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<= /span>

 

 

<= span lang=3DES>https://doi.org/10.37815/rte.v35n2.1056

<= span lang=3DES>Artículos originales

 

Modelo de Visualización de Datos de Juegos Serios de Atención y Memoria Cognitiva

Data Visualization Model of Serious Games of Cognitive Attention and Memory

 

Juan-Se= bastián Toledo1, 2 https://orcid.org/0000-000= 1-5120-9486, Juan-Fernando Lima2 https://orcid. org/0000-0003-350= 0-3968, María-Iné= ;s Acosta-Urigüen1,= 2 https://orcid.org= /0000-0003-4865-2983,=

Marcos Orellana= 2 https://orcid.org/0000-0002-= 3671-9362

 

1Escuela de Ciencias de la Computación, Universidad del Azuay,= <= /span>

Cuenca, Ecuador

= sebastiantoledo@es.uazuay.edu.ec

 =

2Labora.torio de Investigación y Desarrollo en Informática (LIDI), Universidad del Azuay, Cuen= ca, Ecuador, Cuenca, Ecuador flima@uazuay.edu.ec, macosta@uazuay.edu.ec= , marore@uazuay.edu.ec<= span lang=3DES style=3D'font-family:"Courier New";mso-bidi-font-family:"Times Ne= w Roman"'>

 

 

=

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

Enviado: 2023/07/15 Aceptado: 2023= /08/22 Publicado: 2023/10/15

 


Resumen

En el campo de la psicología, los juegos serio= s se han transformado en herramientas digitales que permiten la aplicación de test psicológicos, el entrenamiento de competencias, y la detección de trastornos o patologías. Si bien los sistemas interactivos generan grande= s cantidades de datos que pueden ser almacenados, surge la necesidad de identificar patrones de juego que permitan al especialista tomar decisiones basadas en datos. En este contexto, los modelos de visualización se han convertido en<= span style=3D'letter-spacing:-.35pt'> una herramienta moderna y precisa para solventar estas representaciones. El ob= jetivo del presente trabajo es crear un modelo de visualización aplicado a datos extraídos de un juego serio orientado al entrenamiento de atenci&oacu= te;n y memoria. Para ello, se propuso una metodología que permitió el desarrollo de un entorno unificado de análisis visual compuesto por tres tableros interactivos. Finalment= e, el modelo fue evaluado a través del modelo de aceptación tecnológica, demostrando una fiabilidad sobresaliente.

 

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Palabras clave: datos cognitivos, juegos serios, modelo de visualización, comportamiento de jugadores, mapeo visual.

 

Abstract

In the field of psychology, serious games have been transformed into digital tools that allow the application of psychological t= ests, skills training, and the detection of disorders or pathologies. Although interactive systems generate large amounts of data that can be stored, the need arises to identify game<= span style=3D'letter-spacing:-.75pt'> patterns that allow the specialist to make data-driven decisions. In<= span style=3D'letter-spacing:-.75pt'> this context, visualization models have become a modern and precise tool to solve these representations. This work aims to create a visualization model applied to = data extracted from a serious game aimed at training attention and memory. For t= his purpose, a methodology was proposed that allowed the development of a unified visual analysis environment composed of three interactive dashboards. Finally, the model was evaluated through the technology acceptance model, which s= howed reliability equivalent to outstanding.

 

Keywords: cognitive data, serious games, visualization model, players behavior, visual mapping.

 

 

Introducción

En los campos de la psicolog&iacut= e;a y la medicina, los juegos serios se han transformado en herramientas digitales que permiten la aplicación = de test psicológicos, el entrenamiento de competencias y la detección de trastornos o patologías, desempeñando un papel fundamental para la salud mental y proporcionando al especialista una efectiva herramienta de soporte a los métodos de terapia tradicionales (Abd-Alrazaq et= al., 2022; Manera et al., 2017; Mezrar & Bendella, = 2022). Al igual que cualquier otro sistema altamente interactivo, los juegos serios generan grandes cantidades de datos, que reflejan directamente las acciones= y decisiones del jugador (Dö= ;rner et al., 2016). Las técnicas de minería de datos (MD), que hoy en día son muy comunes en diversos campos como la educación, medicina y finanzas (K= umar & Bhardwaj, 2011), pueden aplicarse a la gran cantidad de información que se deriva de la interacción de los usuarios c= on los juegos serios (Alonso-Fernández et al., 2019), de esta forma es posible identificar patrones de juego que permitan al especialista tomar decisiones basadas en datos (Provost & Fawcett, 2013).

 

Aún en la actualidad, es comú= n que en el ámbito cotidiano los especialis= tas encargados de controlar y llevar a = cabo las sesiones de entrenamiento no posean información detallada acerca= del estado del juego, el progreso o las implicaciones que conlleva a las decisi= ones del jugador; y en consecuencia se trate al sistema como una caja negra= 1 (Alonso-Fernandez et al., 2021). A pesar de los avances tecnol&o= acute;gicos y las nuevas herramientas que permiten interactuar con la informaci&oac= ute;n recolectada de juegos serios, los datos generados no reciben un tratamiento adecuado y la falta de comunicación de hallazgos resulta en la pérdida de información relevante y una incorrecta gestión del conocimiento (Loh et al., 2015). En este contexto surge la necesidad de aplicar técnicas de MD que permitan registrar, procesar y analizar la información, con el propósito de desarrollar un modelo de visualización adecuado para juegos serios en el contexto de atención y memoria. El análisis de datos puede revelar creencias, comportamientos y estrategias de resolución de problemas del jugador, mientras que la visualización permite transformar la información en gráficos apropiados, para facilitar la interpretación de resultados a las partes interesadas (G. Wallner & Kriglstein, 2013).

