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=
Esta o=
bra
está bajo una licencia internacional Creative
Commons Atribución-NoComercial 4.0
Enviado: 2023/07/16 Aceptado: 2023=
/08/21
Publicado: 2023/10/15
Resumen La inadecuada capacidad para resolver conflictos en
estudiantes de ingeniería automotriz puede tener consecue=
ncias negativas en el ámbito laboral. El desarrollo del pensamient=
o lógico matemático puede
ayudar a los estudiantes a desarrollar habilidades de anál=
isis crítico, mejora de la capacidad de resolución de problemas,
desarrollo de habilidades de razonamiento y comunicación efectiva, lo que les permite abordar
los conflictos de manera efectiva
y encontrar soluciones
creativas. Esta investigación pretende identificar predictores de la
capacidad para resolver problemas usan=
do
algoritmos de clasificación. Metodología: En este estudio se
aplicaron tres algoritmos de clasificación y se usó el
proceso KDD para identificar predictores de la capacidad para resolver problemas. El conjunto de datos cuenta
con 60 registros de estudiantes de la carrera de ingeniería automotriz de la Universidad Equinoccial en Quito, Ecuador, a los que se les aplicaron tres herramientas: una ficha sociodemográfica, un test de Shatnawi relacionado con el pensamiento lógico matemático y un test de Watson Glaser sobre capacidad de resolución
de conflictos. Resultados: El mejor modelo de clasificación e=
s el
algoritmo K-vecinos más cercanos=
y su capacidad de predicción es muy buena,
con una tasa de verdaderos positivos
&nbs=
p;
frente a<=
span
style=3D'letter-spacing:-.3pt'> la tasa de falsos positivos
AUC de 0.75, junto a un buen desempeño en clasificar casos negativos. El modelo puede ser
mejorado con un aumento de la muestra, validación cruzada o ajuste de híper
parámetros. Conclusión: La edad y el pensamiento lógico
matemático están fuertemente asociados con la capacidad de
resolver conflictos. En futuras investigaciones es importante considerar variables adic=
ionales como la experiencia en proyectos de resolución de problemas, el conocimiento<=
span
style=3D'letter-spacing:-.3pt'> técnico y la habilidad
comunicativa; explorar el uso de algoritmos
de
aprendizaje automático más avanzados; diseñar intervenciones educativas específicas basadas
en el desarrollo del pensamiento lógico ma=
temático; o comparar la capacidad de resolución de conflictos entre diferentes disci=
plinas
de ingeniería.
Palabras clave:
Algoritmos
de clasificación=
, aprendizaje automático, pensamiento lógico
matemático,
psicología, resolución de conflictos.
Abstract Inadequate conflict re=
solution
skills among automotive engineering students can negatively affect <=
span
lang=3DES>the workplace. Mathematical and logical thinking can help students
develop critical analysis skills, problem-solving ability, reasoning skills,
and effective communication, enabling them to deal effectively with conflic=
ts
and find creative solutions. This research aims to identify predictors
of problem-solving =
ability using sorting algorithms. Methodology: In this study, three classification
algorithms were applied, and the KDD process was used to identify predictor=
s of
problem-solving ability. The data set includes 60 records of students from =
the
automotive engineering program at the
Universidad Equinoccial in Quito, Ecuador, to whom three tools were =
applied: a sociodemographic card, a Shatnawi
test related to mathematical logical
thinking, and a Watson
Glaser test on conflict resolution ability. Resul=
ts: The best classification model is the K-nearest neighbor’s algo=
rithm;
its predictive ability is excellent, with an actual positive rate versus a false positive
rate AUC of 0.75 and a good performance in classifying negative
cases. The model can be improved with an increased sample size, cross-validation, or hyper-parameter
adjustment. Conclusion: Age and mathematical and logical thinking are strongly associated with conflict
resolution ability. Future research: It is essential to consider additional
variables such as experience in problem-solving projects,
technical knowledge, and communication skills;
to explore the use of more advanced machine
learning algorithms; to design specific
educational interventions based on the development of mathematical, logical thinking; or to compare conflict
resolution ability between different engineering disciplines. =
Keywords: Classifica=
tion
algorithms, machine learning, mathematical logical thinking, psychology,
conflict resolution.
Introducción En el campo de la ingeniería
automotriz es común que los proyectos se realicen en equipos
multidisciplinarios donde los estudiantes deben colaborar. Y comunicarse eficientemente.
