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.
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.
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).
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.
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.
Wondering why Interpreting Learning Analytics is vital to eLearning? Check why Interpreting Learning Analytics is vital when you design or refine eLearning.
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
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.
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.
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.
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?
An interesting question arose at a recent xAPI Camp hosted by The eLearning Guild: “What happened to objectives in xAPI?” We should be able to use xAPI to document successful completion of eLearning, but without statements of learning objectives in the content, this is not possible.
I'll start this article by making one simple statement: Feedback loops work. Why? That’s the way we human beings learn, as feedback provides us with a sense of where we stand and an evaluation of our progress.
As Massive Open Online Courses (MOOCs) generate a huge amount of learning activity data through its thousands of users, great potential is provided to use this data to understand and optimize the learning experience and outcome.
For about 10 years, from 2005 – 2015, much of the discussion about tracking eLearning revolved around the Shareable Content Object Reference Model (SCORM) and learning management systems (LMS).
Game Learning Analytics (GLA) is the process of applying Learning Analytics techniques to Serious Games in order to get insight about how the game is being used and improve the educational experience.
It never bodes well to dive into the unknown without preparation. To define, design and enable learning analytics, it’s essential to have a clear strategy in place. Prep yourself with these evaluation questions before you dive into learning analytics.
Jisc has been supporting seven research projects in learning analytics at UK universities over the past year. These have been in the areas of curriculum analytics, mental health and wellbeing and the evaluation of institutional learning analytics projects.Join us to hear the projects present their interesting findings.
There’s no question that the shift to remote and flexible learning has highlighted the importance of technology in education, but at the same time, this shift has also complicated some key aspects of a teacher’s job.
The Experience API (xAPI for short) is far more than just an update to SCORM, the popular standard for tracking data from a learning management system. xAPI opens up a whole new world of possibilities for learning analytics. Examples of what real organizations are doing with it in real-life situations make it easier to grasp the scale of this advance and apply the learnings to your own situation.
According to The Kirkpatrick Model, Level 3: Behavior is the degree to which participants apply what they learned during training when they are back on the job. The prevailing belief that a Level 3 plan for post-training support and accountability is difficult, expensive and out of training’s purview is untrue. Here are the deceptively simple steps to create learning experiences with true value.
The OLC Quality Scorecard - Benchmarking Tools, Checklists, & Rubrics for Evaluating the Quality and Effectiveness of Online Learning Programs & Courses
How can product developers use data analytics to improve products, prove their effectiveness, and increase the fidelity of implementation? Learn more in the latest Nexus story by Rachel Schechter
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)