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=
.
Esta =
obra está bajo una licencia
internacional
Creative Commons Atribución-NoComercial 4.0
Enviado: 2024/07/02 Aceptado: 2024=
/10/03
Publicado: 2024/10/15
Resumen La transf=
ormación digital
en la educación ha generado
la necesidad de capacitar a los docentes y mejorar sus competencias
digitales. Sin embargo, los procesos de formación no suelen ser
efectivos dada la falta de personalización en las propuestas y la
variedad en los perfiles de los docentes. Dado lo anterior, este artículo aborda el desarrollo de una herramienta web diseñ=
ada
para diagnosticar las capacidades digitales de los profesores y proporcionar
recomendaciones personalizadas de MOOCs
para apoyar el desarrollo de sus competencias. Para esto se propone
un sistema que utiliza técnicas de Procesamiento de Lenguaje Natural
(PLN) y un modelo de recomendaci&oacut=
e;n basado en contenido. Los datos de los cursos se obtienen mediante web scraping
y se procesan con técnicas de PLN, utilizando un modelo de
lenguaje avanzado (BERT) para generar=
representaciones vectoriales de los cursos
y las competencias. =
El sistema
web permite a los docentes autoevaluar sus competencias=
, visualizar los resultados y recibir recomendaciones personalizadas de cursos MOOC.
La implementació=
n de este sistema
ha mostrado ser eficaz en la
identificación d=
e las áreas de mejora de los docentes
y en la sugerencia<=
span
style=3D'letter-spacing:-.15pt'> de cursos relevantes para su desarrollo profesional. Los resultados sugieren
que el uso de este sistema puede ser una solución efectiva para la
capacitación en competencias digitales de los docentes.
&nbs=
p;
Palabras clave: competencias, inteligencia artificial, pent=
ágono TIC.
<=
/o:p>
&nb=
sp;
Abstract Digital transformation in education has created the n=
eed to
train teachers and improve their digital competencies. However, training
processes are often ineffective due to the lack of personalization in the
proposals and the variety in teacher profiles. Given this, this article
addresses the development of a web tool designed to diagnose teachers’=
; digital competencies and provide
personalized recommendations for MOOCs to support the development of their
skills. To achieve this,<=
span
style=3D'letter-spacing:-.55pt'> a system that uses Natural
Language Processing (NLP)
techniques and a content-based recommendation model is proposed. Course data is obtained through web scraping
and processed with NLP techniques, using an advanced language model
(BERT) to generate vector representations of the courses and competencies. =
The
web system allows teachers to self-assess their competencies, visualize the<=
span
style=3D'letter-spacing:-.75pt'> results,
and receive personalized recommendations for MOOCs. The implementation of this system
has proven effective in identifying teachers’ areas for improvement and suggesting relevant
courses for their professional development. The results suggest that the use of this system can be an
effective solution for training teachers in digital competencies.
&nbs=
p;
Keywords: pedagogical tools, artificial intelligence, =
digital competencies, educational technology.
&nb=
sp;
Introducción La era digital ha provocado una
transformación significativa en diversos aspectos de la sociedad, con la educación=
como
uno de los sectores más afectados. Este cambio profundo ha impulsado=
una
revisión de las metodologías de enseñanza y aprendizaj=
e,
situando a las Tecnologías de la Información y la Comunicación (TIC) en el núcleo de la interacción educativa
(Figueredo, 2013). La incorporación de las TIC en la
educación no solo favorece un enfoque más interactivo y
dinámico en los procesos educativos, sino que también exige u=
na
evolución en el papel del docente. En este contexto, los educadores =
se
enfrentan al reto de adaptarse a estos=
nuevos entornos
digitales, lo que requiere el desarrollo y perfeccionamiento constante de sus competencias digitales y relacionadas con las TIC.
&nb=
sp;
No obstante, las instituciones
educativas enfrentan serios desafíos en el proceso de capacitar eficazmente a los docentes
en el uso de las TIC. Estos retos incluyen
desde la carencia de recursos tecno=
lógicos adecuados, como computado=
ras y acceso a Internet, hasta
limitaciones en la formación y desarrollo profesional de los docentes en el ámbito tecnológico (Ayabaca et al., 2019). Además, uno de los obstáculos más complejos es la falta de una referencia estandarizada que permita evaluar con precisión el ni=
vel
inicial de competencia TIC de los docentes. Esta ausencia de criterios
uniformes complica la identificación de necesidades específic=
as
de formación, lo que dificulta la personalización de los
programas de capacitación para mejorar las habilidades dig=
itales. La disparidad en los niveles
de competencia digital
entre docentes de diferentes regiones, instituciones e incluso dentro de la misma institución subraya la importancia
de enfoques adaptativos en la capacitación docente.
&nb=
sp;
Para superar estos desafíos=
, se
han desarrollado diversos modelos adaptados a los distintos contextos
regionales, con el objetivo de establecer una base para evaluar y mejorar l=
as
competencias digitales de los docentes. Entre estos, destacan los “Es=
tándares
ISTE para educadores” publicados por la Sociedad Internacional de
Tecnología Educativa de Estados Unidos (ISTE) en 2008, que proporcio=
nan
directrices para la integración de las TIC en la práctica docente
(Trust, 2018). En Europa, el Instituto Nacional
de Tecnologías Educativas y de Formación del
Profesorado (INTEF) ha propuesto el “Marco Común de Competencia
Digital Docente”, alineado con el Marco Europeo de Competencia Digital
para Ciudadanos y
&nb=
sp;
Educadores, el cual sirve como una herramienta para
identificar las necesidades de formación en el ámbito digital
(INTEF, 2023; Redecker, 2022). En América Latina, el Ministerio de
Educación de Colombia (MEN, 2013) presenta un modelo enfocado en cin=
co
competencias TIC fundamentales.
=
;
Los=
modelos antes mencionados brindan un marco <=
span
lang=3DES style=3D'letter-spacing:-.6pt'> de referencia para establecer una base <=
span
lang=3DES>que permita evaluar
las competencias digitales de los docentes. Sin embargo, el verdadero valor de
estas evaluaciones radica en utilizar
sus resultados para apoyar la formación docente.
