MUP/PLE lecture series
This event took place on Thursday 23 June 2011 at 14:00
Hendrik Drachsler Open University in the Netherlands
Technology-enhanced learning aims to design, develop and test socio-technical innovations that will support and enhance learning practices and knowledge sharing of individuals and organizations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. With the increasing use of Learning Management Systems, Personal Learning Environments, and Data Mashups the TEL field, became a promising application area for information retrieval technologies and Recommender Systems to suggest most suitable learning content or peers to learners. The renewed interest in information retrieval technologies in TEL reveals itself through an increasing number of scientific events and publications combined under the research term Learning Analytics. Learning Analytics has the potential for new insights into learning processes by making so far invisible patterns in the educational data visible to researchers and develop new services for educational practice.
This lecture attempts to provide an introduction to Recommender Systems for TEL, as well as to highlight their particularities compared to recommender systems for other application domains. Finally, it will outline the latest developments of Recommender Systems in the area of Learning Analytics.
This event took place on Thursday 23 June 2011 at 14:00
Technology-enhanced learning aims to design, develop and test socio-technical innovations that will support and enhance learning practices and knowledge sharing of individuals and organizations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. With the increasing use of Learning Management Systems, Personal Learning Environments, and Data Mashups the TEL field, became a promising application area for information retrieval technologies and Recommender Systems to suggest most suitable learning content or peers to learners. The renewed interest in information retrieval technologies in TEL reveals itself through an increasing number of scientific events and publications combined under the research term Learning Analytics. Learning Analytics has the potential for new insights into learning processes by making so far invisible patterns in the educational data visible to researchers and develop new services for educational practice.
This lecture attempts to provide an introduction to Recommender Systems for TEL, as well as to highlight their particularities compared to recommender systems for other application domains. Finally, it will outline the latest developments of Recommender Systems in the area of Learning Analytics.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Multimedia and Information Systems is...
We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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