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Weppner J.,Embedded Intelligence Laboratory | Lukowicz P.,Deutsche Forschungszentrum fur Kunstliche Intelligenz GmbH | Blanke U.,ETH Zurich | Troster G.,ETH Zurich
IEEE Sensors Journal | Year: 2014

We describe a system that leverages users voluntarily having their smartphones scan the environment for discoverable Bluetooth devices to analyze crowd conditions in urban environments. Our method goes beyond mere counting of discoverable devices toward a set of more complex, robust features. We also show how to extend the analysis from crowd density to crowd flow direction. We evaluate our methods on a data set consisting of nearly 200 000 discoveries from nearly 1000 scanning devices recorded during a three day city-wide festival in Zurich. The data set also includes as ground truth 23 million global positioning system location points from nearly 30 000 users. © 2014 IEEE.

Narciss S.,TU Dresden | Sosnovsky S.,Deutsche Forschungszentrum fur Kunstliche Intelligenz GmbH | Schnaubert L.,University of Duisburg - Essen | Andres E.,Deutsche Forschungszentrum fur Kunstliche Intelligenz GmbH | And 3 more authors.
Computers and Education | Year: 2014

Personalized tutoring feedback is a powerful method that expert human tutors apply when helping students to optimize their learning. Thus, research on tutoring feedback strategies tailoring feedback according to important factors of the learning process has been recognized as a promising issue in the field of computer-based adaptive educational technologies. Our paper seeks to contribute to this area of research by addressing the following aspects: First, to investigate how students' gender, prior knowledge, and motivational characteristics relate to learning outcomes (knowledge gain and changes in motivation). Second, to investigate the impact of these student characteristics on how tutoring feedback strategies varying in content (procedural vs. conceptual) and specificity (concise hints vs. elaborated explanations) of tutoring feedback messages affect students' learning and motivation. Third, to explore the influence of the feedback parameters and student characteristics on students' immediate post-feedback behaviour (skipping vs. trying to accomplish a task, and failing vs. succeeding in providing a correct answer). To address these issues, detailed log-file analyses of an experimental study have been conducted. In this study, 124 sixth and seventh graders have been exposed to various tutoring feedback strategies while working on multi-trial error correction tasks in the domain of fraction arithmetic. The web-based intelligent learning environment ActiveMath was used to present the fraction tasks and trace students' progress and activities. The results reveal that gender is an important factor for feedback efficiency: Male students achieve significantly lower knowledge gains than female students under all tutoring feedback conditions (particularly, under feedback strategies starting with a conceptual hint). Moreover, perceived competence declines from pre- to post-test significantly more for boys than for girls. Yet, the decline in perceived competence is not accompanied by a decline in intrinsic motivation, which, instead, increases significantly from pre- to post-test. With regard to the post-feedback behaviour, the results indicate that students skip further attempts more frequently after conceptual than after procedural feedback messages. © 2013 Elsevier Ltd. All rights reserved.

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