Tag Archives: Poverty

A Summary: Working Memory Differences between Children Living in Rural and Urban Poverty – Michele Tine

Working memory (WM) is the process by which we transiently hold and process information required for obtaining a short-term goal. It can be broadly considered to have two main components, verbal WM and visuospatial WM. WM is essential to everyday life, for example, it is used when remembering your friends’ order in a coffee shop or adding up the correct change to pay the man at the checkout counter. WM is essential to higher-processing and cognition including decision-making, learning, complex problem-solving, strategic thinking and sustained attention. In recent years, research has indicated that children from a low socioeconomic background display deficits in WM capacity when compared with children from a high socioeconomic background. (For further reading on this see: Fernald, Weber, Galasso and Ratisfandrihamanana, 2011; Noble, Norman and Farah, 2005.) With the knowledge that a lower WM capacity impacts on attention and learning this finding is of particular significance when considering children’s education. It has been shown that children with a low WM capacity can exhibit poor attention in class, poorer academic performance, learning difficulties and more difficult behaviours. (See our previous post for more insight into the relationship between WM and ADHD https://forgingconnectionsblog.wordpress.com/2015/02/05/adhd-and-low-working-memory-a-comparison/ ).

Given that a low socioeconomic background can impact on WM in children, it is important to explore why this occurs and which socioeconomic factors are the largest contributors. The following paper, by Michele Tine, (http://www.tandfonline.com/doi/full/10.1080/15248372.2013.797906#tabModule) considers how coming from a low-income background can impact on working memory (WM) in children whilst distinguishing between participants based on their ‘developmental context’ (settlement area), either rural or urban. Whilst it has been established that low-income children have deficits in verbal and visuospatial WM when compared with their high-income counterparts, this paper is the first, to my knowledge, that considers whether poverty is associated with a greater deficit in verbal or visuospatial WM and how settlement area influences this relationship.

Considering settlement area is a good way of determining if environmental factors mediate the relationship between low-income and decreased WM capacity. Whilst each settlement area will be unique in it is own way we can assume that some features are more prominent in one environment than another. For example, stress is thought to be a highly important factor when considering the impacts of socioeconomic background on an individual. Grouping via settlement area, takes into account exposure to different stressors in different environments.

Some of the differing stressors identified in the paper for those from a low-socioeconomic  background  include:

For an URBAN environment For a RURAL environment
Substandard and crowded housing Isolation from people
Excessive noise Isolation due to reduced access to technologies the internet and reduced computer ownership
Inadequate health care, education and inadequate health care Isolation from institutions and services
Increased crime rate Decreased social support*
Increased physiological disorders Less everyday visual stimulation
Increased divorce rates
Exposure to chronic aircraft noise

* Social support is a known buffer for stressors

Prior research has determined that the relationship between socioeconomic status and visuospatial WM is ‘fully mediated by allostatic load’, where allostatic load is the strain on the individual that accumulates over time with chronic stress. Thus, we would expect that the higher the amount of stress experienced, the greater the reduction in visuospatial WM capacity. On this basis we would perhaps expect there to be a difference in WM deficits once settlement area is considered as a result of exposure to differing stressors. Whilst the relationship between stress, socioeconomic status and visuospatial WM has been investigated, there has been little definitive research into how this compares to the relationship between stress, socioeconomic status and verbal WM.

 The individuals that participated in this study were recruited via their schools and their income measures (high or low) were determined by the following thresholds:


The individual attends a school that ‘serves a community with a median family income below the national median family income’. The school must have >75% of students qualified for free or reduced school meals and the individual themselves must qualify for free school meals.


The individual attends a school that ‘serves a community with a median family income above the national median family income’. The school must have <25% of students qualified for free or reduced school meals and the individual must not qualify for reduced or free school meals.

To determine if an area was rural or urban The US Census Bureau (2010) and McCracken and Barcina’s (1991) work was utilised along with population per county and the average number of students per class in the county.

With these thresholds, four groups of individuals could be defined:

1) Urban and high-income

2) Rural and high-income

3) Urban and low-income

4) Rural and low-income

There were no significant differences in gender between the four groups but there was, however, a significant difference in ethnicity distribution across the groups.

