Now that the “the only constant is change” in society, our capacity to engage with novel challenges is of first order importance. What are the personal dispositions that authentic learning needs to cultivate, and can we make these assessable and visible to learners and educators?
Recommender systems provide users with content they might be interested in. Conventionally, recommender systems are evaluated mostly by using prediction accuracy metrics only. But, the ultimate goal of a recommender system is to increase user satisfaction.
Selten war ein Gesetz so dysfunktional wie das Leistungsschutzrecht für Presseverleger. Die Bundesregierung weigert sich, das einzugestehen - weil sie es in der ganzen EU einführen will.
Most institutions say they value teaching. But how they assess it tells a different story. University of Southern California has stopped using student evaluations of teaching in promotion decisions in favor of peer-review model. Oregon seeks to end quantitative evaluations of teaching for holistic model.
Presentation used by Tinne De Laet, KU Leuven, for a keynote presentation during an event: organised by Leiden University, Erasmus University Rotterdam, and Delft University of Technology.
The presentations presents the results of two case studies from the Erasmus+ project ABLE and STELA, and provides 9 recommendations regarding learning analytics
Wondering why Interpreting Learning Analytics is vital to eLearning? Check why Interpreting Learning Analytics is vital when you design or refine eLearning.
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.
I’m included this link as the idea of player and team assessment in professional sports has begun to change. I just find this a fascinating topic in how our society is seeing a shift in how we evaluate in general including in the realm of professional sports. In the past player evaluation was done by experts who would watch and make a decision – the process is very subjective. Analytics provide ways to quantify in numbers what we see happen on the ice or field. The same goes for teams. While at the end of the day the score is what matters, analysts have found metrics to identify keys to long term success for teams as well.
J. Choi, A. Khlif, and E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), page 23--27. Online, Association for Computational Linguistics, (2020)
J. Choi, A. Khlif, and E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), page 23--27. Online, Association for Computational Linguistics, (2020)