Why children differ in motivation to learn: Insights from over 13,000 twins from 6 countries
This study explored the etiology of individual differences in enjoyment and self-perceived ability for several school subjects in nearly 13,000 twins aged 9–16 from 6 countries. The results showed a striking consistency across ages, school subjects, and cultures. Contrary to common belief, enjoyment of learning and children’s perceptions of their competence were no less heritable than cognitive ability. Genetic factors explained approximately 40% of the variance and all of the observed twins’ similarity in academic motivation. Shared environmental factors, such as home or classroom, did not contribute to the twin’s similarity in academic motivation. Environmental influences stemmed entirely from individual specific experiences.
Moreover, attending different classrooms did not increase dissimilarity between twins in their levels of enjoyment and self perception of competence. Equal similarity between twins attending same and different classrooms cannot be explained with equalising effect of the shared home environment as no such effect was found in this study. These results suggest that similarity in academic motivation for any unrelated individuals stems from their chance genetic similarity, as well as similar individual-specific environmental experiences, rather than similar family/classwide experiences. Whatever the environmental influences on the levels of enjoyment and self-perceived ability are, they seem to act in a non-shared, individual-specific way, potentially interacting with genetic make-up, experiences and perceptions. Multiple individual-specific life-events, such as birth complications, missing school due to illness, and peer-relations, may contribute to motivation. Effects of family members, teachers, classes, and schools may also be non-shared: parents, siblings, and teachers may actually treat children in the same family/class differently, responding to their individual characteristics (Babad, 1993; Harris & Morgan, 1991; Spengler, Gottschling, & Spinath, 2012). On the other hand, children may perceive their parents, teachers, classmates, and schools differently (Zhou, Lam, & Chang, 2012) – depending on other non-shared environmental and genetic effects. In addition, genetic effects may differ as a function of environment. For example, research suggested that heritability of reading might be moderated by teacher quality or SES status (Taylor, Roehrig, Hensler, Connor, & Schatschneider, 2010).
True grit and genetics: Predicting academic achievement from personality.
Twin analyses of Grit perseverance yielded a heritability estimate of 37% (20% for consistency of interest) and no evidence for shared environmental influence. Personality, primarily conscientiousness, predicts about 6% of the variance in GCSE grades, but Grit adds little to this prediction. Moreover, multivariate twin analyses showed that roughly two-thirds of the GCSE prediction is mediated genetically. Grit perseverance of effort and Big Five conscientiousness are to a large extent the same trait both phenotypically (r = 0.53) and genetically (genetic correlation = 0.86). We conclude that the etiology of Grit is highly similar to other personality traits, not only in showing substantial genetic influence but also in showing no influence of shared environmental factors. Personality significantly predicts academic achievement, but Grit adds little phenotypically or genetically to the prediction of academic achievement beyond traditional personality factors, especially conscientiousness.
Understanding and Influencing Pupils' Choices as they Prepare to Leave School
The data collected here suggests, simply, that pupils who like and admire their teachers perform better than students for whom this is not the case, and this is partly for environmental reasons. It is important to note that our study design does not allow us to identify the direction of effects and a positive teacher-pupil relationship could as easily be a consequence as a cause of high achievement. A related point is that Phase 3 analyses noted that pupils with relatively high g expressed higher average opinions of their teachers. This was particularly clear for Maths and Science.
In summary, it remains unclear whether and how we can influence pupils’ choices and behaviour at this important developmental stage. However, our study has identified some key areas for discussion and further exploration. Given the prevalence of idiosyncratic experiences in our data we would also emphasise a need for ‘sensitive schooling’ in the form of personalisation and attention to individual differences. Great swathes of empirical data, including that presented here, suggests that all pupils are ‘special snowflakes’ who are likely to be helped (not harmed) by being recognised as such.
Inequality in Human Capital and Endogenous Credit Constraints
We find substantial evidence of life cycle credit constraints that affect human capital accumulation and inequality. The constrained fall into two groups: (a) the chronically poor with low initial endowments and abilities and low levels of acquired skills over the lifetime, and (b) the initially well-endowed persons with high levels of acquired skills. The first group has flat life cycle wage profiles. They remained constrained over most of their lifetimes. The second group has rising life cycle wage profiles. They are constrained only early on in life because they cannot immediately access their future earnings. As they age, their constraints are relaxed as they access their future earnings.
