<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Volodymyr  Boublik</style></author><author><style face="normal" font="default" size="100%">Mariya  Gornostay</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">AN APPROACH TO DEVELOPING PERSONALIZATION ENVIRONMENT FOR E-LEARNING SYSTEMS</style></title><secondary-title><style face="normal" font="default" size="100%">Problems of Education in the 21st Century </style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">personalization</style></keyword><keyword><style  face="normal" font="default" size="100%">recommender systems</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">January/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://oaji.net/articles/2014/457-1392222150.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">3</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Personalized support for users becomes very important and integral part of many information systems. It allows proposing to a user courses or news which the user may be interested in. Personalization makes information systems more user-friendly, increases their popularity, users more often come back to such systems and generally users' satisfaction increases. This paper provides comprehensive overview of recommender systems applicable for e-Learning, their architecture. It shows role of personalization for e-Learning and opportunities which can be covered by this technique. The work describes types of recommender systems and the ways they can be used in e-Learning; data-mining algorithms and methods are provided. Ways of their optimization for e-Learning environment which allow decreasing recommendation time and increasing recommendation quality are shown. А hybrid personalization system for e-Learning environment has been proposed.




</style></abstract><work-type><style face="normal" font="default" size="100%">Original article</style></work-type><section><style face="normal" font="default" size="100%">87-92</style></section></record></records></xml>