 


1 Si= stema que produce resultados útiles s= in revelar información acer= ca de su funcionamiento interno.

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El presente trabajo se enfoca en a= plicar técnicas de MD en un conjunto de datos provenientes de un juego serio orientado al entrenamiento de atención y memoria, con la finalidad de desarrollar un modelo de visualización interactivo, que permita al especialista de la salud explorar, analizar y evaluar a los participantes a través de sus características propias (atributos demográficos, socioeconómicos y conductuales) y aquellas variables que denotan el rendimiento durante cada sesi&oa= cute;n (puntaje y tiempo de duración).

 

 

Revisión de literatura

Durante la última dé= cada los desarrolladores de juegos serios han comen= zado a recolectar grandes volúmenes de datos durante las sesiones de juego, con el fin de comprender el comportamiento del jug= ador y permitir la toma de decisiones acertada, lo cual conlleva al surgimiento de la analítica de datos en el contexto de los juegos serios<= span style=3D'letter-spacing:-.75pt'> (G. Wallner & Kriglstein, 2013), que según Alonso-Fernandez et al. (2021) se define como el análisis de datos y la extracci&oacu= te;n de información, y la analítica visual de juegos para apoyar a la interpretación de datos complejos (Günter Wallner et al., 2018). Una investigación comparable a este trabajo ha sido descrita por Minović et al. (2015). Aqu= í los investigadores presentan un modelo para la visualización del aprendizaje en juegos serios, que permite al profesor obtener una retroalimentaci&oac= ute;n en tiempo real del desempeño del estudiante, con la finalidad de facilitar la = toma de decisiones en áreas de gran importancia como el nivel de dificultad del juego o el camino de aprendizaje apropiado para el estudiante.= Por otro lado, la herramienta permite al estudiante conocer su progreso en el juego. La técnica de visualización utilizada consiste en una modificación del diagrama de vista circular propuesto por (D. A. Keim et al., 2008).

 

De Troyer et al. (2016) realizaron= una investigación, que consistió en el desarrollo de tres técnicas de visualización que permiten informar acerca del es= tado del jugador y mejorar la comprensión de los resultados, en un juego serio orientado al cibera= coso en redes sociales. Cada una de las técnicas presentadas propone un enfoque específico y detallado en áreas de interés. La primera visualización se denomina time-oriented-visualization, y se enfoca en detallar la frecuencia relacionada a las interacciones del jugador con cada elemento del juego. L= a segunda, character-oriented visualization, se enfoca en el jugador y provee una visión general de las relaciones entre personajes en el juego. La tercera visualización, interaction-o= riented visualization, resalta las interacciones entre jugadores. Como resultad= o, los investigadores demostraron que las visualizaciones ayudan a los participantes = a comprender mejor el resultado del juego. La visualización que obtuvo la mejor puntuación en la evaluación fue interaction- oriented visualization, que se caracteriza por proporcionar un entorno interactivo para la exploración de las interacciones entre los jugadores, a través de una representación de nodos (jugadores= ) y aristas (interacciones entre jugadores).

 

Las= técnicas de visualizaci&o= acute;n utilizadas han demostrado alta efectividad para representar los datos según su propósito, entre las disponibles destacan: gráficos de pastel, gráficos de barras, gráficos de líneas y mapas de calor y gráficos de burbujas. Si bien existen investigaciones que utilizan técnicas de analítica de datos en juegos serios con diversos propósitos que abarcan desde la evaluación del aprendizaje hasta la validación del dise&ntild= e;o del juego, no existen estudios que implementen un modelo de visualización adecuado para la representación <= /span>de datos extraídos de un juego serio orientado al entrenamiento cognitivo, la atención y la memoria.

 

Método y Materiales

El presente trabajo se fundamenta en el estudio de la ciencia de datos y la visualización d= e información con la finalidad de desarrollar un modelo de visualización que permita identificar patrones entre las características propias de cada jugador (demográficas, socioeconómicas y conductuales), <= span style=3D'letter-spacing:-.1pt'>contra aquellas variables que denotan el rendimiento cognitivo (puntaje y tiempo) en el juego serio de pares. Para ello, se ha desarrollado una metodología basada en el método Cross Industry Standard Process for Data Mining (CRISP-DM) (Wir= th & Hipp, 2000) y el Modelo Unifi= cado de Visualización (MUV) de (Martig et al., 2003). Todo el proceso= se ve reflejado en la Figura 1.

 

Figura 1

Metod= ología del macroproceso para el tratamiento, análisis = y visualización de datos provenientes de juegos serios


 

El Juego de Pares es un juego en línea desarrollado por psicólogos y estudiantes de la Universidad “Nombre”, con el objetivo de evaluar y reforzar habilidades asociadas al área cognitiva (atención y memoria). La Figura 2 muestra parte del entorno de ejecución. El juego consiste en encontrar el par de cada imagen, para esto las imágenes se muestran un determinado tiempo y luego se ocultan, con el fin de que el jugador recuerde la posición inicial. Para completar el juego se deben superar cuatro niveles, cada uno con dificultad incremental (dos pares más por cada nivel). El rendimiento del jugador se determina po= r el tiempo empleado para completar cada nivel y el puntaje final (penalizado por cada error) con un máximo de 2000 puntos. Se puede acceder al juego mediante un navegador web, a través de la URL: https://jserionew-8e818. web.app.

 

Figura 2

Juego de Pa= res


 

Descripción= del conjunto de datos

Los datos recolectados corresponden a= sesiones registradas desde mayo del 2022 hasta septiembre del 2022 y se dividen en dos categorías. 1) Aquellos adquiridos mediante un único registro de usuario, en el cual se solicitaron datos de tipo demográficos, socioeconómicos y conductuale= s. 2) Datos generados durante la ejecución del juego, aquellos que denotan directamente la interacción del usuario con el sistema, como el tiempo de juego y el puntaje. En el estudio participaron un total de 248 personas, de las cuales 129 pertenecen al género femenino y 119 al género masculino. En la Tabla 1 se presentan las variables recolectadas.