Each individual completed four WM tasks from the Automated Working Memory Assessment battery. Two of the subtests measured verbal WM and two measured visuospatial WM.  As expected, the low-income children were found to have significantly lower composite WM percentile scores than the high-income students and there were no differences between the urban high-income and the rural high-income groups. The urban low-income group had significantly lower WM scores than both the high-income groups and the rural low-income group showed the same trend, having significantly lower WM scores than both of the high-income groups.

However, a comparison of the two low-income groups showed that they had differing WM profiles. The rural low-income group had a significantly higher verbal WM profile than the urban low-income group but also a significantly lower visuospatial WM profile than the urban low-income group. The urban low-income group showed no significant differences across the verbal and visuospatial WM scores, whilst the rural low-income group had significantly different verbal and visuospatial WM scores.

Based on past literature, Tine speculates that the differences in verbal WM between the two low-income groups may be accounted for by chronic noise pollution amongst the low-income urban population, for example through living close to airports and train tracks. However, the reason for this difference and for the decreased visuospatial WM capacity in the rural low-income group needs further investigation before any conclusion can be drawn.

Whilst this paper presents solid and much needed research into how income and settlement area/developmental context impacts on WM capacity in children, the author states that caution must be taken in inferring any causal relationships from studies such as this one. Due to a lack of information on the participants’ parents, although unlikely, it is possible that ‘low-income parents (and in turn, children) with particularly weak verbal WM abilities tend to seek out urban low-income areas, or on the contrary, low-income parents (and children) with relatively weak visuospatial WM capacity self-select into rural low-income areas.

Additionally, as previously mentioned, there was a significant difference in racial distribution across the groups and thus it cannot be ruled out that some of the differences seen in the WM profiles are a result of cultural differences and the ‘experience of race-based stereotype threat’ which has been shown to reduce WM capacity (Schmader and Johns, 2003). The study also does not consider language ability amongst the populations or the proportion of non-native English speakers, which is also known to be related to WM ability. Whilst the insights this paper provides are unique, the research could be furthered still by taking into account factors other than income and developmental context, for example, maternal education, parental employment or family structure, other well recognised and substantial components of what it means to be from a low-socioeconomic background.

So, in conclusion, whist we cannot assume causality from the findings, this paper presents, in my opinion, a very good first step into developing a much deeper understanding of the impact of SES on cognitive ability in children.  The hope is that this kind of research will eventually allow us to develop a successful intervention to aid those children from disadvantaged backgrounds so that they do not experience the cognitive deficits and disadvantages in education that their high-socioeconomic peers evade.

The full paper can be accessed here: http://www.tandfonline.com/doi/full/10.1080/15248372.2013.797906#tabModule)


Noble, K.G. Norman, M.F. and Farah, M.J. (2005) Neurocognitive correlates of socioeconomic status in kindergarten children. Developmental Science, 8, 74-87.

Fernald, L.C.H. Weber, A. Galasso, E. and Ratisfandrihamanana, L. (2011) Socioeconomic gradients and child development in a very low income population: Evidence from Madagascar. Developmental Science, 14, 832-847.

McCracken, J.D. and Barcinas, J.D. (1991) Differences between rural and urban schools, student characteristics, and student aspirations in Ohio. Journal or Research in Rural Education, 7, 29-40.

Schmader, T. and Johns, M. (2003) Converging evidence that stereotype threat reduces working memory capacity. Journal of Personality and Social Psychology, 85, 440-452.

Tools of the Mind: An Effective Intervention for High Poverty Schools?

Early childhood interventions are believed to be a key step towards ending the cycle of poverty. This belief is based upon the large evidence base that demonstrates that good childhood development (measured in various ways) is highly predictive of a large variety of positive outcomes later in life.  If we give children the emotional, social and cognitive support they need in their early years it is hoped that this will make a lasting improvement that persists for the rest of their lives and halt the transmission of negative social problems from one generation to the next.