Los conflictos pueden surgir debido a diferencias en opiniones, enfoques
o ideas. Rivera et al. (2023) afirman que la capacidad =
de
resolver conflictos permite a los estudiantes abordar estas discrepancias de manera constructiva, encontrar soluci=
ones y mantener un ambiente de trabajo
armonioso. Los conflictos no resueltos pueden tener un impacto negativo en =
la
productividad de un equipo. Si los estudiantes no saben cómo manejar
adecuadamente los desacuerdos y conflictos, el progreso del proyecto puede
retrasarse, lo que afecta el tiempo de entrega y la calidad del trabajo fin=
al.
&nbs=
p;
Andajani-Sutjahjo et al. (2018)
proclaman que la capacidad de resolución de conflictos
permite a los estudiantes=
abordar
rápidamente los problemas
y mantener el flujo de trabajo sin interrupciones innecesarias. L=
a resolución de conflictos<=
span
style=3D'letter-spacing:-.15pt'> eficaz
fomenta un ambiente
propicio para la innovación y la creatividad, puesto que cuando los estudiantes pueden expresar y discutir
abiertamente sus ideas sin temor a conflictos negativos, se promueve=
la
generación de nuevas soluciones=
y enfoques de innovación. La capacidad de resolver conflictos fomenta el pensamiento
crítico y la búsqueda de alternativas, lo que puede ll=
evar
a avances significativos en el campo de la ingeniería automotriz.
La resolución de conflictos=
ayuda
a construir y mantener relaciones interpersonales sólidas <=
span
style=3D'letter-spacing:-.1pt'>entre los estudiantes de ingeniería automotriz. A medida que trabajan juntos para superar
los desafíos y conflictos, desarrollan habilidades de
comunicación, empatía y comprensión mutua. Estas
habilidades son valiosas
tanto en el entorno académico como en el profesional, ya que
facilitan la colaboración y el trabajo
en equipo, según Celi Rojas et al. (2021).
La capacidad de resolución de conflictos es importante para los estudiantes de ingeniería automotriz porque les
permite trabajar
de manera efectiva en equipo, mejorar la productividad, fomentar la innovación
y mantener relaciones interpersonales sólidas. Estas
habilidades son esenciales para el éxito en el campo de la ingeniería automotriz, donde el trabajo colaborativo y la resolución de problemas
son fundamentales.
La ingeniería automotriz im=
plica
el uso intensivo de conceptos matemáticos, desde cálculo
diferencial e integral hasta álgebra lineal y estadística. Un
pensamiento lógico, matem&aacut=
e;tico, sólido es fundamental para comprender y aplicar estos conceptos de manera efectiva
(Alcívar Martínez, 2018). Medir el pensamiento
lógico matemático en los estudiantes ayuda a identificar si tienen una base sólida en
matemáticas según Rosero Ojeda (2021) y si están
preparados para abordar
los desafíos técnicos que se encontrarán en su campo de estudio
según Núñez (2021). Los ingenieros automotrices se enfrentan a problemas complejos
que requieren un enfoque
lógico y analítico. La capacidad de razonar de manera
lógica y aplicar conceptos matemáticos adecuados es esencial =
para
descomponer problemas complicados en pasos más manejables y encontrar
soluciones eficientes. Morales et al. (2018) describen que, al evaluar el
pensamiento lógico matemático de los estudiantes, se puede
determinar su capacidad para abordar problemas complejos y resolverlos de
manera efectiva.
En el campo de la ingeniería
automotriz, es necesario tomar decisiones informadas basadas en datos y
análisis matemáticos. Esto puede incluir decisiones relaciona=
das
con el diseño de vehículos, la optimizaci&=
oacute;n del rendimiento, el análisis de fallas, entre
otros aspectos. As&iacu=
te; mismo,
Godoy Cedeño (2020)
refiere que el pensamiento lógico matemático permite a los
estudiantes evaluar y analizar datos de manera crítica, lo que les ayuda a tomar decisiones más fundamentadas y efectivas en su práctica
profesional.
La ingeniería automotriz está en<=
span
style=3D'letter-spacing:-.2pt'> constante
evolución y requiere
la capacidad de pensar
de manera creativa e innovadora. El pensamiento lógico matemá=
tico
es una habilidad clave para plantear y resolver problemas complejos;
según Yasig Salguero (2=
021),
permite explorar nuevas ideas y desarrollar tecnologías innovadoras =
en
el campo. Medir el pensamiento lógico matemático en los
estudiantes permite identificar su capacidad para abordar desafíos
técnicos y contribuir a la innovación en la industria automot=
riz.