Si bien es importante medir las competencias TIC, es aún más cruci=
al actuar sobre esa información. No obstante, organizar capa=
citaciones que lleguen a grandes grupos
de docentes y que aborden
las competencias y el nivel requerido para cada uno según su perfil evaluado es costoso y complicado
para las instituciones educativas. Esto evidencia la necesidad de una
herramienta tecnológica que permita ayudar a la formación docente
de manera masiva,
automática y personalizada. Para asegurar que este sea un proceso automatizado que no requiera mayores esfuerzos organizativos,
se pueden utilizar técni=
cas de
Procesamiento de Lenguaje Natural (PLN) para analizar contenidos en =
la
web y determinar su pertinencia con respecto a los objetivos de
formación planteados.
&nb=
sp;
Este trabajo propone el desarrollo=
de
una herramienta web diseñada para evaluar y presentar el perfil de competencias digitales de los docentes, y utilizar esos datos para recomendar
automáticamente c=
ursos MOOC que ayuden
a mejorar cada competencia evaluada.
Para lograr esto, se define un marco de referencia
para la evaluación de competencias digitales, se recolectan
y preprocesan datos de cursos utilizando técnicas de PLN para
identificar su relevancia en relación con cada competencia, y se gen=
eran
representaciones vectoriales tanto de los cursos como de las descripciones de las competencias. A partir de estas representaciones, se calcula la similitud entre ellas, se implementa un modelo de clasificación personalizado y se optimizan sus hiperparámetros. Los cursos se clasifican según su nivel cognitivo para asegurar recomendaciones
personalizadas. Finalme=
nte, se implementa un sistema web que realiza
búsquedas autom&=
aacute;ticas
y periódicas de cursos, actualizando continuamente las recomendacion=
es,
y permitiendo a los docentes evaluar sus competencias y acceder a sugerenci=
as
personalizadas.
&nbs=
p;
El artículo se encuentra estructurado de la siguiente
manera: en la sección 2 se describe la metodología utilizada para el desarrollo del sistema de recomendación. La sección 3 presenta
los resultados de la evaluación del sistema y finalmente la
sección 4 presenta las principales conclusiones de este trabajo.
&nb=
sp;
Metodología En la Figura 1 se detalla la
metodología empleada para desarrollar de cursos utilizando un enfoque de filtrado basado
en contenido. El enfoque de filtrado basado en contenido elige los elementos a recomendar basándose en la correlación entre el
contenido de esos elementos y las preferencias o necesidades previamente
identificadas del usuario (Agga=
rwal, 2016). En este caso, no se requiere
información de otros docentes ni de su interacción con otros cursos; lo único necesario es la correlación entre el contenido de los cursos
y las descripciones de las competencias a evaluar, que representan las necesidades identificadas.
Esta metodología se divide en tres etapas o secciones princip=
ales
que son:
&nbs=
p;
• =
Etapa 2: Implementación del Modelo de Recomendación.
• =
Etapa 3: Implementación del Sistema.
&nb=
sp;
Figura 1 Metodología
aplicada para el desarrollo del sistema de recomendación
Fase 1: Preparación de los datos Definición del Marco de Referencia El primer paso en esta primera etapa
es definir el marco de referencia para la evaluación de las competenci=
as
digitales. Después de revisar varios marcos (ver sección 1), =
se
optó por utilizar el Pentágono de Competencias TIC para el Desarrollo Profesional Docente del Ministerio
de Educación Nacional (MEN) de Colombia (MEN, 2013). El
Pentágono de Competencias ha
sido específicamente desarrollado teniendo en cuenta el contexto
latinoamericano, lo que lo hace más relevante y aplicable a las
realidades y necesidades específicas de nuestra región. Este
modelo se centra en cinco competencias TIC
principales que cubren una amplia gama de habilidades necesarias para la
práctica docente efectiva en el ámbito digital, asegurando una
formación completa y equilibrada para los docentes. Además, el modelo ha sido
reconocido y validado por instituciones educativas de la región,
garantizando su fiabilidad y efectividad en la
mejora de las competencias digitales
de los docentes. La implementación de este modelo ha
mostrado resultados positivos en la
identificación de áreas de mejora y en la capacitación
efectiva de los docentes en competencias digitales, lo que respalda =
su
elección sobre otros marcos que pueden no estar tan ajustados a las necesidades locales. Este marco sugiere un camino y guía para que los docentes utilicen
las TIC de forma estructurada, a travé=
s de la evaluación y el desarrollo de cinco competencias qu=
e se
describen en la Tabla 1.
&nb=
sp;
Tabla 1 Definición
de las competencias dig=
itales según el marco del MEN de Colombia
COMPETENCIA
DESCRIPCIÓN
=
Tecnológica
Comprensión
de =
las
herramientas =
tecnológicas,
desarrollo =
de
contenidos =
y
consideración
étic=
a en la
utilización de las TIC=
.
=
Comunicativa
Capacidades=
de los docentes para transmit=
ir y compartir información en entornos
mediados por
las TIC.
=
Pedagógica
Aprecia el aprendizaje, la resolución de problemas educativos y el diseño de estrategias
didá=
cticas apoyadas por las TIC.
De =
gestión
Aplicación de las TIC en los procesos=
educativ=
os, las pol&iacu=
te;ticas y =
el crecimie=
nto profesio=
nal mediante
el uso de TIC.
=
=
Investigativa
Comprueba que las actividades de investigación fomenten=
la reflexi&=
oacute;n sobre el uso de las TIC,
las fuentes de información, el acceso a información a
través de TIC y la creación de nuevos conocimientos.
Estas competencias se desarrollan y
manifiestan en diferentes niveles o etapas de complejidad. En la primera
etapa, conocida como momento de exploración, se facilita el acceso
a un conjunto
de conocimientos básicos que sirven como fundamento para avanzar hacia niveles
más profundos de comprensión. Posteriormente, en la segunda
etapa o momento
de integración, =
se promueve
la aplicación de los conocimientos adquiridos par=
a resolver
problemas en diversos contextos. Finalmente, en la tercera
etapa o momento de innovación, se destaca la creatividad,
permitiendo ir más allá de lo aprendido y concebir nuevas for=
mas
de acción o explicación. En la Figura 2 se muestra un ejemplo=
del
resultado de evaluar las competencias digitales según este modelo.
