Lindsay Clements – Education & Teacher Conferences

About Lindsay Clements

Lindsay is a researcher and Ph.D student in Boston College's Applied Developmental and Educational Psychology department where she studies early childhood development. As a member of the national ‘DREME’ research team, she focuses on the role that parents and teachers play in early math and executive function learning. In 2012, she earned her Ed.M in Mind, Brain, and Education from Harvard University. Lindsay has worked and taught in both public and private schools and, most recently, she spent three years at Punahou School in Honolulu where she created and led the middle school learning support program. In her spare time, Lindsay enjoys walking around Boston finding delicious foods and friendly dogs.

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Lindsay Clements

October-21-2017

Hands-on and Hands-off Learning

When we walk into a classroom, especially an early learning or elementary school one, manipulatives are almost always within reach. Look to your left, and notice the group of children spinning the hands on a pretend clock, trying to figure out what 6:30 should look like. Glance to you right, and watch the students sort pretend money into the dollar slots of a dinging cash register. And peer over your shoulder, as students use square, circle, and triangle magnets to create geometric worlds on a magnetic easel.

In a previous article, I discussed some of the cognitive research on problem-solving and decision-making. And while that piece focuses primarily on how conscious and unconscious thoughts make sense of questions and choices, this article turns to another important aspect of problem-solving: classroom manipulatives.

How do physical objects help us make sense of questions and concepts?

Manipulatives in Mathematics

Manipulatives are a type of symbol that can take nearly any form. One of the most common types of manipulatives that we may come across are base-10 blocks; small foam squares that can be combined and separated to help students understand basic math concepts (e.g., addition). Other common manipulatives in the classroom include pretend money, model buildings, and modeling clay.

Now, fiddling with manipulatives can be pretty enjoyable; but, as a learning tool, they come with a fair amount of controversy. This is especially so with mathematics manipulatives.

The more traditional school of thought tends to suggest that manipulatives help children learn math by reducing the abstractness of math problems. [1] They do this by substituting mathematical symbols with concrete objects. For example, the symbolic character “3” can be represented with three blocks. And if you toss in another three blocks, you’ve represented both the concept of addition and “6”.

But, more recent arguments have asserted that manipulatives can only really promote mathematics learning when teachers assist children in understanding the symbolic relation between physical objects and the math concepts they represent. The dual-representation hypothesis posits that when children perceive manipulatives as only being objects (e.g., a single base-10 block as just a squishy square), it is challenging to understand their relation to the mathematical expression they represent (e.g., the number one).

Style vs. Substance

One study that demonstrated just how tricky manipulatives can be investigated the ways in which elementary school students used pretend money when solving math word problems. [2]

First, fourth, and sixth grade students were asked to complete ten world problems that involved money. Half of the participating students received manipulatives: realistic bills and coins along with the suggestion that these materials could be used to help solve the problems. The other half of the students did not receive any manipulatives.

At all grades, the students who did not have access to the manipulatives performed better on the word problems than the students who did. Access to the pretend money actually appeared to interfere with students’ accuracy.

But why?

In a second experiment, fifth grade students were asked to complete ten more word problems. This time, the students were assigned to one of three manipulatives conditions:

realistic, perceptually rich bills and coins
bland bills and coins
no physical manipulatives

The students were also asked to show their work on their answer sheets. This allowed the researchers to analyze students’ incorrect answers to determine whether they made conceptual or computational errors.

The researchers found that the students who used the perceptually rich pretend money made more errors than both the children who used the bland money and the children who did not use manipulatives.

The students who used the bland money performed at the same level as the students who had no access to the manipulatives.

Further, when analyzing the pattern of errors made by students in each condition, it appeared that strategy selection was influenced by the students’ access to the perceptually rich money. Compared to the students in the other two conditions, students in the perceptually rich condition were more likely to select a particular strategy (such as multiplication or division) that often resulted in an incorrect answer.

However, even though these students made more errors overall, their written work indicated that their conceptual understanding of the word problems was the strongest of the three groups.

Thus, there appears to be somewhat of a trade-off when using manipulatives. While these materials can help students relate their learning to real-world experiences, as well as promote conceptual understandings, perceptually rich manipulatives may distract children–and that distraction ultimately results in computational errors.

Two Sides to Every Coin

Interestingly, although research suggests that physical manipulatives can be distracting in a not-so-good way, it also seems that symbols can sometimes distract in a not-so-bad way.

This finding has been shown in preschoolers who participate in the Less is More task. In this tricky game, children must point to a small tray holding two candies in order to receive a larger tray with five candies. To succeed, children must inhibit their urge to point to the tray with more candies on it when asked which one they would like.

Given that young children generally have difficulty inhibiting themselves under such conditions, one study asked whether variations of the Less is More task might reduce the affective component of the game through symbolic distancing. [3] That is, would three year olds’ performance on the task improve if the large and small quantities of candy were represented by something else?

Children were randomly assigned to one of four conditions:

the traditional representation of smaller and larger quantities of candies (real treats)
rocks representing the candies, with children shown one-to-one correspondence between the rocks and candies (i.e., if children chose the tray with two rocks, they got five candies)
arrays of dots to represent the candies without one-to-one correspondence (i.e., one set of dots was larger than the other, but the number of dots was not the same as the number of candies)
one picture of mice and one picture of an elephant to represent small and large rewards, respectively

It turned out that the preschoolers’ performance on the mouse/elephant condition was significantly better than on the real treat condition. In other words, children more often pointed to the mice (small symbol) in order to get the elephant (large reward) than they did the two candies (small quantity) in order to get the five candies (large quantity).

Performance on both the mouse/elephant condition and the dots condition were significantly better than the real-treat and rock conditions. It appears, then, that the use of symbols can also distract in a helpful way. In particular, symbols with greater psychological distance from their referent (i.e., the mouse and elephant seem less related to the candies than the one-to-one corresponding rocks do) can reduce the emotional component of the Less is More task.

With this buffer from the emotional temptation of the larger tray of candies, children seem better able to inhibit their instinct to point to it.

Use ‘Em or Lose ‘Em

Despite the controversy that surrounds manipulatives and symbolic reasoning, most researchers seem to agree that there is a time and a place for each. And, most certainly, each has its own learning curve.

In order for manipulatives to be beneficial, researchers generally suggest that teachers:

a) should strive to explicitly connect the manipulatives to the concepts they represent; and

b) should select objects that easily allow children to understand their relation to concepts.

For example, the best math manipulatives tend to be objects that are only used for math learning (e.g., base-10 blocks); are not particularly interesting or familiar; and possess an internal structure that explicitly represents the relevant math concept.

But, when aiming to distract from emotionally-charged situations, symbols that seem unrelated to the emotionally charged object or event generally set students (especially young children) up for success.

References:

[1] Uttal, D. H., Scudder, K. V., & DeLoache, J. S. (1997). Manipulatives as symbols: A new perspective on the use of concrete objects to teach mathematics. Journal of Applied Developmental Psychology, 18, 37-54.

