KMi Seminars
MUP/PLE lecture series
This event took place on Friday 24 June 2011 at 14:00

 
Hendrik Drachsler Open University in the Netherlands

The growth of data in the knowledge society creates opportunities for new insights through advanced analysis methods based on information retrieval technologies. Educational institutions also create and own huge datasets on their students and course activities. But they make little use of the data when considering new educational services, recommending suitable peers or content, and improving the personalization of learning. Nevertheless, personalized learning is expected to have the potential to create more effective learning experiences, and accelerate the study time for students. In the educational world, only very limited datasets are publicly available and no agreed quality standards exist on the personalization of learning.
The dataTEL Theme Team aims to address these issues by advancing data driven research to gain verifiable and valid results and to develop a body of knowledge about the personalization of learning. In this context, new challenges emerge like unclear ethical, legal and privacy issues, suitable policies and formats to share data, required pre-processing procedures and rules to create sharable datasets, common evaluation criteria for personalization and recommender systems in TEL.
The lecture will give an overview about the latest developments in educational datasets research and give an outline how a dataset driven future in TEL could look like.

 
KMi Seminars Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

Multimedia and Information Systems is...


Multimedia and Information Systems
Our research is centred around the theme of Multimedia Information Retrieval, ie, Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval, Query Languages and Query Mediation.

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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