What is dyscalculia?

Many people may struggle to develop strong mathematical abilities for many different reasons and thus mathematical difficulties are best thought of as a continuum (BDA, 2019). Dyscalculia falls on one end of that continuum and is a specific learning difficulty that affects a person’s ability to understand numerical information and perform mathematical operations (American Psychiatric Association, 2013).

Watch our explainer video

Here’s a video we produced as part of our NeuroSENse project

How is dyscalculia defined?

Although definitions may vary,  individuals with dyscalculia may have difficulty with mental maths, trouble understanding mathematical concepts, difficulty with sequencing and organising information, and challenges with time and money management. These difficulties manifest during the early school years and must persist for at least 6 months to be diagnosed, according to DSM-V criteria. In addition, these learning difficulties cannot be attributed to intellectual disabilities, developmental disorders, or neurological or motor disorders. While dyscalculia is often diagnosed in childhood, it can also affect adolescents and adults.

How common is it?

Prevalence rates of dyscalculia have proven difficult to ascertain given that different inclusion criteria for dyscalculia are often used (Szűcs & Goswami, 2013). Based on a small number of previous studies, the prevalence of dyscalculia has been estimated to range between 1.3% and 10% of the population (Devine et al., 2013). This is equivalent to roughly 3 children in every class of 30 children, making it a relatively common condition that can affect people of all levels of intelligence.

How is dyscalculia diagnosed?

Dyscalculia can be diagnosed through a comprehensive evaluation by a qualified professional, and there are strategies and interventions that can help individuals with dyscalculia improve their mathematical skills and make progress.

Dyscalculia is not a neuromyth. The exact causes of dyscalculia are not yet fully understood, but researchers believe that there may be a combination of genetic, environmental, and brain-related factors that contribute to the condition (Van Herwegen, 2020). It is important to understand the facts about dyscalculia to provide appropriate support and accommodations for individuals who may have this condition.

Yet, there are several common misconceptions (neuromyths) about dyscalculia. Here are our top 5 myths!

Myths about dyscalculia

Neuromyth 1: If a person struggles with mathematics they have dyscalculia

This is not necessarily the case. Dyscalculia is a specific learning difficulty that affects an individual’s ability to understand and perform mathematical operations. This it is not the same as simply having difficulties with mathematics. A child may struggle with mathematics for a myriad of reasons, including lack of interest, poor teaching, or the curriculum being too delivered too quickly for their capacity. In addition, maths anxiety can be a contributing factor to difficulties with maths, but that doesn’t mean people with maths anxiety necessarily have dyscalculia (Devine et al., 2018). Most people with dyscalculia have specific mathematical difficulties such as understanding how numbers relate to each other (number sense), memorising and retrieving numerical facts as well as make counting errors.

Neuromyth 2: Individuals who are dyscalculic usually only have problems with numbers and can read and write at typical levels

Individuals with dyscalculia typically experience difficulties with their working memory and visuo-spatial skills (Kroesbergen et al., 2022). As such, dyscalculia impacts all areas of the curriculum, not just mathematics. In addition, up to 20-60% of those with dyscalculia also have other learning difficulties, such as, ADHD, dyslexia, and dyspraxia (Morsanyi et al., 2018; von Aster & Shalev, 2007), with co-occurrence of maths and reading difficulties as high as 70% (Moll et, al, 2019). This can mean individuals with dyscalculia also have problems with attention, reading and writing. It is thought that the overlap between dyscalculia and other learning difficulties is caused by shared difficulties with procedural learning (Evans & Ullman, 2016), the learning and control of skills and habits.

Neuromyth 3: Individuals with dyscalculia can be best helped by teaching them to remember number facts

Difficulties with number facts is only one aspect of dyscalculia. Although the actual cause of dyscalculia has not yet been established, many individuals with dyscalculia show difficulties  with reasoning about quantities, and with a sense of what numbers represent (Butterworth, 2018). As such, they may need targeted interventions and support to succeed in academic and everyday life. Additionally, accommodations such as extra time for assessments, use of a calculator, or modifications to assignments can help students with dyscalculia succeed in the maths curriculum (Fuchs et al., 2008). While there are strategies and interventions that can help individuals with dyscalculia improve their maths skills, there is no cure for dyscalculia, which is a lifelong difficulty.

Neuromyth 4: It is often thought that individuals with dyscalculia are impaired across the entire maths curriculum

Indeed, most individuals with dyscalculia may struggle with basic arithmetic, number sense, and mathematical reasoning, which can impact their ability to learn and apply maths concepts across different areas of the curriculum. Anecdotal evidence suggests that some individuals with dyscalculia can be very good at geometry and algebra but there is scant evidence on the knowledge of geometry and algebra in individuals with dyscalculia. It is important to note that the severity and scope of dyscalculia can vary from person to person (see for example studies that have examined the existence of sub-groups within dyscalculia: Bartelet et al, 2014; Costa et al., 2018). While most people with dyscalculia struggle with many different mathematical concepts and procedures, some individuals with dyscalculia may have strengths in particular areas of maths and with good teaching and practice individuals with dyscalculia can make progress in maths, especially if targeted early intervention is provided.

