The nature of statistical learning theory
The nature of statistical learning theory
Affective gaming: measuring emotion through the gamepad
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Efficient retrieval of life log based on context and content
Proceedings of the the 1st ACM workshop on Continuous archival and retrieval of personal experiences
PWS & PHA: posture web server and posture history archiver
Proceedings of the the 1st ACM workshop on Continuous archival and retrieval of personal experiences
Automating the detection of breaks in continuous user experience with computer games
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Feature Subset Selection and Feature Ranking for Multivariate Time Series
IEEE Transactions on Knowledge and Data Engineering
Presence: Teleoperators and Virtual Environments
Game development for experience through staying there
Proceedings of the 2006 ACM SIGGRAPH symposium on Videogames
Measuring visual consistency in 3d rendering systems
ACSC '10 Proceedings of the Thirty-Third Australasian Conferenc on Computer Science - Volume 102
The 'interactive' of interactive storytelling: customizing the gaming experience
ICEC'10 Proceedings of the 9th international conference on Entertainment computing
Usability attributes in virtual learning environments
Proceedings of The 8th Australasian Conference on Interactive Entertainment: Playing the System
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We present a continuous and unobtrusive approach to analyze and reason about users' personal experiences of interacting with virtual and game environments. Focusing on an immersive educational game environment that we are developing, this is achieved through the capture and storage of user's movements and events that occur as a result of interactions with and within immersive environments. Termed immersidata, we then query and analyze immersidata to make sense of user behavior.Two example approaches are described. The first describes an application ISIS (Immersidata analySIS) that provides a tool for analysis of user behavior/experience through the indexing of immersidata with video clips of students' gaming sessions. This approach is described by way of an example to identify the causes of interruptions or breaks in interactions/focus of attention to facilitate the identification of problematic design. In our second example we describe our work towards classifying students' performance through immersidata. To this aim, we describe one example of transforming immersidata into multivariate time series and then by applying feature subset selection techniques we identify the features that differentiate students. We describe the application of this approach to identify novice and expert players with 90\% accuracy. One proposal is to use this to customize the game environment appropriate to the students' ability. Finally, we present future directions for the continuation of the work presented herein and also, the application of the immersidata system to capture, store and analyze personal behavior/experiences and provide appropriate feedback in our work and home environments.