International Learning Analytics & Knowledge Conference
Categories: Learning analytics
|Avg. acceptance rate:||30.4|
|Table of Contents|
International Learning Analytics & Knowledge Conference (LAK) has an average acceptance rate of 30.4% .
The following events of the series LAK are currently known in this wiki:
|Ordinal||From||To||City||Country||General chair||PC chair||Submitted papers||Acceptance rate||Attendees|
|LAK 2021||11||Apr 12||Apr 16|
|LAK 2020||Mar 23||Mar 27||Frankfurt am Main||Germany||Katrien Verbert|
|LAK 2019||Mar 4||Mar 8||Tempe||USA||Christopher Brooks|
|LAK 2018||Mar 5||Mar 9||Sydney||Australia||Abelardo Pardo|
|Simon Buckingham Shum|
|LAK 2017||Mar 13||Mar 17||Vancouver||Canada|
|LAK 2016||Mar 25||Mar 29||Edinburgh||UK|
|LAK 2015||Mar 16||Mar 20||Poughkeepsie||USA|
|LAK 2014||Mar 24||Mar 28||Indianapolis||USA|
|LAK 2013||Apr 8||Apr 12||Leuven||Belgium|
|LAK 2012||Apr 29||May 2||Vancouver||Canada|
|LAK 2011||Feb 27||Mar 1||Banff||Canada|
The International Conference on Learning Analytics & Knowledge is the premier research forum in the field, providing common ground for all stakeholders in the design of analytics systems to debate the state of the art at the intersection of Learning and Analytics — including researchers, educators, instructional designers, data scientists, software developers, institutional leaders and governmental policy makers.
The conference is held in cooperation with ACM in association with ACM SIGCHI and SIGWEB, with the double-blind, peer-reviewed proceedings archived in the ACM Digital Library. The ACM Digital Library (DL) is the world's most comprehensive database of full-text articles and bibliographic literature covering computing and information technology. This renowned repository includes the complete collection of ACM publications plus an extended bibliographic database of core works in computing from scholarly publishers. This guarantees that the proceedings will be available to the widest possible audience of computing professionals. ACM has an enlightened copyright policy with liberal author rights: authors may self-archive their own papers as Open Access Preprints, as long as they carry the specified ACM statement.