[2] McNeil, N. M., Uttal, D. H., Jarvin, L., & Sternberg, R. J. (2009). Should you show me the money? Concrete objects both hurt and help performance on math problems. Learning and Instruction, 19, 171-184.

[3] Carlson, S. M., Davis, A. C., Leach, J. G. (2005). Less is more: Executive function and symbolic representation in preschool children. Psychological Science, 16, 609-616.

Decisions, Decisions: Helping Students with Complex Reasoning

Lindsay Clements

September-23-2017

Decisions, Decisions: Helping Students with Complex Reasoning

Most of us have heard the adage about the two ways that someone can get into a swimming pool: jump right in, or enter slowly to acclimate to the temperature a few inches at a time.

Most of us have probably also witnessed (or experienced) the varied ways that someone might approach an assignment: one could start and finish it right away; work on it in small chunks over an extended period of time; or wait until the last moment to start, likely rushing to finish.

And for those that are keeping an eye on back-to-school sales events, there are of course different ways to shop: one could impulse purchase an item, or do some research beforehand to get the best possible deal.

The common thread in all of those scenarios is that different methods, strategies, and thought processes can be employed to solve problems or complete tasks. And each has its own time and place. So how do we decide exactly which ones to use in a given situation?

Algorithms and heuristics

The science behind problem solving and decision-making comprises a robust portion of cognitive research and involves the study of both conscious and unconscious thought.

Overall, there are two primary ways that a problem can be tackled: with algorithms or with heuristics. [1] An algorithmic approach refers to a series of steps that are more or less guaranteed to yield the solution. While this approach is most easily thought of in the context of mathematics (e.g., following a mathematical formula), an algorithmic approach also refers to such procedures as following a recipe or backtracking your steps to find a lost object.

Heuristics, on the other hand, are associative strategies that don’t necessarily lead to a solution, but are generally pretty successful in getting you there. These include conscious strategies (such as solving a maze by making sure your path stays in the general direction of the end point) and unconscious strategies (such as emotional instincts). Because heuristics are more subjective and less systematic than an algorithmic approach, they tend to be more prone to error.

In the classroom, solving problems with an algorithmic approach is fairly straight-forward: students can learn the needed procedural steps for a task and identify any places where they might have gone wrong, such as a miscalculation or a typo.

Heuristics are more complicated, however, and much of the research on problem solving aims to understand how children and adults solve problems in complex, confusing, or murky situations. One question of particular interest involves transfer: how do children apply, or transfer, their knowledge and skills from one problem-solving scenario to another?

Six of one, half-dozen of the other

Research suggests that students tend to have trouble transferring knowledge between problems that share only the same deep structure. For example, two puzzles that can be solved with the same logic, but that have different numbers, settings, or characters, are tricky.

In contrast, problems that share both their deep structure and shallow structure can be solved with relative ease.

A seminal study that illustrates the challenges of transfer asked students to solve the Radiation Dilemma: a medical puzzle of how to destroy a tumor with laser beams. [2] Some of the students were first told to read The General: a puzzle (and its solution) based on the common military strategy of surrounding an enemy and attacking from all sides. The solution to the Radiation Dilemma was analogous to the solution for The General: radiation beams should target the tumor from all sides until destroyed.

The researchers found that the students who first read the solution to The General successfully solved the Radiation Dilemma more often than those who did not.

However, students who received a hint that the solution to The General problem would help them solve the Radiation Problem were actually more successful in solving it than those who read both problems but received no hint.

This finding suggests that analogies can certainly be a helpful guide when children (or adults) are trying to make sense of a problem or find similarities between different contexts. But, they can also be confusing. Presumably, people become distracted by or hyper-focused on shallow structural features (e.g., reading the Radiation Dilemma and trying to remember what medical strategy was used on a TV drama) and thus overlook the deep structure similarities that are present.

So, when we ask students to make connections between two problems, scenarios, or stories that have surface-level differences, a little hint may just go a long way.

The less the merrier?

In addition to better understanding how to make decisions or think about problems, researchers also aim to understand how much we should think about them. And, contrary to popular thought, it appears that reasoned and evaluative thinking may not always be best.

In fact, there is evidence for the deliberation-without-attention effect: some problem-solving situations seem to benefit more from unconscious cognitive processing. To investigate this, scholars at the University of Amsterdam set out to determine whether better decisions result from unconscious or conscious thought. [3]

In their experiment:

participants (college students) read information about four hypothetical cars
the descriptions of the cars were either simple (four features of the car were listed) or complex (12 features were listed)
some of the features were positive and some were negative; the “best” car had the highest ratio of positive-to-negative features
four minutes passed between participants reading about the cars and being asked to choose the best one
some participants spent those four minutes thinking about the cars, while the others were given a puzzle to solve in order to distract them from such thinking

When asked to choose the “best” car, two groups stood out:

Group A: participants that (1) read the simple car description and (2) consciously thought about the cars were more likely to identify the best car than those who read the simple description and then worked on the puzzle
Group B: participants who: (1) read the most complex car descriptions and (2) were then distracted by the puzzle were more likely to identify the best car than those who read the complex description and consciously thought about the car options

The participants in Group B actually had a higher overall success rate than those in Group A.

Thus, it appeared that conscious thinkers made the best choices with simple conditions, but did not perform as well with complex circumstances. In contrast, the unconscious thinkers performed best with complex circumstances, but performed more poorly with simple ones.

Buyer’s Remorse

Of course, the cars that the participants evaluated were fictional. The researchers therefore wanted to see if their results would hold up in similar real-word circumstances. They traveled to two stores: IKEA (a complex store, because it sells furniture) and a department store (a simple store, because it sells a wide range of smaller items, such as kitchen accessories).

As shoppers were leaving the store with their purchases, the researchers asked them:

What did you buy?
How expensive was it?
Did you know about the product before you purchased it?
How much did you think about the product between seeing it and buying it?

The researchers then divided the shoppers into two groups: (1) conscious and (2) unconscious thinkers, based on amount of time they reportedly spent thinking about their purchased items.

After a few weeks, the researchers called the shoppers at home and asked them about their satisfaction with their purchases. In a similar vein to the first experiment, here the conscious thinkers reported more satisfaction for simple products (department store) and the unconscious thinkers reported more satisfaction for complex products (IKEA).

Thus, these experiments indicate that conscious thinking is linked to higher satisfaction with decisions when conditions are simple (less to evaluate), whereas unconscious thinking leads to higher satisfaction when conditions are complex (many factors to evaluate).

Why don’t you sleep on it

While these studies are only a snapshot of the problem-solving and decision-making research field, they offer some valuable thoughts for how we can support students in the classroom.

First, we know that students need to understand problems in order to solve them. It is likely a good habit to continually remind ourselves that our students do not all make sense of the same problems in the same way or at the same rate. Thus, as we saw in The General, when we offer students problem guides, strategies, or templates, a little nudge as to how to use them can be enormously beneficial.

Second, we often push our students to think deeply and critically about problems and context. And that is probably true now that, more than ever, thoughtful, evidence-based, and logical reasoning is critical for tackling both local and global issues.