La determinación o medición del pensamiento lógico matemático en los estudiantes de ingeniería automotriz es importante para garantizar que tengan una base sólida en matemá=
;ticas,
puedan abordar problemas compl=
ejos, tomar decisiones informadas y contribuir a la innovación
&nbs=
p;
en su campo. Estas habilidades son esenciales para el éxito en la ingeniería automotriz y para
enfrentar los desafíos técnicos en constante cambio en la industria.
Algunos de los resultados de la
búsqueda analizan la importancia del pensamiento matemático y lógico en la ingeniería, y la capacidad de identificar, formular y resolver problemas
de ingeniería complejos utilizando los<=
span
style=3D'letter-spacing:-.05pt'> principios de la ingeniería, la ciencia y las matemáticas (Kolajo, 2020). =
Se
sugiere que en cuanto a calidad y monitoreo de trabajo se refiere, la
importancia de la probabilidad<=
span
style=3D'letter-spacing:-.1pt'> y la estadística en la toma de decisiones y reducció=
;n de riesgos
en las actividades in=
dustriales es primordial, según Ortiz Aguilar<=
span
style=3D'letter-spacing:-.75pt'> et al. (2021).
Además, algunos
resultados discuten la importancia del razonamiento lógico y las
habilidades de resolución de problemas para los ingenieros. La
evaluación de habilidades del pensamiento computacional para predeci=
r el
aprendizaje y retención de estudiantes, si bien no se enfoca
directamente en la capacidad de
resolución de conflictos, proporciona información sobre la
evaluación de las habilidades <=
/span>cognitivas en estudiantes de ingeniería según González-Quiñones
et al. (2022). “E=
l razonamiento lógico como requisito funcional en ingeniería”: Este artículo analiza los conceptos
de lógica, resolución de problemas y razonamiento
lógico como requisitos funcionales en ingeniería. Si bien no menciona específicamente la capacidad de resolución de conflictos, enfatiza
la importancia del pensamiento lógico en la ingeniería. Según R=
ojas-López &
García-Peñalvo (2020),
“el desarrollo del pensamiento lógico matemático en los estudiantes costarricenses de undécimo, año de colegios académicos diurnos y su nivel de logro en el aprendizaje de las matemáticas”. Este estudio tuvo como
objetivo aportar evidencia empírica respecto a la relación en=
tre
el desarrollo del pensamiento lógico matemático en estudiantes
costarricenses de grado 11º en escuelas académicas diurnas y su
nivel de aprovechamiento en matemáticas, específicamente en l=
as
áreas algebraica y geométrica. El estudio encontró un =
bajo
nivel de desarrollo del pensamiento lógico matemático,
significativamente alejado de los parámetros aceptados internacionalmente. Las escalas, el razonamiento lógico y la demostración matemática
fueron las más desafiantes. El nivel de logro en álgeb=
ra y
geometría también fue claramente insuficiente (Vargas Salazar,
2017). Aunque los resultados de la búsqueda no proporcionan estudios=
específicos relacionados c=
on la capacidad de resolución de conflictos en estudiantes de ingeniería automotriz usando los algoritmos de clasificación, estos estudios ofrecen información
sobre la evaluación de las habilidades cognitivas y la
importancia del pensamiento lógico en estudiantes de educaci&oac=
ute;n superior
y de secundaria. Es=
posible
que se necesite m&aac=
ute;s investigación para encontrar
estudios que aborden específicamente la capacidad de resolució=
;n
de conflictos en el ámbito académico de la ingeniería
automotriz.
El test de Watson Glaser est&aacut=
e;
formado por varias dimensiones, una de ellas es la inferencia que es una
conclusión que una persona puede extraer de ciertos hechos observado=
s o
supuestos; reconocimiento de suposiciones: Una suposición es algo qu=
e se
presupone o se da por sentado; La Deducción: En esta prueba, cada
ejercicio consta de varias afirmaciones (premisas) seguidas de varias
propuestas de conclusiones; Interpretación: Cada uno de los ejercici=
os
propuestos consiste en un breve párrafo seguido de varias conclusion=
es
sugeridas. Se supone que todo lo que aparece en el párrafo corto es
cierto, pero el problema consiste en juzgar
si cada una de las conclusiones propuestas se deduce lógicamente, más allá de=
toda duda
razonable, de la información contenida en el párrafo;
Evaluación de argumentos: A la hora de tomar decisiones
sobre cuestiones importantes, es conveniente poder distinguir entre argumentos
sólidos y argumentos débiles en lo que respecta
a la cuestión. Para que un argumento sea sól=
ido,
debe ser importante y estar directamente relacionado con la cuestión=
. Un
argumento es débil si no está directamente relacionado con la
cuestión (aunque pueda tener una gran importancia general) (Possin,
2014).
