Recommending educational video games based on game features and student's Learning Styles

Students learn and process information in different ways according to their learning styles. There are many learning styles and teachers are usually unable to deliver learning material that matches every possible combination. In this context, several tools, called Adaptive Hypermedia Systems (AHS), have been developed to automatically adapt and recommend learning material according to the students' learning styles. In recent years, educational video games have been used to motivate and engage students, providing a promising form of instruction. However, these video games possess several features. Thus, a particular educational video game could be preferred by a student with a specific learning style, but not by other student with a different style. For this reason, we analyze which students' behaviors described by Felder's learning styles are related to educational video game features. Moreover, we developed an approach to recommend educational video games whose features match the students' learning styles. Experimental results obtained from a population of undergraduate Computer Science students showed several relations between educational video games features and students' behaviors.