Ruben Verborgh’s data

Matches in Ruben’s data for { <https://ruben.verborgh.org/publications/lieber_lile_2018/#publication> ?p ?o }

• publication author me.
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• publication about interoperability.
• publication about Provenance.
• publication about SPARQL.
• publication about Linked_Data.
• publication about RDF.
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• publication type ScholarlyArticle.
• publication type Article.
• publication type Document.
• publication type Document.
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• publication type Q386724.
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• publication P50 me.
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• publication title "Linked Data Generation for Adaptive Learning Analytics Systems".
• publication isPartOf proceedings_of_the_linked_learning_workshop.
• publication name "Linked Data Generation for Adaptive Learning Analytics Systems".
• publication label "Linked Data Generation for Adaptive Learning Analytics Systems".
• publication name "Linked Data Generation for Adaptive Learning Analytics Systems".
• publication topic interoperability.
• publication topic Provenance.
• publication topic SPARQL.
• publication topic Linked_Data.
• publication topic RDF.
• publication subject interoperability.
• publication subject Provenance.
• publication subject SPARQL.
• publication subject Linked_Data.
• publication subject RDF.
• publication authorList b0_b2085.
• publication topic interoperability.
• publication topic Provenance.
• publication topic SPARQL.
• publication topic Linked_Data.
• publication topic RDF.
• publication abstract "According to the Learning Analytics (LA) reference model, LA is used to collect, explore and analyze diverse types and interrelationships of data. Specifications like the Experience API (xAPI) work towards interoperability with respect to interrelationship of diverse learning data. Algorithms for adaptive learning could be improved by incorporation of user-related data, not present in learning activities. Linking these user-related data with learning activity data would fully exploit the potential of interrelationships with data. Conventional solutions, as well as current Linked Data-based solutions focus purely on learning activity data, whereas solutions based on Linked Data could be used to integrate data of different domains. We propose a provenance-aware pipeline to transform xAPI learning activity statements to Linked Data. The integration of learning activities with other user data, provides a more complete set of user data, improving an adaptive learning analytics system. We use the proposed pipeline to build a Linked Learning Record Store based on the Resource Description Framework (RDF). SPARQL queries are used to link data about learning activities, enriched with fine-grained exercise descriptions, with data describing the abilities of users. In this paper, we show how Linked Data can be generated from xAPI statements in a streaming approach, based on existing tools and interfaces. Our solution demonstrates the usage of Linked Data to combine learning activity data with user ability data, to get a more complete set of user data aiming to assist in adaptive learning.".
• publication datePublished "2018".
• publication mainEntityOfPage lieber_lile_2018.
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• publication isPrimaryTopicOf lieber_lile_2018.
• publication page lieber_lile_2018.