativa, sumada a su adaptación
específica al español en contextos informales y con alta carg=
a emocional, lo convierte en una opción idónea =
para el análisis de comentarios estudiantiles dentro del presente
estudio.
&nbs=
p;
Pes=
e a estos avances, la literatura aún carece =
de un enfoque que integre simultáneamente competencias pedagógicas, uso de tecnologías educativas y la voz estudiantil en los procesos
de asignación docente. Asimismo,
pocos modelos actualizan dinámicamente los perfiles académico=
s ni
se alinean con marcos institucionales de competencias. En este contexto, el
problema representa un nicho claro en la investigación y la práctica actual que requiere atención específica.
&nbs=
p;
Para atender estas limitaciones, e=
ste
estudio propone un sistema recomendador que permita asignar de manera efici=
ente
a los docentes a las materias que mejor se alineen con su perfil profesional y académi=
co. El sistema integra
múltiples fuentes=
de información relevantes, como los resultados
históricos de evaluaciones estudiantiles, las competencias
pedagógicas y técnicas declaradas o verificadas
institucionalmente y los comentarios cualitativos extraídos de dichas
evaluaciones. Con esta integración, se busca facilitar una
asignación docente más i=
nformada y alineada con las necesidades académicas, favoreciendo así una gestión administrativa
más eficiente. A diferencia de soluciones existentes, el sistema propuesto
agiliza la asignación considerando
simultáneamente competencias disciplinares, pedagógicas y
tecnológicas, junto con resultados provenientes de evaluaciones
estudiantiles periódicas y análisis basados en modelos integrales de competencias, como el
pentágono de competencias (Maldonado-Mahauad, Lozano, y Pache=
co,
2024).
&nbs=
p;
El presente trabajo
se estructura de la siguiente
manera: en la sección 2 se describe en detalle la metodolog&iac=
ute;a
utilizada, donde se presentan los datos y métodos aplicados para el =
diseño e implementación del si=
stema
recomendador, incluyendo las técnicas de procesamiento de lenguaje
natural, la construcción de perfiles docentes y el modelo matemático de recomendación
empleado. En la sección 3 se presentan los resultados obtenid=
os
en una fase piloto de validación, donde a través de un
análisis comparativo entre las asignaciones tradicionales y la
retroalimentación recibida por parte de los responsables
académicos, se discute la valoración del prototipo func=
ional. Finalmente, en la sección 4 se abordan
las conclusiones generales del estudio, se discuten =
sus
implicaciones prácticas y se plantean recomendaciones clave para
trabajos futuros y mejoras del modelo.
&nb=
sp;
Metodología La
definición de las fases del proyecto se basó en el enfoque de
Integrated L (ILD),
caracterizado por una iteración continua entre investigación,
diseño, implementación y evaluación (Bannan-Ritland, 2003). La
Figura 1 muestra un gráfico que representa los pasos de esta metodología. En primer lugar, se
realizó una exploración informada mediante el análisis del contexto, la identificación de
necesidades, la revisión de datos existentes y la evaluación =
de
desafíos técnicos. Luego, se conceptualizaron soluciones a
través del diseño de funcionalidades clave. Posterior=
mente, el sistema fue desarrollado e implementado, integrando modelos de machine learni=
ng y
algoritmos de recomendación, además de realizar ajustes y pru=
ebas
preliminares. Finalmente, la solución fue validada con usuarios real=
es,
recopilando retroalimentación para ajustes finales y evaluando su
viabilidad de escalamiento.
&nbs=
p;
Figura 1 LD =
framework - lista de procesos para el sistema
de recomendación
Selecc=
ión de herramientas y modelos =
Sistema desarrollado (versión
inicial).
Métricas y recomendaciones
Fase 1. Exploración informada En esta fase, se establecieron los
objetivos del proyecto a partir del análisis integral de fuentes de datos clave sobre el
desempeño y la trayectoria docente. Se incluyeron registros
históricos de materias impartidas y evaluaciones de desempeño
para obtener indicadores cuantitativos, así como comentarios estudia=
ntiles
que aportaron una visión cualitativa sobre aspectos pedagógic=
os y
actitudinales. Además, se analizaron portafolios docentes y hojas de
vida para identificar form=
ación, experiencia e investigacione=
s. Estos datos se complementaron
con marcos institucionales existentes sobre competencias docentes, que
sirvieron como base para definir el perfil docente y estructurar el modelo =
de
competencias empleado en el sistema de recomendación.
&nbs=
p;
En cuanto al proceso de
recolección y estructuración de la información, los da=
tos
fueron obtenidos a partir de registros institucionales existentes, tales co=
mo
bases de datos de evaluaciones docentes, portafolios académicos y ho=
jas
de vida proporcionadas directamente por los responsables del proceso de
acreditación ABET de la
universidad, lo que garantiza la actua=
lización y validez de los datos. Este procedimiento se realizó de manera manual, incluyendo
la verificación<=
span
style=3D'letter-spacing:-.05pt'> de títulos y competencias declaradas, con el fin de asegurar
que la información sobre habilidades y capacidades derivada=
s de
los títulos académicos estuviera correctamente descrita, lo cual no podía garantizarse mediante un proceso
automatizado. Adem&aacu=
te;s, el objetivo
de
este proceso es marcar el inicio de un marco de competencias docentes específico para nuestro
caso de estudio y que sirva como base para el sistema de
recomendación.