Neuromyth 5: The dyscalculic brain is wired differently, which causes problems with maths but is often associated with strengths like creativity, strategic thinking, and intuitive thinking

There is currently no scientific evidence to support the claim that people with dyscalculia are more creative than those without dyscalculia. Dyscalculia is a learning difficulty that affects a person’s ability to understand and work with numbers. It does not necessarily affect a person’s creativity or artistic abilities. However, it is worth noting that people with dyscalculia may have developed compensatory strategies to deal with their difficulties in mathematics, which could enhance their creativity in other areas. For example, they may have developed stronger verbal and visual reasoning skills, or they may have developed a more intuitive approach to problem-solving. These compensatory strategies could potentially translate into enhanced creativity in certain domains. Nevertheless, it is important to recognise that dyscalculia is a real and significant learning difficulty that can have a negative impact on a person’s academic and professional success. It is essential to provide appropriate support and accommodations for individuals with dyscalculia to help them overcome their challenges and reach their full potential, regardless of their creativity levels.

Here’s your take home

In sum, although the exact causes of dyscalculia are not yet fully understood, dyscalculia can have a profound impact on people’s lives, especially in terms of educational outcomes and financial success. Early intervention is required to help people with dyscalculia to achieve their full mathematical potential. However, neuromyths can prevent timely diagnosis, create stigma and impact on intervention practices (Gini et al., 2020) and thus, it is important to continue our understanding of dyscalculia and reflect on any beliefs, knowledge and practices. Further research on dyscalculia especially to how it manifests over time is required.

References

American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th Edition: DSM-5 (5th ed.). American Psychiatric Publishing.

Bartelet, D., Ansari, D., Vaessen, A., & Blomert, L. (2014). Cognitive subtypes of mathematics learning difficulties in primary education. Research in Developmental Disabilities, 35(3), 657-670. doi: 10.1016/j.ridd.2013.12.010.

Butterworth, B. (2018). Dyscalculia: From science to education. Routledge.

Costa, H.M., Nicholson, B., Donlan, C., & Van Herwegen, J. (2018). Low performance on mathematical tasks in preschoolers : the importance of domain-general and domain-specific abilities. Journal of Intellectual Disability Research, 62(4), 292-302.

Devine, A., Soltész, F., Nobes, A., Goswami, U., & Szűcs, D. (2013). Gender differences in developmental dyscalculia depend on diagnostic criteria. Learning and Instruction, 27, 31–39. https://doi.org/10.1016/j.learninstruc.2013.02.004

Devine, A., Hill, F., Carey, E. and Szűcs, D. (2018) Cognitive and emotional math problems largely dissociate: Prevalence of developmental dyscalculia and mathematics anxiety. Journal of Educational Psychology, 110(3): 431–44.

Evans, M. & Ullman, M.T. (2016). An extension of the procedural deficit hypothesis from developmental language disorders to mathematical disability. Frontiers in Psychology 7 ,1-9.

Fuchs, L. S., Fuchs, D., Powell, S. R., Seethaler, P. M., Cirino, P. T., & Fletcher, J. M. (2008). Intensive Intervention for Students with Mathematics Disabilities: Seven Principles of Effective Practice. Learning Disability Quarterly, 31(2), 79- 92. https://doi.org/10.2307/20528819

Gini, S., Knowland, V., Thomas, M.S.C. & Van Herwegen, J. (2021). Neuromyths about neurodevelopmental disorders: Misconceptions by educators and the general public. Mind, Brain & Education, 15(4), 289-298.

Kroesbergen, E. H., Huijsmans, M. D. E., & Friso-van den Bos, I. (2022). A Meta-Analysis on the Differences in Mathematical and Cognitive Skills Between Individuals With and Without Mathematical Learning Disabilities. Review of Educational Research, 0(0). https://doi.org/10.3102/00346543221132773

Moll, K., Landerl, K., Snowling, M. J., & Schulte-Körne, G. (2019). Understanding comorbidity of learning disorders: Task-dependent estimates of prevalence. Journal of Child Psychology and Psychiatry, 60(3), 286–294. https://doi.org/10.1111/jcpp.12965

Morsanyi, K., van Bers, B.M.C.W., McCormack, T., & McGourty, J. (2018). The prevalence of specific learning disorder in mathematics and comorbidity with other developmental disorders in primary school-age children, British Journal of Psychology, 109(4), 917-940, ISSN: 0007-1269. DOI: 10.1111/bjop.12322.

