Adding features of educational games for teaching physics uri icon


  • Virtual Laboratories (VLs) have to overcome important challenges to improve student knowledge, understanding and motivation. This research aims to test the hypothesis that, through adding features of serious games to VLs and integrating artificial intelligence (AI) techniques, an enhancement of student motivation, knowledge and understanding can be attained. This work introduces the Olympia architecture, which is based on a previous architecture that combines VLs and intelligent tutoring systems (ITSs). In addition, Olympia enables the combination of serious games with ITSs, resulting in an educational game virtual laboratory (GVL). The GVL provides affective feedback through sound, a more engaging look-and-feel and defines student actions through the game mechanics module. Olympia was tested in a case study on teaching linear momentum in an undergraduate Physics course. For the first evaluation, a VL and a GVL were implemented. The results showed that students were motivated and learned in a similar way with both the GVL and VL environments. Later, several additions were integrated in both environments: the probabilistic student model was improved, tutorial videos were added, and the feedback was refined. For the second evaluation the results suggest that using the GVL resulted in higher learning gains than using VL. ©2009 IEEE.

Publication date

  • January 1, 2009