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    Active paper for active learning

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    Recent research into distance learning and the virtual campus has focused on the use of electronic documents and computer‐based demonstrations to replace or reinforce traditional learning material. We show how a computer‐augmented desk, the DigitalDesk, can provide the benefits of both paper and electronic documents using a natural interface based on real paper documents. Many electronic documents, particularly those created using the guidelines produced by the Text Encoding Initiative (TEI), include detailed semantic and linguistic information that can be used to good effect in learning material. We discuss potential uses of TEI texts, and describe one simple application that allows a student's book to become an active part of a grammar lesson when placed on the DigitalDesk. The book is integrated into an interactive point‐and‐click interface, and feedback is related to the currently visible pages of the boo

    Learning Active Learning from Data

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    In this paper, we suggest a novel data-driven approach to active learning (AL). The key idea is to train a regressor that predicts the expected error reduction for a candidate sample in a particular learning state. By formulating the query selection procedure as a regression problem we are not restricted to working with existing AL heuristics; instead, we learn strategies based on experience from previous AL outcomes. We show that a strategy can be learnt either from simple synthetic 2D datasets or from a subset of domain-specific data. Our method yields strategies that work well on real data from a wide range of domains
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