Szűcs, D., & Goswami, U. (2013). Developmental dyscalculia: Fresh perspectives. Trends in Neuroscience and Education, 2(2), 33–37. https://doi.org/10.1016/j.tine.2013.06.004

Van Herwegen, J. (2020). Math Disorder. In: S. Hupp & J. Jewell. The Encyclopedia of Child and Adolescent Development. John Wiley & Sons: Chichester, UK.

von Aster, M. G., & Shalev, R. S. (2007). Number development and developmental dyscalculia. Developmental Medicine and Child Neurology, 49(11), 868–873. https://doi.org/10.1111/j.1469-8749.2007.00868.x

Alexandra Moroti is part of the global customer research team at Amazon. Alexandra recently completed Birkbeck-UCL-IoE’s Masters in Educational Neuroscience degree. She was attracted to the course due to the novelty of the field, with its multifaceted approach of connecting different disciplines such as biology, neuroscience, and psychology with education – as well as the fact that it is a conjoined programme offered by three leading institutions. In this blog, we asked Alexandra to tell us about the independent research project she completed as part of her masters degree, in which she investigated educational games. Over to you, Alexandra.

“A quick search for “educational toy” on Google yields 191 million results in under a second, most of which are blog posts with affiliate links or recommendations from media outlets. A search for “educational toys” on Mumsnet, a popular parenting blog in the UK, shows numerous inquiries seeking age-appropriate recommendations. While many articles highlight the best educational toys for specific age groups, there are often no clear criteria for selecting these toys.

The purpose of educational toys is to aid a child’s development in a specific area, such as teaching coding or promoting motor skills. These toys should be active, engaging, meaningful, and socially interactive. However, the labelling of toys as “educational” is not regulated, and the development of educational toys often lacks sufficient research into age-appropriate developmental principles relevant to the claimed outcome.

To evaluate the claims of a popular toy marketed as enhancing social cognition in children through socio-emotional learning, I conducted a small-scale pre-test/post-test experimental design, to investigate the effects on young children of playing with a particular “educational” toy over a period of 14 days.

The selected toy

The toy selected for the research was ‘Big Feelings Pineapple,’ marketed for children aged three years and above. The aim of the toy is to build preschool social-emotional learning skills by supporting the recognition of emotional facial expressions. The toy came with a leaflet of 24 expressions, including the six universal emotions: happiness, sadness, anger, fear, disgust, surprise, which can be constructed using various pieces including eyebrows, eyes, and mouths. Here’s the product image (taken from its Amazon page).

The study

Child participants played with the Pineapple toy and were assessed before and after the intervention on four tasks – two experimental and two control tasks. I evaluated the child’s interaction with the toy through an in-the-moment play assessment tool, and then coded parental observations from a daily play diary.

I found that, although the Pineapple toy was good at promoting communication, it scored lower than predicted in the “Thinking and learning” and “Social interaction” dimensions of my measures. Children were mostly engaged with the toy when their parents were involved, but the toy lacked context and explanation when used alone. Parents who engaged their children with the toy in a meaningful way had more in-depth conversations about emotions later in the trial, and the toy was seen as a positive facilitator for conversations on emotions.

The children showed higher levels of emotion recognition post-play than they did pre-play, but the improvement was not related to the number of times the child played with the toy. This makes it ambiguous whether it was the toy having the effect or natural development. I wish I had included a ‘control group’ of children who had played with another type of toy, to check for this!

Broader lessons from my research study

While initiatives like Common Sense Media exist to help parents choose the best products for their kids, they do not include educational toys. Recently, some researchers have started to pay closer attention to the overuse of the label “educational” in marketing toys, with some researchers also turning their attention to educational value of “educational” apps. For example, see papers by Kathy Hirsh-Pasek and colleagues, Marisa Meyer and colleagues, and Shayl Griffith and colleagues.

In my view, an important future step is to establish guidelines for the development, marketing, and testing of educational toys to ensure that they are truly beneficial for a child’s development. This could involve consultation with researchers in the field, qualitative research such as focus groups and in-depth interviews with stakeholders, and longitudinal studies to assess the educational claims made by manufacturers. By doing so, parents can make informed decisions about which toys truly aid their child’s development. But my study suggests that a key role for toys may be how they support interactions between parents and children that in turn stimulate learning.

I think children’s learning should be a collective effort that goes beyond the household. Society should ensure that cities and environments, curricula, and manufacturers’ claims support educational experiences that prepare children for a future where adaptability and mental balance are crucial.”

Thanks, so much, Alexandra! If you are interested in this topic, take a look at this article which considers whether toys and games improve children’s thinking generally or just make kids better at playing games. And this article by Yuval Noah Harari, the author of Sapiens, speculating on what skills children may have to learn in 2050!

In this blog, the CEN’s Professor Chloë Marshall describes the findings of a recent project to investigate how easily hearing adults can learn sign language.