&nbs=
p;
La evaluación del pensamien=
to
crítico de Watson Glase=
r. Es
una prueba diseñada para evaluar la capacidad de un individuo para
pensar críticamente. La prueba proporciona una puntuación tot=
al
que se puede utilizar para clasificar a las personas según sus
habilidades de pensamiento cr&i=
acute;tico. Un puntaje de al menos 70% en la prueba.
Indica una capacidad
avanzada de pensamiento crítico; un puntaje de al menos el 40=
% Se
relaciona con un nivel básico de pensamiento crítico.; mientras que un puntaje
o porcentaje menor al 40% indica problemas o un nivel deficiente en la
habilidad de pensamiento crítico según Rivera et al. (2023). =
Una
puntuación más alta en la prueba puede indicar habilidades de pensamiento crítico más s&oac=
ute;lidas
que son esenciales para la resolución efectiva de conflictos.=
Materiales y Métodos Proceso de descubrimiento de
conocimiento en base de utilizado el proceso Knowledge Discove=
ry
Database (KDD), un proceso automático que combina descubrimie=
nto
y análisis para extraer patrones en forma de reglas en funciones de =
los
datos. El proceso KDD consta de múltiples capas; su principal objeti=
vo
es extraer conocimientos valiosos, pero no triviales de los datos a los que
tiene acceso. Las etapas del proceso KDD se describen a continuación: 1. Identificación del problema e identificación del dominio de trabajo.
2. Creaci=
ón del conjunto de datos. 3. De procesamiento de los datos. 4. Reducción y proyecci&oacut=
e;n de los datos. 5. Formulación de los objetivos de la KDD. 6. Selección de méto=
dos de minería de datos. 7. Evaluación de los patrones descubiertos. 8. Presentación y visualización. 9. Integración
del conocimiento descubierto en el sistema
(Camilo Giraldo Mejía & Alberto Vargas
Agudelo, 2019).
Base de datos e instrumentos de recolección de datos El conjunto de datos cuenta con 60
registros de estudiantes de la carrera de ingeniería automotriz de la
Universidad tecnológica Equinoccial, a quienes se les aplicó 3
instrumentos: El primero corresponde a una ficha
sociodemográfica; el segundo
a una prueba para determinar el pensamiento ló=
gico
matemático denominado test de Shatnawi (Vargas Salazar, 2017); y el
tercer instrumento corresponde a la prueba
de Watson Glaser
Relacionada con la medición del pensamiento crítico.
La variable a predecir se denomina
BUENAS_CUALI la cual hace referencia a la cantidad de respuestas exitosas. =
Como
resultado de la aplicación del test de Watson Glaser; es de tipo nominal y puede ser clasi=
ficada
como deficiente o básica avanzada. Las variables predictoras son el sexo y el estado civil,
ambas de tipo nominal; la edad, y las 6 dimensiones que forman parte del test que mide =
el
desarrollo del pensamiento lógico matemático, estas últimas de tipo cuantitativas. La Tabla 1 muestra la descripción de las variables
y sus posibles valores=
.