 

Tabla 1

Descripción de las variables recol= ectadas del juego de pares


Atributos        &= nbsp;           &nbs= p;            &= nbsp;            Tipo de Dato

= <= span lang=3DES style=3D'font-size:1.0pt;mso-bidi-font-size:12.0pt'>

C= édula        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;     Nominal

N= ombre        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;   Nominal

C= iudad        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;    Nominal

G= énero        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;     Nominal

P= eso        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;         Numérico

A= ltura        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;      Numérico

D= iscapacidad        &= nbsp;           &nbs= p;            &= nbsp;     Booleano

Tipo Discapacidad     &= nbsp;           &nbs= p;           Nominal Antecedentes neurodegenerativos Booleano COVID-19   Booleano=

Fecha de nacimiento           &nbs= p;            &= nbsp; Fecha

Nivel de Instrucción            =             &nb= sp;  Ordinal

Tipo de colegio            &= nbsp;           &nbs= p;           Nominal

Uso del computador           &nbs= p;            &= nbsp;  Numérico

Actividad física            =             &nb= sp;          Numérico


Ingres= os económicos        &= nbsp;           &nbs= p;     Ordinal

 

 

= <= span lang=3DES style=3D'font-size:1.0pt;mso-bidi-font-size:12.0pt'>


Atributos  &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;     Tipo de Dato

Fecha de juego=             &nb= sp;            =             <= span style=3D'letter-spacing:-.1pt'>Fecha

Tiempo nivel 1&nbs= p;            &= nbsp;           &nbs= p;           Fecha

Tiempo nivel 2&nbs= p;            &= nbsp;           &nbs= p;           Fecha

Tiempo nivel 3&nbs= p;            &= nbsp;           &nbs= p;           Fecha

Tiempo nivel 4&nbs= p;            &= nbsp;           &nbs= p;           Fecha

Tiempo total        &= nbsp;           &nbs= p;            &= nbsp;       Fecha


Puntos      = ;            &n= bsp;            = ;            &n= bsp;       Numérico

 

Preparación= de los datos

La aplicación de técnicas de preprocesamiento previo al modelado puede incrementar la eficienc= ia del proceso y mejorar la calidad de los patrones encontrados. Es común que los datos recolectados no se encuentren en un estado óptimo, por lo tanto, deben ser tratados considerando las observaciones detectadas en la fase previa (Han, 2012). En la Figura 3, se presenta el proceso de preparación de los datos, que a su vez corresponde con la primera transformación de= l MUV.

 

Figura 3

Modelado <= /span>de la preparación de los datos


 

La Transformación de Datos Crudos a Datos Abstractos (DC – DA) es la responsable de procesar los datos provenientes del dominio de aplicación (datos crudos) y llevarlos a un formato manejable por el sistema, y como resultado se obtiene un conjunto de datos potencialmente visualizables denominados datos abstractos (Martig et = al., 2003). Como parte de los datos abstractos podemos tener un subconjunto de D= atos Crudos a los cuales se les puede haber mejorado de alguna forma (Martig et al., 2003). En esta fase, se procesaron los datos abstractos mediante tareas de limpi= eza, estructuración, discretización y selección de variable= s. Como resultado, se obtuvo un conjunto de datos preparados que servir&aacut= e;n como punto de partida para la continuación del proc= eso de transformación DC – DA, misma que finalizará en la siguiente fase (clasificación según el rendimiento).

 

Limpieza de datos<= /span>

La limpieza de datos se puede aplicar para eliminar el ruido, tratar los valores atípicos y corregir las= inconsistencias en los datos (Kotu & Deshpande, 2019). En este contexto, se realizó una optimización de los datos, mediante la corrección de: 1) unidades de medida y tiempo erróneas correspondientes a los atributos: “peso”, “altura”, “uso del computador&#= 8221; y “actividad física”. 2) Expresiones que contienen caracteres (P.ej. “aprox”, “h”, “cm”), en campos numéricos.

3) Corre= cción de “fecha de juego”, para ello se restó seis horas de cada campo registrado por el servidor. 4) Eliminaci&o= acute;n de registros con inconsistencias en la fecha de nacimiento, únicamente aquellos que ingresaron la fecha actual al momento del registro. Como resultado del proceso de

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limpieza de datos se obtuvieron un total<= span style=3D'letter-spacing:-.05pt'> de 471 registros.

 

Discretización de datos

En este paso del preprocesamiento,= los datos se transforman o consolidan para que el proceso de miner&iacut= e;a resultante sea más= eficiente y los patrones encontrados se puedan interpretar con mayor facilidad (Han, 2012). Como parte del proceso se discretizó la edad de los jugadores según la clasificación de edades sugerida por la psicología del desarrollo (Wertsch &= Tulviste, 1992), y los rangos asignados a cada grupo etario se pueden observar en la Tabla 2. Finalmente, se realizó la di= scretización del índice de masa corporal de acuerdo a la escala sugerida por el Centers for Disease Control and Prevention (Division of Nutrition, Physical Activity, and Obesity, 2022), los rangos empleados se pueden apreciar en la Tabla 3.

