Matches in Ruben’s data for { <https://ruben.verborgh.org/publications/demeester_call_2018/#publication> ?p ?o }
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- publication author me.
- publication author me.
- publication author erik_mannens.
- publication author me.
- publication author me.
- publication creator me.
- publication creator me.
- publication creator erik_mannens.
- publication creator me.
- publication creator me.
- publication about Linked_Data.
- publication about JSON.
- publication author me.
- publication author me.
- publication author erik_mannens.
- publication author me.
- publication author me.
- publication coparticipatesWith me.
- publication coparticipatesWith me.
- publication coparticipatesWith erik_mannens.
- publication coparticipatesWith me.
- publication coparticipatesWith me.
- publication type ScholarlyArticle.
- publication type Article.
- publication type Document.
- publication type Document.
- publication type Document.
- publication type Q386724.
- publication type CreativeWork.
- publication type Work.
- publication P50 me.
- publication P50 me.
- publication P50 erik_mannens.
- publication P50 me.
- publication P50 me.
- publication maker me.
- publication maker me.
- publication maker erik_mannens.
- publication maker me.
- publication maker me.
- publication title "Interoperable User Tracking Logs using Linked Data for improved Learning Analytics".
- publication isPartOf proceedings_of_the_19th_international_computer_assisted_language_learning_research_conference.
- publication name "Interoperable User Tracking Logs using Linked Data for improved Learning Analytics".
- publication label "Interoperable User Tracking Logs using Linked Data for improved Learning Analytics".
- publication name "Interoperable User Tracking Logs using Linked Data for improved Learning Analytics".
- publication topic Linked_Data.
- publication topic JSON.
- publication subject Linked_Data.
- publication subject JSON.
- publication authorList b0_b2057.
- publication topic Linked_Data.
- publication topic JSON.
- publication abstract "Learning analytics can provide adaptive learning and performance support by analyzing user tracking logs. However, data-driven learning is usually confined to a specific context (e.g., learning English within one application), and thus not interoperable across systems or domains. In this paper, we investigate ways to improve integration of data across applications and educational domains, by means of Linked Data, using existing standards such as the Experience API. Using JSON-LD, existing Learning Record Store tools can be used to store the tracking logs, which are then interpreted and aligned as Linked Data. We have applied the solution in an initial data capture resulting in more than two million statements spanning two different applications. This way, we aim to enrich adaptive learning and performance support across contexts.".
- publication datePublished "2018".
- publication mainEntityOfPage demeester_call_2018.
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- publication isPrimaryTopicOf demeester_call_2018.
- publication page demeester_call_2018.