It is most people’s experience that learning a language is much harder in adulthood than it is in childhood, whether or not there is a critical or sensitive period for language-learning (Most learning happens in the first 3 years | Centre for Educational Neuroscience; Hartshorne et al., 2018). Research has shown that babies and young children possess powerful cognitive mechanisms for extracting statistical regularities from a stream of speech or sign language, and that these mechanisms allow them to ‘break into’ language by mapping word forms to meanings (Berent et al., 2021; Hay et al., 2011). But what about adults? Do they still retain these mechanisms? And if so, can they utilise them when confronted not only with a new language but a language in a modality that they have not previously encountered, namely a sign language?

What can hearing adults learn from viewing a few minutes of naturalistic sign language?

A project led by the CEN’s Chloë Marshall and funded by the Leverhulme Trust set out to investigate what hearing adults can learn from viewing just a few minutes of naturalistic sign language. She and her colleagues Dr Julia Hofweber, Prof. Marianne Gullberg, Lizzy Aumônier and Dr Vikki Janke adapted a paradigm used by Gullberg in previous work. Gullberg et al. (2010) had demonstrated that adult speakers of Dutch were able, after just a few minutes of watching a weather forecast presented in Mandarin Chinese, to learn something about word forms, word meanings, and sound regularities of this unfamiliar language.

Weather forecasts in Swedish Sign Language

Chloë and her team wanted to investigate whether English-speaking adults who had never learned any sign language would be able to learn similar linguistic information after short exposure to Swedish Sign Language (Svenskt teckenspråk, STS). They developed a four-minute weather forecast in STS, within which were embedded 22 ‘target’ signs that varied in occurrence frequency (they occurred either three or eight times in the forecast) and in iconicity (how closely the form of the sign resembled its meaning).

They also created three experimental tasks. In Task 1, participants were shown a mix of target signs and signs that they had not viewed in the forecast, and they had to respond ‘yes’ or ‘no’ when asked whether they had seen the signs before. In Task 2, participants were shown each target sign and had to write down what they thought it meant. In Task 3, participants were shown target signs and signs that could or could not be signs of STS, and they had to make a judgement as to whether they thought each sign was a real sign of the language or not. Participants viewed the weather forecast and then did just one of the three tasks. The task was a surprise for them – they were not warned beforehand that they would have to do it. In this way the researchers were testing implicit and unattended learning.

A still image from the Swedish Sign Language weather forecast

What the study found

Although participants found all three tasks challenging, the results from Task 1 (which assessed the recognition of sign forms) and Task 2 (which assessed whether participants could work out the meaning of the signs) indicated that they had managed to learn something. Participants were more accurate in recognising and assigning meaning to signs that occurred more frequently in the forecast and that were more iconic. Unlike in the original Mandarin Chinese study of Gullberg et al. (2010), however, participants did not appear to have learnt anything about what forms possible signs can take. Nevertheless, taken as a whole this exciting project has shown that the cognitive mechanisms that adults bring to ‘breaking into’ a new language are not limited to just speech, but can be employed even when the language modality is an unfamiliar one.

A still image from one of the target signs used in the experimental tasks, meaning ‘rainbow’. This is an example of a highly iconic sign, because it is visually very similar to a rainbow. Other target signs were less iconic. For example, the sign for ‘mountain’ involves the two fists rubbing past one another, which does not resemble the shape of a mountain at all. Sign-naïve adults found signs like ‘mountain’ harder to remember and guess the meaning of than highly iconic signs such as ‘rainbow’.

What are the implications?

Although the project was not designed to evaluate the effectiveness of sign language teaching, the findings have potential implications for education. The fact that both the form and the meaning of signs were better learnt when they occurred with higher frequency in the input is not surprising given what scientists already know about the role of frequency in language learning (Ellis, 2002), but it provides support for teachers manipulating the frequency of signs in their teaching materials and motivation for learners to seek repeated exposure to such materials. More innovatively, the findings also support the inclusion of signs that are high in iconicity because their meaning is more guessable.

An additional finding from the study, namely that participants’ scores on a range of cognitive tasks such as English vocabulary, executive functions and non-verbal reasoning did not correlate with their learning, suggests that at the early stages of sign language learning the characteristics of the learning materials might matter more for learning than students’ cognitive abilities.

More work needed on second language learning of sign

This set of studies needs replicating in different sign languages and with different input materials. Nevertheless, the findings make an important contribution to the field of the second language learning of sign, where much less is known compared to spoken language learning (Schӧnstrӧm & Marshall, 2022). One paper from the project has been published (Hofweber et al., 2022), another has been accepted for publication (Hofweber et al., 2023), and others are in process. Please contact Chloë if you would like further information (chloe.marshall@ucl.ac.uk).

