000220334 001__ 220334
000220334 005__ 20190317000510.0
000220334 037__ $$aPOST_TALK
000220334 245__ $$aCLEO - Workshop on Contextual Learning Analytics Enforcing data Ownership. Building Interactive Learning Dashboards with Logstash, Elasticsearch and Kibana (ELK)
000220334 269__ $$a2016
000220334 260__ $$c2016
000220334 336__ $$aTalks
000220334 520__ $$aEducational data continues to grow in volume, velocity and variety. Making sense of the educational data in such conditions requires deployment and usage of appropriate scalable, real-time processing tools supporting a flexible data schema. Elasticsearch is one of the popular open-source tools meeting the enlisted requirements. Initially envisioned as a search engine capable of operating at scale and in real time, Elasticsearch is used by organisations such as Wikimedia and Github, which deal with big data on daily basis. In addition, Elasticsearch is used increasingly often as analytics platform thanks to its scalable architecture and expressive query language. Until recently, the exploitation of Elasticsearch for (learning) analytical purposes by practitioners was hindered by a high entrance barrier due to the complexity of the query language and the query specificities. This is currently changing with the ongoing development of Kibana, an open-source tool that allows to conduct analysis and build visualisations of Elasticsearch data through a graphical user interface. Kibana does not require the user to dive into technical details of the queries (although it is still possible) and hence makes big educational data visualisations accessible to regular users. The additional value of Kibana comes in play whenever several visualisations are combined on a single dashboard, enabling to use multiple coordinated views for an interactive explorative analysis. Both Elasticsearch and Kibana, together with Logstash are part of an analytics stack often referred to as ELK. Logstash supports data acquisition from multiple sources (including twitter, RSS, event logs) thanks to its rich set of available connectors. Custom connectors can be developed for case-specific sources. In addition to the mentioned values, ELK enables building analytics infrastructures decoupled from the learning platform, i.e., it allows to host separately the learning environment (with the analytics functionalities) and the data storage without affecting the end-user experience.
000220334 6531_ $$aLearning Analytics
000220334 6531_ $$aLearning Dashboards
000220334 6531_ $$aLogstash
000220334 6531_ $$aElasticsearch
000220334 6531_ $$aKibana
000220334 6531_ $$aData ownership
000220334 6531_ $$aInteractive Analytics
000220334 6531_ $$aInteractive Dashboards
000220334 700__ $$0245685$$g210961$$aVozniuk, Andrii
000220334 700__ $$g248809$$aRodriguez Triana, Maria Jesus$$0248291
000220334 700__ $$aGillet, Denis
000220334 7112_ $$dJune 27-28, 2016$$cBilbao, Spain$$aLearning Analytics Summer Institute
000220334 8564_ $$uhttp://graasp.eu/ils/572cb573c3ddb608c844b933/?lang=en$$zURL
000220334 8564_ $$uhttp://www.slideshare.net/vozniuk/interactive-learning-analytics-dashboards-with-elk-elasticsearch-logstash-kibana-lasi-2016-andrii-vozniuk-maria-rodrigueztriana-denis-gillet$$zURL
000220334 909C0 $$xUS09295$$0252406$$pREACT
000220334 909CO $$qGLOBAL_SET$$pSTI$$ooai:infoscience.tind.io:220334$$ppresentation
000220334 917Z8 $$x248809
000220334 917Z8 $$x210961
000220334 937__ $$aEPFL-TALK-220334
000220334 973__ $$aEPFL
000220334 980__ $$aTALK