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We investigated the feasibility of crowdsourcing full-fledged tutorial videos from ordinary people on the Web on how to solve math problems related to logarithms. This kind of approach (a form of learnersourcing) to efficiently collecting tutorial videos and other learning resources could be useful for realizing personalized learning-at-scale, whereby students receive specific learning resources -- drawn from a large and diverse set -- that are tailored to their individual and time-varying needs. Results of our study, in which we collected 399 videos from 66 unique teachers on Mechanical Turk, suggest that (1) approximately 100 videos -- over $80%$ of which are mathematically fully correct -- can be crowdsourced per week for $5/video; (2) the crowdsourced videos exhibit significant diversity in terms of language style, presentation media, and pedagogical approach; (3) the average learning gains (posttest minus pretest score) associated with watching the videos was stat.~sig.~higher than for a control video ($0.105$ versus $0.045$); and (4) the average learning gains ($0.1416$) from watching the best tested crowdsourced videos was comparable to the learning gains ($0.1506$) from watching a popular Khan Academy video on logarithms.
In this paper we propose applying the crowdsourcing approach to a software platform that uses a modern and state-of-the-art 3D game engine. This platform could facilitate the generation and manipulation of interactive 3D environments by a community o
Modern machine learning is migrating to the era of complex models, which requires a plethora of well-annotated data. While crowdsourcing is a promising tool to achieve this goal, existing crowdsourcing approaches barely acquire a sufficient amount of
The main goal of this paper is to discuss how to integrate the possibilities of crowdsourcing platforms with systems supporting workflow to enable the engagement and interaction with business tasks of a wider group of people. Thus, this work is an at
We consider the problem of learning user preferences over robot trajectories for environments rich in objects and humans. This is challenging because the criterion defining a good trajectory varies with users, tasks and interactions in the environmen
In this paper we report the results of a pilot study comparing the older and younger adults interaction with an Android TV application which enables users to detect errors in video subtitles. Overall, the interaction with the TV-mediated crowdsourcin