Interoperable adaptivity and learning analytics for serious games in image interpretation

Personalization and adaptivity in computer simulations and serious games are being used to achieve positive long term effects on the users' engagement, motivation and ultimately on the learning outcome. Interoperability regarding the collection of usage data allows for an effective analysis of the interaction and learning progress data. This paper presents an interoperable adaptivity framework combined with a web-based tutoring interface which gives learning analytics insights. The developed framework "E-Learning A.I." (ELAI) acts as an intelligent tutoring agent for simulations and serious games and uses the Experience API (xAPI) protocol. The application of the ELAI has been demonstrated in an adaptive map-based learning game for aerial image interpretation. The scientific research questions affect the possible usages of the collected interaction data, how to manifest adaptivity in games, how to realize interoperable adaptivity mechanisms for simulations and serious games, and how to make use of collected usage data.