&nb=
sp;
Fase 2=
. Diseño A partir de la base teórica, los objetivos establecidos y el alcance
definido en la fase anterior, se inició la
etapa de diseño del sistema, centrada en la construcción del
perfil docente y la lógica de recomendación. Esta fase se enfocó en estructurar y operacionalizar los
&nb=
sp;
criterios necesarios para vincular, de manera objetiv=
a, las
características del docente con los requerimientos específico=
s de
cada asignatura, respetando las directrices institucionales y la realidad
académica de la universidad.
&nbs=
p;
El<=
/span> primer paso fue la integración y procesamiento =
de las distintas fuentes de información recopiladas. En particular, se aplicaron técnicas de procesamiento =
del lenguaje natural (PLN) para
analizar los comentarios cualitativos emi=
tidos por los estudiantes en las evaluaciones docentes. Mediante el u=
so de
un modelo de análisis de sentimientos basado en transformers
(RoBERTuito), se logró transformar esas percepciones subjetiv=
as
en un indicador cuantificable, permitiendo incorporar la voz estudiantil co=
mo
un componente estructurado dentro del perfil docente. Este indicador no solo reflejaba la carga emocional de los
comentarios (positiva, negativa o neut=
ral), sino que también contribuía a captar dimensiones actitudinales no evidentes en los datos numéricos
tradicionales. Simultáneamente, se incorporaron los resultados de las
evaluaciones docentes instituc=
ionales como una medida
directa del desempeño. Estas evaluaci=
ones aportaron indicadores cuantitativos sobre distinta=
s dimensiones del trabajo docente,
como planificació=
;n, metodología, cumplimiento y relación con los estudiantes. Asimismo, se incorporaron datos sobre la
formación académica, experiencia profesional,
participación en investigación y actividades de
actualización docente.
&nbs=
p;
Uno de los elementos clave en esta etapa fue la definición de un marco de competencias docentes, que permitió estructurar la comparación entre el perfil del docente y los requerimientos
de cada asignatura. Cada=
asignatura del plan de estudios fue caracterizada mediante
este marco, especificando
qué tipo de competencias eran prioritarias para su enseñanza
efectiva. Esto permitió construir una matriz de competencias por asignatura, contra
la cual se podía evaluar
la idoneidad de cada docente. Con toda esta información estructurada, se desarroll&o=
acute; un modelo de coincidencia usando la similitud
del coseno que calculaba el grado de alineación entre el perfil de cada docente y las necesid=
ades
académicas de las asignaturas.=
Asimismo,
se incorporó el pentágon=
o de competencias docentes, que distingue cinco dimensiones: pedagógica, tecnológica,
comunicativa, de investigación y de gestión. Estas
dimensiones se integran en el modelo mediante un score que actúa como uno de los componentes del sistema =
de
recomendación.
&nb=
sp;
Figura 2 Proceso
de selección de candidatos, que parte del conjunto general de docent=
es
y, mediante una función<=
span
style=3D'letter-spacing:-.2pt'> de evaluación, genera una lista ordenada con aquellos que cumplen los criterios
definidos
Fin=
almente, con cada uno de los componentes<=
/span> cuantificables mencionados se diseñó una función de evaluación que, a partir del conjunto total =
de docentes disponibles, =
genera una lista =
de candidat=
os para cada materia.
Esta lista se ordena en función del nivel de afinidad entre
el perfil del docente y=
los
requerimientos de la asignatura, permitiendo a los responsables
académicos contar con una herramienta objetiva y transparente para la
toma de decisiones. El perfil final
&nb=
sp;
resultante de este proceso combina datos duros y blandos, generando una representación integral
de cada docente. Este <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>flujo de procesamiento =
se resume en la Figura 2, donde se ilustra el proceso de selección de candidatos.
&nbs=
p;
Con el fin de sintetizar de manera=
clara
la lógica metodológica aplicada, la Figura 3 presenta
un diagrama que integra las distintas fuentes de información y los procesos intermedios
que conforman el sistema de recomendación. En este esquema se
representan las entradas principales: evaluaciones docentes, competencias
técnicas, comentarios estudiantiles y el pentágono de
competencias de la universidad, los procedimientos de clasificación y
afinidad que permiten transformar la información en métricas
cualitativas y cuantitativas, así como el modelo matemático q=
ue
combina dichos indicadores mediante pesos dinámicos. Finalmente, se
muestran las salidas del proceso, consistentes en el ranking docente-asigna=
tura
y el perfil docente interactivo, que construyen los productos finales del
sistema.
&nb=
sp;
Figura 3 Diagrama metodológico del sistema de recomendación docente,
desde las fuentes
de datos
hasta el perfil
interactivo
Fase 3=
. Implementación El sistema de recomendación fue implementado mediante
un proceso que incluyó
la preparación de datos, modelado matemático y la
integración de técnicas de aprendizaje automático. La información recopilada fue sometida a procesos de limpieza, transformación y selección de variables clave, con el fin de garantizar una base sólida y coherente para el análisis posterior. Particular atención =
se dio al tratamiento de los comentarios estudiantiles, q=
ue fueron preprocesados mediante
técnicas de normalización textual, eliminación de ruido
lingüístico y tokenización
con Byte-Pair Encoding, permitiendo su=
conversión a representaci=
ones numéricas compatibles con mo=
delos
de lenguaje.
