- Insight into the variability of adaptive learning solutions, based on their Approach to adaptivity and selected Taxonomy and Maturity attributes introduced and defined- A framework for how an organization might use a set of contextual considerations and instructional objectives to determine a best-fit approach to adaptive learning- Detailed profiles of eight adaptive learning suppliers and a list of other organizations to watch
...a framework for thinking about adaptive learning in higher education
An algorithm that extends an artificial-intelligence technique to new tasks could aid in analysis of flight delays and social networks.
Here is a great and simple explanation of a new network algorithm. I believe that another form of amplification of results that I seem to have seen concerns the overfitting problem. If I select a solution from a range and just chase after the highest or best criteria (of any sort: correlation, max error, r-squared, mean error, or more complex criteria) then the predictor equation seems to be over confident.
This clarified something I had confused for a while. I always thought that supervised machine learning was with humans in the loop, but according to this definition, it is just when there is a search on for an explanitory relationship to explain some y in terms of some array of x's. I guess the supervision can then also be the fitness criteria.
Think of learning analytics as the warning lights of online learning's dashboard – that gleaming assemblage of dials and icons that alerts a driver at once if gas in the tank is running low, a door is open, or the cooling system ...
It's great to have multiple projects to scan over and learn from. The one thing I would say is that learning analytics is a term that is being stretched to mean more than learning (and that is fine until some new name comes along). I think the best practice will be to talk about big data analytics for learning, retention, recruitment, alumni networks, business practices as all part of a new wave of using near real time analysis methods on big data sets to better understand and model learners.
Higher ed institutions around the globe are exploring the potential of adaptive technology to revolutionize the way students learn.
I disagree slightly in the sense that adaptation is (I think) the only way to personalize 'at scale.' so it is not just one of a set of methods to personalize, but the only realistic method to use when going to scale.
In a new U.N. report released on Monday morning (Japan time) scientists come to a stark conclusion: Unless the world changes course immediately and dramatically, the fundamental systems that support human civilization are at risk. The Intergovernmental Panel on Climate Change’s new report—which is seven years in the making—draws on...
Sorry to be a bit off topic, but this report is one in a long series of highly consistent messages that the world seems to be ignoring.
Check out this beautiful infographic which answers why would colleges want to have analytics, how can educational data mining and learning analytics improve and personalize education, and how does this process work.
An infrographic to share with an educational data science team
How To Build A Successful Data Science Team InformationWeek The second data science role is that of machine-learning expert, a statistics-minded person who builds data models and makes sure the information they provide is accurate, easy to...
For a learning organization, I think we need to add a learning scientist and a games and simluations developer/researcher to make an all-around team.
Gamification is the use of game design concepts to create a layer on a real world setting. Typical gamification focuses on the use of rewards like points and badges to change the behavior of users, which can cause long-term damage to intrinsic motivation. Meaningful gamification is the use of design concepts from games and play to help people find personal connections to a real-world setting.