TAILIEUCHUNG - A generic model for the context-aware representation and federation of educational datasets: Experience from the dataTEL challenge

Research on online interactions during a learning situation to better understand users' practices and to provide them with quality-oriented features, resources and services is attracting a large community. As a result, the interest for sharing educational data sets that translate the interactions of users with elearning systems has become a hot topic today. However, the current systems aggregating social and usage data about their users suffer from a series of weaknesses. In particular, they lack a common information model that would allow for exchanges of interaction data at a large scale. To tackle this issue, we propose in this paper a generic model able to federate heterogeneous context metadata and to facilitate their share and reuse. This framework has been successfully applied to several data sets provided by the research community, and thus gives access to a big data set that could help researchers to increase efficiency of existing learning analytics technics, and promote research and development of new algorithms and services on top of these data. | A generic model for the context-aware representation and federation of educational datasets: Experience from the dataTEL challenge

• Giáo dục học
• The context aware representation
• Federation of educational datasets
• The dataTEL challenge
• Knowledge management
• Learning analytics
• The epublication era
• Roles and strategies of learning analytics
• Learning strategies
• Designing personalized learning environments
• The role of learning analytics
• Personalized learning environment
• Educational data mining
• Learning analytics platform
• Higher education in Japan
• Higher education
• Education data analysis
• Support education
• Learning through research
• Closing the assessment loop
• Data mining
• Data warehouse
• Distance education
• Data envelopment analysis
• Efficiency analysis
• Predictive analytics
• Machine learning
• Linear regression
• BMC Pulmonary Medicine
• Cystic fibrosis
• Health care analytics
• Health care delivery
• Mixed methods
• Personalized medicine
• Statistical learning