&nb=
sp;
Figura 2 Resultado =
de la evaluación de competencias según el modelo del MEN de Colombia
Obtención <=
/span>de los datos de
cursos Una de las principales motivacione=
s para
este trabajo es la necesidad de apoyar la formación docente con contenidos actualizados, al mismo tiempo que se minimizan los esfuerzos
de la universidad en términos de tiempo y presupuesto. Una manera de automatizar el proceso de
recolección de datos de cursos es emplear
una técnica <=
span
style=3D'letter-spacing:-.45pt'> utilizada para extraer datos de plataformas
web accesibles a través de Internet denominada web scraping. =
Esto
implica el uso de scripts que recorren páginas web y recopilan
información específica de manera automatizada (Patel, 2020). =
Por
tal motivo, la recolección de datos se llevó a cabo utilizando
herramientas como Python y bibliotecas como BeautifulSoup y Requests,
respetando los términos de servicio de los sitios web objetivo. Las plataforma=
s de cursos MOOC incluyeron sitios
como Coursera y Udemy. =
El
proceso consistió en extraer información relevante de cada cu=
rso,
incluyendo títulos, des=
cripciones y enlaces, asegurando la limpieza y estructuración adecuada
de los datos recolectados para su posterior procesamiento. Al tratarse de un proceso
calculado se utilizaron términos de
búsqueda específicos relacionados con las competencias digita=
les
evaluadas. Estos términos fueron seleccionados cuidadosamente para
abarcar una amplia gama de
cursos pertinentes y asegurar que los resultados fueran relevantes. <=
/p>
&nb=
sp;
Preprocesamiento =
y filtrado
inicial de los cursos Para preprocesar los textos de los cursos,
se aplicó una serie de pasos de procesamiento
de lenguaje natural (PLN) tanto al título como a la descripció=
;n.
Primero, se llevó a cabo la tokenización, un proceso que divi=
de
el texto en unidades más pequeñas llamadas tokens, que =
&nb=
sp;
pueden incluir palabra=
s,
partes de palabras, caracteres, números, signos de puntuación=
o
símbolos (Lee, =
2023). Luego, se aplicó la lematización, que convierte cada palabra a su forma base llamada lema de la palabra (Khyani & B S,=
2021)
(por ejemplo, "corriendo" se convierte en "correr").
Adicionalmente, se eliminaron las stop words, que son palabras comunes
y de poca relevancia =
(como "y", "el", =
"de") que no aportan
significado al análisis (Kedia & Rasu, 2020). Estos pasos
permitieron obtener un conjunto de palabras representativo para cada curso,
facilitando su posterior análisis y filtrado.
=
;
Durante
la obtención automatizada de los cursos,
se recolectaron varios cursos que no correspondían a las
competencias debido a la naturaleza de los motores de búsqueda en las páginas de cursos, que en
ocasiones devuelven resultados irrelevantes. Para abordar este problema, se implementó un proceso de filtrado inicial
utilizando términ=
os clave obtenidos de las descripciones de las cinco competencias evaluadas. Para identificar=
estos términos clave, se
utilizó el método TF-IDF (Term Frequency-Inverse Document Frequency), que es ampliamente
utilizado en el análisis de textos para resaltar palabras significativas en grandes conjuntos de datos (Leskovec, Rajaraman, & Ullman,<=
span
style=3D'letter-spacing:-.35pt'> 2014).
Este método asegura
que los términos seleccionados sean verdaderamente representativos de las
competencias.
&nbs=
p;
A partir de las descripciones de las competencias, se gener&oacut=
e; una lista de palabras clave para cada una de ellas. Estas
palabras clave se compararon con el conjunto
de palabras obtenido de cada curso durante el preprocesamiento. Los cursos se filtraron en función de las coincidencias
entre las palabras clave correspondientes a la competencia y el conj=
unto
de palabras de cada curso. Aqu=
ellos cursos
que no contenían ninguna de las palabras
relevantes fueron descartados, asegurando que solo los cursos pertinentes a las competencias evaluadas fueran considerados en las etapas
posteriores del análisis. Esta estrategi=
a de filtrado inicial
no solo mejora
la calidad de los
datos, sino que también optimiza
los recursos al eliminar cursos irrelevantes desde el inicio.
=
;
Etiquetado manual<=
/span> Se creó un conjunto de datos, extrayendo los 100 cursos
con las coincidencias más alta=
s para cada competencia con el fin de facilitar el proceso de etiquetado manual, garantizando que el conjunto
de datos fuera manejable y representativo. El objetivo del etiquetado manual es evaluar
la pertinencia de cada curso
en relación con la competencia para la cual fue seleccionado. Los especialistas revisaron los títulos y descripciones de los cursos
y los clasificaron m=
anualmente
como "pertinentes" o "no pertinentes". El resultado de =
este
procedimiento fue la creación de un conjunto de datos etiquetados denominado gold<=
span
style=3D'letter-spacing:-.4pt'> estándar, que representa el resultado que se
espera del modelo y que sirvió como base para el entrenamiento y
evaluación del modelo de recome=
ndación.
=
;
Fase 2: Modelo de=
recomendación En esta etapa se implementó=
el
modelo de recomendación, para lo cual se utilizó un modelo de lenguaje para convertir tanto las definiciones de las competencias como los títulos y descripciones =
de los cursos en vectores numéricos denominados embe=
ddings y posteriormente
calcular la similitud entr=
e estos embeddings. Usando
estos valores de similitud entre
los cursos y su respectiva
competencia, se implementó un modelo de clasificación que decide la pertinencia
del curso. Finalmente, para asegurar la personalización del curso, se implementó una clasificación
según el nivel cognitivo de los cursos.
En la Figura 3 se explica de manera gráfica el proceso de implementación del modelo =
de
clasificación.
&nb=
sp;
Generación =
de Embeddings Par=
a representar <=
/span>mediante embeddings los títulos y descripciones =
de los cursos, así como
las desc=
ripciones de las competencias, se utiliza BERT (Bidirectional Encoder
Representations from Tra=
nsformers), el cual es un modelo de lenguaje
avanzado que destaca
por poder generar representaciones contextuale=
s de
las palabras, ya que considera el contexto en el que aparece cada palabra
dentro del texto (Devlin et al., 2019). Este modelo tiene un tamaño comparable al de
BERT-Base y fue entrenado
utilizando la técnica de Whole Word Masking (Cañete et al., 2023).