But there is also much to be said about instinct, conscience, and whatever it is that goes on in our subconscious. So if we see our students dwelling on a problem, or sweating a decision, the best way that we can help them delve into a solution may just be to first have them step away for a little while.

References:

[1] Novick, L., & Bassok, M. (2006). Problem solving. In K. Holyoak & R. Morrison (Eds.), The Cambridge Handbook of Thinking and Reasoning (pp. 321-349). London: Cambridge University Press.

[2] Gick, M. & Holyoak, K. (1980). Analogical problem solving. Cognitive Psychology 12(3), 306-355.

[3] Dijksterhuis, A., Bos, M., Nordgren, L., & van Baaren, R. (2006). On making the right choice: The deliberation-without-attention effect. Science, 311, 1005-1007.

Lindsay Clements

July-10-2017

A Tale of Two Analyses

For researchers and research-readers alike, the data analysis portion of a study is many things: complex, exciting, frustrating, intriguing, and sometimes even befuddling.

And, analytics are always on the move. With each new study, researchers are developing increasingly intricate and elegant ways to make meaning of their data. At the same time, powerful statistical software, like SPSS or Stata, is continuously expanding its capability to process such sophisticated research designs.

Certainly, many long hours go into choosing a study’s analytic approach. Researchers must develop and refine their hypotheses; organize their data in such a way that statistical software can read it; and choose a statistical method (i.e., a mathematical approach) to test their research questions.

That last part about choosing a statistical method is where things can get tricky. In general, different statistical methods are not simply multiple ways of doing the same thing. Whereas something like a division problem may use different techniques (e.g., long division, trial- and-error) to get the same result, different statistical methods can analyze the same data yet produce differing, and even contradictory, results.

Differences in design: A little goes a long way

Just as French philosopher Jean-Paul Sartre liked to say “we are our choices,” in many ways our research results are our choices, too.

A study conducted by Burchinal & Clarke-Stewart illustrates this well. [1] These authors noticed that two different research teams had analyzed the same longitudinal data set, yet found (and published) substantially different results.

These two research teams analyzed data from the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care: a large, national study that followed the development of 1,364 children from six months of age. Both teams were also interested in the same question: what is the impact of mothers’ employment, and children’s subsequent nonmaternal child care, on children’s early cognitive growth?

The NICHD Early Child Care Researchers (Team 1) were first in line to test this question. After a series of analyses, this team concluded that the age at which children entered nonmaternal care, and the amount of time spent in such care, showed no relation to children’s cognitive performance up to age three. [2]

Next, Team 2 (Brooks-Gunn, Han, & Waldfogel, 2002) tested this same question. However, in contrast to the Team 1, they concluded that mothers’ full-time employment during children’s first nine months was indeed associated with impaired cognitive functioning when the children were three years of age. [3]

Speaking different analytic languages

The contradictory findings between these two research teams were not only curious, but also important to reconcile. After all, the difference between advising mothers of young children to work or not work is a big one. And, such a recommendation has implications for state and federal programs, such as Temporary Assistance for Needy Families, that assist young mothers in finding employment.

Burchinal & Clarke-Stewart therefore conducted a new, third study investigating how each team’s analytic design may have engendered the contradictory results.

Two approaches

First, Team 1 team used a conservative, top-down analytic approach. This approach:

uses all available information, such as multiple outcome variables and data from all participants
begins with a general test of significant relations between variables and works its way down to more specific comparisons
helps researchers avoid exaggerating the significance of associations found when working with large data sets

Team 2, on the other hand, used an a priori comparison approach. This technique:

examines hypotheses and variable relations chosen by researchers before (a priori) data exploration
utilizes a small subset of participants and/or variables in order to conduct a small set of comparisons between explicitly chosen participants and/or variables
is helpful when theory or previous research strongly implies a relation between specific variables or constructs

Thus, it seemed likely that investigating a smaller group of participants, and analyzing a smaller set of outcome data, contributed to Team 2’s finding of a relation between maternal employment and children’s cognitive growth. On the other hand, utilizing the full set of study participants, and analyzing all possible child outcome data, seemed to result in Team 1’s lack of such a finding.

To confirm this hypothesis, Burchinal & Clarke-Stewart analyzed the same participants and variables that Team 2 did; but, they used the top-down approach this time. The result of these new analyses? No significant findings.

The authors therefore reported Team 1’s findings—that is: it doesn’t hurt young children for their mothers to get a job—as being a more reliable take-away.

A cautionary tale

It is important to note that both the top-down approach and the a priori comparison approach are well-respected and well-established analytic techniques. And, as with all analytic techniques, each has strengths, weaknesses, and research questions for which its use is optimal.

But a study such as the one conducted by Burchinal & Clarke-Stewart provides an important cautionary tale. That is, when we, as consumers of research findings, draw conclusions from empirical work, it is important to remain attentive to the type of analyses that were used to engender such claims.

Of course, we probably won’t all end up being experts in all areas of analytic approach. But perhaps a good rule of thumb is that when we see a small amount of data being used to make big claims, it’s best to take a second look, get a second opinion, or see if the study has been replicated a second time.

References

[1] Burchinal, M.R. & Clarke-Stewart, K.A. (2007). Maternal employment and child cognitive outcomes: The importance of analytic approach. Developmental Psychology, 43, 1140-1155.

[2] Brooks-Gunn, J., Han, W.J., & Waldfogel, J. (2002). Maternal employment and child cognitive outcomes in the first three years of life: The NICHD Study of Early Child Care. Child Development, 73, 1052–1072.

[3] National Institute of Child Health and Human Development Early Child Care Research Network. (2000). The relation of child care to cognitive and language development. Child Development, 71, 960–980.

Use Your Words: The Impact of Parent and Teacher Speech on Early Language Growth

Lindsay Clements

June-01-2017

Use Your Words: The Impact of Parent and Teacher Speech on Early Language Growth

It’s finals time!

As the promise of spring and summer days rolls in, the increase in sunshine can mean only one thing for students: assignments, exams, papers, and projects are due.

Not surprisingly, this time of year arrives with no shortage of stress for those with tight deadlines, writer’s block, computer glitches, or myriad other dilemmas inherent to the academic world.

And for those faced with the daunting sight of a blank page needing to be filled, I like to offer the charming words of Sylvia Plath: “let me live, love, and say it well in good sentences.”

Of course, taken from Plath’s novel The Bell Jar, the quote’s implied ease of eloquent composition belies much of the strife of writing. Which brings us to an important question: where do good writers come from? Or, better yet, how do children acquire the words that will eventually be put on paper to make them good writers (and to ace those finals)?

Given that language and literacy skills are ubiquitous, their development has been an interest of researchers for years. And it turns out that early language exposure has long-lasting implications for childhood skill growth.