However, high quality intervention studies are rare, making it difficult to know which type of approach will work (if any).  The lack of research is unsurprising due to the fact that this kind of study is very hard to do well. They usually require a huge investment in time, money and effort. Furthermore, they involve a number of complex factors that might affect both the validity and applicability of the results, such as individual teaching style and child demographics.

This is what makes the recent study into the effectiveness of Tools of the Mind [1], an early intervention programme, by Drs Blair and Raver from New York University [2] a particularly interesting research piece. They argue that children receiving the Tools of the Mind program showed a significant improvement in learning in comparison to children in typical kindergarten classrooms. Importantly, the authors claim that some of these benefits continue into the first grade, after the program had finished, and that many of these effects were stronger in high-poverty schools.

In this post, I will be putting Tools of the Mind to the test, loosely using Dorothy Bishop’s framework for identifying red flags in interventions (It’s a good read. For those interested see [3]). So, what is Tools of the Mind? Is it credible? How solid is the scientific evidence behind it? Does it really work and, if so, are the effects worth the effort and cost involved?

What is Tools of the Mind?

Tools of the Mind is an educational program, developed over 18 years and now used in prekindergartens and kindergartens across the USA and Canada. I will be focussing on the kindergarten program here (that’s ‘reception’ for the British). It is based on the Vygotskian approach: the idea that it is important to teach children to master ‘mental tools’ such as attention and emotion-regulation skills that promote intentional and self-regulated learning. This is expected to develop their executive functions, social and emotional competence at a greater rate. In practice, it involves 60 or more Vygotskian-based activities including activities that require children to create their own learning plans, reflect on their learning and work in pairs with a strong focus on intentional make-believe play tied to stories and literature. Tools of the Mind was named an exemplary education program by the International Bureau of Education at UNESCO in 2001. Maybe this sounds marvellous, but does it actually work?

Who is behind the program and what are their credentials?

The program has largely been developed by Drs Bodrova and Leong. Encouragingly, both appear to be experts in the field. Dr Elena Bodrova was, until recently, Principal Researcher at Mid-continent Research for Education and Learning, a non-profit, non-partisan education research and development corporation, and Dr Deborah Leong is the Professor Emerita of Psychology, Metropolitan State College of Denver. They’ve both written a number of papers, articles and books, and, to the best of my knowledge, have each authored 10 papers in peer review journals in topics related to the Tools of the Mind program. I can’t find any red flags here!

Is there credible science behind the program?

The program claims to improve academic achievement and socio-emotional skills through improving executive functions, and in particular, the ability to self- regulate. Executive functions encompass many different processes involved in the management of cognition and actions: the ability to pay attention to and remember relevant details, to plan, to solve problems strategically and to regulate emotions and behaviour successfully. If you think of your brain as an orchestra, executive functions would be the conductor,  organizing many different instruments to play together as a coherent whole, bringing some in and fading others outand changing the pace and intensity of the music. Indeed, they seem to be involved in pretty much every higher-order cognitive process, calling into question whether the idea of executive functions is too vague and general a concept to be of any use and they are sometimes seen as a controversial topic amongst cognitive scientists.

Putting this issue aside for now, performance on tasks purporting to tap executive functions are excellent predictors of educational progress. More and more research is pointing to the development of these as being one of the most important domains in early childhood for positive short and long-term outcomes. The argument goes something like this: in the same way that a bad conductor is likely to produce dreadful music, no matter how excellent the individual musicians are, poor development of executive function is a very strong predictor of poor social and academic outcomes even if the rest of the cognitive system is well developed. Great claims are made about the extent to which these skills can be boosted by intervention. When you combine this with the fact that growing up in a deprived environment is frequently found to have a profound negative effect on executive functions, it seems a credible and good target for a program like Tools of The Mind.

The approach used to develop executive functions is based on the relatively well known and researched Vygotskian Approach. Unfortunately, I don’t have time and space to review this here but the Tools of the Mind website provides a lot of information on the science behind this.

Is there evidence that the intervention is effective from controlled trials?

