What is a retention early warning system? What is it good for? What are its limitations? And how are its failings representative of the unfulfilled potential of so many ed tech products? You’ve got questions, we’ve got answers.
In this post, I explore the relationship between learning engineering and learning design, talk about language as a design artifact, and provide an example about how Caliper could be the centerpiece of a learning engineering process for developing better learning analytics.
One of the challenges facing higher education is a huge amount of tacit knowledge—things that we don’t know we know—about both our academic expertise and our teaching expertise. We need to make that knowledge explicit in order to make progress. This post unpacks a peculiar kind of literacy problem.
The IMS has been amazingly successful. I take a deep dive into both the what and the why, and then look at how the next challenge of learning analytics is going to mean the next decade of interoperability work will be different from the last one.
The revolution will be televised. Eventually. For now, there will be audio.
This is a great example of the kind of collaboration I expect to see more of from the Empirical Educator Project and CMU’s OpenSimon contribution.
EDwhy: The answer may be 42, but what’s the question?