“Curiosity and openness matter more than diligence and perseverance”

Margherita Malanchini
Margherita Malanchini

Developmental psychologist Margherita Malanchini explains why curiosity, creativity, and self-confidence are more important for a child’s success in reading and math than commonly assumed. She also discusses recent findings about the role of genes in educational attainment and the implications of this research for future interventions.

Sabine Gysi: We often assume that an intelligent child will do well in reading or math. But what ultimately contributes most to an individual child’s success? Is it intelligence, or self-control, or perhaps the child’s drive and curiosity?

Margherita Malanchini: These are some of the questions that I focus on in my research: how self-regulation in children works, and how different aspects of self-regulation relate to differences in academic achievement, beyond intelligence.

Of course, there is a large body of research showing that children’s intelligence or cognitive ability predicts how well they will do in reading and mathematics, even later in life. But I want to learn more about the factors that matter beyond intelligence. In a recently published study my colleagues and I investigated a host of factors that are involved in what is called “self-regulation.”

Self-regulation refers to how well children are able to control their behaviors and internal states against a backdrop of conflicting or distracting situations, drives, and impulses. However, self-regulation is a very broad construct, including aspects that are more closely related to intellectual abilities, such as being able to store and update information in their working memory, and others that are more closely linked to personality traits, such as conscientiousness and diligence.

We also wanted to look beyond these self-regulatory factors to identify other aspects that contribute to performance in reading and mathematics, beyond cognitive skills. We examined a host of cognitive, self-regulatory, and motivational factors and found that components of openness to experience, such as intellectual curiosity, creativity, and self-confidence, are important in predicting differences in reading and mathematics ability – more important than self-discipline, conscientiousness, and diligence.

Because we conducted this investigation on a twin sample, the Texas Twin Project, we were also able to explore how genetic and environmental factors accounted for the links between these measures and children’s success. We found that not only intellect, but several other factors, including executive functions, openness to new challenges, intellectual interest, and self-confidence, share genetic links with reading and mathematics abilities.

SG: Does this mean that low-achieving children with high levels of interest and motivation might turn into high achievers?

MM: I published a paper that looked at how reading achievement and reading motivation influence each other over time. Does reading achievement develop as a function of motivation, or is it the other way around? Is it true, in other words, that the better you do, the more interested and confident you become in reading?

“Students who are anxious but also highly motivated can achieve just as much as students who are highly motivated but not anxious.”

We found that there are mutual associations that create an upward spiral: The more interested you are in reading, the more you will achieve; but your achievements also have an effect on your level of interest. This same reciprocal relationship works the other way round by creating a negative feedback loop that links low levels of interest and self-confidence to diminished performance.

In another study, which is currently under review, we observed the same for mathematics skills: the more interested students are in mathematics and the more confident they are in their mathematics abilities, the greater their future mathematics achievement is likely to be. In turn, the greater children’s achievements in mathematics, the more interested and confident in the subject they are likely to become.

SG: Does this also explain math anxiety?

MM: The interplay among anxiety, motivation, and achievement is really interesting. We looked at this in another paper and found that students clustered into several groups based on their levels of motivation and anxiety in mathematics.

The surprising finding was that although there is generally a negative relationship between math anxiety and math achievement, students who are anxious but also highly motivated can achieve just as much as students who are highly motivated but not anxious. It was when combined with low motivation that anxiety resulted in lower performance. This may be due to a negative feedback loop in which anxiety hinders performance, and this in turn prevents students from developing self-confidence and interest in mathematics. And, as we know from longitudinal work, these mutual relationships are reinforced over the course of a person’s development.

Interestingly, the group of students characterized by high levels of motivation and high anxiety were also the students who were dedicating the most time to studying mathematics, which suggests that time invested in studying could be one of the mechanisms through which students who are highly anxious, but also highly motivated, compensate for the detrimental effects of anxiety on performance.

SG: Another aspect of educational success is attracting more and more attention these days: our genes. Can DNA explain why one child does better in school than another?

MM: Yes, differences in DNA can predict differences between students in academic achievement, as we have shown in a recent paper examining the power of such predictions throughout the course of students’ compulsory education in the United Kingdom. However, the accuracy of predictions based on DNA is still limited. When using DNA to make a prediction, you need to add up all the genetic variants – each with very small effects – that are associated with educational attainment to arrive at what we call a genome-wide polygenic score. This score predicts how well an individual will do in school, and its prediction has been found to increase as the student grows older.

Even more recent work looked at predictions combining different polygenic scores, and found that we can predict about 15% of individual differences in academic achievement at age 16 – so yes, DNA predicts how well children will do in school, with a little less accuracy than family socioeconomic status or IQ (~20-25%), which are currently the best predictors we have. However, our predictions are still far from perfect.

“What role do environments play in moving from a genetic predisposition to these observed differences in cognition and learning?”

There are two main things to bear in mind when considering this pathway from DNA to observed differences in student achievement. First, the prediction from DNA is always probabilistic and not deterministic, and second, the genetic predisposition to observed differences in behavior is likely to interact with many environmental and biological factors. This is something I intend to explore further in my research: What role do environments play in moving from a genetic predisposition to these observed differences in cognition and learning?

SG: This also seems to be a very important topic in communicating scientific findings – when people hear “prediction” or “disposition,” they tend to conclude that “this is something I have no influence on.”

MM: Absolutely. Most traits show a genetic predisposition, but that doesn’t mean we can’t intervene in an environmental way, because dispositions are likely to interact with the environment to promote certain outcomes. But without taking into account a person’s genetic disposition, we can never confidently separate genetic and environmental effects. Only by considering the complexity of the genetic and environmental architecture of educational achievement will we be able to create and improve interventions, leading to more successful outcomes.

SG: What implications do these findings have for parents, teachers, and eventually policymakers?

MM: This is a very delicate topic, and we ought to think long and hard about how this could work.

There are a number of reasons why we might want to predict how a child will do in school. One of them might be so that we can implement interventions; we might identify children who are at risk of low achievement and focus on them. It’s important to understand that this would not mean selecting children for discriminatory purposes, but the opposite – selecting them to receive special help. Resources are limited, and when it comes to deciding where to allocate them, it makes sense to devote resources to children who need them the most.

“Resources are limited, and when it comes to deciding where to allocate them, it makes sense to devote resources to children who need them the most.”

But we might also want to use this information to personalize the learning experiences of all children, and this can be done by leveraging recent technological developments, for example by creating online platforms that allow children to work at a level that is appropriate to their abilities. This can help avoid a negative feedback loop that will lead to frustration, anxiety, and diminished performance.

Although we are moving in the right direction and gaining essential new knowledge every day, we are still in the early stages. We need to reach a much more exhaustive understanding of the numerous factors that contribute to explaining why children differ so widely in their achievement and cognitive abilities, whether these involve genetic or environmental pathways or both. Only then can we think about how to use this knowledge to help students on a day-to-day basis.

In the second part of our interview, Margherita Malanchini talks about leveraging technologies to create personalized learning environments, and how such environments might help all students reach their full potential.

Margherita Malanchini is a postdoctoral research fellow at the University of Texas at Austin, where she works on the Texas Twin Project under the mentorship of Elliot Tucker-Drob and Paige Harden. During the last year of her PhD studies, Malanchini founded the MILES research project, a Multi-Cohort Investigation into Learning and Educational Success. Malanchini is also an Affiliate Postdoctoral Fellow at the Social, Genetic and Developmental Psychiatry Centre, King’s College London. Her research at the intersection of developmental psychology, genetics and education seeks to inform educational practice and interventions, with the ultimate goal of enabling students to achieve their full potential.

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