Let’s Talk about School Talk

Hoping to better understand if the amount and type of vocabulary that preschool teachers use around their students would predict children’s language skills at kindergarten and beyond, Dickinson & Porche (2011) conducted a longitudinal study. [1]

The researchers observed and videotaped classroom interactions in a number of Head Start preschools. Teacher-child talk was then separated into several categories:

teacher extending utterances were those times when teachers tried to keep conversation going by encouraging children to talk further;
sophisticated vocabulary reflected the number of low-frequency or sophisticated words used by teachers;
attention-related utterances were those times when teachers tried to gain or hold their students’ attention;
correcting utterances arose when teachers corrected the accuracy of what students said;
and analytic utterances during book reading happened when teachers prompted their students to explore reasons for characters’ actions or discussed the meanings of words.

The researchers also examined children’s literacy skills. To do this, the children completed tests targeting storytelling, receptive vocabulary, reading comprehension, and word recognition at both kindergarten and 4th grade.

Analyses showed that reading comprehension in 4th grade was positively related to preschool teachers’ use of sophisticated vocabulary and attention-related utterances.

As well, preschool teachers’ use of sophisticated vocabulary positively influenced 4th grade decoding skills.

Finally, a mediation model, or an indirect chain effect, emerged among preschool teachers’ sophisticated vocabulary, children’s kindergarten decoding skills, and children’s 4th grade reading comprehension. That is, students who had a preschool teacher that used more sophisticated vocabulary were better able to decode words in kindergarten, and in turn had better reading comprehension skills in 4th grade.

So, in essence, preschool teachers who used more varied words, and maintained their students’ attention to these words, seemed to provide more opportunities for their students’ language skills to grow.

Let’s Talk about Home Talk

Of course, no discussion of schooling’s impact on early skill development is complete without a look toward the home’s impact.

One such home-based study investigated the relation between socioeconomic status (SES) and children’s early language development. [2]

There were two primary hypotheses here:

that SES-related differences in children’s vocabulary could be the result of SES-related differences in language-learning experiences;
that maternal speech would mediate the relation between SES and child vocabulary development

33 high-SES families and 30 mid-SES families agreed to video record daily activities in their home and allowed researchers to transcribe them. Parent-child interactions were recorded twice, 10 weeks apart, and included such activities as getting dressed, eating breakfast, and playing with toys.

The researchers then used a variety of measures to analyze maternal speech.

They tallied word tokens (i.e., the number of different words used), word types (i.e., the number of root words used, such that using both ‘run’ and ‘running’ counted as one use), and word totals (i.e., the total number of words, even if some were used more than once).

They also counted the number of times the parent built upon something the child said (topic-continuing replies).

Finally, children’s vocabulary skill was assessed as a measure of productive vocabulary, or the number of word types used in an average 90-utterance speech sample.

These data showed that SES was significantly associated with both child vocabulary and maternal speech: high-SES mothers produced more utterances, more word tokens, more word types, spoke for longer periods of time, and produced more topic-continuing replies than did mid-SES mothers.

The average length of the mothers’ speech significantly predicted child vocabulary. And, the association between SES and child vocabulary became statistically weaker once the researchers subtracted maternal speech from the equation. In other words, the difference in vocabulary that the researchers found between the high-SES and mid-SES children was due (almost fully) to the differences seen in their mothers’ speech.

It seems, then, that SES-related differences in child-directed speech may arise from more general SES-related differences in language use. That is, the style of language use among higher-SES mothers appears to influence the way they talk to their children, which in turn affects the rate at which their children build their vocabularies.

Small Steps toward Big Words

These studies contribute to a large literature that suggests early language experiences have a substantial long-term impact on children’s language and literacy skill development.

So should parents and teachers grab the thesaurus in hopes that that their children will fast-track to the Dean’s List?

Probably not.

But a little mindfulness about how we (as parents, teachers, and/or caregivers) use our words around our youngest learners will probably go a long way.

In fact, studies have shown that even small increases in the richness of language that children are exposed to can have a lasting positive effect. [3]

So here and there, we can ask ourselves:

The next time I ask her to put her toys away, can I say it in a new way?

How can I push them to think about why the story character feels both happy and sad at the same time?

And my personal favorite: Am I saying this well, in good sentences?

References:

Connor, C.M., Morrison, F.J., & Slominski, L. (2006). Preschool instruction and children’s emergent literacy skills. Journal of Educational Psychology, 98, 665-689. [link]

Dickinson, D. K., & Porche, M. V. (2011). Relation between language experiences in preschool classrooms and children’s kindergarten and fourth-grade language and reading abilities. Child Development, 82, 870-886. [link]

Hoff, E. (2003). The specificity of environmental influence: Socioeconomic status affects early vocabulary development via maternal speech. Child Development, 74, 1368-1378. [link]

Understanding Racial Imbalances in Special Education

Lindsay Clements

May-21-2017

Understanding Racial Imbalances in Special Education

As another April has come and gone, so has another World Autism Month. The Light It Up Blue campaign celebrates each spring with a renewed push for greater understanding and acceptance of individuals with autism spectrum disorder.

And with greater attention to autism (hopefully) comes greater attention to learning and developmental disabilities more broadly. In the context of education, this means greater attention to the who, what, and why of special education (SPED) services.

Special education provides a public education, generally through implementation of individualized curricula, to students with intellectual, learning, developmental, and/or physical disabilities. [1]

Or does it? In the last decade, researchers and policymakers have begun to take a closer look at the students enrolled in SPED. Red flags have emerged, to say the least.

The Numbers Don’t Add Up

At the forefront of concern is evidence of substantial disproportionality in SPED enrollment. Disproportionality arises when a group, such as a racial or ethnic minority, is represented in SPED at a greater rate than they comprise within their school, community, or nation. For example, if a school is comprised of around 65% white students, we should expect that the SPED classrooms are also comprised of around 65% white students.

Yet in nearly every state, rates of SPED enrollment show evidence of overrepresentation of minority groups. [2]

Now, before delving into the possible factors contributing to such disproportionality, it is worth noting that special education is still relatively new to U.S. public education. SPED was first enacted in 1973 and has gone through several policy iterations to reach its current form: the Individuals with Disabilities Education Act (IDEA).

IDEA mandates that education services for children with disabilities must meet students’ individual needs and must take place in the least restrictive environment possible (ideally with non-disabled students). As well, and perhaps most important for the current discussion, is the mandate that SPED assignment can happen only after appropriate enrollment procedures have concluded. These procedures include aptitude and achievement tests, teacher recommendations, and considerations of the student’s cultural background. [1]

Despite IDEA’s requirements, however, SPED services do not appear to be distributed equitably. [3] Enrollment data show that students of color consistently experience disproportionate inclusion in SPED, and this issue has actually come to the attention of Congress more than once. During both the 19th and 22nd Annual Reports to Congress, the Office of Special Education Programs (OSEP) and the Office of Civil Rights (OCR) reported that students of color may be being misclassified or inappropriately placed in SPED, that such placement may be a form of discrimination, and that SPED students may be receiving services that do not meet their needs. [4]

Red Flags

What kind of disproportionality are we talking about? Let’s look at a snapshot of some of the numbers and contexts that researchers have been tracking:

despite Black children constituting only 17% of total school enrollment, they comprise 33% of children diagnosed with mental retardation (now referred to as intellectual disability, ID) [5]
Black boys are, on average, 5.5 times more likely to be diagnosed as emotionally disturbed (ED) than are white girls [6]
American-Indian boys are, on average, nearly three times more likely than white girls to be diagnosed with a learning disability (LD) [6]
among students with disabilities, 57% of Hispanics are educated in partially separate or substantially separate settings and denied access to inclusive settings, compared to 45% of whites [7]
English language learners (ELL) are 27% more likely to be placed in special education during the elementary school grades [8]

Where to Begin?

