This article reviews the research and evidence about multi-touch tables to provide an analysis of their key design features and capabilities and how these might relate to their use in educational settings to support collaborative learning. A typology of design features is proposed as a synthesis of the hardware and physical characteristics of the tables so that the longevity of these factors and the associated analysis can be better preserved, particularly in relation to the range of ways in which they may be used collaboratively in classrooms. The variability of features relating to software is also analysed and key pedagogic issues identified. The aim that underpins this review is to relate the design of the technical features with key pedagogic issues concerning the use of digital technologies in classrooms, so as to provide a more robust basis for their integration in classrooms in terms of their potential to support or to improve learning.
The potential of tabletops to enable simultaneous interaction and face-to-face collaboration can provide novel learning opportunities. Despite significant research in the area of collaborative learning around tabletops, little attention has been paid to the integration of multi-touch surfaces into classroom layouts and how to employ this technology to facilitate teacher-learner dialogue and teacher-led activities across multi-touch surfaces. While most existing techniques focus on the collaboration between learners, this work aims to gain a better understanding of practical challenges that need to be considered when integrating multi-touch surfaces into classrooms. It presents a multi-touch interaction technique, called TablePortal, which enables teachers to manage and monitor collaborative learning on students' tables. Early observations of using the proposed technique within a novel classroom consisting of networked multi-touch surfaces are discussed. The aim was to explore the extent to which our design choices facilitate teacher-learner dialogue and assist the management of classroom activity.
We present the experiences from building a web-scale user modeling platform for optimizing display advertising targeting at Yahoo!. The platform described in this paper allows for per-campaign maximization of conversions representing purchase activities or transactions. Conversions directly translate to advertiser's revenue, and thus provide the most relevant metrics of return on advertising investment. We focus on two major challenges: how to efficiently process histories of billions of users on a daily basis, and how to build per-campaign conversion models given the extremely low conversion rates (compared to click rates in a traditional setting). We first present mechanisms for building web-scale user profiles in a daily incremental fashion. Second, we show how to reduce the latency through in-memory processing of billions of user records. Finally, we discuss a technique for scaling the number of handled campaigns/models by introducing an efficient labeling technique that allows for sharing negative training examples across multiple campaigns.
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