From Openresearch
Jump to: navigation, search
Event series
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:

 OrdinalFromToCityCountryGeneral chairPC chairAcceptance rateAttendees
LAK 202111Apr 12Apr 16
LAK 2020Mar 23Mar 27Frankfurt am MainGermanyKatrien Verbert
Maren Scheffel
Niels Pinkwart
Vitomir Kovanovic
LAK 2019Mar 4Mar 8TempeUSAChristopher Brooks
Ulrich Hoppe
LAK 2018Mar 5Mar 9SydneyAustraliaAbelardo Pardo
Kathryn Bartimote-Aufflick
Grace Lynch
Simon Buckingham Shum
Rebecca Ferguson
Agathe Merceron
Xavier Ochoa
LAK 2017Mar 13Mar 17VancouverCanada
LAK 2016Mar 25Mar 29EdinburghUK
LAK 2015Mar 16Mar 20PoughkeepsieUSA
LAK 2014Mar 24Mar 28IndianapolisUSA
LAK 2013Apr 8Apr 12LeuvenBelgium
LAK 2012Apr 29May 2VancouverCanada
LAK 2011Feb 27Mar 1BanffCanada




Loading map...

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.