Those are some pretty troubling statistics, and researchers have endeavored to get to the bottom of them. But as one might expect, disproportionality is a complex, layered issue.

And a potentially misleading one. For example, a natural response to reading the numbers above might be that disproportionate overrepresentation is worse than disproportionate underrepresentation. We would be remiss to take that thought as a blanket statement, though. After all, while overrepresentation may reflect heightened disapproval of minority students’ behavioral or academic performance, underrepresentation may reflect minority students’ struggles going unnoticed. And that latter possibility isn’t any better than the former.

For example, the diagnosis of Intellectual Disability in Black students has been shown to decline as poverty increases. [2] In other words, the poorest Black students may be the least likely to be identified as having ID. But, there is still a disproportionately high rate of Black children in SPED who are diagnosed with ID overall.

Such nuanced findings may suggest that Black students are being over- and under-monitored based on their socioeconomic background in addition to, or in lieu of, their academic profile.

Thus, the story that needs to be uncovered is not only the extent of disproportionality (i.e., the raw numbers) but also the forms (i.e., the diagnoses) and the causes.

What’s Happening with SPED Assessment?

Turning to causality in particular, some researchers have hypothesized that the assessment procedures required by IDEA for SPED enrollment may be less rigorous in practice than on paper. In an investigation of how qualitative factors, such as personal beliefs, may affect the rigor of psychological/educational evaluation, Harry, Klinger, Sturges, & Moore (2005) investigated community perceptions of the validity of SPED referrals throughout urban schools in Southern Florida. [9] Extensive interviews with teachers, administrators, and families uncovered a high level of confidence in school-ordered assessments.

That is, the interviewees believed that students would be referred for, and enrolled in, SPED only after a true need for such services was found. Which sounds good! But, paradoxically, this high level of confidence may actually lead to harmful results.

Because from the get-go, students may be vulnerable to inappropriate SPED placement if members of their family, school, and community are unlikely to examine a referral critically. Further, given that studies have found Limited English-Proficient students to be more likely to be placed in SPED, families that experience a language or cultural barrier to their child’s school may face particular disadvantage in advocating for their child.

These same researchers also found that teachers’ perceptions of a student’s learning difficulties, as well as their perception of dysfunction existing within a student’s family, predicted their students’ SPED assessment results. This may indicate a complex process through which a teacher’s perception of a student influences the nature of their interactions (e.g., challenging the student less due to lower expectations), which in turn contributes to lower levels of student achievement and, eventually, consideration for SPED.

Other researchers, however, have suggested that psycho-educational assessment is not the main event in SPED placement at all. [8] Rather, disproportionate referrals may arise from the ongoing failure of regular education classrooms to serve racial and ethnic minority students. They argue that it is the quality of a student’s classroom instruction, and the level of management within the classroom, that should be most emphasized during student assessment. This emphasis would allow for underachievement to be seen as the result of a poor learning environment rather than individual student failure.

What Other Factors Underlie Disproportionate Representation?

Overall, most researchers have concluded that disproportionality in SPED is the result of:

subjective student identification practices (e.g., teachers’ interpretation of the same behaviors differently depending on the student);
blatant violations of IDEA’s guidelines;
and antiquated systems of SPED funding based on category of disability (i.e., schools receive more money if a student is diagnosed with Intellectual Disability than if diagnosed with Dyslexia). [10]

Yet other studies have begun to take a new, ecological approach in their investigations. For example, based on the assumption that low-income students are more likely to be students of color, several researchers have asked: is poverty is associated with increased risk for SPED enrollment?

In one such study, Strand & Geoff (2009) analyzed the 2005 Pupil Level Annual School Census – a data set of 6.5 million students in England. [11] The authors found that poverty and gender explained more disproportionality in SPED enrollment than did ethnicity; but, the overrepresentation of students of color in SPED was still significant even after controlling for poverty. It appears, then, that some degree of interplay between individual (e.g., academic strengths and weaknesses, learning support at home) and environmental (e.g., socioeconomic conditions, teacher and societal beliefs) factors significantly contribute to placement in SPED classrooms.

Getting to the Bottom of it

So far, researchers seem to have a lot of pieces of the disproportionality puzzle in a lot of places. How do we put them all together–at least enough so that we can begin to do something about it?

To start, Oswald, Coutinho, & Best (2005) recommend a new research agenda. They advocate for disproportionality studies to focus specifically on disentangling social factors (such as systemic bias) from individual factors (such as differential susceptibility) as an underlying cause of over- or underrepresentation in SPED. [6]

These authors argue that studies should investigate whether students of certain racial or ethnic groups are differently susceptible to schooling contexts such as low-quality instruction, loose classroom management, or particular academic interventions. Under the theory of differential susceptibility, it is perhaps so that some students fare better in special education classrooms than others, making them more likely to be placed back into regular education.

They also advocate for assessment procedures that compare an individual’s performance to the performance of students of similar characteristics. For example, the achievement of a Hispanic female youth should be compared to the average performance of similar female students within their school or district (i.e., not their non-Hispanic classmates). If differential susceptibility to an aspect of the educational environment exists for some racial or ethnic minorities, assessment procedures that compare similar students may provide the most accurate depiction of an individual successes or challenges.

No Time Like the Present

It is clear that disproportionality exists within SPED. But what it less clear is why, or how to fix it. Given that it is a relatively new addition to public education, however, we can hope that the inequity currently seen in SPED may not yet be as deeply rooted as some of the challenges that regular education faces (e.g., school segregation).

Nonetheless, time is of the essence for new research! It is only with a better understanding of the roles that various factors play in SPED disproportionality that the development (and enforcement) of policy interventions can commence.

[Editor’s note: this post was written by Lindsay Clements. The initial byline, saying that it had been written by me, was incorrect. My apologies for the mistake.]

References

[1] U.S. Department of Education Office for Civil Rights (2010). Free Appropriate Public Education for Students With Disabilities: Requirements Under Section 504 of the Rehabilitation Act of 1973, Washington, D.C.

[2] Parrish, T. (2005). Racial disparities in the identification, funding, and provision of Special Education. In D.J. Losen & G. Orfield (Eds.), Racial Inequity in Special Education (pp.15-37). Cambridge, MA: Harvard Education Press.

[3] McDonald, K.E., Keys, C.B., & Balcazar, F.E. (2007). Disability, race/ethnicity and gender: Themes of cultural oppression, acts of individual resistance. American Journal of Community Psychology, 39, 145-161. doi:10.1007/s10464-007-9094-3 [link]

[4] U.S. Department of Education (1997). Nineteenth annual report to Congress. Washington, DC: Author.; U.S. Department of Education (2000). Twenty-second annual report to Congress. Washington, DC: Author.

