Looking around with your brain in a virtual world

IEEE Computational Intelligence SocietySymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, CCMB 2011 -- 11 April 2011 through 15 April 2011 -- Paris -- 85913Offline analysis pipelines have been developed and evaluated for the detection of covert attention from electroen-cephalography recordings, and the detection of overt attention in terms of eye movement based on electrooculographic measurements. Some additional analysis were done in order to prepare the pipelines for use in a real-time system. This real-time system and a game application in which these pipelines are to be used were implemented. The game is set in a virtual environment where player is a wildlife photographer on an uninhabited island. Overt attention is used to adjust the angle of the first person camera, when the player is tracking animals. When making a photograph, the animal will flee when it notices it is looked at directly, so covert attention is required to get a good shot. Future work will entail user tests with this system to evaluate usability, user experience, and characteristics of the signals related to overt and covert attention when used in such an immersive environment. © 2011 IEEE

Looking around with your brain in a virtual world

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MOOC dropouts: A multi-system classifier

In recent years, technology enhanced learning platforms became widely accessible. In particular, the number of Massive Open Online Courses (MOOCs) has—and still is—constantly growing. This widespread adoption of MOOCs triggered the development of specialized solutions, that emphasize or enhance various aspects of traditional MOOCs. Despite this significant diversity in approaches to implementing MOOCs, many of the solutions share a plethora of common problems. For example, high dropout rate is an on-going problem that still needs to be tackled in the majority of MOOCs. In this paper, we set out to analyze dropout problem for a number of different systems with the goal of contributing to a better understanding of rules that govern how MOOCs in general and dropouts in particular evolve. To that end, we report on and analyze MOOCs from Universidad Galileo and Curtin University. First, we analyze the MOOCs of each system independently and then build a model and predict dropouts across the two systems. Finally, we identify and discuss features that best predict if users will drop out or continue and complete a MOOC using Boosted Decision Trees. The main contribution of this paper is a unified model, which allows for an early prediction of at-risk or dropout users across different systems. Furthermore, we also identify and discuss the most indicative features of our model. Our results indicate that users’ behaviors during the initial phase of MOOCs relate to their final results