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December 2015


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Vitomir Kovanovic <[log in to unmask]>
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Classification, clustering, and phylogeny estimation
Tue, 22 Dec 2015 13:55:53 +0000
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(apologies for cross-posting)
The Third ACM Conference on Learning at Scale (L@S) invites contributions to its Work in Progress and Demonstration tracks.

Deadline for both tracks is January 15th, 11:59pm PST. Please see the website for formatting guidelines:

The Work-in-Progress (WiP) track showcases recent findings or other types of innovative or thought-provoking work. Accepted WiP papers will be presented in the form of a poster at the session.

The Demonstrations track show aspects of learning at scale in an interactive hands-on form. A live demonstration is a great opportunity to communicate ideas and concepts in a powerful way that a regular presentation cannot.

Learning at Scale is at the intersection of computer science and the learning sciences, seeking to improve practice and theories of learning at scale. Work presented at Learning at Scale reports on rigorous research on methodologies, studies, analyses, tools, or technologies for learning at scale. Learning at Scale includes MOOCs, games (including massively multiplayer online games), citizen science communities, and other types of learning environments which (a) provide learning experiences to large number of learners and/or (b) produce detailed, high volume data about the learning process.

Please see the website for a list of accepted full papers:

The keynotes in Learning at Scale 2016, which will take place in Edinburgh, are Sugata Mitra, Mike Sharples, and Ken Koedinger.

The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.

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