[5] Losen, D.J. & Orfield, G. (2005). Racial inequity in special education. In D.J. Losen & G. Orfield (Eds.), Racial Inequity in Special Education (pp.xv-xxxvii). Cambridge, MA: Harvard Education Press.

[6] Oswald, D.P., Coutinho, M.J., & Best, A.L.M. (2005). Community and school predictors of overrepresentation of minority children in Special Education. In D.J. Losen & G. Orfield (Eds.), Racial Inequity in Special Education (pp.1-13). Cambridge, MA: Harvard Education Press.

[7] Garcia Ferros, E. & Conroy, J.W. (2005). Double jeopardy: An exploration of restrictiveness and race in special education. In D.J. Losen & G. Orfield (Eds.), Racial Inequity in Special Education (pp.39.70). Cambridge, MA: Harvard Education Press.

[8] Artiles, A.J., Rueda, R., Salazar, J.J. & Higareda, I. (2005). Within-group diversity in minority disproportionate representation: English language learners in urban school districts. Exceptional Children, 71(3), 283-300. [link]

[9] Harry, B., Klinger, J.K., Sturges, K.M., & Moore, R.F. (2005). Of rocks and soft places: Using qualitative methods to investigate disproportionality. In D.J. Losen & G. Orfield (Eds.), Racial Inequity in Special Education (pp.71-92). Cambridge, MA: Harvard Education Press.

[10] Reschly, D.J. (2000). IQ and Special Education: History, current status, and alternatives. Unpublished paper, National Academy of Sciences, National Research Council, Washington, DC.

[11] Strand, S., Geoff, L. (2009). Evidence of ethnic disproportionality in special education in an english population. The Journal of Special Education, 43(3), 174-190. [link]

Early Education Program Evaluation: “Differential Susceptibility” to Success

Lindsay Clements

March-26-2017

Early Education Program Evaluation: “Differential Susceptibility” to Success

Show me the Money

As most parents, teachers, and education policy folks know well, early childhood education is expensive. Whether federally-funded, state-funded, or family-funded, preschool and structured early care generally operate on a pretty tight budget. They also generally operate on pretty high hopes: academic achievement, personal growth, reduced delinquency, and much more.

And they should! As Ralph Waldo Emerson wrote, “there is no knowledge that is not power.” We certainly need to maintain high expectations for youth to get the most out of their academic careers. As well, we should expect the programs that we invest in to set children up for the success that they promise.

Show us the Results

So what happens when we don’t see those hopes result in program outcome data; in particular, at the state- and federally-funded program level?

Do we launch an investigation into what went wrong?
Do we take the money away?
Do we blame the teachers, or parents, or school districts?

The “what now?” of underwhelming achievement is a challenging road to venture down. For some context, check out my colleague Austin’s recent blog post regarding a newly published study looking at the infamous fadeout effects in Head Start preschools.

Unfortunately, questions of whom to blame have dominated much of the “what now?” conversation over the years. Yet some studies, like the one Austin discussed, are trending in a new, positive direction for developmental and educational research alike.

Let’s Re-think ‘Results’

This new genre of studies does two things. First, it looks at such factors as fidelity to a particular program’s plan. Let’s take Head Start as an example. Researchers will ask: how well and how often are Head Start’s specialized strategies actually being implemented in classrooms?

Second, and most important, these studies don’t stop there. Instead, they go on to broaden the idea of an outcome to include measures of mental health and social growth, and the image of a learning environment to include the home and child care centers.

Broadening what we think achievement is, and where we think learning happens, is an important movement. Of course, many developmental psychologists have been advocating for this broadening for years. Social psychologist Urie Bronfenbrenner, for example, began studying ways in which intra- and inter-person factors affect learning back in the 1970’s. But the merging of research questions that focus on individual context with research questions that focus on school program evaluation is an exciting new empirical endeavor.

Differential Susceptibility

An endeavor that we stand to gain a lot from. One way that these new context+program evaluation research questions are making an impact is in studies of early achievement and differential susceptibility (DS).

DS is a theoretical model that aims to understand why some things affect some people differently. In developmental research, DS refers to children who are more behaviorally or biologically reactive to stimuli and, as a result, more affected by both positive and negative environments. [1]

Study 1

Let’s look at a longitudinal study conducted by researchers at Birkbeck University of London. [2] They investigated the effects of early rearing contexts on children of different temperaments. The following data was collected from 1,364 families:

predictive measures

parents reported the temperament of their child at 6 months (general mood, how often they engage in play behavior, how well they transition to a babysitter, etc.);
parenting quality (i.e. maternal sensitivity) was assessed at 6 and 54 months during laboratory and home observations;
quality of child care (e.g. daycare) was assessed at 6, 15, 24, 36, and 54 months via observation

outcome measures

academic achievement, behavior problems, teacher-child conflict, academic work habits, and socio-emotional functioning were assessed regularly between 54 months and 6th grade

Results showed that children who had a difficult temperament in infancy were more likely than children who didn’t to benefit from good parenting and high-quality childcare. They also suffered more from negative parenting and low-quality child care.

Most pronounced was the finding of differential effects for child care quality. Here, high quality care fostered fewer behavior problems, less teacher-child conflict, and better reading skills while low quality care fostered the opposite — but, only for those children who had a difficult temperament.

The takeaway: children that had a difficult temperament in infancy were differentially susceptible to quality of parenting and child care. For them, the good was extra good, and the bad was extra bad.

Study 2

Researchers at Stanford University engaged high- and low-income kindergartners in activities designed to elicit physiological reactivity (measured by the amount of the stress hormone cortisol in their saliva). [3] In other words, the children completed activities that were difficult and kind of frustrating. They also completed a battery of executive function assessments.

It turns out that children who displayed higher reactivity (more cortisol) during the activities were more susceptible to their family’s income. That is, family income was significantly associated with children’s EF skills — but only for those children with high cortisol response. Highly reactive children had higher EF skills if their family had a higher income, but lower EF skills if their family was lower income.

The takeaway: children that were highly reactive when faced with challenging activities were differentially susceptible to their family’s resources. Their EF was particularly strong if their family had high income, yet particularly weak if their family had a lower income.

Evaluating Program Evaluation

How is being mindful of phenomena like differential susceptibility helpful when we receive the news that children made no special long-term gains after being enrolled in a publicly-funded program?

First, we should recognize that we may have set ourselves up for some disappointment at the outset if we assumed that all children would be equally susceptible to the positive effects of home or school interventions.

Of course, at school entry, we don’t necessarily know which students are arriving with difficult temperaments. Or whether their child care environment has exacerbated or buffered it. Which means that we’re also not going to be able (practically or ethically) to separate students by level of disadvantage in order to decide which program they should be enrolled in. So let’s just accept that we’re going to see some variation in individual outcomes.

