The e-Design Assessment Tool (eDAT) is a tool to help tutors represent and evaluate effective blended or distance learning designs. The eDAT combines a simple analysis of the learning activities with reflections on the teaching and learning perspective that underpins the design.
This pilot project collects problems and metrics/datasets from the AI research literature, and tracks progress on them. You can use this notebook to see how things are progressing in specific subfields or AI/ML as a whole, as a place to report new results you've obtained, as a place to look for problems that might benefit from having new datasets/metrics designed for them, or as a source to build on for data science projects. At EFF, we're ultimately most interested in how this data can influence our understanding of the likely implications of AI. To begin with, we're focused on gathering it.
This presentation explores shortcomings of learning analytics for the wide adoption in educational organisations. It is NOT about ethics and privacy rather than focuses on shortcomings of learning analytics for teachers and students in the classroom (micro-level).
Not all learning analytics are the same. Discover how proactive learning analytics help you influence and improve ongoing learning processes by predicting the future and creating recommendations for action. Identify the 4 key elements that will determine the success of your analytics journey.
K. Dellschaft, and S. Staab. Proceedings of the 5th International Conference on The Semantic Web, page 228--241. Berlin, Heidelberg, Springer-Verlag, (2006)