Equalizing cognitive ability has dramatic effects on reducing inequality in education (Table 7). Equalizing non-cognitive ability has a similar strong impact. Earnings and consumptions, including family background, has much less dramatic effects after controlling for the other first order effects of cognitive ability. Reducing tuition substantially promotes schooling, but has only minor effects on our measures of inequality. Enhancing student loan limits has minor effects on all outcomes studied. There are dramatic effects of equalizing cognition but equalizing other factors
What grades and achievement tests measure
Cognitive skills predict life outcomes. This paper reinterprets the evidence on the relationship between cognitive skills and a variety of important life outcomes by analyzing the constituent components of widely used proxies for cognitive skills—grades and achievement tests. Measures of personality predict achievement test scores and grades above and beyond IQ scores. Analyses using scores on achievement tests and grades as proxies for IQ conflate the effects of IQ with the effects of personality. Both measures have greater predictive power than IQ and personality alone, because they embody extra dimensions of personality not captured by our measures.
Why do these findings matter? Achievement tests are widely used to measure the traits required for success in school or life. It is important to know what they measure to design effective policy and use these measures to evaluate schools and teachers (evidence of teacher effectiveness on personality and its consequences for high school graduation is in ref. 28). Understanding the sources of differences in the test scores and grades used to explain the black–white achievement gap (29), the male–female wage gap (30), and other gaps by social class directs attention to what factors might be remediated (5). For example, personality or noncognitive skills are more malleable at later ages than IQ, and there are effective adolescent interventions that promote personality but are much less successful in boosting IQ (31, 32). The predictive power of grades shows the folly of throwing away the information contained in individual teacher assessments when predicting success in life.
G Is for Genes
Reading ability is distributed normally – a classic bell curve. That is, most people cluster around average ability, with a small proportion excelling and a small proportion struggling. Our ability to read is heavily influenced by our genes: estimates of heritability tend to hover between 60 and 80%. This means that a significant proportion of the differences between individuals in how well they can read can be explained by genetic influence, leaving as little as 20% to be explained by the environment in some studies (Kovas, Haworth, Dale, and Plomin, 2007; Wilcutt et al., 2010). Similar results have recently been reported from China, in spite of the very different orthography of Chinese (Chow et al., 2011). (p. 24)
Is mathematical ability heritable? Yes it is. Kovas estimated the heritability of mathematical ability among 10-year-old children, as rated by their teachers, as about two-thirds. Shared environment accounted for 12% of the ability differences between children, and nonshared environment accounted for 24%. She had carried out a similar analysis when the TEDS twins were 7 years old and reached a very similar conclusion: teacher-assessed mathematics achievement was 68% heritable, with shared environment accounting for 9% and nonshared environment 22% of the differences between children. Similar results also emerged when the children were 9 years old. In this sample at least, which is representative of the wider UK population, a heritability estimate of 60 to 70% appears to be robust throughout the early school years. This mirrors our results for reading and writing.
This is what primary school teachers are dealing with. Genetic differences at this stage are more important to mathematics achievement than differences in family income, family Monopoly or Rummikub sessions, parental education, gender, or school quality. Yet teacher training does not take them into account. In one sense, a heritability estimate of 60 to 70% tells the teacher nothing at all about what is possible, or even to be expected, from any particular child, but it should confirm that, for partly biological reasons, all of the children in her class are starting from different points and therefore need to take different next steps to develop their understanding and their ability. It should tell her that her job is to gradually draw out each child's potential rather than aiming, as a class, at some arbitrary, externally imposed target. Teachers already know this, but their methods are too often challenged by a political will to defy nature. Some kids start with a biological advantage in mathematics. It is not unreasonable to propose that those kids will develop differently from those who do not share their advantage. Is it unreasonable for education to reflect this? (pp. 44-45)
So, by the end of our study we were left with the hypotheses that positivity about school, “flow” in the classroom, and peer stress, work as nonshared environmental influences on achievement. We also saw significant relationships between peer and academic stress and “flow”; and, in some subjects at least, between “flow” and academic achievement, which suggests a possible chain-reaction. We also saw that stress was negatively associated with “flow,” suggesting the hypothesis that classroom stress is linked to low morale and that this low morale, in terms of “flow” and positivity, has a negative knock-on effect on academic achievement. Perhaps there would be merit in teaching children how to handle stress and achieve “flow” as a means of boosting their academic performance? (p. 122)