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Implementation of Clustering Techniques to Data Obtained from a Memory Match Game Oriented to the Cognitive Function of Attention

Abstract:

Serious games are software applications with an explicit educational objective that have been thoroughly thought out and designed as a learning instrument or tool. They allow the user to experience situations similar to real life and learn from their mistakes through immediate feedback. These games have been developed in various fields, including business, industry, marketing, health, government, among others. For instance, some are used for cognitive training in human beings, where attention and memory are fundamental axes during the various stages of the human life cycle. In this context, the “memory match game” is a card game where many pairs of cards are laid face down, and its objective is to match pairs in the lowest time and with the minimum number of wrong clicks. This information is registered and stored along with the player’s sociodemographic information. Thus, this article aims to analyze the dataset, applying clustering techniques in order to find behavioral patterns. The age variable was used to generate 4 age groups that served as the basis for applying the unsupervised machine learning algorithm, k-means. The results show the behavior of the data in relation to the age groups, it is evident that the more experience the players gain, times and scores improve, regardless of age.