REFERENCES

Berent, I., de la Cruz-Pavía, I., Brentari, D. & Gervain, J. (2021). Infants differentially extract rules from language. Scientific Reports, 11, 20001. https://doi.org/10.1038/s41598-021-99539-8

Ellis, N. (2002). Frequency effects in language processing: A review with implications for Theories of implicit and explicit language acquisition. Studies in Second Language Acquisition, 24(2), 143-188. https://doi.org/10.1017/S0272263102002024

Gullberg, M., Roberts, L., Dimroth, C., Veroude, K., & Indefrey, P. (2010). Adult language learning after minimal exposure to an unknown natural language. Language Learning, 60, 5-24. https://doi.org/10.1111/j.1467-9922.2010.00598.x

Hartshorne, J., Tenenbaum, J., & Pinker, S. (2018). A critical period for second language acquisition: Evidence from 2/3 million English speakers. Cognition, 177:263-277. https://doi.org/10.1016/j.cognition.2018.04.007

Hay, J. F., Pelucchi, B., Graf Estes, K., & Saffran, J. R. (2011). Linking sounds to meanings: Infant statistical learning in a natural language. Cognitive Psychology, 63, 93-106. https://doi.org/10.1016/j.cogpsych.2011.06.002

Hofweber, J. E., Aumônier, L., Janke, V., Gullberg, M., & Marshall, C. (2022). Breaking into language in a new modality: The role of input and individual differences in recognising signs. Frontiers in Psychology, 13:895880. https://doi.org/10.3389/fpsyg.2022.895880

Hofweber, J., Aumônier, L., Janke, V., Gullberg, M., & Marshall, C. R. (accepted). Which aspects of visual motivation aid the implicit learning of signs at first exposure? Language Learning.

Mott, M., Midgley, K., Holcomb, P., & Emmorey, K. (2020). Cross-modal translation priming and iconicity effects in deaf signers and hearing learners of American Sign Language. Bilingualism: Language and Cognition, 23, 1032-1044. doi:10.1017/S1366728919000889

Schӧnstrӧm, K., & Marshall, C.R. (2022). SLA²: Linking the domains of second language acquisition and sign language acquisition. Introduction to special issue ‘second language acquisition of sign languages’. Language, Interaction and Acquisition, 13, 145-158. https://doi.org/10.1075/lia.00014.eng

In this blog, Dr. Emma Meaburn, our resident genetics expert, discusses the latest research in using direct measures of DNA variation to predict educational outcomes. Does this work? How can it be useful?

Individual differences in educational traits are heritable

We each contain in every cell in our body the complete set of genetic instructions to build a human, with the distinctly human characteristics of a highly developed brain and the capacity to reason and communicate. Despite the overarching genetic similarity between us, there are numerous – and important – differences in our DNA sequence. If you were to pick any two unrelated individuals at random and examine their DNA sequence, you would find that they differ at roughly 1 in every 1,200 DNA letters (bases). It is now beyond doubt that these genetic differences account for a portion of the differences we see between individuals in how they think, feel and behave. This is termed ‘heritability’. Twin and DNA-based studies have robustly demonstrated that individual differences in educationally relevant traits such as time spent in education (Lee et al., 2018), general cognitive function (Davies et al., 2018) and even academic subjects studied (Rimfeld et al., 2016) are heritable. To illustrate the size of this genetic influence, a recent DNA-based study by Donati et al identified SNP-heritabilities ranging from 41-53% for performance in National Curriculum-based Standardised Assessment tests (SATs) of English, Maths and Science at 11 and 14 years of age (Donati et al, 2021).

Polygenic scores capture a portion of the heritability of educational traits

Let’s refer to a difference in a DNA base between individuals as a ‘genetic variant’. One key insight from recent large-scale genetic studies is that there are many thousands of common genetic variants that together contribute to the heritability of educational traits and outcomes. It transpires that even though each individual DNA variant makes a small contribution, they can be summed together into a single genetic ‘score’ that predicts a portion of the differences we observe or measure between people. This aggregate measure has been termed a ‘polygenic score’ (or polygenic index). To calculate a person’s polygenic score for a particular trait, you sum up the total number of risk-increasing and risk-decreasing variants found in their genome, each weighted by their magnitude of impact. The polygenic score for number of ‘years of education’ completed predicts around 11% of the variance in years of schooling in adolescents and adults (Lee et al., 2018). To put the size of this explanatory power into context, this is better than household income, although not quite as good as maternal education as a predictor of child educational attainment.

Studies measuring DNA variation directly and attempting to predict educational outcomes struggled for a long time because the signal they were trying to detect was so tiny. The studies had to include thousands and thousands of participants before statistically reliable links between DNA variation and years spent in education could be detected. Lee et al.‘s (2018) study involved 1.1 million participants. The same group of researchers last year pushed the number to over 3 million participants and now reported that they could predict up to 16% of differences in educational attainment from direct measures of DNA variation (Okbay et al., 2022).

Polygenic scores for early identification of individuals at risk

Polygenic scores are normally distributed in a population: some people will have a higher score relative to everyone else, while some people will have lower score, but most people will be average. In an out-of-sample prediction, 75% of individuals in the top 10% of the ‘years of schooling’ polygenic distribution go to university, as compared to 25% of individuals in the bottom 10% (Plomin & von Stumm, 2018).  Educational systems have limited resources, and these resources are currently targeted on interventions designed to support students who struggle. Given the same finite resources, low polygenic scores could be a mechanism for triggering in-person assessment or early (or more frequent) monitoring, before the emergence of overt problems. In principle, measures of DNA variation are available at birth.