&nb=
sp;
Una vez transformados los textos, =
se
procedió a su clasificación automática. Para ello, se<=
span
style=3D'letter-spacing:-.65pt'> entrenó y ajustó=
; un modelo de análisis de sentimientos RoBERTuito, especializado =
en textos en español y previame=
nte
entrenado sobre tareas emocionales. El objetivo era clasificar cada comentario
en una de cuatro categorías: positivo, neutral, negativo y alerta (esta última reservada
para situaciones especialmente críticas o sensibles, como reportes<=
span
style=3D'letter-spacing:-.75pt'> de maltrato, discriminación o favoritismo). Para garantizar la calidad del entrenamiento, se utilizó un conjunto de comentarios
filtrados y etiquetados manualmente, res=
petando principios éticos co=
mo el anonimato tanto del
emisor (estudiante) como del receptor
(docente). El ajuste
del modelo se realizó utilizando dos enfoques complementarios. En primer lugar, se aplic&oac=
ute;
una división clásica de los datos en un 80 % para entrenamien=
to y
un 20 % para prueba. En segundo lugar, se utilizó una validaci&oacut=
e;n cruzada
estratificada en cinco pliegues, asegurando que todas las clases estuvieran representadas
proporcionalmente en cada subconjunto. Ambos métodos emplearo=
n un
criterio de parada temprana basado en la métrica F1-score, para evit=
ar
el sobreajuste y mantener un equilibrio entre precisión y sensibilid=
ad.
Como resultado de este proceso, se generaron varios modelos alternativos de
clasificación.
&nb=
sp;
En cuanto al tratamiento de datos
sensibles, se siguieron los protocolos establecidos por la Dirección=
de
Innovación Educativa de la Universidad de Cuenca, entidad responsabl=
e de
regular los procesos de evaluación docente. Todos los comentarios
estudiantiles fueron anonimizados desde su origen, lo que garantiza que ni =
los
emisores ni los receptores puedan ser
identificados. Cuando RoBERTuito clasificó un comentario en la
categoría de alerta, asociada a expresiones de maltrato,
discriminación o situaciones críticas, este no se
incorporó al perfil docente final ni se mostró públicamente, sino que fue gestionado únicamente como un indicador
interno con fines de detección y prevención. De esta manera, el sistema reconoce la existencia de
datos sensibles, pero asegura
su tratamiento ético sin comprometer la privacidad ni la integridad
de los actores involucrados.
&nbs=
p;
Para
mejorar la robustez
del sistema y reducir la dependencia de un único modelo, se implementó un esquema de votación
mayoritaria (ensemble). Bajo este esquema, las predicciones de los modelos
individuales se combinaron y se asignó a cada comentario la
categoría elegida por la mayoría de los modelos. Esta
técnica aumentó la confiabilidad de las predicciones, especialmente en casos de ambigüedad o textos cortos.
Finalmente, para integrar esta clasificación
textual en el perfil docente de forma cuantitativa, se diseñó=
una
escala de evaluación que asigna una calificación normalizada =
de 0
a 100.
&nbs=
p;
La expresión
matemática empleada para =
esta normalización se presenta a continuación:
<=
/o:p>
&nb=
sp;
e: Positivo =3D 2, Neutral =3D 0, Negativo =3D -=
1, Alerta =3D -5
Donde:
conteo(e) conteototal es el total=
de
comentarios sobre el docente. peso(e) corresponde al peso correspondiente a la categoría e. pesomax =3D 2
máximo valor de ponderación
pesomin =3D -5
mínimo valor de ponderación
&nb=
sp;
Con los datos procesados, se imple=
menta
un modelo matemático que genera una puntuación de
recomendación R(D,A) para cada docente frente a una
asignatura específica. Esta puntuación integra diversas fuent=
es
de información, ponderadas mediante la siguiente fórmula, basada
en el modelo propues=
to originalmente por Calle-L&oa=
cute;pez et al (2018).
y adaptado para incorpo=
rar
aspectos adicionales pertinentes a nuestro estudio:
<=
/o:p>
&nb=
sp;
Cada término en la ecuación representa un aspecto específico del perfil docente y su
relación con la asign=
atura.
&nbs=
p;
Evaluación =
histórica ponderada
=
Donde:
ϕk (D,A) :
evaluación histórica del docente D en la asignatura A durante=
el
período k, combinando indicadores cuantitativos y cualitativos.
N : número total de períodos considerados.
: factor sigmoide que otorga mayor peso a evaluaciones recientes.
&nbs=
p;
Comentarios estudiantiles C(D,A) : calificaci&o=
acute;n derivada del anális=
is de comentarios proporcionados por estudiantes.
&nbs=
p;
Similitud de compe=
tencias Sim(D,A) :
mide la afinidad entre el vector competencial del docente D , obtenido del portafolio=
docente actualizado y el vector competencial requerido
por la asignatura A , definido
por la planificación curricular. Se calcula mediante la similitud del
coseno:
<=
/o:p>
&nbs=
p;
Pentágono <=
/span>de competencias docentes <=
/h2>
Ψ(D) : métrica compuesta que evalú=
a cinco dimensiones competenciales institucionales (tecnológica, comunicativa,
pedagógica, gestión e investigativa). Cada dimensión se
obtiene mediante encuestas normalizadas en el rango [0,1]:
<=
/o:p>
&nb=
sp;
<=
span
lang=3DES style=3D'letter-spacing:-.2pt'>Además,
el modelo =
incorpora dos agrupaciones <=
/span>funcionales definidas desde <=
span
lang=3DES style=3D'letter-spacing:-.2pt'>observaciones
<=
span
lang=3DES>de la Dirección de Innovación Educativa: doce=
ncia, que considera evaluaciones recientes
&nb=
sp;
(p1 ) y percepción
estudiantil (p2 ); y
conocimiento, que incluye similitud competencial (p3 ) y competencias institucionales (p4 ).
