![]() In the demo, we will show interested instructors how they can use OneUp in their classes. The system currently supports the following game elements: points (challenge points, skill points, and activity points), progress bar, virtual currency, badges, leaderboard, skill board, learning dashboard, and avatars. The gamification related configuration includes the choice of the game elements to be used along with specification of gaming rules for them. It is highly configurable and supports tailoring gamification features to meet the vision of the instructor. The platform enables gamifying these practice activities. OneUp enables instructors to define course activities and create exercise problems for practicing and self-assessment, as well as exams or quizzes for testing particular skills. OneUp Learning, a platform aimed at facilitating the gamification of academic courses. Meanwhile, gamification - the application of game design principles and game mechanics to a non-game context - increasingly attracts the interest of educators due to its potential to foster motivation and behavioral changes in learning contexts. The low performance and drop-outs in Computer Science classes are frequently attributed to lack of engagement and motivation. We experimentally evaluate the different properties of these algorithms on toy graphs and demonstrate how our approach can be used to study relative importance in real-world networks including a network of interactions among September 11th terrorists, a network of collaborative research in biotechnology among companies and universities, and a network of co-authorship relationships among computer science researchers. We define a general framework and a number of different algorithms, building on ideas from social networks, graph theory, Markov models, and Web graph analysis. In interactive analysis of such data a natural query is "which entities are most important in the network relative to a particular individual or set of individuals?" We investigate the problem of answering such queries in this paper, focusing in particular on defining and computing the importance of nodes in a graph relative to one or more root nodes. Large and complex graphs representing relationships among sets of entities are an increasingly common focus of interest in data analysis-examples include social networks, Web graphs, telecommunication networks, and biological networks.
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