 

Tabla 2

Clasificación por grupos etarios


Edad (años)        &= nbsp;    Grupo etario

= <= span lang=3DES style=3D'font-size:1.0pt;mso-bidi-font-size:12.0pt'>

2 – = 11        &= nbsp;           &nbs= p;   Niñez<= /span>

12 - 19        &= nbsp;           &nbs= p; Adolescencia<= /span>

20 - 35        &= nbsp;           &nbs= p; Juventud<= /span>

36 - 60        &= nbsp;           &nbs= p; Madurez<= /span>


61 o m&aac= ute;s        &= nbsp;         Vejez<= /span>

 

Tabla 3

Categorías por índice de masa corporal


IMC  &nbs= p;            &= nbsp;           &nbs= p; Peso

= <= span lang=3DES style=3D'font-size:1.0pt;mso-bidi-font-size:12.0pt'>

Inferior a 18.5          <= /b>Bajo peso

18.5 – 24.9        &= nbsp;        Peso saludable

25.0 – 29.9        &= nbsp;        Sobrepeso<= /span>


30.0 o m&a= acute;s        &= nbsp;         Obesidad<= /span>

 

Modelado d= e Visualización

El<= /span> modelado de visualizaci&= oacute;n propuesto constituye un extracto del MUV,<= span lang=3DES style=3D'letter-spacing:-.65pt'> el cual consiste en un proceso interactivo entre las siguientes transformaciones: = Datos Abstractos - Datos a Visualizar (DA <= /span>– DaV), Mapeo Visual (TMV) y Visualización (TV), como se p= uede observar en la Figura 4.

 

Figura 4

Modelado para la visualización

 

A continuación, se presenta una breve descripción de las transformaci= ones que involucró la fase del modelado de visualización= . Se tomaron como guía de referencia las investigaciones realizadas por Luján (2018) y Martig et al. (2003) en el MUV:=

 

D= A – DaV

El objetivo de esta transformación es definir el conjunto de datos que va a estar presente en la visualizaci&o= acute;n y permitir la exploración de distintas regiones del espacio de información y su comparación. Para ello, se seleccionaron los datos a visualizar, y para un mismo conjunto de Datos Abstractos (DA)= se generaron varios conjuntos de Datos a Visualizar (DaV), cada uno correspond= e a una vista en particular.

 

TMV

El principal objetivo de esta transformaci&= oacute;n es conseguir un mapeo <= span lang=3DES style=3D'letter-spacing:-.1pt'>expresivo y efectivo.

El mapeo es expresivo cuando se consigue representar = todos los datos del conjunto de DaV. Por otra parte, la efectividad del mapeo estará definida por la manera en que la representación visual sea percibida por el usuario. En esta transformación se especificó cómo visualizar los datos presentes en el conjunto de DaV. Se generaron las estructuras necesarias con la finalidad de soportar el sustrato espacial, los elementos visuales (marcas y canales visuales) y los atributos gráficos de los elementos visuales.

 

El sustrato espacial está f= ormado por la configuración entre la cantidad de ejes involucrados en la visualización, su orientación (radial, rectilínea, libr= e o paralela), la colección de ejes asociados y su organización en pantalla (por composici&oacut= e;n, alineación, sobrecarga o recurs= ión). Por otro lado, el sustrato gráfico (elementos visuales y atributos gráficos) se compone de todos los elementos utilizados para representar la vista. La información presente en el sustrato gráfico se descompone en marcas (elementos visuales) y en can= ales visuales (controlan la apariencia de las marcas) (Luján, 2018). Según Luján (2018), para conseguir un mapeo expresivo y efectivo, es necesario establecer:

 

•

 

JSViz_1

Es un entorno unificado de análisis visual para datos provenientes del juego serio de pares. El sistema fue diseñado para el análisis y exploración de variables demográficas y aquellas propias del juego que denotan el rendimiento= de los jugadores durante cada sesión de entrenamiento. La herramienta incorpora técnicas de visualización como gráficos de barras y un diagrama de dispersión, y además provee un sistema de categorización interactivo de acuerdo con el grupo etario,<= span style=3D'letter-spacing:-.5pt'> así como tambi&eac= ute;n la clasificación de intentos según el rendimiento cognitivo. En la Figura 6 se pueden observar las transformaciones por las cuales atraviesan los datos, de= bido a que las tres vistas exploran el mismo conjunto de Datos a Visualizar, las <= span style=3D'letter-spacing:-.1pt'>representaciones correspondientes y los procesos que atraviesan en el modelo son similares hasta la transformación <= /span>DA – DV. Mientras que la transformación de mapeo visual y la transformación de visualización son únicas para cada vista.

 

Figura 6

JSViz_1 en el MUV. Ramificaciones del modelo propuesto generando tres vistas

correlacionadas


 

Transformaci&oacu= te;n de Mapeo Visual

Se realizó la TMV para cada una de las vistas de forma independiente y como resultado del proceso se obtuvo un conjunto de Datos Mapeados Visualmente, que corresponden al estado previo a la visualización final.

 

•      =    Transformación de Mapeo Visual para la vista. Gráfico de barras 1

◦      =    Sustrato espacial: se definiero= n 2 ejes, organizados por composición, con orientación rectilínea. El eje vertical es de tipo categórico y el horizo= ntal cuantitativo.=

◦      =    Marcas: Todos los ítems de dato se representan a través de la misma marca, un área (2D).

◦      =    Canales: El canal visual utilizado fue el tamaño.

•      =    Transformación de Mapeo Visual para la vista. Gráfico de barras 2

◦      =    Sustrato espacial: se definiero= n 2 ejes, organizados por composición, con orientación rectilínea. El eje vertical es de tipo categórico y el horizo= ntal cuantitativo.=

◦      =    Marcas: Todos los ítems de dato se representan a través de la misma marca, un área (2D).

◦         Canales: Los canales visuales utilizados fueron el tamaño, que se asocia con la cantidad de puntos, y el color mediante una escala secuencial azul que representa la edad del jugador.

&nbs= p;

•      =    Transformación de Mapeo Visual para la vista. Gráfico de dispersión

◦      =    Sustrato espacial: se definiero= n 2 ejes, organizados por composición, con orientación rectilínea. Ambos ejes (horizontal y vertical) son de tipo cuantitativo. El rango establecido para el eje horizontal es de 0 a 600 y el vertical de 0 a 2000.

◦      =    Marcas: Todos los ítems de dato se representan a través de la misma marca, un punto.

◦         Canales: El canal visual utilizado es el color, que representa la clasificación con respecto al rendimiento= al cual cada punto pertenece. El mapeo del color con el rendimiento asociado se puede observar en la Tabla 5.

 

JSViz_2

Es<= /span> el segundo entorno unificado de anális= is visual = que forma<= span lang=3DES style=3D'letter-spacing:-.6pt'> parte <= span lang=3DES style=3D'letter-spacing:-.1pt'>del modelo= propuesto. A diferencia de JSViz_1, este entorno fue diseñado para el análisis y exploración de variables conductuales contra aquellas propias del juego que denotan el rendimiento de los jugadores durante cada sesión de entrenamiento. La herramienta incorpora técnicas de visualización como: gráficos de barras, diagrama de dispersión, diagrama de árbol y gráficos de violín, además provee un sistema de categorización interactivo de acuerdo con el Índice de Masa Corporal y la clasificación de intentos según el rendimiento cognitivo.

 

En<= /span> la Figura 7, se pueden observar las transformaciones por las cuales atraviesan los datos y para cada vista se generó una ra= ma. Debido a que las tres vistas exploran el mismo conjunto de Datos a Visualizar, las representaciones correspondientes y los procesos que atraviesan en el modelo son similares hasta la transformación DA – DV. Mientras que la transformación de mapeo visual y la transformación de visualización son únicas= para cada vista.

 

Figura 7

JSViz= _2 en el MUV. Configuración del proceso mediante cuatro ramificaciones para representar las vistas proporci= onadas por JSViz_2


 

Transformaci&oacu= te;n de Mapeo Visual

Se realizó la TMV para cada una de las vistas de forma independiente y como resultado del proceso se obtuvo un conjunto de Datos Mapeados Visualmente, que corresponden al estado previo a la visualización final.

 

•      =    Transformación de Mapeo Visual para la vista. Gráfico de barras

◦

&nbs= p;

orientación rectilínea. El eje vertical= es de tipo categórico y el horizontal cuantitativo.

◦         Marcas: Todos los ítems de dato se representan a través de la misma marca, un área (2D).

◦      =    Canales: El canal visual utilizado fue el tamaño.

•      =    Transformación de Mapeo Visual para la vista. Gráfico de dispersión

◦      =    Sustrato espacial: se definiero= n 2 ejes, organizados por composición, con orientación rectilínea. Ambos ejes (horizontal y vertical) son de tipo cuantitativo. El rango establecido para el eje horizontal es de 0 a 600 y el vertical de 0 a 2000.

◦      =    Marcas: Todos los ítems de dato se representan a través de la misma marca, un punto.

◦         Canales: El canal visual utilizado es el color, que representa la clasificación con respecto al rendimiento= al cual cada punto pertenece. La codificaci&= oacute;n del color se presenta en la Tabla 5.

•      =    Transformación de Mapeo Visual para la vista. Gráfico de violí= n 1

◦      =    Sustrato espacial: se definiero= n 2 ejes, organizados por composición, con orientación rectilínea. El eje vertical es de tipo cuantitativo y representa el tiempo de actividad física con un rango de 0 a 4 y el horizontal categórico con valores que corresponden a la clasificación por clústeres según el rendimiento: Alto, Bajo 1, Promedio y Bajo 2.

◦      =    Marcas: Todos los ítems de dato se representan a través de la misma marca, un área (2D).

◦         Canales: Los canales visuales utilizados fueron el tamaño, y el color mediante una escala categórica.

•      =    Transformación de Mapeo Visual para la vista. Gráfico de violí= n 2

◦      =    Sustrato espacial: se definiero= n 2 ejes, organizados por composición, con orientación rectilínea. El eje vertical es de tipo cuantitativo y representa el tiempo de uso del computador con un rango de 0 a 25 y el horizontal categórico con valores = que corresponden a la clasificación por clústeres según el rendimiento: = Alto, Bajo 1, Promedio y Bajo 2.

◦      =    Marcas: Todos los ítems de dato se representan a través de la misma marca, un área (= 2D).

◦         Canales: Los canales visuales utilizados fueron el tamaño, y el color mediante una escala categórica.

•      =    Transformación de Mapeo Visual para la vista. Diagrama de árbol=

◦      =    Sustrato espacial:<= /span> se<= span lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1= 03%; letter-spacing:-.65pt'> definieron= 2 ejes, organizado= s por= recursi&oa= cute;n, con= orientaci&= oacute;n rectilínea. = El eje vertical es de tipo cuantitativo y representa el tiempo de uso del computador con un rango de 0 a = 25 y el horizontal categórico con valores que corresponden a la clasificación por clústeres según el rendimiento: Alto, Bajo 1, Promedio y Bajo 2.

◦      =    Marcas: Todos los ítems de dato se representan a través de la misma marca, un área (= 2D). Cada una de las marcas corresponde a la clasificación por clústeres según el rendimiento: Alto, Bajo 1, Promedio y Bajo 2.

◦      =    Canales: Los canales visuales utilizados fueron el tamaño del rectángulo, que tiene un área proporcional a la cantidad de horas promedio de uso del computador, y el color, mediant= e una escala secuencial proporcional a= l tiempo promedio de actividad física, codificada con un matiz azul.

 

JSViz_3

Es el tercer entorno unificado de análisis visual que forma parte del modelo propuesto.

&nbs= p;

A diferencia de los anteriores, este entorno fue diseñado para el análisis y exploración de variables temporales contra aquellas variables que denotan el rendimiento de los jugadores durante cada sesión. La herramienta<= span style=3D'letter-spacing:-.2pt'> incorpora técnicas de visualización como mapas de calor, gráficos de polilíneas, gráficos de barras y gráficos de dispersión, además provee un sistema de categorización interactivo de acuerdo con el número de intento, en el cual es posible comparar el rendimiento<= span style=3D'letter-spacing:-.3pt'> de todos jugadores e identificar valores atípicos mediante= un sistema de control interactivo.

 

En<= /span> la Figura 8 se pueden observar las transformaciones por las cuales atraviesan los datos y par= a cada vista se generó una rama. Al igual que en los entornos anteriores las cuatro vistas que conforman JSViz_3 exploran el mismo conjunto de Datos a Visualizar y las representaciones correspondientes y los procesos que atraviesan en el modelo son similares hasta la transformación DA – DV. Mientras que la transformación de mapeo visual y la transformación= de visualización son únicas para cada vista. Para conseguir esta visualizació= n, se seleccionó de los DA la fecha de juego y las variables propi= as del juego que denotan el rendimiento de los jugadores. Los atributos selecciona= dos pasarán a formar parte de los DaV, descartando a todos los restantes= .

 

Figura 8

JSViz_3 en el MUV. Las ramificaciones de color se generan de forma dinámica a partir de

interacciones en el sistema de visualización

 


 

Transforma= ción de Mapeo Visual

Se realizó la TMV para cada una de las vistas de forma independiente y como resultado del proceso se obtuvo un conjunto de Datos Mapeados Visualmente, que corresponden al estado previo a la visualización final.

 

•      =    Transformación de Mapeo Visual para la vista. Mapa de Calor

◦      =    Sustrato espacial: se definiero= n 2 ejes, organizados por composición, con orientación rectilínea. El eje vertical es de tipo categórico y el horizo= ntal de tipo fecha.

◦      =    Marcas: Todos los ítems = de dato se representan a través de la misma marca, un área (2D). Mediante una alineación en forma= de matriz, donde cada celda codifica valores cuantitativos mediante el color.<= o:p>

◦      =    Canales: El canal visual utiliz= ado fue el color, mediante una escala divergente naranja-azul, con un rango de = 850 a 1975 puntos, el indicador de alerta tiene un valor de 1469, que correspon= de al prototipo de un jugador con rendimiento Bajo 1, de acuerdo con la clasificación resultante del proceso de “Clasificación

&nbs= p;

Según el Rendimiento”.

•      =    Transformación de Mapeo Visual para la vista. Gráfico de líneas

◦      =    Sustrato espacial: se definiero= n 3 ejes, organizados por composición, con orientación rectilínea. Todos los ejes son de tipo cuantitativo. El rango establecido para el eje horizontal varía dependiendo de la cantidad de intentos registrados de cada jugador, el primer eje vertical tiene un rango establecido de 0 a 2000 y el segundo eje vertical se establece de acuerdo con el tiempo total máx= imo entre todos los intentos de cada jugador.

◦         Marcas: Todos los ítems de dato se representan a través de la misma marca, una polilínea.

◦         Canales: El primer canal visual utilizado es el color, mediante una escala categórica, que inicialmente se asignará a un color por defecto. El color naranja representa el tiempo total y= el color azul el puntaje. El segundo canal visual corresponde al tamaño que varía de acuerdo con el número de intentos registrados por cada jugador.

•      =    Transformación de Mapeo Visual para la vista. Gráfico de barras

◦      =    Sustrato espacial: se definiero= n 2 ejes, organizados por composición, con orientación rectilínea. El eje vertical es de tipo cuantitativo y el horizontal = es categórico.<= /p>

◦      =    Marcas: Todos los ítems de dato se representan a través de la misma marca, un área (2D).

◦         Canales: El canal visual utilizado fue el tamaño y el color de las marcas. El color se asignará mediante u= na escala categórica, con colores predefinidos. El color rojo corresponde a la marca que representa el Nivel 1, el color turquesa al Nivel 2, el color amarillo al Nivel= 3 y el color violeta al Nivel 4.

•      =    Transformación de Mapeo Visual para la vista. Gráfico de dispersión

◦      =    Sustrato espacial: se definiero= n 2 ejes, organizados por composición, con orientación rectilínea. El eje horizontal es de tipo fecha y el eje vertical es = de tipo cuantitativo.

◦      =    Marcas: Para esta vista se establecen dos tipos de marca. Un punto, que representa cada uno de los ítems del dato. Y dos líneas, que representan los lími= tes superior e inferior de acuerdo con un parámetro calculado a partir d= e la desviación estándar= del puntaje de todos los intentos mostrados en la vista.

◦         Canales: El canal visual utiliz= ado es el color de cada elemento visual. Las marcas que correspond= en a los ítems de dato pueden tener dos colores, azul para aquellas que = se encuentran dentro de los límites inferior y superior, y naranja para aquellas marcas que se encuentren fuera de los límites. De esta form= a se facilita la identificación de valores atípicos.

 

 

Resultados y Discusión

Como resultado de la metodolog&iac= ute;a propuesta para el desarrollo de un modelo de visualización, se obtuv= o un entorno unificado para la exploración y análisis visual de da= tos provenientes del juego serio de pares. El análisis a través d= e la visualización conduce al descubrimiento de patrones (p. ej. tendenci= as, brechas, valores atípicos o agrupaciones), la verificación de hipótesis y el soporte para el razonamiento y la toma de decisiones.=

 

JSViz_1

Provee un conjunto de gráfi= cos coordinados que permiten la exploración y análisis de las variables demográficas cont= ra aquellas que representan el rendimiento en el juego. En la

&nbs= p;

Figura 9, se = puede observar el entorno resultante.=

 

Figura 9

Entorno de visualización unificado: JSViz_1


 

El entorno unificado JSViz_1 está compuesto por<= span style=3D'letter-spacing:-.25pt'> las siguientes vistas:

 

•      =    Brushing = y<= span lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1= 03%; letter-spacing:-.55pt'> = Linking: = JSViz_1 proporcion= a esta interacci&= oacute;n que= es<= span lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1= 03%; letter-spacing:-.55pt'> aplicada a múl= tiples vistas, permitiendo seleccionar un subconjunto de datos en una vista y resaltarlo en todas= las demás vistas coordinadas. Los cambios que se generen mediante una interacción a una vista se reflejan de forma automática en las vistas restantes. En particular, esta interacción se puede realizar al seleccionar uno de los intentos en el gráfico de dispersión o una de las barras del gráfico de barras 2.