Tabla 1 Variables Analizadas
Variables
=
Código
=
Tipo
Género
SEXO
nominal
Estado civil
CIVIL
nominal
Edad
EDAD
cuantitativa
Generalizació=
;n
GG
cuantitativa
Inducción
II
cuantitativa
Deducción
DD
cuantitativa
Variables
=
Código
=
Tipo
Uso =
de
símbolos matemáticos
SS
cuantitativa
Razonamiento =
lógico
RR
cuantitativa
Demostración=
matemática
PP
cuantitativa
Global del =
test pensamiento
lógico
DPLM_10
cuantitativa
Global del test Watson-Glaser
BUENAS_CUALI
cuantitativa
Descripci&=
oacute;n de variables El género se refiere a
cómo se auto identifican los alumnos estudiados, es decir si se
identifican como hombres o mujeres para este caso específico. En el
estado civil existen dos opciones que son: soltero o casado. La edad indica los años de vida de los estudiantes, que en promedio es de 22,5 años. Las dimensiones del =
test
de desarrollo de pensamiento lógico matemático están conformadas por:=
La generalización, que comprende la búsqueda de modelos
o modelización como un componente fundamental del pensamiento
lógico matemático; la deducción, que se refiere
al proceso que conlleva a conclusiones válidas, siempre y cuando
las premisas de partida
también lo sean. La inducción, que se caracteriza por conducir al hallazgo
de modelos partiendo de casos
específicos; el<=
span
style=3D'letter-spacing:-.75pt'> uso de símbolos y el uso del lenguaje
matemático, que s=
e refiere
al proceso de generalización matemática, unificando su representación de forma
concisa, precisa y clara. El razonamiento lógico, que se refiere a la
habilidad para decidir si las proposiciones son o no lógicamente
verdaderas; la capacidad de realizar demostraciones matemáticas, que=
se
relaciona con los métodos usados por los estudiantes para validar sus
construcciones teóricas o fundamentar los resultados obtenidos; el t=
est
global de pensamiento lógico, representado por DPLM_10, es el resultado global obtenido de aplicar el test de Shatnawi;
y el test global de Watson-Glaser, llamado
BUENAS_CUALI, que es el valor
global obtenido al apli=
car el
test de pensamiento crítico (Aiyub et al., 2021).
Pasos
para ejecutar el proceso KDD Se muestra
en la Figura 1 en primer lugar
los datos relativos
a la valoración del pensamiento crítico de los estudiantes de ingeniería automotriz de la
Universidad Tecnológica Equinoccial, de los últimos niveles séptimo y octavo, respectivamente
son subidos en una hoja de Excel
junto a los resultados obtenidos de la ficha
sociodemográfica y el test de desarrollo del pensamiento
lógico matemático aplicado a los 60 estudiantes. En este estu=
dio
se utilizó el desarrollo del pensamiento lógico matemá=
tico
y su influencia en el pensamiento crítico. El segundo paso es la
preparación de los datos, es decir almacenar los valores obtenidos
en el pensamiento l&o=
acute;gico matemático y la valoración global en un archivo de Excel en formato csv delimitado por comas para hacerlo visible en la aplicación estadística JASP. Es necesario
indicar que en esta etapa se depura la base, verificando que los valores
decimales están d=
efinidos por puntos y los nombres
de las variables van sin tilde porque el idioma nativo
del programa estadístico es el inglés; también se procede a la verificación del respectivo tipo de
variable para finalmente verif=
icar que no hay celdas vacías ni ningún=
símbolo especial, tanto<=
span
style=3D'letter-spacing:-.6pt'> en los datos como en los nombres de las variables según lo indicado en (Martin Guambuguete Rea et al., 2023). En el tercer paso, los datos se modelizan
mediante JASP para predecir el pensamiento
crítico de los estudiantes de ingeniería automotriz a
partir de las variables sociodemográficas, así como de las dimensiones y el global del test de desarrollo de pensamiento lógico matemático
de Shatnawi; En este paso, hay una secuencia de 5 fases: entrenamien=
to,
patrón de prueba, evaluación de resultados y conocimiento,
representación.
&nbs=
p;
Los datos depurados se dividen en =
dos
partes: la fase de entrenamiento y la fase de prueba. Según Ber=
ástegui Arbeloa
(2018), el algoritmo
K-vecinos más cercanos
para construir un modelo=
en la
fase de entrenamiento, es un algoritmo que usa el método de
decisión de tipo binario para clasificar el problema. Para evaluar el
rendimiento del modelo de aprendizaje generado, utiliza la validación
cruzada k-fold, para evaluar el rendimiento del modelo de aprendizaje gener=
ado.
El algoritmo divide el conjunto de datos en K, subconjuntos o pliegues del<=
span
style=3D'letter-spacing:-.4pt'> mismo tamaño y, a continuación, el modelo se entrena k veces, cada una de ellas utilizando un subconjunto diferente como prueba y los otros K menos 1 subconjuntos como entrenamiento.