Let’s also remind ourselves that variation is not necessarily reflective of an ineffective program. At Head Start, for example, it is probably safe to speculate that most families are juggling some amount of stress, financial instability, and social tension. And according to the DS model, students who are predisposed to be highly reactive will be hit hardest by these things. As a result, reactivity is probably going to interfere with their reaching what we define as success. But DS also tells us that they have the most to gain from a nurturing, consistent environment.

So let’s not take the money away. Let’s hold off on passing the blame around. And let’s not refer to these data as something going “wrong”. Let’s instead look at the students who continue to struggle and ask what contextual factors — such as a child’s weak self-regulation skills and their parent’s inability to address it in the way their teacher wants because they work two jobs — are at play.

I’m no gambler, but if we can figure those things out, and commit to doing something about them, then I say we double-down when it comes to funding.

References

Ellis, B. J., Boyce, W. T., Belsky, J., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2011). Differential susceptibility to the environment: An evolutionary–neurodevelopmental theory. Development and Psychopathology, 23, 7–28. doi:10.1017/S0954579410000611 [link]

Pluess, M., & Belsky, J. (2010). Differential susceptibility to parenting and quality child care. Developmental Psychology, 46, 379-390. [link]

Obradovic, J., Portilla, X. A., & Ballard, P. J. (2015). Biological sensitivity to family income: Differential effects on early executive functioning. Child Development, 87(2), 374-384. doi: 10.1111/cdev.12475 [link]

Lindsay Clements

March-01-2017

“Nevertheless, She Persisted”

President Barack Obama greets 2010 Fermi Award recipients Dr. Burton Richter, right, and his wife Laurose, and Dr. Mildred S. Dresselhaus, third from right, and her husband Gene, in the Oval Office, May 7, 2012. (Official White House Photo by Pete Souza) This official White House photograph is being made available only for publication by news organizations and/or for personal use printing by the subject(s) of the photograph. The photograph may not be manipulated in any way and may not be used in commercial or political materials, advertisements, emails, products, promotions that in any way suggests approval or endorsement of the President, the First Family, or the White House.Ê — President Barack Obama greets 2010 Fermi Award recipient Dr. Mildred S. Dresselhaus, in the Oval Office, May 7, 2012. (Official White House Photo by Pete Souza)

If you watched the Oscars this past weekend, or simply had lucky t.v. timing over the past few weeks, you may have caught GE’s newest commercial featuring MIT scientist Millie Dresselhaus. The ad aims to promote GE’s upcoming diversity endeavor: 20,000 women in science, technology, engineering, and math (STEM) jobs by 2020. It’s a lofty goal, and I’m rooting for ‘em.

This initiative comes in response to only 18% of GE’s technical workforce being female. Although worrisome for both equity and economic reasons, this statistic is not unusual in the STEM student or professional world. We may be wondering: how has GE, and numerous other similar companies, achieved such low female employment and retention? Which is also to ask: what does it mean for women to persist in the STEM world, and what kind of internal oomph does it take? Luckily, researchers have begun to tackle both questions.

(It’s Not Just GE)

Fewer girls and young women engage with STEM at the advanced placement, college, and career levels than do males. A report published by the National Science Foundation (NSF) found that women represent only around 35% of college students enrolled in physics, mathematics, and computer science courses, and less than 10% of those studying physics and engineering at the graduate level. [1]

Also of concern is the high rate of attrition seen in those who complete undergraduate study and enter the workforce. This turnover leaves women holding only 22% of the math and science jobs available in the U.S.

A Couple of Questions Out of the Way…

Researchers have approached this gender disparity from different angles in hopes of better understanding what is happening.

Some have asked: is it perhaps so that males are just more able mathematicians than females? This conclusion seems unlikely, given that gender differences in math performance barely exist early in development, and tend to emerge at the high-school and college levels. [2]

Others hypothesized: maybe boys are just more interested in STEM than girls? This also seems a stretch, with research showing that throughout at least the elementary school years, a high percentage of both boys and girls (68 and 66 percent, respectively) report liking science. [3]

(See also my fellow-blogger’s post about raising girls’ levels of math participation in the US and India.)

A different approach, then. Several studies have gone beyond theories of disproportionate aptitude and interest and begun to question if social pressures and expectations affect girls’ pursuit of STEM. If yes, then how?

How?

In one such study, researchers at Brown University and Williams College studied the interaction of stereotype threat and mathematics performance in female students. [4]

Stereotype threat is a social occurrence whereby the targets of intellectual inferiority stereotypes, such as women or racial or ethnic minorities, perform more poorly at a task when in an environment that reminds them of this stereotype, such as in the presence of males or the racial or ethnic majority. [5]

In the experiment, college students completed a challenging math or verbal task in groups of three. Each trio included the study participant plus two others: two people of the same gender as the participant (the same-sex condition) or two people of the opposite gender of the participant (the minority condition).

The researchers found that women in the minority condition performed more poorly on the math test than did women in the same-sex condition (males did well on the test despite condition).

The female participants’ performance was also found to be proportional to the number of males in their group, such that women in a mixed-sex majority condition (i.e. two women and one male) still experienced performance deficits as compared to women in the same-sex condition.

Given their findings, the researchers suggested that women in STEM courses or jobs, where their colleagues are predominantly male, may experience stereotype threat. As a result, they are at-risk of performing below their ability, and thus at-risk for attrition.

In another study, students at two universities completed assessments of working memory and mathematics, as well as a self-report anxiety scale regarding their feelings about math. [6] The results statistically demonstrated a chain effect: female students had higher anxiety about math, which in turn affected the working memory resources they needed to complete the math tasks, which in turn lead to lower performance.

The researchers discussed this chain as lending support for Processing Efficiency Theory, which suggests that anxiety negatively affects the central executive component of working memory. The central executive is responsible for processing information stored in verbal and visuospatial working memory, both of which come in pretty handy when completing mathematics tasks.

There’s Probably More to Persist Through than we Think

Studies such as these suggest that our social environment likely has a large impact on how women navigate, among other things, the STEM world. So we ask: what engenders women’s worried feelings around mathematics? How are messages of inferiority transmitted? After all, most girls grow up with some combination of family members, teachers, and/or other role models reciting the message that girls can excel in STEM just as boys can. Yet we still see that, by age six, girls are more likely to group boys into the category of “really, really smart” than they are to categorize their fellow ladies as such. [7]

First, let’s not underestimate the messages that waft in the background of girls’ daily lives. For example, picture a middle school student sitting down at her kitchen table to work on science homework. She can faintly hear CNN from the living room t.v. as she works on a diagram of Newton’s laws of motion. And what CNN happens to be covering that evening is Nobel scientist Tim Hunt’s rationale for promoting gender-segregated workplaces, which is that women in science laboratories are too at-risk of falling in love with their male colleagues.

Collective groan…but so what? Surely CNN will move to another story, the diagram will be skillfully completed, and the student will clear her books from the table so that she can eat her dinner. But not so surely will that story’s message be erased from her subconscious. And that’s a big ‘what’.