=
;
La relevancia relativa
de cada grupo
de competencias varía según e=
l avance
académico, que se modeló mediante la asignación=
de
pesos dinámicos. En los ciclos iniciales se otorga mayor importancia=
a
la dimensión pedagógica o de docencia, ya que en esta etapa se
busca fortalecer las bases metodológicas y el acompañamiento
formativo de los estudiantes. En los ciclos superiores, en cambio, se prior=
iza
la dimensión de conocimiento disciplinar, pues el énfasis académico =
se orienta hacia la especialización y la profundidad técnica de los contenidos.
Esta transición progresiva refleja con mayor fidelidad las
expectativas institucionales y fue implementada a travé=
s de funciones lineales
que ajustan los pesos de cada dimensión en funci&oacut=
e;n
del nivel académico de la asignatura, como se ilustra en la Figura 4=
.
&nb=
sp;
Figura 4 Varia=
ción de los pesos asignados a las dimensiones docencia y conocimiento en funci&oacut=
e;n del ciclo académico
1.0
0.8
0.6
0.4
0.2
0.0
1 =
&nb=
sp; 5 &nbs=
p; &=
nbsp; &nbs=
p; 10
Ciclo académico
Como parte de la fase de
implementación, se desarrolló un componente clave para mejora=
r la
visualización y comprensión del desempeño docente: el
perfil docente, diseñado no solo como una interfaz informativa, sino como una herramienta estratégica para apoyar la=
toma
de decisiones académicas fundamentadas. Este se presenta como un dashboard que agrupa elementos
gráficos orientados a facilitar el análisis y la toma de
decisiones mediante una representación clara y precisa de
información clave. El objetivo principal de este perfil es convertir
datos dispersos y complejos, provenientes de diversas fuentes como
evaluaciones, comentarios, historial de
asignaturas y competencias, en conocimiento estructurado y accionable. Para
ello, se incorporaron distintos tipos de visualizaciones que permiten
representar tanto indicadores cuantitativos como cualitativos de manera
integrada.
&nbs=
p;
Los elementos que conforman el per=
fil
docente son: (1) las gráficas de barras de la Figura 5, que muestran=
las
asignaturas más adecuadas para cada docente ordenadas según el
nivel de afinidad calculado por el sistema de recomendación. Estas
visualizaciones permiten identificar =
de forma
inmediata las coincidencias más rel=
evantes entre
el perfil del docente y los
requerimientos académicos, facilitando una asignación eficien=
te y
basada en evidencia; (2) el conteo categorizado de comentarios estudiantiles
representado en la Figura 6, mediante una barra apilada que segmenta las
opiniones positivas, neutrales y negativas. Este componente proporciona una
visión sintética sobre la percepción general que los
estudiantes tienen del docente y funciona como un indicador cualitativo
adicional.
&nb=
sp;
Asimismo, (3) la gráfica de
líneas mostrada en la Figura 7, que refleja la evolución
histórica del rendimiento docente a partir de las calificaciones
obtenidas en evaluaciones institucionales a lo largo de distintos ciclos académicos. Esta representación temporal
permite detectar tendencias, identificar mejoras o retrocesos y valorar el impacto de procesos formativos
o cambios metodológicos en el tiempo; (4) las nubes de palabr=
as
ilustradas en la Figura 8, generadas a partir del análisis de
comentarios estudiantiles, que agrupan los términos más
frecuentes asociados a dimensiones positivas y negativas del desempeñ=
;o
docente. Estas visualizaciones cualitativas permiten
captar de manera
intuitiva los aspectos
más mencionados =
por
los estudiantes, destacando fortalezas recurrentes o señalando
áreas de mejora con un alto nivel de consenso.
&nbs=
p;
Finalmente, (5) el Pentágono TIC de competencias docentes, repr=
esentado en la Figura 9, ofrece una visión
integral del perfil profesional al evaluar el nivel del docente en cinco
dimensiones estratégicas. Cada vértice del pentágono
refleja el grado de desarrollo en una competencia específica, lo que permite analizar
de forma conjunta
las habilidades pedagógicas, tecnológi=
cas e
investigativas en relación con el marco institucional adoptado.
