Learning Analytics and Linked Data 2012

1st International Workshop on Learning Analytics and Linked Data (LALD2012)
at the 2nd International Conference on Learning Analytics and Knowledge (LAK12), Vancouver, Canada

In Technology-Enhanced Learning (TEL), a multitude of datasets exists that offer new
opportunities for teaching and learning. The available datasets can be roughly distinguished between (a) Open Web Data to (b) Personal Learning data originating from different learning environments.

Open Web data covers educational data publicly available on the Web, such as Linked Open Data (LOD) published by institutions about their courses and other resources; examples include (but are not limited to), e.g., The Open University (UK), the National Research Council (CNR, Italy), Southampton University (UK) or the mEducator Linked Educational Resources. It also includes the emergence of LD-based metadata schemas and TEL-related datasets. The main driver in the adoption of the LOD approach in the educational domain is the enrichment of the learning content and the learning experience by making use of various connected data sources.

Personal Learning data from different learning environments originate from tracking learners’ interactions with different tools and resources. The main driver for analyzing these data is the vision of personalized learning that offers potential to create more effective learning experiences through new possibilities for the prediction of and reflection over the learning processes.

To this end, Learning Analytics can be seen as an approach which brings together two
different views: (i) the external view on publicly available Web data and (ii) an internal view on personal learner data, e.g. data about individual learning activities and histories. Learning Analytics aims at combining these two in a smart and innovative way to enable advanced educational services, such as recommendation of suitable educational resources to individual learners.

To enable synergies and alignment of those efforts, communities like the Special Interest
Group (SIG) dataTEL of the European Association of Technology Enhanced Learning
(EATEL) and the LinkedEducation.org open platform, emerged very recently. The SIG
dataTEL
 aims to advance data-driven TEL research and to develop a body of knowledge
about personalization driven from analyzing and visualizing personal data derived from
learning environments. Connecting information derived from such personal tracking data with the Web of (Linked Open) Data offers interesting perspectives to enrich learning processes with suitable resources available on the Web.

The main objective of the LALD workshop is to connect the research efforts on LinkedData
and Learning Analytics to create visionary ideas about how the synergy of Web of Data and Learning Analytics can transform and support TEL processes and applications. Therefore, the workshop will explore, collect and review datasets for TEL to discuss Learning Analytics approaches which make use of the Web of Data. During the workshop, an overview of available educational datasets will be given. The participants will have the opportunity to present own datasets or dataset descriptions, show their own data products and tools, and discuss major challenges to collect, use and share educational datasets and their products. Different promising initiatives and solutions for the above mentioned challenges will be presented.

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