LAK
| LAK | |
|---|---|
International Learning Analytics & Knowledge Conference
| |
Categories: Learning analytics
| |
| WikiDataId: | Q105491174 |
| Scholia: | https://scholia.toolforge.org/event-series/Q105491174 |
| DblpSeries: | lak |
| WikiCFP Series: | 1931 |
| Bibliography: | dblp.uni-trier.de/db/conf/lak/ |
| CORE Rank (2018): | nan |
| Avg. acceptance rate: | 30.4 |
| Avg. acceptance rate (last 5 years): | 30.4 |
| Table of Contents | |
International Learning Analytics & Knowledge Conference (LAK) has an average acceptance rate of 30.4% (last 5 years 30.4%).
Events
There are 11 events of the series LAK known to this wiki: LAK 2011, LAK 2012, LAK 2013, LAK 2014, LAK 2015, LAK 2016, LAK 2017, LAK 2018, LAK 2019, LAK 2020, LAK 2021
Submission/Acceptance
Locations
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.