The (current) challenges for polygenic prediction

Aside from the (very real) practical and ethical challenges of requiring genetic data for children, what are the key barriers for polygenic prediction of educational attainment?

Firstly, it is important to remember that polygenic scores indicate propensity, not inevitability. This is because they do not capture all genetic effects, and genetic effects will always be contingent upon the (home and school) environments in which we grow up. This means that many individuals born with a low polygenic score will still flourish academically. Conversely, individuals with very high polygenic scores may not perform well academically for other reasons, such as experiencing a large environmental risk or having genetic effects not captured by the polygenic score. Research to identify the full spectrum of genetic effects is ongoing (Ganna et al., 2016), but in parallel we need a better understanding of how polygenic effects vary as a function of the environment (Domingue et al., 2020).

Secondly, the studies on which polygenic scores are derived have been limited to populations with European genetic ancestries and the current Educational Attainment (EA) polygenic scores are not as accurate in its predictive abilities in non-European samples. This severely limits generalisability, and risks increasing economic and education disparities between European and non-European populations (Martin et al., 2019). To redress this imbalance culturally and ancestrally diverse genetic studies are a research priority, but the results will take time to feed through (Peterson et al., 2019).

Thirdly, polygenic scores for educational prediction will arguably remain of limited practical value until we know what the optimal environments are that will maximise genetic potential. For this, we need a much better understanding of how polygenic influences impact molecular, biological and neural processes to cause cognitive and behavioural differences between people. Important research is addressing this question (see Dreary et al., 2020; van der Meer & Kaufmann, 2022), but we are still some way off from having a good explanative account of polygenic effects.

“How does society want polygenic scores to be used in education? An analogy can be made with attainment-based selection and streaming in schools … but now we are dealing with a marker of academic potential rather than realised performance”

Finally, even if these challenges were overcome, a central question to ask is how does society want polygenic scores to be used in education? An analogy can be made with attainment-based selection and streaming in schools, the benefits of which continue to be debated (Rix & Ingham, 2021). The arguments are the same, but now we are one step removed and dealing with a marker of academic potential, rather than realised performance. For example, polygenic scores could theoretically be used to personalise educational provision and maximise every student’s educational potential. Alternatively, they could be used to focus resources and identify students deemed to have genetically endowed promise. The answer to this difficult – but important – question is not clear cut.

Future perspectives

So where does this leave us? Polygenic scores should not be ignored, but the hype (and concern) around them needs to be informed by what they can and cannot realistically deliver. Polygenic scores will never definitively predict complex educational outcomes, as heritability is not 100%. However, they do predict (statistically) meaningful differences in educational traits between individuals in a population, and this predictive power is likely to increase.

If their potential in educational settings is to be actualised, we need a clearer understanding of how they relate to, and can be integrated with, existing (non-genetic) measures of educational performance and potential. Only then can we progress in a way that ensures educational and social inequalities in the classroom are mitigated rather than exacerbated.

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If you are interested in these topics, see our recent CEN seminar discussing the book “The Genetic Lottery” by behavioural geneticist Kathryn Paige-Harden:

Citations

Davies, G., Lam, M., Harris, S.E. et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat Commun 9, 2098 (2018).

Deary, I.J., Cox, S.R. & Hill, W.D. Genetic variation, brain, and intelligence differences. Mol Psychiatry 27, 335–353 (2022).

Domingue, Benjamin W., Sam Trejo, Emma Armstrong Carter, and Elliot M. Tucker-Drob.
2020. “Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges.” Sociological Science 7: 465-486.

Donati, G., Dumontheil, I., Pain, O. et al. Evidence for specificity of polygenic contributions to attainment in English, maths and science during adolescence. Sci Rep 11, 3851 (2021).

Ganna A, Genovese G, Howrigan DP, Byrnes A, et al. (2016). Ultra-rare disruptive and damaging mutations influence educational attainment in the general population. Nat Neurosci. 2016 Dec;19(12):1563-1565. doi: 10.1038/nn.4404. Epub 2016 Oct 3. PMID: 27694993; PMCID: PMC5127781.

Lee, J.J., Wedow, R., Okbay, A. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet 50, 1112–1121 (2018).

Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019 Apr;51(4):584-591. doi: 10.1038/s41588-019-0379-x. Epub 2019 Mar 29. Erratum in: Nat Genet. 2021 May;53(5):763. PMID: 30926966; PMCID: PMC6563838.

Okbay A, Wu Y, Wang N, et al. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nat Genet. 2022 Apr;54(4):437-449. doi: 10.1038/s41588-022-01016-z.

Paige Harden, K. (2021). The Genetic Lottery: Why DNA Matters for Social Equality. Published by Princeton University Press 2021.

