Democratizing Game Learning Analytics for Serious Games

Abstract

Interest in the field of serious games (SGs) has grown during the last few years due to its multiple advantages. For example, SGs provide immersive learning environments, where risky or complex scenarios can be tested in safety while keeping players engaged. Moreover, the highly interactive nature of serious games opens new opportunities for applying learning analytics to the interaction data gathered from the gameplays. These interaction data can be used, for example, to measure the impact of serious games on their players. At e-UCM, we have developed open code tools to support serious game learning analytics (GLA), especially an xAPI tracker that collects the player interactions and sends them to a cloud analytic store, SIMVA. Although this tracker uses the xAPI specification as a basis, it includes extensions tailored to our tools. However, not all game developers have the knowledge to operate our analytics infrastructure or are willing to use our tools. We present the design of a GLA system based on existing software modules, focused on collecting and storing analytics generated by SGs in xAPI format. The main elements of this lean architecture are the Learning Record Store (LRS) and the xAPI tracker. With this work, we aim to facilitate and lower the barrier of applying learning analytics in serious games.