=
;
Para este proceso, no es necesario
aplicar ningún tipo de preprocesamiento de textos, ya que puede alte=
rar
el contexto, lo cual no es recomendable para ser procesado por BERT. En primer lugar, se utiliza
la tokenización específica de BERT, que divide el texto en tokens y añade tokens especiales
necesarios para el modelo. Finalmente, para obtener los embeddings, estos
tokens se pasan a través del modelo BERT pre entrenado
(dccuchile/bert-base-spanish- wwm-unca=
sed), generando un vector de alta dimensión para cada token. Estos vectores capturan
información semántica rica sobre las palabras y su
contexto en el texto y se promedian para obtener una representaci=
ón final del texto. Se ha optado por este método de agregaci&oacu=
te;n debido a su capacidad para obtener u=
na
representación general del texto completo.
&nb=
sp;
Cálculo de =
similitudes Eva=
luar la similitud entre dos textos requiere convertir el texto <=
span
lang=3DES style=3D'letter-spacing:-.3pt'> en un conjunto de números y
aplicar operaciones matemáticas para calcular la distancia entre dos
vectores, lo que indica su relación. Distancias pequeñas sugi=
eren
una alta relación, mientras que distancias grandes indican una baja relación. La similitud coseno es una medida utilizada
para evaluar la similitud
entre dos vectores en un espacio multidimensional.
&nbs=
p;
Se realizan dos cálculos de similitud por curso: (1) para el título; (2) para la descripción
del
curso. La función cosine_similarity de Sklearn se utiliza para calcular la similitud del coseno.
Esta función toma dos vectores como entrada y devuelve un val=
or
de similitud. Este valor puede =
variar entre -1 y 1, donde los valores
cercanos a 1 indican que los vectores
son similares, mientras<=
span
style=3D'letter-spacing:-.5pt'> que los valores
cercanos a -1 indican que son diferentes. Los resultados obtenidos
son la similitud entre
el título del curso y la definición de la competencia, así com=
o la similitud entre la descripción del cur=
so y
la definición de la competencia.
&nbs=
p;
Implementación del modelo
de clasificación Se implementa un modelo de
clasificación personalizado, el cual utiliza los valores de la similitud
del coseno calculados para los títulos y descripciones=
de los cursos en relación con las competencias. Para combinar estos dos valores de
similitud en una única puntuación, se calcula un promedio ponderado. El peso del título se denota como w, y el peso de la descripción
se calcula como 1−w.
&nbs=
p;
Basándose en la similitud
ponderada, el modelo clasifica un curso como pertinente o no
pertinente para una competencia. Para tomar esta decisión, se define un umbral denotado
como t, que determina el punto de corte para considerar un curso como
pertinente. Si la similitud ponderada es mayor o igual al umbral, el curso =
se
clasifica como pertinente.
=
;
Optimización y evaluación del modelo En esta sección se
entrenará el modelo con los datos etiquetados para definir los
hiperparámetros ó=
;ptimos para este modelo
de clasificación=
. Al realizar un análisis de los datos etiquetados se pudo observar
que las etiquetas están m=
uy desbalanceadas, por lo que se aplicó la técnica d=
e submuestreo al conjunto de datos etiquetados para evitar el sobreajuste y asegurar
&nb=
sp;
un entrenamiento equilibrado del modelo. Esta
técnica se utiliza para reducir la cantidad de instancias de la clase mayoritaria, lo que ayuda a equilibrar los conjuntos de datos y mejorar la precisión de los modelos
predictivos (Ali et al., 2015).
En este caso,
la clase mayoritaria son los cursos=
etiquetados como no pertinentes y la clase
minoritaria son los etiquetados como cursos
pertinentes. Con este submuestreo, se asegura que el modelo no esté
sesgado hacia la clase mayoritaria, es decir, que el modelo no sea propenso=
a
clasificar siempre como no pertinentes a los cursos.
&nbs=
p;
Figura 3 Proceso de implementación del modelo de recomendación
Con el objetivo de buscar los
hiperparámetros que brinden un mejor rendimiento para el modelo, se
aplicó Grid Search en combinación con validación cruza=
da.
Esta combinación de técnicas es ampliamente utilizada debido =
a su
capacidad para buscar los hiperparámetros óptimos <=
span
style=3D'letter-spacing:-.1pt'>de manera rigurosa
y sistemática. Según un artículo en Scikit-learn documentation, este enfoque asegura
que los modelos
sean evaluados exhaustivamente, utilizando sol=
o una parte del conjunto de datos para la selección de hiperpar&aac=
ute;metros y otra parte para la evaluación final,
lo que reduce el riesgo de sobreajuste (Scikit-learn, 2018). <=
/p>
&nb=
sp;
En la Tabla 2 se observa la descripción de los hiperparametros a optimizar para este modelo, definiendo los ran=
gos
que pueden tener y el incremento que va a ser utilizado para la búsq=
ueda
exhaustiva. Para este proceso se definió un conjunto de todas las
posibles combinaciones de w y t, para posteriormente realizar una
validación cruzada k-fold. En este caso
se utilizó un k=3D5; esto implica dividir
el conjunto de datos en cinco pliegues
para entrenar al modelo =
con
cuatro pliegues y probar con el pliegue restante, reiterando este proceso c=
inco
veces para cada combinación de hiperparámetros.
&nbs=
p;
Para cada combinación de
hiperparámetros se obtuvieron las métricas que indican el
rendimiento del modelo. Los resultados de estas métricas en las
iteraciones de la validación cruzada se promedian para obtener las m=
étricas
representativas para cada combinación de hiperparámetros, =
lo que permite
identificar la configuración óptima que maximiza el rendimiento
del modelo. Existen
diversas métricas=
para evaluar un modelo y depende de la naturaleza del problema abordado cua=
l es
la métrica que se requiere maximizar. En el contexto de esta
clasificación de cursos se optó por maximizar la métri=
ca
de precisión, ya que en este caso es crucial minimizar el núm=
ero
de falsos positivos, es decir, evitar que se recomienden cursos que no sean
relevantes para las competencias definidas. Esta métrica mide la
proporción de verdaderos positivos entre todos los clasificados como
positivos, y puede ser especialmente útil cuando el objetivo
es asegurar que los elementos etiquetados po=
sitivamente sean correctos
(Saito & Rehmsmeier, 2015). Esta métrica es particularmente
relevante en conjuntos de datos desequilibrados, como es el caso en este
problema, donde la cantidad de cursos pertinentes es mucho menor que la
cantidad de cursos no pertinentes.
