Source: Unz.com, Jul 2019
Over the last 50 years in developed countries, evidence has accumulated that only about 10% of school achievement can be attributed to schools and teachers while the remaining 90% is due to characteristics associated with students.
Teachers account for from 1% to 7% of total variance at every level of education. For students, intelligence accounts for much of the 90% of variance associated with learning gains.
The largest effect of schooling in the developing world is 40% of variance, and that includes “schooling” where children attend school inconsistently, and staff likewise.
I further argue that the majority of the variance in educational outcomes is associated with students, probably as much as 90% in developed economies.
A substantial portion of this 90%, somewhere between 50% and 80% is due to differences in general cognitive ability or intelligence. Most importantly, as long as educational research fails to focus on students’ characteristics we will never understand education or be able to improve it.
Source: TechCrunch, Feb 2018
This week, the school announced the launch of the MIT Intelligence Quest, an initiative aimed at leveraging its AI research into something it believes could be game-changing for the category. The school has divided its plan into two distinct categories: “The Core” and “The Bridge.”
“The Core is basically reverse-engineering human intelligence,” dean of the MIT School of Engineering Anantha Chandrakasan tells TechCrunch, “which will give us new insights into developing tools and algorithms, which we can apply to different disciplines. And at the same time, these new computer science techniques can help us with the understanding of the human brain. It’s very tightly linked between cognitive science, near science and computer science.”
The Bridge, meanwhile, is designed to provide access to AI and ML tools across its various disciplines. That includes research from both MIT and other schools, made available to students and staff.
“Many of the products are moonshoots,” explains James DiCarlo, head of the Department of Brain and Cognitive Sciences. “They involve teams of scientists and engineers working together. It’s essentially a new model and we need folks and resources behind that.”
Funding for the initiative will be provided by a combination of philanthropic donations and partnerships with corporations. But while the school has had blanket partnerships in the past, including, notably, the MIT-IBM Watson AI Lab, the goal here is not to become beholden to any single company. Ideally the school will be able to work alongside a broad range of companies to achieve its large-scale goals.
“Imagine if we can build machine intelligence that grows the way a human does,” adds professor of Cognitive Science and Computation, Josh Tenenbaum. “That starts like a baby and learns like a child. That’s the oldest idea in AI and it’s probably the best idea… But this is a thing we can only take on seriously now and only by combining the science and engineering of intelligence.”
Source: ArsTechnica, Feb 2017
<research study HERE>
different spatial tests are all basically testing the same underlying ability—and that this ability is only partly explained by general intelligence. This means that spatial ability is, to some extent, independent: you can have better (or worse) spatial ability than your general intelligence might suggest. The results also suggest that about a third of the differences in people’s spatial scores can be explained by genetics.
A team of researchers led by Kaili Rimfeld of King’s College London studied more than 1,300 pairs of twins to figure out to what extent genes contribute to spatial ability.
But first, they had to figure out which aspect of spacial reasoning to test them on. The researchers scoured the scientific literature to find all the different tests that had been used to assess spatial ability and ran pilot studies using them. They took out any tests that were too easy or difficult and any tests where the same people scored inconsistent results when taking the same test twice. The team also looked at the similarity of people’s scores in different tests—if the scores were very similar, they took out the redundant tests. By doing this, they boiled spatial ability down to 10 core tests.
Comparing the results of the identical and fraternal twin pairs found that 69 percent of the differences in spatial test results could be explained by genetic similarity. Of the remainder, the majority—23 percent—was explained by individual experience.
That only leaves a small bit of ability to be explained by the environment that the twins shared. The researchers emphasize that these estimates are unique to this population: in a less equal environment than the UK, genes might explain less of the difference.
The researchers also compared the genetic overlap with general intelligence. They found that after controlling for general intelligence, 30 percent of the differences in spatial scores could be attributed to genetic differences.
Source: LinkedIn, date indeterminate
<answer is at the source>
Source: Psychology Today, Mar 2017
Intelligence is the most important factor in determining long-term achievement outcomes, and personality is unlikely to compensate for background disadvantage.
Brent Roberts, professor of psychology at the University of Illinois-Urbana Champaign. His work is especially important to consider because he attempts to fully account for the role of intelligence when assessing the impact of other non-cognitive factors.
We found that both cognitive ability and personality traits are important for these outcomes in particular, but that cognitive ability differences have a larger compensatory effect than individual personality traits. … Given the independent effects of cognitive abilities and personality traits, I’d be inclined to argue that both sets of variables are important for education and income but that cognitive abilities are more important than personality traits.
For most achievement-related outcomes like education, cognitive ability is always the strongest predictor. The line that non-cognitive factors do as well or better is just wrong.
My read of the IQ to “soft outcome” literature is that it is vastly overstated.
Source: Unz.com, Feb 2017
What allows groups to behave intelligently? One suggestion is that groups exhibit a collective intelligence accounted for by number of women in the group, turn-taking and emotional empathizing, with group-IQ being only weakly-linked to individual IQ (Woolley, Chabris, Pentland, Hashmi, & Malone, 2010).
Here we report tests of this model across three studies with 312 people. Contrary to prediction, individual IQ accounted for around 80% of group-IQ differences. Hypotheses that group-IQ increases with number of women in the group and with turn-taking were not supported. Reading the mind in the eyes (RME) performance was associated with individual IQ, and, in one study, with group-IQ factor scores.
However, a well-fitting structural model combining data from studies 2 and 3 indicated that RME exerted no influence on the group-IQ latent factor (instead having a modest impact on a single group test). The experiments instead showed that higher individual IQ enhances group performance such that individual IQ determined 100% of latent group-IQ. Implications for future work on group-based achievement are examined.
It is interesting also that groups did not perform better than individuals – a genuine group-IQ might be expected to enable problem solving to scale linearly (or better) with number of subjects.
In group-IQ tasks, coordination costs appear to prevent group problem-solving from rising even to the level of a single individual’s ability. This implicates not only unsolved coordination problems, which are well-known barriers to scale (Simon, 1997) but also reiterates the finding that the individual problem-solver remains the critical reservoir of creativity and novel problem solution (Shockley, 1957).
Source: James Thompson blog, Apr 2014
Asians (Chinese, Koreans, and Japanese) are supposed to have higher IQs (about 105 on average) than North Europeans (100), while sciences have been developed overwhelmingly by Europeans and their offshoots. Why Asians are lacking in scientific success might relate to two factors:
1. Low curiosity, which is expressed by lower Openness to experience (-.59 SD) as shown in various cross-cultural personality comparisons.
2. Collectivism, which is captured by various individualism-collectivism indices such as the Hofstede individualism index (IDV), or Hofstede and Triandis individualism index (about -2 SD). The genetic underpinnings for these traits, such as DRD4, 5HTTLPR, and OPRM1 have also become increasingly apparent.
To integrate these psychological traits, a “q” factor is constructed by factor analysis on measures of Openness and Collectivism, which are then correlated with variables measuring academic achievements and also student assessments. It is found that IQ scores coupled with “q” factor scores neatly predict racial scientific achievements and also world-wide student assessments.
Google DOC presentation: