Desarrollo del Pensamiento Computacional a través de la experiencia con las acciones del jugador en juegos de cartas
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Fecha
2021
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es
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Universidad Andrés Bello
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Licencia CC
Licencia CC
Resumen
En el siguiente estudio se aborda el aprendizaje del pensamiento computacional (CT)
aplicado en juegos de cartas, debido a la lógica de este género de juegos que resulta
favorable para enseñar el CT, a diferencia de los métodos tradicionales, al aprender
jugando y disfrutando del juego. Es desarrollado un juego de cartas coleccionables con
una lógica y apariencia atractiva en el que es posible ver características relacionadas al
CT para poder realizar pruebas en estudiantes universitarios, el cual cuenta con una
selección y creación de mazos basados en facciones de civilizaciones, pueblos e
imperios de la antigüedad con sus propios guerreros y seres mitológicos, y los
enfrentamientos contra una IA capaz de aplicar estrategias en sus jugadas. El objetivo es
probar este juego en estudiantes universitarios y que evalúen la experiencia a través de
una encuesta con categorías que definen la percepción del jugador sobre el juego,
enfocándose en la satisfacción del jugador sobre el juego se busca analizar cómo las
demás categorías y factores del juego afectan al comportamiento de la categoría que
describe el disfrute de este mismo. Al analizar los resultados de las encuestas se aplican
medidas estadísticas en búsqueda de correlaciones entre las categorías, para luego
encontrar un modelo predictivo de regresión lineal que explique en gran medida el
comportamiento de las variables dependientes que definen el disfrute por parte del
jugador en base a las variables independientes que definen las demás características y
factores del juego con correlaciones más altas vinculadas a las variables dependientes.
La interpretación de los modelos encontrados concluye que el disfrute en la experiencia
del juego por parte del jugador está fuertemente relacionado con variables como el efecto
y control de las decisiones del jugador en el juego, la retroalimentación recibida por el
jugador, los desafíos del juego y el mazo escogido por el jugador, sin embargo, debido a
la baja cantidad de encuestados algunos modelos obtenidos carecen levemente de
capacidad predictiva.
The following study addresses the learning of computational thinking (CT) applied to card games, due to the logic of this genre of games that is favorable for teaching CT, unlike traditional methods, by learning while playing and enjoying the game. It is developed a collectible card game with an attractive logic and appearance in which it is possible to see features related to CT in order to test in university students, which has a selection and creation of decks based on factions of civilizations, peoples and empires of antiquity with their own warriors and mythological beings, and confrontations against an AI capable of applying strategies in their moves. The objective is to test this game on university students and have them evaluate the experience through a survey with categories that define the player's perception of the game, focusing on the player's satisfaction with the game to analyze how the other categories and factors of the game affect the behavior of the category that describes the enjoyment of the game. When analyzing the results of the surveys, statistical measures are applied in search of correlations between the categories, to then find a predictive linear regression model that explains to a great extent the behavior of the dependent variables that define player enjoyment based on the independent variables that define the other characteristics and factors of the game with higher correlations linked to the dependent variables. The interpretation of the models found conclude that the player's enjoyment of the game experience is strongly related to variables such as the effect and control of the player's decisions in the game, the feedback received by the player, the challenges of the game and the deck chosen by the player; however, due to the low number of respondents, some models obtained slightly lack predictive capacity
The following study addresses the learning of computational thinking (CT) applied to card games, due to the logic of this genre of games that is favorable for teaching CT, unlike traditional methods, by learning while playing and enjoying the game. It is developed a collectible card game with an attractive logic and appearance in which it is possible to see features related to CT in order to test in university students, which has a selection and creation of decks based on factions of civilizations, peoples and empires of antiquity with their own warriors and mythological beings, and confrontations against an AI capable of applying strategies in their moves. The objective is to test this game on university students and have them evaluate the experience through a survey with categories that define the player's perception of the game, focusing on the player's satisfaction with the game to analyze how the other categories and factors of the game affect the behavior of the category that describes the enjoyment of the game. When analyzing the results of the surveys, statistical measures are applied in search of correlations between the categories, to then find a predictive linear regression model that explains to a great extent the behavior of the dependent variables that define player enjoyment based on the independent variables that define the other characteristics and factors of the game with higher correlations linked to the dependent variables. The interpretation of the models found conclude that the player's enjoyment of the game experience is strongly related to variables such as the effect and control of the player's decisions in the game, the feedback received by the player, the challenges of the game and the deck chosen by the player; however, due to the low number of respondents, some models obtained slightly lack predictive capacity
Notas
Proyecto de título (Ingeniero Civil Informático)
Palabras clave
Pensamiento Computacional, Videojuegos