Peterson RE, Kuchenbaecker K, Walters RK, et al. (2019). Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations. Cell. 2019 Oct 17;179(3):589-603. doi: 10.1016/j.cell.2019.08.051. Epub 2019 Oct 10. PMID: 31607513; PMCID: PMC6939869.

Plomin R, von Stumm S. The new genetics of intelligence. Nat Rev Genet. 2018;19:148–59.

Rimfeld K, Ayorech Z, Dale PS, Kovas Y, Plomin R. Genetics affects choice of academic subjects as well as achievement. Sci Rep. 2016 Jun 16;6:26373. doi: 10.1038/srep26373. PMID: 27310577; PMCID: PMC4910524.

Rix, J., & Ingham, N. (2021).The impact of education selection according to notions of intelligence: A systematic literature review. International Journal of Educational Research Open
Volume 2, 2021, 100037.

van der Meer, D., Kaufmann, T. Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives. Transl Psychiatry 12, 447 (2022).

In this blog, we describe a new project beginning at the CEN looking at the evidence for what interventions work best for pupils with special educational needs and disabilities (SEND). The project is called MetaSENSE, because the full title is pretty long: Raising educational outcomes for pupils with special educational needs and disabilities: A meta-analysis and identifying barriers to change (MetaSENSE)

Why is the project important?

Although previous systematic reviews have examined what works for those with SEND^5,6,7, they have not considered the different tiers used in educational services⁸ and have not separated good quality teaching or universal instruction (Tier 1) from targeted interventions. Targeted interventions can be highly individualised (Tier 3) or not (Tier 2) but include evidence-based interventions or instruction (e.g., Lego Therapy or Colourful semantics) delivered by a trained adult who needs to adhere to the fidelity of the intervention. Targeted interventions are only prescribed to pupils who struggle beyond what can be provided within the regular classroom at classroom level. According to recent figures, this applies to 1,318,300 pupils. Pupils who are most likely to require targeted intervention support include those with Speech Language and Communication needs (23.4%), Moderate Learning Difficulties (22.8%), Social, Emotional and Mental Health needs (18.1)%), and Specific Learning Difficulty (14.9%).

What will the new project do?

The current study will synthesise evidence of what works to raise educational outcomes across different pupils with SEND aged 4 to 25 in a systematic review followed by a meta-analysis (phase 1). In addition to analysis of the quality of the evidence base, this meta-analysis will, for the first time, inform which interventions work best (i.e., largest effect sizes) in relation to different phases of education (preschool, primary, secondary, post-16) and different educational contexts (special vs mainstream) for each category of SEND needs. This will provide greater insight into whether support should be specific or can be generalised across different groups of SEND needs.

This information will be of use to teachers, SENCos, school leaders, and educational psychologists in terms of making provision more effective and cost-effective if provision can be used across different groups of SEND needs. Knowledge of what works for which groups of SEND needs and in which contexts also provides insight into cognitive mechanisms that are important to improving educational outcomes in different SEND groups and this will be of interest to academics and professionals who wish to develop new targeted interventions. As the systematic review will highlight gaps in the research evidence, this will set the future research agenda and be of interest to academics and research funding bodies.

In a second phase of the project, the team will carry out some in-depth interviews with educational professionals to dig into how they select different educational approaches to use, as well as the barriers that they face in implementing the most effective practices highlighted by the first phase.

What will the project produce?

The team will then put together a toolkit featuring a database that can inform practitioners about the evidence-base underpinning different interventions for pupils with SEND and which interventions to select in different context according to the pupils’ needs. The goal is to allow parents, educators, specialist professionals and policymakers to make evidence-informed decisions about how to raise educational outcomes for those with SEND in cost-effective ways and inform the future research agenda of academics and relevant funders.

Who’s on the team and who are our funders?

The project team includes several members of the CEN including: Dr Jo Van Herwegen (PI), Professor Chloe Marshall, Dr Rebecca Gordon and Professor Michael Thomas as well as Professor Julie Dockrell and Thomas Masterman.

The project has been funded by the Nuffield Foundation, but the views expressed are those of the authors and not necessarily the Foundation. Visit www.nuffieldfoundation.org or https://www.nuffieldfoundation.org/project/raising-educational-outcomes-for-pupils-with-sen-and-disabilities

All study materials including review, interview and coding protocols will be made accessible via the Open Science Framework.

You can find more detail about the project here.

