LAK'19
LAK (International Learning Analytics and Knowledge Conference) 2019
https://lak19.solaresearch.org/
Arizona State University, Tempe, Arizona
March 4–8, 2019
9回目のLAK
https://gyazo.com/7395dd75799731e7c473a8ef47b29047
https://gyazo.com/8ccdd565c5a41dbacf4f37fc70acc2ce
https://gyazo.com/617729b8173bfbed87630b5a6ee7fae9
https://gyazo.com/3f9e39dbd19b6fc1d6e193a2279b2851
https://gyazo.com/51bcfb9abc168439cc3bc95a0c15074d
twitterハッシュタグ
https://twitter.com/search?q=%23LAK19
プログラム冊子
名札ケースに入るコンパクトサイズで便利でした
https://gyazo.com/0ca080f478e5a9fa44565a9da7d1c9d5
Welcome and introduction（メイン初日最初のセッション）
過去最大規模とのこと
900+ participants
525 unique individuals
投稿状況
Accepted papers
https://lak19.solaresearch.org/accepted-papers/
Full papers & short papers　69件
Practitioner　10件
Doctral Consortium　10件
Posters and Demonstrations　49件
Workshops and Tutorials　29件
投稿件数
https://gyazo.com/336fc19cbc49dea89b80e95ba43dac95
年々増加（今年は375）
採択率
https://gyazo.com/a7f3a494956281860ef18b832f7ba103
Full, shortの採択率33%
国別の著者数
https://gyazo.com/46009f9c5ccc7b5f34f8250adaa40e11
著者のつながり
https://gyazo.com/5ffcc1e621d5b623700f1c23b35df48b
Reference先
https://gyazo.com/c88a6ba034e84d3f2a885671ed2deb5c
LAK ProceedingsのEducational TechnologyにおけるH5-index
https://gyazo.com/5e8461b49f36d691671d39d0d1a59a62
Topics in submitted methods
https://gyazo.com/36d8a94bab47e598948cd37059b8f474
Method in submitted papers
https://gyazo.com/f97a40c8d92d4873bb9e725808fdd26a
セッション名
Campus Experience
Classroom & Collaboration
Computational Methods
Curriculum
Dashboards I
Dashboards II
Design
Design and Development
Dialogue & Engagement
Educational Theory
Feedback and Measurement
Games and Learning
Imprementation
Intelligent Tutoring System I
Intelligent Tutoring System II
Logging Activity
Machine Learning I
Machine Learning II
Machine Learning III
Multimodal Analytics
Novel Devices
Predictive and Privacy
Reading Analytics
Self-regulated learning
Sequences
Text analytics I
Text analytics II
LAK11-19のセッション名まとめ
今年（LAK19）はちょっとセッションのまとめ方が変わってるような…
Keynoteも含め、predictive modeling系の発表が多い印象だった
手法も、DNN、RNN、LSTMなどをよく見た
学生の「潜在的状態」を含めたモデル化も散見
メソッドの発表が多い印象だったが、そういうセッションばかりをたまたま見たのかもしれない
Keynote（初日）
https://lak19.solaresearch.org/keynotes/
Professor Ryan Baker (University of Pennsylvania, USA)
"Some Challenges for the Next 18 Years of Learning Analytics"
6 Challenges
https://gyazo.com/4289458b90a610d4dd7ff9fdf5468a9f
スライドがRyan Bakerのtwitterにより公開されています
https://twitter.com/BakerEDMLab/status/1106181368713744384
Keynote（2日目）
https://lak19.solaresearch.org/keynotes/
Professor Lise Getoor (University of California Santa Cruz, USA)
"Scalable Collective Reasoning for Richly Structured Socio-Behavioral Data"
MOOCにおける学生の潜在的状態をPSL frameworkでモデル化して、student successやエンゲージメントを早期予測する
https://gyazo.com/d3c0a9c35a6009f84fcef50fcd65e557
https://gyazo.com/bbc0a1f1cc88d82bd35bd74a7cc6a1a7
（あとで調べる）
A Short Introduction to Probabilistic Soft Logic
Modeling Learner Engagement in MOOCs using Probabilistic Soft Logic
Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daume III, Lise Getoor, NIPS Workshop on Data Driven Education, 2013.
Understanding Engagement and Sentiment in MOOCs using Probabilistic Soft Logic (PSL)
Lise Getoor, Machine Learning for Education NIPS Workshop, December 10, 2016
LAK'20
University of Frankfurt, Germany
March 23-27, 2020
LAK10周年
https://gyazo.com/eec70312185a94def19b93e34bc6f803