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In the educational world, only very limited datasets are publicly available and no agreed quality standards exist on the personalization of learning. The SIG dataTEL 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.

Group members

Victoria Pérès-Labourdette Lembé
Chuan Pham
Roi
mohammad daoud
Valentin Butoianu
xara
jmspector
Moji
veecam
Mahmood
Stefan Svetsky
Adam Cooper

dataTEL - EATEL SIG on Data-driven Research and Learning Analytics

Owner: Hendrik Drachsler

Group members: 91

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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. 

The SIG dataTEL 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. It builds upon the positive outcomes of the dataTEL Theme Team funded by the STELLAR Network of Excellence. It’s intentions are to foster the cooperation between different Learning Analytics research units and to act as their representative to other relevant communities. 

Therefore, its main objectives are:

Networking:

  • Fostering of a research network on educational dataset driven research
  • Improving the exchange with relevant research communities
  • Representing dataTEL researchers to promote the release of open datasets from educational providers

Privacy and Ethics:

  • Contributing to policies on ethical implications (privacy and legal protection rights)
  • Suggesting guidelines for the anonymisation of data and reusing publicly available data for Learning analytic research Evaluation of Technologies
  • Fostering a shared understanding of evaluation methods in Learning Analytics
  • Encouraging data competitions similar to TREC and CLEF to compare TEL research and guide people in evaluating and comparing their results

Educational Datasets:

  • Fostering the standardizations of datasets to enable exchange and interoperability
  • Clustering of educational datasets
  • Evaluate how linked data can be applied for the SIG objectives

Data Technologies:

  • Fostering technology for data research to filter, adapt, convert, visualize and self-extract datasets
  • Promoting real-world data applications that show a measureable impact on the TEL target groups

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Website: http://adenu.ia.uned.es/workshops/recsystel2010/datatel.htm

Latest group activity

Tuesday, 24 December

Stefan Svetsky has started a new discussion topic titled: Where could I found so called Educational Datasets for my teaching
Where could I found so called Educational Datasets  and in which way I could it use when teaching bachelors. Does exist any case study?


  (281 days ago)

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