Adaptive educational games based on recommendation strategy and learning style identification

Adaptivity and personalization can enhance student's motivations and acceptance of the usage of educational games. Within educational games, adaptivity defines automatic adaptation of learning objects based on student's learning styles, preferences, weaknesses or strongest, etc. Recently, machine learning algorithms such as recommender systems and… have been applied to develop personalization add adaptation properties in educational games. In this study, we introduce an interactive educational game developed based on first-grade students' math-books or school learning concepts. In this game, according to the student's profile and evaluation tests, the game evaluates students' performance iteratively. Also, student's learning styles and preferences will be extracted through playing game. Then in each iteration, the system recommends suitable learning objects to a learner based on a trade-off between their skills and the difficulty of the task.