Blair and Raver provide the first cluster randomized controlled study into the effectiveness of the Tools of the Mind curriculum in comparison to current practices in kindergartens. They looked at its effects on a range of different cognitive and academic skills. During the two year study, an impressive 795 children took part from 79 different classrooms and 29 schools. The schools were randomly assigned to either the control group who simply continued business as usual or the treatment group who received training to implement the Tools for the Mind programme in their schools. Children were tested in the first term of kindergarten, with follow up tests at a mean of 5 months and 1 year later. It’s questionable as to whether this can be considered an active control group, given that there may have been a potential bias for teachers receiving the new and novel Tools training to expect better results in comparison to those that continued as before, but overall I think this is quite a good study design.

They found that children in the Tools of the Mind classrooms were significantly better at keeping information in their working memory (effect size, ES = 0.14), maintaining attention in the face of distractions (ES = 0.12) and processing information (ES = 0.08) at the first follow up. This was accompanied by a greater rate in academic improvements in mathematics (ES = 0.13), vocabulary (ES not given) and reading (ES = 0.07), in comparison to the control classrooms (although this was only conventionally significant for mathematics). The faster rate of improvement in reading (Figure 1, ES = 0.14) and vocabulary (ES = 0.1) extended into the first grade, becoming significant, after the program had finished, suggesting that it has long term effects.

Most of the effects seen were stronger in high-poverty schools where more than 75% of the pupils are eligible for free or reduced-price lunch. In particular, the effect of Tools of the Mind in comparison to the control group was significant in tests that measured the child’s ability to maintain attention despite emotionally arousing distractors (ES = 0.82), fluid IQ (the ability to solve problems in novel situations, ES = 0.46) and vocabulary (ES = 0.43) in high poverty schools.

Whilst this is the only study of the kindergarten program, it should be noted that both the National Institute for early education research (NIEER) and the Peabody research institute (PRI) have been investigating the pre-kindergarten program. Despite the fact that both studies have included only high-poverty classrooms (around 80%+ children receiving free or reduced price lunch) their results appear to have some substantial disagreements. In one study, NIEER compared 88 children in Tools classrooms with 122 children receiving the district’s balanced literacy curriculum. They found that by the end of the first year, children in the Tools classroom had significantly better classroom experiences, far fewer behavioural problems (an indicator of self-regulation) and some improvement in language performance, although not significant after correcting for multiple comparisons, in comparison to children in the control group. They found no improvement in reading or mathematics. In a subsequent study of the same program but with a slightly different sample (85 children in Tools, 62 in the control classrooms) they found that children in the Tools classrooms performed significantly better in tests of executive functions (stoop battery and flanker tasks) in comparison to children in the control classrooms and that this difference was greater, the more taxing the task.

In contrast, PRI have been conducting a much larger study comparing 498 pupils receiving the Tools curriculum with 379 pupils receiving a range of curricula that would normally be found in pre-school classrooms. They found that the Tools curriculum had no significant effect on direct assessments of achievement or executive function or any teacher ratings of language, executive function, or social behaviour by the end of pre-kindergarten in comparison to the control classrooms. Remarkably, when the children were assessed a year later at the end of kindergarten, they found that the comparison children that had received the normal curricula actually had significantly greater gains in achievement and executive function composite scores and on many of their subtests in comparison to those that received Tools of the Mind!

Are these effects worth the effort, time and cost involved?

Being subjective, this isn’t an easy question to answer. Note that the Tools curriculum can’t simply be taken off the shelf and implemented as other curricula might be: it requires about two years of teacher training including in-classroom coaching and a shift in the teacher’s role in the classroom. However, Blair and Raver appear to believe that: ‘teachers received typical levels of training and implemented the curriculum with materials that are well within the budget of the average kindergarten classroom. Information about the actual cost is difficult to find but in general sources place it at £5000 – £7000 per classroom, which seems reasonable [4, 5] and the materials required appear to be simple, inexpensive and readily available.

