1st International Workshop on Learning Analytics and Linked Data

Drachsler, H., Dietze, S., Greller, W., D'Aquin, M., Jovanovic, J., Pardo, A., Reinhardt, W., & Verbert, K. (2012). 1st International Workshop on Learning Analytics and Linked Data. In S. Dawson, C. Haythornthwaite, S. Buckingham Shum, D. Gasevic, & R. Fergusson (Eds.), LAK '12 Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 9-10). New York, NY, USA: ACMThe 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 in order to create visionary ideas and foster synergies between both young research fields. Therefore, the workshop will collect, explore, and present datasets, technologies and applications for Technology-Enhanced Learning (TEL) to discuss Learning Analytics approaches that 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 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

Closing the chasm between virtual and physical delivery for innovative learning spaces using learning analytics

Purpose – One of the misconceptions of teaching and learning for practical-based programmes, such as engineering, sciences, architecture, design and arts, is the necessity to deliver via face-to-face physical modality. This paper refutes this claim by providing case studies of best practices in delivering such courses and their hands-on skillsets using completely online virtual delivery that utilises different formats of 2D and 3D media and tools, supported by evidence of efficiency using learning analytics. Design/methodology/approach – The case studies were designed using pedagogical principles of constructivism and deep learning, conducted within a mixture of 2D and 3D virtual learning environments with flexible interface and tools capabilities. State-of-the-art coding and scripting techniques were also used to automate different student tasks and increase engagement. Regression and descriptive analysis methods were used for Learning Analytics. Findings – Learning analytics of all case studies demonstrated the capability to achieve course/project learning outcomes, with high engagement from students amongst peers and with tutors. Furthermore, the diverse virtual learning tools used, allowed students to display creativity and innovation efficiently analogous to physical learning. Originality/value – The synthesis of utilised media and tools within this study displays innovation and originality in combining different technology techniques to achieve an effectual learning experience. That would usually necessitate face-to-face, hands-on physical contact to perform practical tasks and receive feedback on them. Furthermore, this paper provides suggestions for future research using more advanced technologies

A Survey on Linked Data and the Social Web as facilitators for TEL recommender systems

Personalisation, adaptation and recommendation are central features of TEL environments. In this context, information retrieval techniques are applied as part of TEL recommender systems to filter and recommend learning resources or peer learners according to user preferences and requirements. However, the suitability and scope of possible recommendations is fundamentally dependent on the quality and quantity of available data, for instance, metadata about TEL resources as well as users. On the other hand, throughout the last years, the Linked Data (LD) movement has succeeded to provide a vast body of well-interlinked and publicly accessible Web data. This in particular includes Linked Data of explicit or implicit educational nature. The potential of LD to facilitate TEL recommender systems research and practice is discussed in this paper. In particular, an overview of most relevant LD sources and techniques is provided, together with a discussion of their potential for the TEL domain in general and TEL recommender systems in particular. Results from highly related European projects are presented and discussed together with an analysis of prevailing challenges and preliminary solutions.LinkedU

1st International Workshop on Learning Analytics and Linked Data

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 in order to create visionary ideas and foster synergies between both young research fields. Therefore, the workshop will collect, explore, and present datasets, technologies and applications for Technology-Enhanced Learning (TEL) to discuss Learning Analytics approaches that 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 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

1st international workshop on learning analytics and linked data

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 in order to create visionary ideas and foster synergies between the two young research fields. Therefore, the workshop will collect, explore, and present datasets, technologies and applications for Technology Enhanced Learning (TEL) to discuss Learning Analytics approaches that 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.status: publishe