&nbs=
p;
Tabla 2 Hiperparámetros del modelo
HIPERPARÁMETRO
DESCRIPCIÓN
RANGO
INCREMENTO
Peso del título
Determina la importancia relativa de la similitud
del título en comparación con la similitud de la descripción.
[0,1]
0.1
Umbral
Define el valor mínimo de la similitud ponderada para
que un curs=
o sea
considerado pertinente. <=
/o:p>
[0,1]
0.1
La Tabla 3 presenta los mejores
resultados obtenidos durante la primera pasada del Grid Search, una técnica utilizada para optimizar
la selección de hiperparámetros en el modelo. En este contexto, los
hiperparámetros más efectivos fueron un peso del título
(w) de 0.2 y un umbral (t) de 0.8. Esta combinación proporcionó una precisi&oacu=
te;n de 0.596, lo que indica que, de las recomendaciones generadas, una
proporción considerable fue correcta. Sin embargo, esta mejora en la
precisión se obtuvo a costa de una disminución en el recall, =
que
fue de 0.381.
=
;
El recall es crucial para asegurar=
que
el modelo identifique la mayoría de los cursos relevantes, y una
disminución en esta métrica implica que hay más cursos
relevantes que no fueron recomendados (falsos negativos). No obstante, en e=
ste
escenario, se decidió priorizar=
la
precisión sobre el recall. La razón detrás de esta
decisión es que se busca asegurar que los cursos que se recomiendan sean altamente pertinentes para los usuarios,
incluso si eso significa
no recomendar todos los cursos que podrían ser relevantes. Este enfo=
que
es aceptable, ya que es prefer=
ible evitar
la inclusión de cursos no pertinentes en las recomendaciones, lo cual podría disminuir la confianz=
a en
el sistema. Al asegurar la alta calidad de las recomendaciones, se mantiene=
la relevancia y efectividad del sistema, alineándose con el objetivo
de proporcionar a los docentes cursos que realmente
contribuyan a mejorar sus competencias.
&nb=
sp;
Además, el análisis =
de los
resultados sugiere que, aunque existe un trade-off entre precisión y
recall, la estrategia seleccionada optimiza el rendimiento del modelo de
acuerdo con las prioridades del problema en cuestión, donde la precisión es más<=
span
style=3D'letter-spacing:-.45pt'> crítica para el éxito del sistema de<=
span
style=3D'letter-spacing:-.2pt'> recomendación. Sin embargo, es importante tener en cuenta que futuras
iteraciones del modelo podrían explorar un balance
diferente o buscar formas de mejorar tanto la precisión como el recall,
dependiendo de las necesidades específicas del sistema y el feedback=
de
los usuarios.
&nbs=
p;
Tabla 3 Mejores Resultados Grid Search (primera
pasada)
PESO DEL TITULO
UMBRAL
PRECISIÓN
RECALL
F1-SCORE<=
/span>
ACCURACY
0.2
0.8
=
0.596
=
0.381
=
0.454
0.789
0.3
0.8
=
0.496
=
0.147
=
0.22
0.777
=
1
0.7
=
0.466
=
0.103
=
0.168
0.766
0.1
0.8
=
0.458
=
0.536
=
0.485
0.726
0.8
0.7
=
0.457
=
0.259
=
0.325
0.748
0.9
0.7
=
0.434
=
0.155
=
0.227
0.756
0.7
0.7
=
0.406
=
0.397
=
0.399
0.724
0.5
0.8
0.4
=
0.017
=
0.033
0.769
0.4
0.8
=
0.35
=
0.043
=
0.076
0.769
0.6
0.7
=
0.325
=
0.543
=
0.404
=
0.63
Después de los resultados
obtenidos en la primera pasada del Grid Search, se decidió realizar =
una
segunda pasada para afinar aún más la precisión del
modelo. En esta iteración, se utilizó un rango más
específico de valores de los hiperparámetros, centrado alrede=
dor
de los valores previamente identificados como prometedores. El objetivo de =
esta
segunda búsqueda era opt=
imizar con mayor precisión los hiperpar&aac=
ute;metros clave, asegurando que se exploraran las configuraciones más cercanas al óptimo para
mejorar el rendimiento del modelo.
=
;
Como resultado de esta segunda
búsqueda, los valores óptimos identificados fueron un peso del título (w) de 0.2=
15 y
un umbral (t) de 0.811. Como se muestra en la Tabla 4, estos hiperparámetros produjeron una precisión=
de
0.624, una mejora notable respecto a los resultados anteriores. Aunque el
recall no mostró una mejora significativa y se mantuvo en valores similares
a los de la primera
pasada (0.251), la precisión fue la métrica que experiment&o=
acute;
el mayor beneficio.
=
;
La<=
/span> mejora =
en la precisión significa que el modelo es ahora <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>más eficaz en la identificaci&=
oacute;n
de cursos altamente relevantes para los usuarios,
minimizando la inclusión de cursos que no cumplen con los criterios deseados. Este=
resultado es especialmen=
te relevante para el contexto en el que se aplica, donde
la exactitud en las recomendaciones es prioritaria=
. La ligera reducción en el recall es un=
costo
aceptable en este caso, ya que la estrategia sigue siendo asegurar que los
cursos recomendados sean lo más pertinentes posible, aún si e=
sto
implica no captar todos los cursos relevantes disponibles.
&nbs=
p;
Además, la Tabla 4 muestra que la precisión lograda con estos
hiperparámetros =
también
está acompañada p=
or un F1-Score de 0.349 y una precisión global (accuracy=
) de 0.796, lo que
&nb=
sp;
indica un buen balance general entre las métri=
cas de
rendimiento. Estos resultados sugieren que el ajuste fino realizado en esta
segunda pasada ha permitido al modelo alcanzar un nivel más elevado
de rendimiento, adecuándose mejor a los objetivos del sistema de recomendación.
&nb=
sp;
Fin=
almente, es importante destacar que este proceso iterativo de ajuste y optimización es
fundamental para mejo=
rar
continuamente la calidad de las recomendaciones, y futuras iteraciones podrían contin=
uar afinando
los hiperparámetr=
os para adaptarse a cambios en los datos o en las
necesidades de los usuarios.