&nbs=
p;
Figura 5 Porcentaje=
de afinidad
docente–asignatura, calculado según competencias y<=
span
style=3D'letter-spacing:-.2pt'> experiencia
institucional
Figura 6 Conteo categorizado de comentarios estudiantiles, resultado del modelo
RoBERTuito que
clasifica =
en positivo, neutral, negati=
vo y alerta
Figura 7 Evolu=
ción histórica de las notas
de heteroevaluació=
;n, representada en una gráfica de línea=
s para
identificar tendencias y variaciones en el desempeño docente
Figura 8 Nubes=
de palabras que resumen los aspectos positivos
y negativos más mencionados en los comentarios estudiantiles<=
/o:p>
Figura 9 Pent&=
aacute;gono de competencias TIC en el perfil docente,
que muestra el nivel alcanzado
en cinco dimensiones cla=
ve y
permite identificar fortalezas y áreas de mejora
Fase 4=
. Validación La validación del sistema de
recomendación docente se diseñó como una etapa clave p=
ara garantizar su viabilidad práctica y alineaci&oacut=
e;n con las necesidades institucionales. Se aplicó en tres programas
académicos de la institución y contó con la
participación directa de los directores
de carrera. El proceso se estructuró en dos fases: una preevaluación sin intervención
del
sistema, donde los directores asignaron docentes según su criterio, y una posevaluación donde los
mismos directores accedieron a las recomendaciones generadas por la
herramienta, lo que les permiti=
ó explorar
y analizar las listas ordenadas
de docentes sugeridos
para cada asignatura, junto con los indicador=
es y perfiles
detallados que respaldaban dichas recomendaciones. Esta fase permitió
observar cómo las propuestas automatizadas influían en la
percepción y en la toma de decisiones de los directores, y si estas
recomendaciones aportaban un valor agregado en términos de objetivid=
ad,
rapidez y alineación con los perfiles requeridos.
&nb=
sp;
Esta metodología permiti&oa=
cute;
contrastar las decisiones humanas con las sugerencias automatizadas, en un entorno
real y controlado. A través de encuestas estructuradas, se recogi&oacut=
e;
retroalimentación sobre la utilidad y precisión percibida del
sistema, junto con sugerencias de mejora. El análisis de esta información no s=
olo
evidenció patrones de aceptación, sino que también aportó insumos fundam=
entales para perfeccionar la herramienta. Así, la validaci&oa=
cute;n no se
limitó a comprobar
el funcionamiento técnico del sistema, sino que sirvió como un ejercicio institucional para reflexionar sobre los procesos actuales de
asignación docente y explorar nuevas posibilidades apoyadas en
datos, fortaleciendo su valor como herramienta de apoyo a la toma de decisi=
ones
académicas.
&n=
bsp;
Result=
ados y Discusión Con=
el objetivo de evaluar la eficacia y pertinencia del sistema propuesto, se llevó =
a cabo un análisis detallado basado en: (1) múlti=
ples dimensiones <=
/span>que abarcan componentes <=
/span>técnicos del
recomendador y (2) percepciones de usuarios clave. La sección de resultados presenta
de forma estructurada los
principales hallazgos derivados de los resultados y evaluación,
permitiendo valorar la adopción del sistema. Se abordan aspectos
como la efectividad del modelo de análisis
de comentarios estudiantiles, los criterios tradicionales utilizados en la
asignación docente, la validación cualitativa por<=
span
style=3D'letter-spacing:-.2pt'> parte de los directores de carrera, la comparación entre asignaciones
manuales y automáticas, así como las percepciones generales s=
obre
la utilidad del sistema y sugerencias para su mejora.
&nb=
sp;
Result=
ado 1 El modelo RoBERTuito demostr&oacut=
e; un
desempeño robusto y consistente en la tarea de clasificación de comentarios estudiantiles. <=
span
style=3D'letter-spacing:-.65pt'> A partir de un conjunto de datos de 3243 comentarios
etiquetados manualmente, como se puede observar en la Figura 10, las
categorías positiva y negativa =
dominaron la distribución de clases, abarcando
en conjunto aproximadamente el 60 % del total de datos. En contraste, la categoría neutral presentó una representación significativamente
menor, con un 17.08 % de los comentarios, mientras que la clase alerta, diseñada para identificar
situaciones críticas, ocupó una posición intermedia en cuanto a cantidad de ejemplos disponibles.
&nbs=
p;
El desbalance observado plante&oac=
ute;
un reto adicional para el entrenamiento, ya que los modelos de aprendizaje profundo
tienden a privilegiar las clases mayoritarias, reduciendo la sensibilidad en las minoritarias. Para mitigar
este efecto, se empleó la función de pérdida
CrossEntropyLoss adaptada con pesos inversamente proporcionales a la frecue=
ncia
de cada clase, lo que otorgó mayor relevancia a las categorías
menos representadas durante el proceso de retropropagación. Esta estrategia permitió que el modelo ajustara sus parámetros de manera
&nb=
sp;
más equilibrada. Complementariamente, se aplicó la técnica de label smoothing , que distribuye
parte de la probabilida=
d de la clase
verdadera entre las demás, reduciendo la excesiva confianza del modelo en una sola
categoría y mejorando su capacidad de generalización. <=
/p>
&nbs=
p;
Para garantizar una evaluación rigurosa,
se empleó una estrategia de validación cruzada estratificada de cinco pliegues, complementada =
con un esquema clásico de divisi&oacu=
te;n 80/20
para entrenamiento y prueba.
Los resultados obtenidos
en términos de accuracy
oscilaron entre
0.81 y 0.85, mientras que la métrica macro-F1 , que pondera equitativame=
nte el
desempeño en todas las c=
lases,
mostró valores entre 0.80 y 0.85. Esta consistencia en las
métricas across folds ev=
idencia
la estabilidad y capacidad de generalización del modelo sobre datos =
no vistos. RoBERTui=
to
demostró alta capacidad para analizar la complejidad de los comentar=
ios
estudiantiles, caracterizados por lenguaje variado, coloquialismos y
subjetividad. Aunque el conjunto de datos no tenía un balance en las categorías, la estabilidad en accuracy y macro-F1
confirma la robustez del modelo, gracias al uso de muestreo ponderado y
validación cruzada estratificada, lo que mitigó el desbalance=
de
clases.