•      =    Filtro interactivo: Mediante es= ta interacción es posible partir los datos en s egmentos, con la finalidad de enfocarse en subconjuntos de interés. En JSViz_1 esta interacción es viable mediante la selección de los siguientes elementos: 1) Categorías correspondientes a grupos etarios en el gráfico de barras 1. 2) Objetos del panel que incluyen filtros para el número de intentos, el nivel de instrucción, = el tipo de

&nbs= p;

colegio, los ingresos económicos, el géner= o y la consulta por campo de texto según

el identificador del jugador.

•      =    Zoom Semántico: Esta interacción permite seleccio= nar un elemento de la vista con la finalidad de obtener más información sobre la misma. Estos valores se muestran mediante un área de= texto sobre la vista del elemento seleccionado.

•

 

En la Figura 10 se puede observar= un ejemplo de la exploración de datos utilizando JSViz_1, con la finalidad de responder la pregunta: ¿Cuál es el grupo etario con el rendimiento cognitivo más bajo? A simple vista se puede deducir que el grupo etario del adulto mayor posee el rendimiento promedio más bajo del juego, en comparación a los demás grupos. Tambi&= eacute;n es posible determinar que ning&uacu= te;n adulto mayor posee un desempeño asociado al grupo de alto rendimiento (marcas de color azul).

 

JSViz_2

Para el análisis de las variables que se asocian a la conducta o hábitos de los jugadores, se desarrolló JSViz_2. En la Figura 11 se puede observar el entorno resultante.

 

Figura 11

Entorno de visualización unificado: JSViz_2


 

El entorno unificado JSViz_2 está= ; compuesto por las siguientes vistas:<= /p>

•      =    Filtro interactiv= o: En<= span lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1= 00%; letter-spacing:-.45pt'> JSViz_2 esta interacci&= oacute;n es<= span lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1= 00%; letter-spacing:-.45pt'> posible mediante la<= span lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1= 00%; letter-spacing:-.45pt'> selecci&oa= cute;n de<= span lang=3DES style=3D'font-size:12.0pt;mso-bidi-font-size:11.0pt;line-height:1= 00%; letter-spacing:-.45pt'> los siguientes elementos: 1) categor&iacut= e;as correspondientes al IMC en el gráfico de barras 1, 2) objetos del panel que incluyen filtros para el tiempo de uso del computador y el tiempo de actividad f&ia= cute;sica mediante un deslizador de rangos y la consulta por campo de texto según el identificador del jugador.

•      =    Navegación: Esta interacción permite navegar entre tableros. En JSViz_2 esta interacci&oac= ute;n es posible mediante la selección de botones que se encuentran disponibles en el panel.

 

Figura 12

Ejemplo de exploración y anális= is de datos con JSViz_2


Al= igual = que en JSViz_1, es posible utilizar las interacciones= disponibles con la finalidad de potenciar las capacidades cognitivas humanas y permitir el análisis = de grandes volúmenes de datos a través de la visualizació= n. En la Figura 12, se puede observar que el grupo de alto rendimiento est&aac= ute; asociado a un hábito regular de actividad física. Mientras que los jugadores con un rendimiento inferior, en su gran mayoría no realizan actividad física.

 

JSViz_3

Provee un conjunto de gráfi= cos coordinados que permiten la exploración y análisis de las variables temporales con= tra aquellas que representan el rendimiento en el juego. El principal objetivo d= e este entorno unificado es proporcionar al especialista una herramienta interacti= va que facilite la evaluación de resultados con respecto a la evolución del rendimiento, a medida que se realizan las sesiones de entrenamiento. Este entorno unificado también proporciona visualizaciones diseñadas para la exploración con respecto a = la hora en la que se registran sesiones = y el contraste contra otros jugadores en un intento determinado, lo cual permite resaltar la presencia de valores atípicos (jugadores que denotan un desempeño estadísticamente fuera de lo común). En la Figura 13 se presenta la integración de vistas = que conforman JSViz_3.

 

El entorno unificado JSViz_3 está= ; compuesto por las siguientes vistas:<= /p>

•      =    Gráfico de líneas: Utilizado para representar el rendimiento de un jugador en particular seleccionado en el mapa de calor. Los ejes verticales representan el desempeño del jugador, mientras que el eje vertical, el númer= o de intentos.

•      =    Gráfico de barras: Utilizado para representar el tiempo de cada nivel, agrupado por

números de intentos.

&nbs= p;

•

 

Entre las distintas interacciones p= roporcionadas por JSViz_3 tenemos:

•      =    Zoom Semántico: Esta interacción permite seleccio= nar un elemento de la vista con la finalidad de obtener más información sobre la misma. Estos valores se muestran mediante un área de= texto que resalta sobre la vista del elemento seleccionado.

•      =    Crear nueva vista: Esta interacción es exclusiva para JSViz_3, permite incorporar una nueva vista al sistema de visualización y como consecuencia de la interacción se generará una nueva ramificación a partir del estado DA. En la Figura 13 se pueden observar las ramas involucrad= as.

•      =    Cerrar vista: Con esta interacción es posible cerrar vistas abiertas en el sistema de visualización y como consecuencia de la interacción se eliminar&aacu= te; las ramificaciones generadas a partir de la interacción “Crear nueva vista= 221;. En la Figura 13 se pueden observar las ramas involucradas.

