MUP/PLE lecture series Recommender Systems and Learning Analytics in TEL Hendrik Drachsler
This event took place on 23rd June 2011 at 2:00pm (13:00 GMT)
Knowledge Media Institute, Berrill Building, The Open University, Milton Keynes, United Kingdom, MK7 6AA
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.
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.
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