Visualmente, la etapa de prueba se refleja
en el área bajo la curva de características operativas del receptor (ROC),
donde el clasificador seleccionado se<=
span
style=3D'letter-spacing:-.45pt'> muestra
en función de las observaciones relacionadas con las categorías o dimensiones de la variable
objetivo a predecir. Ver Figura 1.
Figura 1 Proceso KDD
Resultados y Discusión Se<=
/span> aplicaron tres algoritmos de clasificación tales como <=
span
lang=3DES style=3D'letter-spacing:-.1pt'>K-vecinos más cercanos, Bosque alea=
torio, el
árbol de decisión; de los cuales el mejor modelo de
clasificación resultó ser el primero, es decir el modelo
K-vecinos más cercanos. Esto se puede evidenciar en la siguiente Tab=
la
2, con las respectivas métricas de evaluación.
Tabla 2 Precisión
de los Algoritmos de<=
span
style=3D'letter-spacing:-.2pt'> Clasificación
Clasificación de los K-Vecinos más cercanos
Vecinos m&aacut=
e;s
próximos
Ponderaciones
Distancia
n (Entrenamiento)
n (Contraste)
Precisión del =
Contraste
3
rank
Manhatan
48
12
0.7500
Clasificación de Árbol de decisión
Separadores
n (Entrenamiento)
n (Contraste)
Precisión del =
Contraste
6
24
36
0.6111
Clasificación en Bosques Aleatorios
Árboles
Características por
separador
n (Entrenamiento)
n (Validación)
n (Contraste)
Pr=
ecisión
de la Validaci&oac=
ute;n
Pr=
ecisión
del Contraste
Precisión
de OOB
11
3
38
10
12
=
0.5000
0.5833
0.5789
La Figura 3, muestra el diagrama de curvas ROC, donde la evaluación de la capacidad del modelo para distinguir entre las diferentes categorías de la variable a predecir es muy buena. La tasa de verdaderos positi=
vos
frente a la tasa de falsos positivos (1- especificidad) bajo la curva AUC es de 0,75 lo que indica una alta capacidad discriminante. Aun así,=
podría ser mejor en clasificación, ya que es sensible al tamaño del conjunto de datos. Un aumento en la muestra de datos observad=
os, así como té=
cnicas de la validación cruzada o el ajuste de híper parámetros. Para evaluar y m=
ejorar
el modelo actual, es una recomendación.
Figura 2 Diagrama de curvas
ROC (JASP 16.0)
El valor predictivo negativo (VPN)<=
span
style=3D'letter-spacing:-.45pt'> es la proporción de casos negativos. Correctamente clasificadas en relación con todos los
casos clasificados como negativos. En este caso, los valores son 0.6667, 0.=
8333
y 07500, respectivamente para las diferentes clases. Un VPN más alto <=
/span>indica que el modelo
es más efectivo
en predecir correctamente los casos negativos. Ratio de verda=
deros negativos (RVN) es la proporción de casos negativos correctamente c=
lasificados
en relación con todos los casos reales negativos. Los valores que
proporcionados son 0.8000, 0.7143 y 07571, respectivamente. Un RVN má=
;s
alto indica que el modelo es más efectivo en identificar correctamente los casos que son realmente negativos. Ratio de falsos negativos (RFN) es la proporción de casos negativos
que fueron clasificados incorrectamente =
como positivos
en relación con todos los casos reales negativos. Un RFN
más bajo indica que el modelo comete menos errores al clasificar incorrectamente los casos negativos como positivos. El ratio de falsos negativos RFN y el ratio de falsas comisiones RFO son relativamente bajos, lo que indica que el
modelo comete menos errores al clasificar incorrectamente los casos negativos
como positivos y viceversa. La puntuaci&oa=
cute;n de amenaza es relativamente baja,
lo que sugiere que modelo es efectivo en minimizar los errore=
s de
clasificación de falsos negativos en comparación con los
verdaderos negativos. En general, estos indicadores
muestran un buen desempeño del modelo K-vecinos más cercanos =
como
clasificador. Ver Tabla 3.
