Learning how to learn: Applying social recommenders to technology-enhanced learning
This week MyStrands was very pleased to provide a video presentation to be used for a keynote address opening the SIRTEL 2007 (Social Information Retrieval for Technology-Enhanced Learning) workshop. This workshop was held In conjuction with the 2nd European Conference on Technology Enhanced Learning (EC-TEL’07) in Crete, Greece.
While MyStrands’ presenters Jim Shur and Rick Hangartner sat a stone’s throw from the foothills of Oregon’s coastal range, the workshop attendees were 10,448 kilometers away watching and discussing with them the video and its implications over Internet Chat. Thanks to Riina Vuorikari and all the SIRTEL organizers and participants for inviting us!
(We have uploaded the video to YouTube in four pieces (one, two, three, four), as we cannot upload the entire file at once. The entire video can be downloaded from here).
Abstract
Technology Enhanced Learning (TEL) represents an interesting new application domain for Social Recommender Technology (SRT). Many, and perhaps most, successful applications of SRT to date have involved recommending media items and consumer goods. For these domains, the knowledge base for the recommender embodies relatively superficial preference semantics, derived as relatively simple statistics from implicit or explicit preference data. It seems the recommendation tasks in many TEL applications will benefit from, if not require, knowledge bases that embody deeper semantics than just statistically evaluated preferences. This talk explores this issue of the role of semantics in SRT recommenders for TEL applications. We propose that the science of how people learn can guide us in building knowledges bases for SRTs applied to TEL, and for that reason TEL applications might be viewed as an evolutionary step for SRTs that places a new emphasis on the science of making useful recommendations.