&nbs=
p;
Figura 10 Distr=
ibución de comentarios por categoría, mostrando predominancia =
de positivos y negativos, con neutral como la clase menos representada
Esta capacidad del modelo para ext=
raer información significativa desd=
e datos subjetivos y no estructurados re=
sulta
especialmente relevante al integrarse con los criterios utilizados
tradicionalmente por los directores de carrera para la asignación
docente. Previo a la implementa=
ción del sistema, los directores de carreras usaron principalmente la experiencia
previa en la asignatura y conocimiento temático como criterios clave
para asignar docentes.
&nbs=
p;
Result=
ado 2 Los=
criterios de los directores coinciden con los componentes del sistema recomendador, lo que
refuerza su validez institucional. Esta concordancia también se refl=
eja
en la nube de palabras generada=
a partir de las respuestas de los directores, como se muestra
en la Figura 11.
&nb=
sp;
Figura 11 Nube<=
span
style=3D'letter-spacing:-.2pt'> de palabras con los criterios más men=
cionados por los directores para la asignación docente, destacando
“experiencia” y “conocimiento”
La coincidencia entre los criterios
utilizados por los directores y los componentes matemáticos integrados en el sistema
de recomendación=
fortalece la validez institucional y la pertinencia práctica del modelo desarrollado. La
experiencia previa en la asignatura y el desempeño docente
evaluado, por ejemplo,
están directamen=
te representados en el componente histórico-evaluat=
ivo ϕk
(D,A) que pondera la calidad y continuidad del desempeño docente=
a
lo largo del tiempo, enfatizando los resultados más recientes para
reflejar el estado actual del docente.=
Por otro lado, el conocimiento temático y la formación académica quedan reflejados en la similitud de competencias Sim(D,A) que compara el perfil del docente con los requerimientos
específicos de cada asignatura, incorporando valores que consideran la formaci&oac=
ute;n formal.
Esta congruencia sugiere que el sistema no solo replica los criterios
valorados por los gestores académicos, sino que además les of=
rece
una formalización cuantitativa y objetiva que puede potenciar
la consistencia en la toma de decisiones. Cabe destacar, que algunos factores relevantes
para la institución, como el mérito por concurso, no están totalmente contemplados en la versión
actual del modelo debido a la complejidad y variabilidad institucion=
al
de estos procesos.
&nbs=
p;
Resulta=
do 3 Alta valoración cualitativa. Durante la evaluación cualitativa del<=
span
style=3D'letter-spacing:-.5pt'> sistema,
los directores de carrera manifestaron un consenso absoluto en varios aspectos clave: la precisión y pertinencia
de las recomendaciones generadas por el sistema, la calidad y suficiencia de los perfiles docentes
elaborados, la idoneidad de las alternativas propuestas para la
asignación, y la concordancia percibida entre los perfiles docentes
presentados y el desempeño real observado en el entorno
académico. Sin embargo, el análisis estadístico median=
te
el coeficiente de concordancia W de Kendall, aplicado para medir el =
grado
de acuerdo entre los tres evaluadores, arrojó un valor general moder=
ado
(W =3D 0.3333 para el modelo recomendador y general), una concordancia perf=
ecta
únicamente en la dimensión del perfil docente (W =3D 1.0) y el
valor-p asociado (p =3D 0.392) indica que no se cuenta con evidencia
estadística suficiente para afirmar que existe un acuerdo significat=
ivo
entre los directores en la mayoría de las dimensiones evaluadas.
&nb=
sp;
Esta limitación se atribuye
principalmente a la baja variabilidad en las respuestas, que se concentraron en niveles altos de valoración, y al tamaño reducido de la muestra, compuesto únicamente por tres evaluadores. Esta situaci&oac=
ute;n no invalida la validez práctica ni la relevancia de las evaluaciones, sino=
que
subraya la necesidad de ampliar la muestra en futuras fases de
evaluación para obtener
conclusiones más<=
span
style=3D'letter-spacing:.95pt'> generalizables y robustas desde el punto de vista
&nb=
sp;
estadístico. Además, la concentración de respuestas en las puntuaciones más alt=
as puede
indicar un sesgo positivo, reflejo del interés y compromiso de los evaluadores con el proyecto, así como una
percepción favorable inicial hacia la herramienta. En conjunto, estos
resultados, aunque limitados, aportan evidencia preliminar valiosa sobre la
aceptación y potencial impacto del sistema recomendador, si=
rviendo como base para mejoras iterativas y una validación ampliada en contextos institucionales más amplios.
