Abstract
This paper aims to study actual behaviors of Thai children and early adolescents with different levels of game addiction while playing online games from an angle of the interaction between a user and computer. Real-time interaction- based behavior data from a program agent installed in personal computers in 20 sample houses were screened along with consent given by children and their parents. Collection of data about game-playing periods, frequency, game-playing times, text-based chatting, mouse click and keyboard typing during the game was carried out over two months and four case study in-depth interviews for addicted players and their parents. The results revealed a novel method to classify online game addiction level of children and early adolescents by mouse click and keyboard typing data and also found relationship between the playing data recorded and game addiction risk conditions and risk behaviors as explained in the article.
Highlights
From the previous research about online game addiction, we found that most researches relating to behaviors and the factors or mechanisms behind game addiction commonly used self-report methodology [1]
ANOVA and quantity of mouse click and keyboard typing in four game type based on playing characteristics; long term game, casual game, real time game, and turn base game [10].Those data were constructed the user model using Waikato Environment for Knowledge Analysis (WEKA)[11] by features shown in Table 1. using 10-fold cross validation method by Decision Tree (DT) and Backpropagation Neural Networks (BNN)
All sample stay in Bangkok and are not part of the treatment program for game addiction symptoms and Attention Deficit Hyperactivity Disorder (ADHD)
Summary
From the previous research about online game addiction, we found that most researches relating to behaviors and the factors or mechanisms behind game addiction commonly used self-report methodology [1]. Some researchers attempted to introduce technology as a tool to provide explanatory information during the game such as the measurement of electrodermal activity (EDA) and heart rate (HR) in order to describe the player’s experience, cognition and emotions [3, 4], or the real-time emotion diagnosis system which monitor the facial or vocal expressions occurring while playing the game [5]. These are all studies of physically effects on the body when users are playing an online game taking into account mental or psychological conditions. A relationship was found between game chat and externalizing aggressive behavior and game addiction [7, 8]
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