Abstract-This paper presents the pedagogical and technical challenges the authors faced in developing a distributed laboratory for the execution of virtual scientific experiments (VSEs) superimposed on a Grid infrastructure, for a course on sensor networks that is part of the Master's in Information Networking (MSIN) program jointly offered by Carnegie Mellon University (CMU), USA and Athens Information Technology (AIT), Athens, Greece. The MSIN program utilizes virtual classroom technologies because of its strong distance learning component. Courses taught by CMU faculty are attended in real-time by students in Athens, Greece, via video-wall teleconferencing sessions. Vice versa, visiting CMU faculty to AIT teach classes that are attended by students at CMU. Students in both institutions enjoy full interactivity with their classmates on the other side of the Atlantic Ocean. A distributed shared virtual laboratory is needed for many of the more empirical courses. This paper describes the challenges and issues the authors faced in developing such a lab.
Immigration imposes a range of challenges with the risk of social exclusion. As part of a comprehensive suite of services for immigrants, the MASELTOV game seeks to provide both practical tools and innovative learning services via mobile devices, providing a readily usable resource for recent immigrants. We introduce advanced results, such as the game-based learning aspect in the frame of recommender services, and present the rationale behind its interaction design. Benefits and implications of mobile platforms and emergent data capture techniques for game-based learning are discussed, as are methods for putting engaging gameplay at the forefront of the experience whilst relying on rich data capture and analysis to provide effective learning solutions.Keywords. Mobile serious game, social inclusion, incidental learning framework, recommender system, human factors.
Geo-social radar:A volunteer helper service allowing users to find nearby volunteers who can help them with a problem, for example acting as a translator at a doctor's appointment, or negotiating local bureaucracy.TextLens: allows a learner to take a photo of a sign, and have this converted to text. This can then be coupled with a language translation tool such as Google Translate. Images and text can be uploaded for help when the meaning is ambiguous, and if the learner wishes to discuss their social, cultural or legal implications.
We present a system architecture for evolving classifier ensembles of oblique decision trees for continuous or online learning applications. In continuous learning, the classification system classifies new instances for which after a short while the true class label becomes known and the system then receives this feedback control to improve its future predictions. We propose oblique decision trees as base classifiers using Support Vector Machines in order to compute the optimal separating hyper-plane for branching tests using subsets of the numerical attributes of the problem. The resulting decision trees maintain their diversity through the inherent instability of the decision tree induction process. We then describe an evolutionary process by which the population of base classifiers evolves during run-time to adapt to the newly seen instances. A latent set of base-classifiers is maintained as a secondary classifier pool, and an instance from the latent set replaces the currently active classifier whenever certain criteria are met. We discuss motivation behind this architecture, algorithmic details and future directions for this research.
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