Why Diversity Programs Fail [...]

In analyzing three decades’ worth of data from more than 800 U.S. firms and interviewing hundreds of line managers and executives at length, we’ve seen that companies get better results when they ease up on the control tactics. It’s more effective to engage managers in solving the problem, increase their on-the-job contact with female and minority workers, and promote social accountability—the desire to look fair-minded. That’s why interventions such as targeted college recruitment, mentoring programs, self-managed teams, and task forces have boosted diversity in businesses. Some of the most effective solutions aren’t even designed with diversity in mind. (Source)

See also Why do women leave engineering?

Why do women leave engineering? [...]

The women in the study, Silbey and her colleagues observed, are more likely than men to say they are entering the field of engineering with the explicit idea that it will be a “socially responsible” profession that will “make a difference in people’s lives.” But group dynamics seem to affect this specific expectation in two ways: by leading women to question whether other professions could be a better vehicle for affecting positive social change, and by leading them to question if their field has a “commitment to a socially conscious agenda that … was a key motivator for them in the first place.” (Source)

See also Why Diversity Programs Fail

Work Changes Culture [...]

As we have seen from communication campaigns around values in organisations, message can temporarily influence expectations. However, what confirms a change in expectations is when people see new behaviours being practiced consistently, rewarded and ultimately expected by others.  (Source)

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Learning Analytics [...]

Once the Pandora’s Box of data availability has been opened, then individuals lose control of the data about them that have been harvested. They are unable to specify who has access to the data, and for what purpose, and may not be confident that the changes to the education system which result from learning analytics will be desirable. More generally, the lack of transparency in data collection and analysis exacerbates the fear of undermining privacy and personal information rights in society beyond the confines of education. The transport of data from one context to another can result in an unfair and unjustified discrimination against an individual. (Source)

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Marshmallow Scarcity [...]

Time and again, poor children have performed significantly worse than their more fortunate counterparts. A 2011 study that looked at low-income children in Chicago noted how poor children struggled to delay gratification. A 2002 study, which examined the physical and psychological stresses that accompany poverty, did too. And so have many others.

The realization has sparked concerns that poverty begets a certain level of impulsiveness, and that that tendency to act in the moment, on a whim, without fully considering the consequences, makes it all the more difficult for poor children to succeed. But there's an important thing this discussion seems to miss. Poor kids may simply not want to delay gratification. Put another way, their decisions may not reflect the sort of impulsive nature we tend to attribute them to.

"When resources are low and scarce, the rational decision is to take the immediate benefit and to discount the future gain," said Melissa Sturge-Apple, a professor of psychology at the University of Rochester who studies child development. "When children are faced with economic uncertainty, impoverished conditions, not knowing when the next meal is, etc. — they may be better off if they take what is in front of them." (Source)

The experimenters used vagal tone to look at performance under stress and found low-SES inverted decision making for those with high vagal tone -- in other words, maybe they weren't impulsive -- maybe they were acting just like they intended.

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Equity by Design [...]

Rather than viewing inequalities as a natural catastrophe (Coates 2015), equity-minded individuals allow for the possibility that inequalities might be created or exacerbated by taken-for-granted practices and policies, inadequate knowledge, a lack of cultural know-how, or the absence of institutional support—all of which can be changed. (Source)

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GitHub for Lesson Plans [...]

In sum: “Small differences between lessons plans are enormously important, enormously time-consuming to account for and fix, and whatever I already have is probably good enough.” It turns out that even if two lesson plans don’t differ all that much they already too much. (Source)

Stereotype Threat [...]

It is thought that the primary mechanism behind stereotype threat is the anxiety one feels about their performance confirming a stereotype and the subsequent reduced capacity of working memory. Interestingly, the research shows that the effects of stereotype threat are only visible (even if they are still present) when the task or assessment is difficult and when the cognitive load is great. In other words, when student’s identity is under threat and they are engaged in a difficult task their cognitive energy is divided between the task at hand and self-evaluative concerns. (Source)

See also "Forget CVs, here's how to use behavioural science to hire the best people" (Link)

Technology Doesn’t Want [...]

The “hacker” model is better: given imagination and determination, we can bend technologies to our will. Thus we should stop thinking about “what technology wants” and start thinking about how to cultivate imagination and determination. Speaking of “what technology wants” is an unerring symptom of akrasia — a lack of self-control — and a way of deflecting responsibility for our actions. (Source)

See the reverse of this in Brains aren't computers