•      =    Filtro interactivo: En JSViz_3 esta interacción pe= rmite la reducción de ítems y es posible mediante la selección de los siguientes elementos. 1) Marcas propias de cada vista: celdas del m= apa de calor y líneas del gráfico de líneas. 2) Objetos del panel que incluyen un filtro para el número de desviaciones estándar mediante un deslizador de rangos y una consulta por campo de texto según el identificador del jugador.

•      =    Navegación: Esta interacción permite navegar entre tableros. En JSViz_3 esta interacci&oac= ute;n es posible mediante la selección de botones que se encuentran disponibles en el panel.

 

Figura 14

Ejemplo de exploración y anális= is de datos con JSViz_3


 

Además de la exploraci&oacu= te;n en términos de población del conjunto de datos, tambié= n es posible analizar el rendimiento cognitivo de un jugador en particular. En la Figura 14, se puede observar un ejemplo de la exploración de datos utilizando JSViz_3, con la finalidad de responder la pregunta: ¿Qué sucede con el rendimiento cognitivo del jugador “Persona 185” a med= ida que la cantidad de intentos incrementa? En el gráfico de líne= as, se puede apreciar que el jugador demuestra un incremento<= span style=3D'letter-spacing:-.75pt'> en cuanto al rendimiento en términos de puntaje (línea azul) y tiempo= total (línea naranja)<= span style=3D'letter-spacing:-.75pt'> a medida que adquiere experiencia en el juego. Así mismo, se puede observar en el gráfico de ba= rras que el tiempo asociado a los niveles 3 y 4 (color amarillo y violeta respectiva= mente), presenta un decremento a medida que las sesiones de entrenamiento se realiz= an.

 

 

Conclusiones

El<= /span> presente trabajo se centró= en el diseño= y validaci&oac= ute;n de un modelo de vi= sualización, que permite a los especialistas del área de la salud mental explorar y analizar los datos recolectados de un juego serio orien= tado al entrenamiento de atención y memoria, con el objetivo principal de identificar patrones asociados al deterioro cognitivo.

 

Par= a cumplir con los objetivos de este estudio, se propuso un modelo de minería= de datos = cuyo principio teórico se fundamenta en la metodología CRISP-DM y el Modelo Unificado de Visualización. Las etapas que forman parte del modelo consisten en tareas genéricas útiles = para cualquier proyecto de minería de datos, como la comprensión del problema y la comprensión y preparación <= /span>de los datos. Así como tambi&eacut= e;n, tareas específicas para el desarrollo del modelo de visualización, donde se realizan transformaciones que parten del tratamiento de datos abstractos hasta conseguir un conjunto de datos visualizados. Técnicamen= te, el proceso de modelado de visualización consistió en la selección de subconjuntos de datos a visualizar, la definición del sustrato espacial y la codificación visual. Posteriormente, con la transformación de visualización se obtuvo el resultado esperado, un conjunto de gráficos apropiados para la visualización de datos provenientes del juego de pares.

 

La consolidación de los dat= os visualizados resultó en un entorno unificado de análisis visu= al compuesto por tres tableros interactivos: JSViz_1, JSViz_2 y JSViz_3. Como parte de la validación del modelo, se realizó un análisis explicativo de los datos recogidos del juego, donde participaron 248 personas. Se pudo determinar que las características de mayor influencia con

&nbs= p;

respecto al rendimiento de los jugadores son la edad, el tiempo promedio de actividad física y el tiempo de uso del computador. Específicamente, = los jugadores con una costumbre habitual de actividad física demuestran mayor rendimiento con respecto a los demás jugadores; por el contrar= io, los jugadores con un menor desempeño cognitivo se caracterizan por pertenecer al grupo etario mayor y/o un escaso hábito de actividad física. El proceso de agrupamiento reveló la presencia de cuatro aglomerados que se categorizaron de acuerdo con el grado de rendimiento asociado a la habilidad cognitiva.

 

Finalmente, se llevó= ; a cabo una evaluación empírica del modelo de visualización con 16 estudiantes de psicología, siguiendo los lineamientos de un cuasi-experimento aplicado con un modelo de transferencia tecnológica. Los resultados revelaron una recepción positiva hacia la adopción de esta tecnología. En el contexto de la salud mental, este modelo ofrece implicaciones prácticas significativas, permitiendo a los especialistas identificar patrones de deterioro cognitivo y adaptar intervenciones de acuerdo con el perfil de cada paciente. = Sin embargo, es prudente considerar las limitaciones del estudio, particularmen= te su enfoque en un único juego y la representatividad de la muestra seleccionada. Investigaciones futuras podrían beneficiarse al amplia= r la diversidad de la muestra y explorar la aplicabilidad del modelo en otros juegos serios relacionados con la salud mental.

 

 

Reconocimientos

EEste trabajo ha sido financiado parcialmente por el Proyecto de Investigación= Ciencia de los datos en juegos seri= os orientados a la atención y memoria Fase III, y forma parte de la Tes= is titulada “Modelo de visualización de datos en el contexto de juegos serios orientados al entrenamie= nto cognitivo de atención y memoria”. Los autores desean expresar su agradecimiento al Vicerrectorado <= span style=3D'letter-spacing:-.1pt'>de Investigaciones de la Universidad del Azuay por el respaldo en la ejecución de proyectos de investigación.

 

 

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Alonso-Fernández, C., Calvo-Morata, A., Fre= ire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (20= 19). Applications of data science to game learning analytics data: A systematic literature review. Computers a= nd Education, 141(April), 103612. https://doi.org/10.1016/j.compedu.2019.103612

 

Alonso-Fernandez, C., Calvo-Morata, A., Freire, M., Martinez-Ortiz, I., & Manjon, B. F. (2021). Data science meets standardized game learning analytics. IEEE Global Engineering Education Conference, EDUCON, 2021- April (February), 1546–= 1552. https://doi.org/10.1109/EDUCON463= 32.2021.9454134

 

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