I have said in the past that we see what we are looking for. This is confirmation of that. "Our findings provide evidence that the stereotypes we hold can systematically alter the brain’s visual representation of a face, distorting what we see to be more in line with our biased expectations." Our expectations play a critical role in perception. That's why there is no such thing as 'theory-neutral data'. We need to be aware of the way our subjective perceptions in turn shape our expectations. "Men, and particularly black men, were initially perceived 'angry,' even when their faces were not objectively angry; and women were initially perceived 'happy,' even when their faces were not objectively happy."
The key question is "Are math and reading test results strong enough indicators of school quality that regulators can rely on them?" The evidence on this isn't clear. "There is surprisingly little rigorous research linking them to the long-term outcomes we actually care about." There is some evidence, such as this, but frankly it reads like pseudoscience. Why? "Achievement tests are only designed to capture a portion of what our education system hopes to accomplish," for example (says the author) character or life skills. And other skills (such as art or music) may be necessary for students' later-life success. "We should be considerably more humble about claiming to know which teachers, schools, and programs are good or bad based on an examination of their test scores." Agreed.
I think there's a point to this post, which is why I'm linking to it, but I think it could probably have been explained more clearly. Essentially the argument is this: companies have shifted their thinking from treating other agencies as 'externalities' to thinking of them as the network. This shift in thinking is important, because it reflects a change from thinking of them as a net cost to thinking of them as the most effective way to produce certain business outcomes. "What assets were for the industrial firm, network effects are for the post-industrial firm." These network effects reflect a value of a company that is far greater than the assets it may hold. Apple's position, for example, as the centre of a network of developers is far greater than it would be if it had all these developers in-house.
Marcie Bianco makes the point proposed in the headline fairly convincingly by offering a series of ways in which it is true, listing everything from the larger students loans they must take out to the higher proportion of women in low-paying adjunct positions to the observation that as women join a field, average pay in the field drops. But there's more, an undercurrent and an observation, which is encapsulated in the discussion of the relation between the attack on the humanities, the increasing number of women in the humanities, and the accusation that there is a predominance of 'liberal values' in such fields.
"Mistakes are not all created equal," writes the author, "and they are not always desirable. In addition, learning from mistakes is not all automatic. In order to learn from them the most we need to reflect on our errors and extract lessons from them." Eduardo Briceño makes this point clear by identifying four types of mistakes, two of which can be seen as beneficial, and two of which really should be avoided.
The Maker movement began as a free-form exercise. "Typically, 'Making' involves attempting to solve a particular problem, creating a physical or digital artifact, and sharing that product with a larger audience. Often, such work is guided by the notion that process is more important than results." But as it began to be applied more in schools, it began to evolve. Diversity and inclusiveness became more important, and questions began to be asked about what was learned. This article is a good overview of some of the recent research. And it's interesting to compare the similarities between the evolution of MOOCs and the evolution of making.