Detecting playfulness in educational gamification through behavior patterns

Games are not a new concept in learning. Game-based learning, simulations, and serious games are known pedagogical methods used to build on the inherent playfulness of learners. Technological advances and the popularity of learning management systems are making it easier to implement gamification, analyze the resulting engagement and playfulness, and modify the implementation if needed. However, knowledge is often missing about how different combinations of game mechanics and dynamics create playfulness. We discuss the concept of gamification behavior patterns, which are sequences of actions performed by a user that can be attributed to the application of a gamification design pattern. A preliminary experiment was conducted in an academic course where perceived playfulness was analyzed with respect to three different sets of independent variables: personality, perceived enjoyment from game mechanics, and gamification behavior patterns. Results show that it is practical to measure gamification behavior patterns and that they have a significant predictive power. We propose the development of an open-source, cloud-based gamification behaviors database that will collect specific gamification engagement events from systems worldwide, along with metadata about each implementation. With such a database, Big Data, machine-learning, and recommender-system algorithms can be applied to increase knowledge regarding steering user behaviors through gamification.