However, I think the key to answering this question is to consider the effect sizes produced by the program, the number of standard deviations between the mean score of the control and target groups. In general, the effect sizes found for the kindergarten version of Tools of the Mind in Blair and Raver’s study are relatively low: they all hover around the 0.1 mark, 5 months into the program. This is around half the average effect size for childhood interventions in 2006 found by Duncan et al. [6]. In addition, whilst the effects in high poverty schools look much more promising, it should be noted that the confidence intervals are also much larger for these effects.

To get a feel for what this means, we can get a rough estimate for how this effect size translates into months of development. Bloom et al. [7] found that children progress with an effect size of 1.52 in reading and 1.14 in maths over the first year of schooling. Averaging this and dividing by 12 gives as a rough expected progress estimate of 0.1 per month. This means that many of the effects of Tools of the Mind put children only one month ahead of children not receiving the program.  However, when considering vocabulary in high poverty schools, for example, with an effect size of 0.46, this offers nearly 5 months advantage. Of course, this analysis is something of an oversimplification, but it provides a little context as to whether these effect sizes are meaningful.

How do these effect sizes compare to differences between children coming from disadvantaged and advantaged children?  Whilst it is difficult to put numbers on these differences as many researchers use different measures of what counts  as ‘advantaged’ and ‘disadvantaged’, in order to get an idea of this, I’ll compare the ES to a few studies that found differences within the usual range. I’ll steer clear of the issues in measuring something as vague of ‘executive functions’ and concentrate on one aspect of them, working memory. Noble et al [8] found a difference of 0.31 SD between high and low SES children in the first year of school. Given that Tools of the Mind was found to improve working memory by 0.14, this would be the equivalent of closing half the income related gap. Vocabulary also shows promising results. A study comparing children receiving free school meals (FSM) and those that didn’t at the start of school found a gap of 0.62 SD between the groups [9]. The 0.46 ES for vocabulary in high poverty schools would close this gap by three quarters. Unfortunately, they don’t report what this figure was in high poverty schools one year later, which would be a particularly interesting result. Reading and maths are less promising however. The same study found gaps of 0.69 for reading and 0.68 for mathematics between children receiving FSM and those that didn’t. The ES of 0.13 in maths and 0.14 in reading won’t have much effect on closing this gap, which is a shame, because arguably these are the more valuable educational skills.

In conclusion, Tools of the Mind appears to be a relatively well-grounded intervention program. A random controlled study showed that Tools of the Mind improved children’s executive functions and academic skills in comparison to normal kindergarten classrooms. Despite not being one of the most effective interventions available, the costs, effort and time required seem reasonable and the results suggest that some of the benefits of the program are long term. It holds particular potential for high poverty schools, where the effects of the program appear to go some way to closing income related achievement gaps. However, it is questionable whether the control group used can be considered a full active control and it would perhaps be better to see these results replicated in a study that used a similarly new and novel curriculum or simply used just part of the Tools curriculum as the control group. In addition, results from studies of the pre-kindergarten program by two highly distinguished research bodies are inconclusive with a large study indicating that children receiving Tools of The Mind were at a disadvantage in comparison to normal curricula at the start of school. With this in mind, and the fact that we only have one study investigating the kindergarten program, I would suggest that more research needs to be done to establish the overall effectiveness of this curriculum (and ideally to identify which aspects of the Tools have the greatest effect) before we can advocate it as an effective intervention for kindergarten classes in high poverty schools.

[1] http://www.toolsofthemind.org/

[2] The full paper by Blair and Raver can be found here: http://dx.plos.org/10.1371/journal.pone.0112393

[3] http://deevybee.blogspot.co.uk/2012/02/neuroscientific-interventions-for.html

[4] http://www.washingtonpost.com/local/education/dc-school-reform-targets-early-lessons/2011/11/04/gIQAGZ2VCN_story.html

[5] http://economicdiscipleship.com/2010/12/23/profile-tools-of-the-mind/

[6] https://socialinnovation.usc.edu/files/2014/03/Duncan-Two-Policies-to-Boost-School-Readiness.pdf

