The main objective of the 1st International Workshop on Learning Analytics and Linked Data (#LALD2012) is to connect the research efforts on Linked Data and Learning Analytics to create visionary ideas [a] and foster synergies between both young research fields. Therefore, the workshop will collect, explore, and present datasets, technologies and applications [b] for Technology-Enhanced Learning (TEL) to discuss Learning Analytics approaches which make use of educational data or Linked Data sources. During the workshop, an overview of available educational datasets and related initiatives will be given. The participants will have the opportunity to present their own research with respect to educational datasets, technologies and applications and discuss major challenges to collect, reuse and share these datasets.
In TEL, a multitude of datasets exists containing detailed observations of events in learning environments [c]that offer new opportunities for teaching and learning. The available datasets can be roughly distinguished between (a) Linked Data – Open Web Data and (b) Personal learning data 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), 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 learning environments originate from tracking learners’ interactions with tools, resources or peers[d]. 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 predicting and reflecting the individual learning process.
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 (a) of suitable educational resources to individual learners, (b) peer students or external expert to cooperate with.
The workshop is looking for contributions touching the following topics.
Educational (Linked) Data
- Evaluating, promoting, creating and clustering of educational datasets, schemas and vocabularies
- Use of LOD for educational purposes
- Feasibility of standardization of educational datasets to enable exchange and interoperability
- Sharing of educational datasets among TEL researchers
- Technologies for the exploration of educational datasets, i.e., for filtering, interlinking, exposing, adapting, converting and visualizing educational datasets
- Real-world applications that show a measurable impact of Learning Analytics
- Real-world educational applications that exploit the Web of Data
- Tools to use and exploit educational Linked Open Data
- Innovative TEL applications that make large-scale use of the available open Web of data
Evaluation of Technologies and Datasets
- (Standardized) evaluation methods for Learning Analytics
- Descriptions of data competitions
Privacy and Ethics
- Policies on ethical implications of using educational data for learning analytics (privacy and legal protection rights)
- Guidelines for the anonymisation and sharing of educational data for Learning Analytics research
/ACCEPTED FORMATS, REVIEW & PUBLICATION
The workshop is looking for different types of submissions. We accept regular full paper (8-14 pages), short paper (4-6 pages). Moreover, we are interested in anonymized datasets that can then be openly used in evaluating TEL recommender systems. Above all, we encourage you to demonstrate your data products and tools even if they are in a premature state. Datasets and demonstrations should be submitted together with an extended abstract submissions (up to 2 pages). For all paper submissions we require formatting according to the Springer LNCS template
All submitted papers will be peer-reviewed by at least two members of the program committee for originality, significance, clarity, and quality. Final versions of accepted submissions will be published in the CEUR-WS.org workshop proceedings and most promising contributions will be invited to the 2nd Special Issue on dataTEL at the International Journal of Technology Enhanced Learning (IJTEL). In addition, the authors are asked to contribute short summaries of their submissions to the dataTEL group space at TELeurope to encourage early information sharing and discussion also with third persons. Based on workshop submissions, the organizers will identify most pressing research challenges to structure the workshop.
For any questions, please contact Hendrik Drachsler [hendrik.drachsler[at]ou.nl].