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  <front>

    <journal-meta>

      <journal-title>Information Technology Journal</journal-title>

      <issn pub-type="ppub">1812-5638</issn>

      <issn pub-type="epub">1812-5646</issn>

      <publisher>

        <publisher-name>Asian Network for Scientific Information</publisher-name>

      </publisher>

    </journal-meta>


    <article-meta>

      <article-id pub-id-type="doi">10.3923/itj.2013.2306.2314</article-id>


      <title-group>

        <article-title><![CDATA[Reinforcement Learning Approach for Adaptive E-learning Systems using Learning Styles]]></article-title>

      </title-group>


      <contrib-group>

        <contrib contrib-type="author" xlink:type="simple">


          <name name-style="western">

            <surname>Velusamy</surname>

            <given-names>Balasubramanian</given-names>

          </name>


          <name name-style="western">

            <surname>Anouneia</surname>

            <given-names>S. Margret</given-names>

          </name>


          <name name-style="western">

            <surname>Abraham</surname>

            <given-names>George</given-names>

          </name>


        </contrib>

      </contrib-group>


      <pub-date pub-type="collection">




        <month>12</month>


        <year>2013</year>

      </pub-date>


      <volume>12</volume>

      <issue>12</issue>


      <abstract><![CDATA[<p>This study aims to provide a mathematical modelling for an 
  adaptive e-Learning environment using SARSA algorithm, by relating it to the 
  concept of Reinforcement Learning. An adaptive e-learning system based on learning 
  styles is very much alike an intelligent agent. The system needs to assess the 
  various interactions from the user and provide them with the best possible content, 
  so as to enhance the learning experience of the user. Successive interactions 
  with the system by the same user should result in he/she being provided with 
  the best content in the same manner as the previous time, if not for a better 
  option. This is possible as the system will have learned from its previous interaction.</p>]]></abstract>


    </article-meta>

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