[7] http://www.mdrc.org/sites/default/files/full_473.pdf

[8] http://onlinelibrary.wiley.com/doi/10.1111/j.1467-7687.2005.00394.x/pdf

[9] http://www.scotland.gov.uk/Publications/2005/02/20634/51605

State of the Nation Report: Linking Research to Policy

We have recently started a new project focused around children that are considered to be “at risk” of poor educational attainment due to their socioeconomic status. Indexes of socioeconomic status include complex measures of interrelated components, with one key component being family income. As a result, socioeconomic status gives an index of poverty that incorporates other forms of deprivation. There is, at present, an increasing effort to provide better links between research on socioeconomic status, its impact on cognition, and government policy. However, when considering educational attainment, there is still little being done to support children from a lower socioeconomic background. The recently released State of the Nation Report [1] indicates the need for further research into this area. It calls for increased interest and action in order to help close the performance gap between children from different socioeconomic backgrounds and reduce the effects of poverty on children.

Whilst the UK is beginning to recover from the economic crisis, social recovery is lagging behind. For example, a family is described as being in relative poverty when the level of family income is less than 60% of the median UK family income. The State of the Nation report from the Social Mobility and Child Poverty Commission states that, before consideration for housing costs, 17% of children in the UK are living in in a state of relative poverty. After housing costs, 27% are living in relative poverty – that is 3.7 million children. Additionally, the report gives values for absolute child poverty, which are also still worryingly high despite government interventions. The House of Commons Scottish Affairs Committee defined absolute poverty as “the lack of sufficient resources with which to keep body and soul together”. [2]

The State of the Nation Report calls for the UK government to re-think their existing approach to child poverty if they wish to achieve the targets set out in the “2020 challenge” to “reduce child poverty by half and prevent Britain becoming a permanently divided society.” The knock-on implications of poverty on educational attainment are well noted in the scientific literature. In 2013, 38.5% of children receiving free school meals, which is a commonly used indicator for poverty, achieved an A*- C grade in Maths and English, compared to 65.3% of children not receiving free school meals. In addition, research published by the Social Mobility and Child Poverty Commission has shown that children from deprived backgrounds that are considered to be high achieving aged 7 fall behind children from the most affluent backgrounds that were considered to be low achieving at the age of 7. This crossing over in attainment levels is seen by 14-16 years of age (see Figure 1) [3]. This pattern of crossing over indicates that the environmental components of socioeconomic status have an impact on a child’s potential for academic attainment above and beyond genetic influences. Since 2005, the attainment gap in education between children from a low socioeconomic background and children from a high socioeconomic background has only closed by 1.6%. The implication is that those children with a low level of educational attainment are 4 times more likely to remain in poverty.

Figure 1. Trajectories from key stage 1 to key stage 4 by early achievement (defined using key stage 1 writing) for the most deprived and least deprived quintiles of socioeconomic status (state school only)SES attainment crossover_KS1 writing

It is well known that working protects against poverty. Children in working households are only a third as likely to suffer from poverty as those in workless households. However, the report also highlights that working is simply not enough to prevent childhood poverty. In 2012/2013 62% of children deemed to be living in poverty lived in a household where at least one person worked. And perhaps most concerning is that if trends continue, “2010-2020 is set to be the first decade with a rise in absolute poverty since records began in the early 1960s.”

With all this considered, further research is needed to provide a clearer picture of how socioeconomic status influences cognition and education. A key focus of this should involve identification of the factors within socioeconomic status and cognition that mark children as being more susceptible to the risk of poor educational attainment. In turn, greater resources need to be devoted to identifying the most effective interventions. These studies are difficult and complicated to conduct, but as The State of the Nation Report identifies, improving the educational prospects of children growing up in poverty provides one of the best mechanisms for creating a more equal society.

In conclusion, although the issues surrounding the socioeconomic impact on education have in part been researched and evaluated, there is yet to be a thorough exploration of all the factors involved.  Further investigation is needed to identify which cognitive, neural and environmental measures provide the most prominent markers of risk and resilience for children growing up in poverty. Research along these lines is sorely needed if government policy is to target those most in need of support.

[1] https://www.gov.uk/government/publications/state-of-the-nation-2014-report

[2] http://www.bbc.co.uk/news/uk-politics-29686628