&nbs=
p;
Tabla 4 Mejores Resultados Grid Search (segunda
pasada)
PESO DEL TITULO
UMBRAL
PRECISIÓN
RECALL
F1-SCORE<=
/span>
ACCURACY
0.215
=
0.811
=
0.624
=
0.251
=
0.349
0.796
=
0.216
=
0.811
=
0.618
=
0.243
=
0.339
=
0.794
=
0.221
=
0.81
=
0.618
=
0.243
=
0.339
=
0.794
=
0.227
=
0.808
=
0.611
0.243
=
0.338
=
0.792
0.24
=
0.805
=
0.611
0.243
=
0.338
0.792
0.232
=
0.807
=
0.611
0.243
=
0.338
0.792
=
0.231
=
0.807
=
0.611
=
0.243
=
0.338
0.792
=
0.239
=
0.805
=
0.611
=
0.243
=
0.338
=
0.792
=
0.223
=
0.809
=
0.611
0.243
=
0.338
=
0.792
0.228
=
0.808
=
0.611
=
0.243
=
0.338
=
0.792
Los resultados de este proceso de
optimización y evaluación muestran que el modelo actual
proporciona recomendaciones de moderada precisión, lo cual es adecua=
do
para las necesidades actuales del sistema, pero es importante trabajar a fu=
turo
en la optimización de este modelo para poder asegurar
que se recomienden to=
dos los cursos pertinentes posibles y se minimicen los falsos negativos en
futuras iteraciones del sistema.
&nbs=
p;
Clasificación utilizando la clasificaci&o=
acute;n de Bloom La clasificación de Bloom, =
creada
por Benjamin Bloom en 1956, es un marco para clasificar los objetivos
educativos, que ha sido ampliamente utilizado en el ámbito educativo
desde su desarrollo. Los educadores emplean esta clasificación o
taxonomía para establecer objetivos de aprendizaje, diseñar
actividades de enseñanza y evaluación, y garantizar que se
aborden diferentes niveles cognitivos. En su artículo "A Revision of Bloom’s Taxonomy:=
An Overview", Krathwohl (200=
2) enfatiza
la relevancia de la taxonomía revisada en la evaluación
y
planificación educativa. Esta taxonomía inicialmente clasifica el aprendizaje cognitivo en seis niveles: Conocer,
Entender, Aplicar, Analizar, Evaluar y Crear.
En el contexto de la educación
en línea, la taxonomía de Bloom se considera una herramienta
útil para clasificar y analizar la profundidad cognitiva de los cursos MOOCs. =
En
este estudio, se adaptó la taxonomía de Bloom para categorizar
los cursos en tres etapas de competencia según el marco referencial =
seleccionado: "Explorador",
"Integrador" e "Innovador", representando un incremento
progresivo en la complejidad cognitiva. Para=
realizar
esta clasificació=
n, se siguió el proceso que se ilustra en la Figura 4:
&nb=
sp;
Figura 4 Proceso de clasificación según etapas o niveles
cognitivos
• =
Definición de Niveles Cognitivos para los momentos
del Pentágono TIC: Con el fin
de simplificar la taxonom&iacut=
e;a de Bloom y ajustarla a nuestro contexto,
se combinaron sus seis niveles cognitivos en tres categorías (que corresponde=
n con los momentos), como se muestra en la Tab=
la 5.
&nbs=
p;
Tabla 5 Taxonomía de Bloom y categorías del Pentá=
;gono TIC
MOMENTOS
NIVEL COGNI=
TIVO DE BLOOM
COMBINADO
DETALLE
Explorador
Conocer y Entender
Cursos enfocados en la introducción y =
comprens=
ión
fundamental de conceptos.
Integrador
Aplicar y Analizar
Cursos
que =
fomentan
la =
aplicación
de
conocimient=
os y el análisis crítico.
Innovador
Evaluar y Crear
Cursos avanzados que impulsan la evaluació=
;n
crítica y la generación de nuevos conocimientos o productos.
• =
Identificación de Verbos Clave
para cada momento:
Se seleccionó un conjunto
de verbos asociados originales de la taxonomía de Bloom. Estos verbos
fueron lematizados y sirven como indicadores del nivel cognitivo que se esp=
era
que el curso logre.
• =
Procesamiento de Descripciones =
de
Cursos: Las descripciones de los cursos disponibles en las plataformas fuer=
on
procesadas para extraer los verbos clave, utilizando la biblioteca NLTK de
Python. Esta herramienta permitió llevar a cabo dos procesos
fundamentales: la lematización de verbos y la eliminación de
“stop words”. La lematización redujo los verbos a su for=
ma
base, lo cual es crucial para un análisis comparativo preciso, mient=
ras que la eliminación de “stop=
words” ayudó a centrarse en las palabras más relevan=
tes
de las descripciones.
• =
Comparación
y Asignación de Momentos: Utilizando los verbos lematizados
&nb=
sp;
extraídos de las descripciones junto con los v=
erbos
clave definidos para cada nivel cognitivo, se realizó una
comparación detallada. A=
cada curso se le asignó el ni=
vel
correspondiente basado en la mayor frecuencia de verbos asociados. Este enf=
oque
cuantitativo garantiza una clasificación objetiva y sistemáti=
ca
de los cursos según su nivel de profundidad cognitiva.
&nb=
sp;
Fase 3: Implementación del sistema En esta etapa final se implementó un sistema web que permite
a los docentes acceder a realizar una autoevaluación de sus
competencias, ver sus resultados y obtener las recomendaciones =
de cursos para capacitarse en sus competencias digitales. Además, este sistema
utiliza técnicas=
de web scraping
para obtener nuevos
cursos y utiliza
el modelo generado
en la etapa 2 para filtrar
los cursos a recomendarse y almacenarlos en la base de datos. La arquitectura
de este sistema se describe de manera gráfica en la Figura 5.=
&nbs=
p;
Figura 5 Arquitectura del sistema
de recomendación <=
o:p>
Autoevaluación de capacidades A partir del instrumento adaptado,=
se
implementó en el sistema una herramienta de evaluación adaptada
al contexto universitario para medir las competencias TIC de los docentes.
Este instrumento evalúa competencias en áreas
tecnológicas, pedagógicas, comunicativas, de gestión e investigativas. Consta de 80 preguntas y utiliza una escala de calificación basada en la escala de Likert (“1- Nada Competente” a “5- Totalmente competente”). Además, el cuestionario
recoge informació=
n sobre la facultad
y la carrera en las que el docente imparte
clases, así como su
número de identificación personal.
