Here's a thread where any of us can post news items or articles we've seen about analytics and machine learning.
My first contribution:
Unintended Consequences and Goodhart’s Law: The importance of using the right metrics
https://towardsdatascience.com/unintended-consequences-and-goodharts-law-68d60a94705c
"Goodhart’s Law is expressed simply as: “When a measure becomes a target, it ceases to be a good measure.” In other words, when we set one specific goal, people will tend to optimize for that objective regardless of the consequences. "
Let's think about how this could affect learning analytics. If we don't choose our targets carefully, participants may optimize their participation too narrowly. Here's a example given in the article:
"In school, we are given one objective: maximize our grade. This focus on one number can be detrimental to actual learning. High school seemed like one long series of memorizing content for a test, then promptly forgetting it all so I could stuff my brain full of info for the next one, without any consideration of whether I really knew the concepts. This strategy worked quite well given how success was measured in school, but I doubt it is the best approach for a great education."
One of the significant-- and often overlooked-- advantages of learning analytics is their potential to detect hidden or "latent" factors in the learning process, not just predict a learning product. What kinds of processes could we detect that would be less prone to "gaming the system" than a final grade prediction?