Group members

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

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

Tags: educational data, learning analytics, data sharing, knowledge analytics, evaluation of personlisation technologies, tel recommender, privacy and ethical issues

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