La interfaz de usuario para esta sección se muestra en la
Figura 6.
&nbs=
p;
Entre
las preguntas diseñadas para cada competencia, se incluyen preguntas
específicas que describen los diferentes momentos o niveles en
los que un docente puede encontrarse. De este modo, para cada competencia, =
se
ubicará al docente en el nivel correspondiente al grupo de preguntas=
en
el que obtuvo la mayor calificación promedio.
&nb=
sp;
Figura 6 Interfaz <=
/span>para completar
la autoevaluación
Resultados =
y recomendaciones Des=
pués de que los docentes completen la autoevaluación,
podrán <=
span
lang=3DES style=3D'letter-spacing:-.5pt'> acceder a sus resultados en la sección de resultados,=
la cual muestra
una representació=
;n de su nivel
de competencia TIC. Para visualizar los=
resultados, se utiliz&oacu=
te; un gráfico de radar en forma de pentágono, que consta
de tres niveles. Cada punta del pentágono representa una competencia=
, y
cada nivel indica el momento en=
que se encuentra
el docente, siendo el nivel más interno
el Momento Explorador, el nivel intermedio el M=
omento
Integrador, y el nivel más externo el Momento Innovador. Ademá=
;s, se ofrece una interpretación detallada
de cada competencia y sus respectivos niveles, permitiendo a los docentes comprender los resultados obt=
enidos en la encuesta (ver Figura 7).
=
;
Figura 7 Vista de la interfaz de usuario para visualizar los resultados de la evaluación
La sección de cursos recomendados (ver Figura 8) muestra una lista de todos los cursos
que se recomiendan al profesor y que están alineados con su nivel de
competencias TIC. Los cursos r=
ecomendados están organizados en=
grupos
de 3 por cada competencia. Cada curso es
accesible y presenta una imagen, un título, la descripción br=
eve,
puntuación, creador y la plataforma de origen.
&nb=
sp;
Figura 8 Vista de la interfaz de usuario para visualizar las recomendaciones de los cursos
Búsqueda automatizada de cursos El sistema realiza una búsq=
ueda
automatizada de nuevos cursos en las plataformas MOOC de forma periódica. Esto se realiza
para mantener actualizada la base de datos de cursos
para recomendar utilizando técnicas de web scraping y los
términos de búsqueda definidos. Para esto se implementó un script que realiza el web scraping
de manera periódica y se utilizan técnicas de PLN y el
modelo de clasificación implementado para definir la pertinencia de =
los
cursos de manera automatizada.
&n=
bsp;
Evaluación=
del Sistema En esta sección se presenta=
la
evaluación del sistema de recomendación, para esto se
utilizó dos criterios
clave: (1) usabilidad, y (2) calidad
de la recomendación. <=
/span>Para evaluar
(1) la usabilidad, se utilizó el cuestionario System Usability Scale (SUS) (Brooke, 1995), que consta de 10 preguntas con cinco opciones
de respuesta. Para evaluar (2) la calidad
de la recomendación, =
se diseñó un cuestionario específico de 3 preguntas que permite analizar el grado de satisfacción
de los docentes con respecto
a los cursos sugerido=
s. La lista de preguntas se muestra en la Tabla
6. La evaluaci&oacu=
te;n fue realizada por una muestra
de n=3D140 docentes
de la Universidad de Cuenca
utilizando
Google Forms.
&nbs=
p;
Tabla 6 Preguntas <=
/span>para medir el grado de satisfacción de los docentes
(calidad de la
recomendación)
IDENTIFICADOR
PREGUNTA
MÉTRICA
MÍNIMA
MÉTRICA
MÁXIMA
PS1
¿Cómo calificaría la releva=
ncia
de los cursos recomendados?
Nada relevante.
Muy relevante.
PS2
En t=
érminos de ajuste a sus intereses y necesidades, ¿cómo
calificaría las recomendaciones?
Nada útil.
Muy útil.
PS3
¿Considera
que los cursos
recomendados están alineados a las cinco competencias TIC <=
span
style=3D'letter-spacing:.5pt'>(tecnológica, comunicativa, <=
span
style=3D'letter-spacing:.55pt'>de gestión, investigativa y
pedagógica)?
No alineadas en absoluto.
Totalmente
alineadas.
En cuanto al cuestionario de satisfacción sobre la calidad
de la recomendación de
los cursos, la tendencia general mostrada en la Figura 9 revela que las
respuestas tienden a concentrarse en las calificaciones más altas (4=
y
5).
&nbs=
p;
Figura 9 Agrupación
de las respuestas por cada pregunta del cuestionario de satisfacción
En la Tabla
7, se puede observar=
que todos los promedios para cada una de las preguntas
superan el valor de 3.5 (media), lo que sugiere
una percepción generalmente positiva por parte de los docentes sobre=
los cursos
recomendados. Los valor=
es de la desviación está=
ndar
reflejan una ligera variabilidad en las respuestas para cada pregunta, lo q=
ue
indica que, aunque la percepción es favorable, hay algunas diferencias en cómo=
los docentes valoran
ciertos aspectos. En ge=
neral,
los resultados muestran que los cursos recomendados son vistos como relevan=
tes,
bien ajustados a sus intereses
y necesidades, y alineados con las competencias TIC. En relación con el cuestionario SUS, el puntaje medio de usabilidad registrado e=
s de
aproximadamente 62.27, con una mediana de 60. Según la Figura 10, que proporciona una interpretación de los resultados
del cuestionario, la herramienta recibe una calificación de 'D' y un adjetivo descriptor de 'OK'. En términos de aceptabilidad, se clasifica como 'Marginal'. La desviación estándar, que se sitúa en torno a 16.96, indica una variabilidad moderada en los puntajes de usabilidad entre los docentes.
=
;
Tabla 7 Resultados =
del cuestionario de satisfacción<=
/span>
IDENTIFICADOR
MEDIA
DESVIACIÓN
ESTÁDAR
PS1
=
4.12
=
0.95
PS2
=
3.98
=
0.92
PS3
=
4.03
=
0.97
Figura 10 Formas de interpretar el puntaje SUS
Figura 11 Agrupación
de las respuestas por cada pregunta del cuestionario de satisfacción
Como se muestra en la Figura 11, l=
os
resultados sugieren que los docentes consideran que el sistema tiene una usabilidad moderada o promedio.