=
;
Result=
ado 4 El sistema replica
eficazmente decisiones humanas
y ofrece alternativas pertinentes. La =
comparación entre asignaciones humanas y del sistema mostró alta coincidencia en Computación,
lo que podría =
atribuirse a una mayor
longevidad en el puesto, permitiendo un conocimient=
o más profundo de los docentes
y las asignaturas qu=
e imparten, facilitando un=
a mejor
correspondencia con las=
recomendaciones del sistema (Tabla
1). Esta carrera
alcanzó un F1-score =3D0.811, con un
50 % de coincidencia total, mientras que Telecomunicaciones y Eléctr=
ica
tuvieron resultados moderados =
(F1-score =3D0.498 y 0.558, respectivamente). En general, las coincidencias parciales predominaron, sugiriendo
oportunidades de mejora, especialmente en la precisión del orden
recomendado. Las coincidencias parciales predominantes en Telecomunicacione=
s y
Eléctrica no deben interpretarse como fallos, sino como evidencia de=
que
el sistema tiene la capacidad de descubrir asignaciones funcionales, cohere=
ntes
con los criterios institucionales y valoradas positivamente, incluso en contextos donde los directores no cuentan con una visión completa del cuerpo docente
disponible. En este sentido, el sistema se posiciona no solo como un replicador de decisiones humanas, s=
ino
como una herramienta estratégica capaz de sugerir alternativas
fundamentadas y pertinentes. No obstante, la decisión final siempre
recae en los directores de carrera, lo que asegura que cualquier posible er=
ror
sea filtrado antes de tener un impacto en la planificación académica. Es importante mencionar que las recomendaciones se derivan de datos
institucionales, por lo cual, aunque puedan diferir
de las elecciones humanas, resultan igualmente
valiosas como alertas o insumos para revisar con mayor detalle los perfiles
docentes propuestos.
&nb=
sp;
Tabla 1 Distribución
porcentual de la concordancia cualitativa entre asignacion=
es humanas
y
recomendadas, según cuatro
posibles estados
CARRERA
COINCIDENCIA =
TOTAL
PARCIAL
OTRO ORDE=
N
NO COINCI=
DE
=
Telecomunicaciones
7.14 %
50 %
32.14 %
10.71 %
=
Computación
50 %
25 %
22.73 %
2.27 %
=
Eléctrica
22.2 %
50 %
9.26 %
18.52 %
Resultado=
5 El sistema es útil, pero se
recomienda ampliar la información de los perfiles docentes. Con base en el análisis cualitativo de la retroalimentación de los directores, se resaltó la necesidad
de enriquecer dichos perfiles con datos adicionales sobre disponibilidad y carga laboral, aspectos
que podrían mejorar la precisión y funcionalidad de las
asignaciones. En general, los usuarios valoraron positivamente el sistema,
destacando su utilidad institucional y el potencial para optimizar procesos=
.
&nbs=
p;
En resumen, los hallazgos indican =
que el
sistema recomendador docente es altamente aceptado por los directores de carrera que participaron en la evaluación. La percepci&oac=
ute;n general destaca su utilidad, la cali=
dad de
las recomendaciones generadas y la claridad de los perfiles
&nb=
sp;
docentes presentados, lo que refuerza su valor como
herramienta de apoyo a la toma de decisiones
académicas. Esta=
aceptación se ve respaldada tanto por las métricas objetivas como por la retroalimentación cualitativa obtenida
durante las pruebas piloto. Además, el sistema demostró =
capacidad
para identificar alternativas viables incluso en escenarios donde los criterios
tradicionales no ofrecían suficientes opciones, lo que sugier=
e un
potencial real para ampliar y mejorar los procesos de asignación
docente.
&nb=
sp;
Conclusiones En =
este
trabajo, se desarrolló una solución tecnológica orient=
ada
a docente. Esta se sustenta en cinco pilares:
el análisis automatizado de comentarios=
estudiantiles, la medición de afinidad
entre competencias y asignaturas, el análisis del rendimiento histórico del docente, la
evaluación de competencias pedagógicas y la aplicación=
de
pesos dinámicos que ajustan la relevancia de cada criterio seg&uacut=
e;n
el nivel académico. Los principales aportes de este trabajo
son: (1) una herramienta de procesamiento del lenguaje natural
para interpretar
retroalimentación estudiantil; (2) un modelo de competencias que ref=
leja
con precisión las capacidades técnicas del personal docente; =
(3)
un sistema de recomendación alineado con los criterios institucional=
es;
y (4) un prototipo validado positivamente por parte de los directores de carrera,
quienes destacaron su utilidad y coherencia. La relevancia de estos aportes radica en que no se presenta=
n de
manera aislada, sino que convergen en una integración
metodológica, la combinación de técnicas de procesamie=
nto
de lenguaje natural basadas en tran=
sformers , tecnología emergente en el análisis automático de texto en español, con modelos
de optimización matemática aplicados al ámbito educativo. Est=
a propuesta articula infor=
mación
cuantitativa (evaluaciones institucionales), cualitativa (comentarios
estudiantiles analizados automáticamente) y competencial
(Pentágono de Competencias), generando perfiles docentes enriquecidos
que permiten decisiones fundam=
entadas.
Esta contribución constituye un avance tanto en la investigación sobre sistemas de recomendación académica como en la aplicación de inteligencia artificial para fort=
alecer
la planificación universitaria.
&nb=
sp;
Finalmente, este proyecto cumpli&o=
acute;
con los objetivos planteados y mostró que la integración de inteligencia artificial, datos institucio=
nales y conocimiento contextual puede ser una vía prometedora pa=
ra fortalecer procesos estra=
tégicos en educación superior. En particular,=
el uso de modelos
basados en transformers para
el análisis de comentarios estudiantiles, junto con la implementación de un modelo
matemático con pesos dinámicos, representa un aporte
metodológico que complementa los mecanismos tradicionales de
evaluación docente. Sin embargo, al momento de validar esta propuest=
a,
el reducido tamaño de la muestra impidió obtener resultados estadísticamente concluyentes sobre la
concordancia general entre evaluadores (coeficiente W de Kendall con valor bajo y sin significancia estadística debido a la homogeneidad
en las respuestas). A pesar de esta limitación, los hallazgos iniciales proporcionan sólidas señales
sobre la viabilidad del sistema para una implementación
más amplia a nivel institucional.
