The effects of stereoscopic 3D on an incidence response training game

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A critical incident is defined as one of the most serious situations that can occur at any institution. Among these critical incidents are fire hazards, which resulted in 224 civilian deaths, and approximately 1.5$ billion dollars in direct property damage in Canada in 2011 alone. As a result of these costs, much effort is currently being placed in both fire safety prevention and fire safety evacuation. However, many current training techniques come at a high cost, and remain ineffective for real life fire situations, especially those which take place in large institutions. An example of this can be seen in university chemical labs, which hold many hazardous and flammable materials. In this thesis, the development of an incidence response training game for chemical lab fires is described. Serious games leverage the power of computer games to captivate and engage players/learners for a specific purpose such as to develop new knowledge or skills. Not only do serious games allow for higher engagement rates, and easier distribution than current training techniques, but by utilizing stereoscopic 3D (S3D) technologies it is hypothesized by many researchers that even higher engagement rates as well as knowledge retention levels can be reached. However, there is a lack of research examining the effects of S3D within an incidence response serious game. In this thesis, three experiments were conducted to examine potential benefits of employing S3D within a serious game for incidence response: i) engagement effects with S3D, ii) calibration of S3D settings, and iii) knowledge retention levels with S3D. The results of these experiments revealed that users were neither more engaged, nor did they increase their knowledge retention with the incorporation of S3D, contrary to prior research. Furthermore, allowing a user to define their own S3D settings is critical, and the inability to do so may create visual discomfort or an unnoticeable S3D effect.
Serious games, Incidence response, Training, Learning, Stereoscopic 3D