Aunque el puntaje
no es bajo, indica que
existe un margen significativo para mejorar la usabilidad del sistema. Esto
implica que la herramienta podría beneficiarse de una revisión
para abordar los aspectos que han conducido a valoraciones más bajas=
. En
resumen, mientras los docentes están satisfechos con los cursos
recomendados, encuentran dificultades en el uso de la herramienta, lo que subraya
la necesidad de mejorar
ciertos aspectos de su usabilidad para optimizar la experiencia del usuario=
.
&nb=
sp;
Discusión<=
span
style=3D'letter-spacing:2.65pt'> y
Conclusiones El<=
/span> campo =
de las Tecnologías de la Información y la Comuni (TIC) está <=
/span>en constante evolución, lo que impone la necesidad de una revisió=
;n
continua de las competencias docentes en este ámbito. A lo largo
de este estudio,
la implementació=
n de un sistema
de recomendación=
de
cursos personalizados ha demostrado ser una herramienta valiosa para apoyar=
el
desarrollo profesional continu=
o de los docentes, contribuyendo =
así a la mejora
de la calidad educat=
iva en las instituciones.
&nbs=
p;
Los resultados obtenidos
sugieren que, si bien los cursos recomendados son percibidos c=
omo
relevantes y bien alineados con las necesidades y competencias TIC de los docentes, la usabilidad
del sistema presenta ciertos desafíos. El análisis de la usabilidad muestra que, aunque
la herramienta cumple su función, existe un margen considerab=
le
para mejorar la experiencia del usuario, lo cual es esencial para maximizar=
la
efectividad y la adopción de la plataforma.
&nbs=
p;
En términos generales, este
sistema no solo facilita la actualización continua de las competenci=
as
docentes en un entorno educativo dinámico, sino que también
promueve una integración más efectiva
de las TIC en la enseñanza. Esto tiene un impacto directo
en la calidad de la educación y enriquece la experiencia de
aprendizaje de los estudiantes, preparándolos mejor para un mundo ca=
da
vez más digital. No obstante, para que el sistema alcance su pleno
potencial, es fundamental continuar mejorando tanto la calidad de las
recomendaciones como la usabili=
dad del sistema. Estas mejoras son necesarias para garantizar que la herramienta siga siendo relevante y útil en un contexto educativ=
o en
constante cambio.
&nbs=
p;
El uso de técnicas de filtrado basado en contenido
y procesamiento de lenguaje natural (PLN) ha demostrado ser efec=
tivo
para categorizar cursos y alinear las recomendaciones con las necesidades
específicas de los docentes. No obstante, la precisión en la
interpretación del lenguaje y la clasificación de contenidos sigue siendo un área de mejora. La implementación
&nb=
sp;
de un modelo
híbrido, que integra
la Taxonomía de Bloom para identificar el nivel cognitivo de los cursos,<=
span
style=3D'letter-spacing:-.6pt'> ha permitido una mayor personalización, pero también ha subrayado la complejidad
de automatizar estas recomendaciones de manera precisa.
&nbs=
p;
Como lecciones aprendidas se pueden
indicar las siguientes: (1) Respecto de la Evaluación de Competencias TIC: La diversidad en las habilidades y conocimiento=
s TIC entre
los docentes destacó l=
a necesidad
de herramientas de evaluación más precisas y objetivas. La subjetividad en las
autoevaluaciones puede afectar la exactitud de las recomendaciones, sugirie=
ndo la necesidad de métodos más estandarizados y menos dependientes de la percepción individual. (2) Respecto del Desarrollo Tecnológico: La colaboración con expertos en diseño y desarrollo web fue crucial
para crear una herramienta que no solo fuera funcional, sino tambi&eac=
ute;n
alineada con la identidad institucional. La flexibilidad y escalabilidad del
sistema permitieron ajustes co=
ntinuos y mejoras basadas
en la retroalimentación de los usuarios. (3) Respecto de la
Retroalimentación y Ada=
ptación:
La retroalimentación de los docentes indicó que, aunque la
mayoría estaba satisfecha con los cursos
recomendados, la usabilidad de la interfaz
necesitaba mejoras.
=
;
Lo anterior resalta
la importancia de considerar tanto
el contenido como la experiencia del usuario
en el diseño de sistemas educativos. Sin embargo, este desarrollo logró exitosamente
diseñar e implementar un sistema de recomendación de
cursos en línea para mejorar las competencias TIC de los docentes de la Universidad de Cuenca. Esta herramienta no solo facilita
el acceso a recursos educativos personalizados, sino que tambi&eacut=
e;n
ayuda significativamente a la adaptación de la educación supe=
rior
a las demandas digitales actuales. La integración de la Taxonom&iacu=
te;a de Bloom y técnicas de procesamiento de lenguaje natural
(PLN) permitió realizar recomendaciones más precisas y
relevantes, aunque todavía hay espacio para futuras mejoras.
&nbs=
p;
A pesar del éxito, el desarrollo enfrentó varias limitac=
iones. La precisión en la medición de competencias s=
e vio afectada por la subjetividad de la autoevaluación y la complejidad de las compete=
ncias
TIC, lo que subraya la necesidad de herramientas de evaluación
más objetivas. Además, la constante evolución de las
tecnologías y metodologías pedagógicas requiere que el=
sistema
sea dinámico y adaptable. Los desafíos del PLN, como la interpretación correcta
de términos y contextos, también presentaron dificultades, especialmente en la clasificación precisa
del contenido educativo. En resumen, el desarrollo ha establecido una base sólida para futuras
investigaciones y desarrollos, ofreciendo valiosas perspectivas para la mej=
ora
continua de las herramientas educativas digitales.
=
;
Para trabajos futuros, se recomien=
da
evaluar la eficacia del uso de la Taxonomía de Bloom para categorizar los cursos según su nivel cognitivo. Además, se sugiere explorar posibles
soluciones basadas en té=
cnicas
de machine learning para mejorar la personalización de los cursos. <=
/span>Esto
podría incluir el uso de modelos más avanzados de aprendizaje
automático para analizar patron=
es en las preferencias y necesidades de los docentes, con el fin de ofrecer recomendaciones
de cursos aún más precisas y adaptadas.
&n=
bsp;
Agradecimientos Este artículo recibió el apoyo =
de la Vicerrector&i=
acute;a Académica de la Universidad <=
/span>de Cuenca y la Dirección de Innovación Educativa.
&nb=
sp;
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