References

1. DfE, June 2021: https://explore-education-statistics.service.gov.uk/find-statistics/special-educational-needs-in-england#releaseHeadlines-dataBlock-tables
2. Masters, G. N., et al. (2020). Ministerial Briefing Paper on Evidence of the Likely Impact on Educational Outcomes of Vulnerable Children Learning at Home during COVID-19. Australian Government Department of Education, Skills and Employment. https://research.acer.edu.au/learning_processes/24
3. Department for Education and Department of Health (2015). Special educational needs and disability code of practice: 0 to 25 years. Available at: https://www.gov.uk/government/publications/send-code-of-practice-0-to-25
4. Gini, S., et al. (2021). Neuromyths about neurodevelopmental disorders: Misconceptions by educators and the general public. Brain Mind and Education.
5. Davis, P. & Florian,. L. (2004). Teaching Strategies and Approaches for Pupils with Special Educational Needs: A Scoping Study. Brief No RB516 (London: DfES). Available online at: www.dfes.gov.uk/research/data/uploadfiles/RB516.doc
6. Carroll, J., et al. (2017). SEN support: A rapid evidence assessment. UK Government (Home Office). https://www.gov.uk/government/publications/special-educational-needs-support-in-schools-and-colleges
7. Cullen, M. A et al (2020). Special Educational Needs in Mainstream Schools: Evidence Review. London: Education Endowment Foundation. The report is available from: https://educationendowmentfoundation.org.uk/public/files/Publications/Send/EEF_SE ND_Evidence_Review.pdf
8. Ebbels, S.H., et al. (2019), Evidence-based pathways to intervention for children with language disorders. International Journal of Language & Communication Disorders, 54, 3-19. https://doi.org/10.1111/1460-6984.12387

The CEN received an enquiry from a parent whose 17-year-old daughter has autism and is preparing to move to university. The daughter is bright but has executive functioning difficulties in ‘not being productive’ and being ‘slow at everything’. Executive functioning is the technical term for processes of cognitive control, including attention, task selection, and planning. It also includes working memory: keeping information in mind and manipulating it to achieve current task goals. The parent enquired whether current research points towards any specific structured programmes designed to develop executive functioning skills that would benefit their daughter.

We asked Dr. Petri Partanen, one of the leading researchers in planning skills in children with learning difficulties, based at the Mid Sweden University, who offered the following advice.

“I will try my best to answer the question, considering interventions that can be managed at home and that might bring improvements in executive functions. This is general advice that might not be suitable in the specific case, since that would require more background information – particularly since there are many different cognitive profiles underlying the diagnosis of autistic spectrum disorder (ASD). As I have been working as a practitioner with children and youth with learning difficulties, I will also share some thoughts from that perspective.

To start with I would say that there is scarce evidence of specific methods for improving executive functions, including planning via training protocols implemented outside the school context, for children and youth with ASD.

I am hesitant to recommend working memory training, even though there are some studies with children and adolescents with ASD showing positive effects (see for example, this study by Weckstein et al., in 2017). The dilemma here is that such training regimes build on the idea of training abilities separated from the content and context. Thus, they require the child to process far transfer. Far transfer means when learned knowledge and skills are extended from the taught context to another dissimilar context. Far transfer still needs to be proven, in my humble opinion.

There are some pilot studies which indicate that combining such cognitive stimulus training programs with metacognitive strategy coaching might increase the effects of such interventions on executive functions (see for example this study by Macoun and colleagues published in 2020). Metacognitive training teaches children explicit strategies about how to apply their current knowledge to new situations. For example, in the aforementioned pilot study, 6-12 year old children with ASD were taught metacognitive strategies using a 5-step script: (1) identify the issue/difficulty, (2) state the reason for the issue/difficulty, (3) select and implement a strategy, (4) evaluate the outcome of the strategy, and (5) once a strategy works, celebrate success (i.e., provide positive reinforcement).

On the other hand, the CogMed working memory training program can be managed at home quite easily, and in combination with a raised metacognitive awareness it can stimulate the adolescent to apply cognitive functioning in different situations – for example through a discussion about important strategies that can be used in studying. Sometimes this discussion can be dealt with by parents, sometimes it has to be someone else, a counsellor or educational psychologist following this. I do think there is ASD support organised at universities in UK, which will be very important. In Sweden there are centers at each university, and I have followed several cases of adolescents with ASD that have been successful, so there are grounds for optimism.

I am particularly interested in interventions that help adolescents become metacognitively aware and help them to find good academic self-regulation strategies, and hopefully together with raised awareness among teachers, implement the study strategies.

As an experienced practitioner, I would say that this would be one of the important keys to success, and help the soon-to-be-adult to plan their studies, try out strategies that fit them, and develop some planning skills. I think finding opportunities as a parent to discuss these questions with the adolescent will be important. This could be very helpful for an adolescent taking the step to university studies, and clearly the adolescent besides challenges has a lot of cognitive resources and strengths.

If we instead look at intervention protocols addressing more specific subject skills for children and adolescents with ASD, there is much more promising research. Particularly, Self-Regulated Strategy Development (SRSD) is well-researched and includes planning facilitation in different subject areas like reading, writing, and mathematics (see, for example this systematic review of writing instruction by Asaro-Saddler published in 2016, and this meta-analysis on reading interventions by Sanders and colleagues in 2019). However, the SRSD protocol is meant to be implemented by teachers and not parents. These protocols still might inspire what to focus on in the support, even in the role as a parent.”