Tabla 3 Métricas de evaluación (JASP 16.0)
Métricas de Evaluación =
&=
nbsp;
basica_avanzada
deficiente
Average / Total
Apoyo
7
5
12
=
Precisión
0.7500
0.7500
0.7500
Precisión (Valor
Predictivo Positivo)
0.8333
0.6667
0.7639
Exhaustividad (Ratio
de Verdaderos Positivos)
0.7143
0.8000
0.7500
Ratio de Falsos Positivos
0.2000
0.2857
0.2429
Ratio de Falsos Hallazgos
0.1667
0.3333
0.2500
Puntuación F1
0.7692
0.7273
0.7517
Coeficientes de Correlación de Matthews
0.5071
0.5071
0.5071
Área Bajo la C=
urva
(ABC, AUC)
0.6571
0.6571
0.6571
Valor Predictivo Negativo <=
/o:p>
0.6667
0.8333
0.7500
Ratio de Verdaderos Negativos
0.8000
0.7143
0.7571
Ratio de Falsos Negativos
0.2857
0.2000
0.2429
Ratio de Falsas Omisiones
0.3333
0.1667
0.2500
Puntuación de Amenaza
1.2500
0.8000
1.0250
Paridad Estadística
0.5000
0.5000
1.0000
Nota. Todas las métricas se calculan para cada clase
frente a todas
las otras clases.
Conclusiones De acuerdo al modelo de clasificación, K-vecinos más cer=
canos, las <=
/span>que mejor permiten clasificar al Pensam=
iento
Crítico, son: Edad (EDAD) con un aporte individual el modelo de
clasificación de 0.4167; Global de desarrollo de pensamiento
lógico matemático (DPLM_10), Demostración matemá=
;tica
(PP), Razonamiento Lógico (RR), cada uno con un aporte individual
de 0.25; luego, las dimensiones de Inducción, Deducción, y Uso de símbolos y
Lenguaje matemático, con un aporte individual cada una de 0.1667; a continuación, la dimensión
generalización (GG), con un aporte individual al modelo de
0.08333. Finalmente, la variable SEXO=
resta precisión al modelo, y la variable
CIVIL tiene un comportamiento neutral, por l=
o cual
las dos variables no son buenas predictoras. Este proceso se hizo manualmen=
te,
según Espinosa-Pinos et al. (2022), pues se retiraron las variables del modelo general clasificador, para determinar
su impacto individual en el mismo.
Dad=
o que la edad <=
span
lang=3DES style=3D'letter-spacing:-.55pt'> tiene =
un aporte significativo =
en la clasificación del pensamiento<=
/span> crítico, se pueden desarrollar estrategias y programas específicos adaptados a diferentes grupos de edad. Esto implica abordar
de manera diferenciada las necesidade=
s y características de los estudiantes más jóvenes y los más adultos, dándoles oportunidades de desarrollo y práctica acordes a su nivel
de madurez cognitiva.
La dimensión =
global del desarrollo del pensamiento lógico matemático ha demostrado ser un predictor importante del=
pensamiento crítico. Por lo tanto, se puede proponer implementar actividades y recursos educativos que promuevan el desarrollo de habilidades en esta área. Esto podría incluir
ejercicios de razonamiento lógico =
matemático, problemas desafiantes que requieran de inducción y
deducción, la exploración de símbolos y el lenguaje
matemático en contextos=
reales
y relevantes para la ingeniería automotriz. Dad=
o que tanto la demostración
matemática como el razonamiento lógico contribuyen sig=
nificativamente a la clasificación del
&nbs=
p;
pensamiento crítico, se pueden diseñar
actividades específicas que promuevan y desarrollen estas habilidade=
s.
Esto implica brindar oportunidades para que los estudiantes practiquen la
resolución de problemas usando demostr=
aciones lógicas y razonamiento=
matemático riguroso. Se puede proponer diseñar intervenciones
educativas basadas en los resultados y variables identificadas como relevantes en el modelo
de clasificación=
. Luego,
se debe evaluar
el impacto de estas
intervenciones en la mejora del pensamiento crítico de los estudiant=
es
de ingeniería automotriz. Esto permitirá verificar la efectividad de las estrategias implementadas =
y ajustarlas según sea necesari=
o.
En futuras investigaciones sobre la capacidad de resoluci&oa=
cute;n de conflictos en estudiantes
de ingeniería automotriz, es importante considerar variables
adicionales, la experiencia en proyectos de resolución de problemas, el conocimiento técnico y la habilidad
de comunicación.
Además, podría explorar el uso de algoritmos de aprendizaje
automático más avanzados que mejoran la precisión de la
clasificación. Un estudio longitudinal que analice el desarrollo de
estas habilidades a lo largo del tiempo proporcionaría informaci&oac=
ute;n
valiosa sobre los factores que influyen en su mejora.
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