&nb=
sp;
Las limitaciones identificadas en este trabajo
se relacionan principalmente con el acceso restringido a información detallada y con la dificultad de estandarizar las competencias específicas
asociadas a cada asignatura. Si bien se contó con registros académicos y de especialización de los
docentes, su revisión y estandarización se realizó manualmente, lo que representó una dificultad
metodológica. A ello se suma la
ausencia de información precisa sobre su experiencia profesional fue=
ra
del ámbito universitario, lo que restringió la posibilidad de
construir perfiles más completos y redujo la representación integral de sus competencias. De manera complementaria, la definición
de competencias puede volverse compleja cuando no se cuenta con un
conocimiento directo del &aacut=
e;rea de especialización, lo que evidencia
la necesidad de mantener un contacto estrecho
&nb=
sp;
con expertos que dominen tanto la disciplina como los
objetivos de cada asignatura. A=
esto se suma que la validación se realiz&oacu=
te; únicamente con una muestra
reducida de directores de carrera en =
la
Facultad de Ingeniería, lo que, junto con la influencia de factores
contextuales como afinidades p=
ersonales o estilos de gestión, limita
la generalizació=
n estadística de los resultados. En consecuencia, los hallazgos no deben interpretarse como conclusiones definitivas, sino como una evidencia
preliminar sobre el potencial del sistema para evolucionar hacia una herramienta recomendadora institucional, capaz de apoyar la asignación docente de manera más transparente,
fundamentada y alineada con los criterios académicos de la
universidad.
&nbs=
p;
Como líneas de trabajo futu=
ro, se
plantea ampliar significativamente la validación del sistema
con una muestra mayor y más diversa de evaluadores para robustecer estadísticamente los resultados obtenidos. Además, se propone
recolectar informaci&oa=
cute;n directamente de los docentes sobre sus áre=
as específicas de experiencia=
y formación, enriqueciendo así la<=
span
style=3D'letter-spacing:-.75pt'> precisión y utilidad
práctica de los perfiles generados. De forma complementaria, se
considera relevante explorar mecanismos para capturar factores contextuales=
que
actualmente no están sistematizados, tales como las preferencias
individuales de los docentes respecto a determinadas asignaturas o modalidades de enseñanza, así como los estilos
de gestión aplicados
por los directores de carrera. Asimismo, se considera relevante inte=
grar
la variable de disponibilidad y carga horaria docente, aspecto que en la
Universidad de Cuenca presenta particularidades debido a la ausencia de
horarios fijos y a la combinación de actividades de docencia,
investigación y gestión
administrativa, que se desarrollan con base en su disponibilidad horaria. Su
incorporación requerirá el diseño de mecanismos
específicos para formalizar esta información de manera
estructurada, lo que permitiría enriquecer la precisión del
sistema de recomendación. No obstante, en el caso específico =
de
la Universidad de Cuenca, es importante destacar que la asignación de horarios constituye un proceso distinto
y complementario al de la asignación de docentes a las asignaturas, aunque ambos se relacionan de manera estrecha
en la planificación ac=
adémica.
Finalmente, considerando los resultados favorables obtenidos con el modelo
basado en transformers , =
se
recomienda desarrollar un modelo propio adaptado específicamente al
contexto institucional, que facilitará una mayor personalización y permitirá abordar
otras tareas complement=
arias como la detección temprana de alertas
o situaciones que afecten el ambiente
académico.
&nb=
sp;
Reconocimientos y Declaraciones Est=
e trabajo recibió el apoyo =
de la Vicerrectoría Académica de la Universidad de Cuenca y de la Dirección de
Innovación Educativa.
=
;
Est=
e trabajo forma parte de una tesis =
de grado de la carrera de Ingeniería en Computaci&oa=
cute;n. Los autores desean expresar su agradecimiento a la Direcció=
;n de
Innovación Educativa, al Departamento de Ciencias de la
Computación y a la Facultad de Ingeniería de la Universidad de
Cuenca, cuyo apoyo y acompañamiento resultaron fundamentales para la
elaboración de este trabajo.
&nbs=
p;
En cuanto a los criterios de
autoría, todos los autores han tenido una participación
equitativa en el desarrollo de la investigación, de acuerdo con la
taxonomía CRediT.
=
;
En relación con el uso de
herramientas de inteligencia artificial, los autores declaran que en la
elaboración del presente artículo se empleó la herrami=
enta
ChatGPT versión 4o. Su uso se limitó a la revisión de ortograf&iacu=
te;a y redacción en todas las secciones del manuscrito, con excepción de las referencias y del resumen,
que fueron elaborados íntegramente <=
/span>por los autores.
&nb=
sp;
Respecto =
a la financiación, el estudio ha sido autofinanciado por los autores,
sin participación de fondos externos.
&nb=
sp;
Referencias Bannan-Ritland, <=
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/08/2.-RCE-CES.pdf
&nb=
sp;
Anexos =
Códigos fuente
Los c&oac=
ute;digos fuente desarrollados para este proyecto
se encuentran disponibles públicamente en l=
os
siguientes repositorios de GitHub:
=
;
•=
; =
https://github.com/cuervo61=
6/RoBERTuito-EduCommentss
•https://github.com/Gh0stJJ/api_recomendador
•https://github.com/Gh0stJJ/Recome=
ndador_docente
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