Ultimate 3D Game Engine Design & Architecture
Ultimate 3D Game Engine Design & Architecture
Fundamentals of Game Design
A semi-open learning environment for virtual laboratories
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Guest Editorial Virtual Laboratories: Enhancing Deep Learning in Model-Based Knowledge Domains
IEEE Transactions on Education
Inferring Knowledge from Active Learning Simulators for Physics
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
PlayPhysics: an emotional games learning environment for teaching physics
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
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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.