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Inferring knowledge acquisition through Web navigation behaviours

Yu


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  <identifier identifierType="DOI">10.5281/zenodo.2640890</identifier>
  <creators>
    <creator>
      <creatorName>Yu</creatorName>
      <affiliation>The University of Manchester</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Inferring knowledge acquisition through Web navigation behaviours</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <subjects>
    <subject>Navigation behaviour</subject>
    <subject>Knowledge acquisition</subject>
    <subject>MOOC</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2018-09-03</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Other</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2640890</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2640889</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/moving-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;As we witness the growing popularity of online learning, we address the problem of knowing if users are actually learning. The traditional assessment approaches involve tests, assignments and peer assessments. We explore if there is a way to measure learning and personalise the user learning experience in an unobtrusive manner. My PhD proposes using data-driven methods to measure learning by mining user interaction data to identify regularities that could be indicators of learning.&lt;/p&gt;</description>
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