Second, as adult women and educators, we should try to get in the habit of taking a look at our own emotional navigation of STEM. Again, let’s not underestimate: one study recently found that heightened math anxiety in female teachers at the beginning of the school year is associated with lower math performance over that school year for their female students. [8] And that anxiety is communicated much more subtly than seeing a math problem and making a run for it.

In other words: better understanding our own subconscious relationship with, and reactions to, STEM disciplines can help us better understand the implicit messages that we transmit to young girls.

Third, let’s talk about it. Now, I would be remiss not to include the caveat that we cannot fully encourage girls to pursue, and persist in, STEM without also considering the importance of encouraging boys and young men to pursue female-dominated fields, such as nursing and early childhood education. Nonetheless, researchers have suggested that efforts to mitigate gender differences in math-related fields are inadequate unless they target specific factors, such as worry about math, in girls and women. [9]

So let’s talk about that worry. And, given what we know about social psychological phenomena (e.g., the prevalence of stereotype threat), the positive effects of such conversations may be maximized within all-girls STEM classes and extracurriculars. A quick Google search can lead us to organizations, such as Girls Excelling in Math and Science (GEMS), the Laurel School’s Center for Research on Girls, and NSF’s National Girls Collaborative Project, that are eager to provide guidance and resources for exactly this purpose.

Because oddly enough, the best way to empower girls to brush off gendered nonsense like Tim Hunt’s argument for workplace segregation, may just be to separate boys and girls for a bit.

National Science Foundation (1996). Women, minorities, and persons with disabilities in science and engineering: NSF Publication No. 96–311. Arlington, VA: Author. [link]
Lindberg, S. M., Hyde, J. S., Petersen, J. L., & Linn, M. C. (2010). New trends in gender and mathematics performance: A meta-analysis. Psychological Bulletin, 136, 1123–1135. [link]
National Science Foundation (2007). Back to school: Five myths about girls and science. NSF Press Release No. 07-108. Arlington, VA: Author. [link]
Inzlicht, M. & Ben-Zeev, T. (2000). A threatening intellectual environment: Why females are susceptible to experiencing problem-solving deficits in the presence of males. Psychological Science, 11, 365-371. [link]
Aronson, J., Lustina, M.J., Good, C., Keough, K., Steele, C.M., & Brown, J. (1999). When white men can’t do math: Necessary and sufficient factors in stereotype threat. Journal of Experimental Social Psychology, 35, 29–46. [link]
Ganley, C. M., & Vasilyeva, M. (2014). The role of anxiety and working memory in gender differences in mathematics. Journal of Educational Psychology, 106 (1), 105-120. [link]
Bian, L., Leslie, S.J., Cimpian, A. (2017). Gender stereotypes about intellectual ability emerge early and influence children’s interests. Science, 355(6323), 389-391. [link]
Beilock, S. L., Gunderson, E. A., Ramirez, G., & Levine, S. C. (2010). Female teachers’ math anxiety affects girls’ math achievement. Proceedings of the National Academy of Sciences, USA, 107, 1860–1863. [link]
Ganley, C. M., & Vasilyeva, M. (2014). The role of anxiety and working memory in gender differences in mathematics. Journal of Educational Psychology, 106 (1), 105-120. [link]

Executive Function: More Than Meets the Eye

Lindsay Clements

January-29-2017

Executive Function: More Than Meets the Eye

Executive functioning (EF) is a burgeoning research area for psychologists, neuroscientists, and educators. For some, EF might seem like the cognitive science flavor of the week. But for others, its study is uncovering a significant piece of the puzzle for how we learn, feel, and act. And those latter folks have a lot to show for it.

In fact, the mainstream interest in EF that has developed over the last two decades may be best summarized by typing “executive functioning” into the search bar on Amazon.com. Here you’ll find a wealth of books illustrating scattered, messy, and forgetful youth. In these works, authors offer parents and educators a pathway to better understand those children that forget their homework, fidget through class, and get lost in thought when they’re supposed to be finishing chores.

Yet while the disorganized adolescent is certainly one component of executive functions in action (or inaction), it merely scratches the surface of what research is uncovering. And with business booming in the EF world, so to speak, it is now more important than ever to take a step back and examine some of the ways that EF research is being applied in classrooms and households.

What is EF?

Executive functioning is an umbrella term that includes the cognitive processes of attention, self-regulation, mental flexibility (the ability to transition from or between one thought or action to another), and working memory [1].

We use EF when we do mental math to calculate a waiter’s tip; when we remember to raise our hand instead of blurting out an answer; when we attend to a science lecture; and when we describe the same event from the varying perspectives of multiple people.

Studies continue to uncover just how entrenched these processes are throughout the lifespan. EF is linked to several positive developmental outcomes, such as school readiness in early childhood [2] and the development of both crystallized and fluid intelligence in middle childhood [3].

Weak EF skills, on the other hand, go far beyond a messy backpack. Low EF has been found to predict difficulties with mathematics [4], externalizing problem behaviors in middle childhood [5], and harsh parenting in adulthood [6]. Challenges with EF also appear to play a role in a number of developmental disorders, such as ADHD and Autism [7].

Imaging studies show that the frontal lobe of the brain is the EF powerhouse, with the most rapid development of these skills occurring in early childhood [8]. While all children are born with the capacity to develop their EF, actual skill growth requires some degree of explicit practice and modeling. For this reason, much of the mainstream EF literature is geared toward K-8 parents and educators. The Harvard Center on the Developing Child, for example, offers a variety of strategies to support children’s EF growth. Picture sorting games for toddlers, memory games like Simon Says for kindergarteners, and fantasy role-play throughout elementary school serve this purpose.

Next Step: Step Back

There is little doubt that executive function skills exist or that they provide an important cognitive foundation for development. But a critical lens is essential when we begin to take empirical EF knowledge and apply it to youth.

In particular, let us tread lightly when we make a qualitative assumption about a child’s skill level, or how to improve it.

How can we be mindful of this caveat in daily practice? A good start is to question the tendency for EF skills to be dichotomized as high or low, good or bad. Of course, some children have different EF skills than others. But the growing instinct to take the attentive, obedient child and the fidgety, distracted child and fit them into either side of this dichotomy risks overlooking important individual contexts.

Imagine middle school student Joe. Joe lives in a high-crime neighborhood but attends a high-resource school in the next town over. As a result of his home environment, Joe has learned to self-regulate in ways that heighten his vigilance and attention to seemingly unimportant details. He is hyper aware of sights and sounds in the distance that, at home, imply an approaching stranger. In his classroom, however, Joe’s attention and self-regulation skills are less fitting, as the distant sound he is attending to instead of his math lesson is simply another student walking the hallway. Compared to his peers, who do not navigate such contrasting environments each day, Joe’s EF skill level concerns his teacher.

In this scenario, Joe is functional at home (high EF) yet distractible and inattentive at school (low EF). And if we can only fit Joe into the dichotomy of high or low EF, instead of on a fluid spectrum, his low-EF presentation at school is likely to make that call.

The factors that engender the presentation of high or low EF skills is an important distinction to make. Here, Joe’s distraction is different from his classmate Jane’s, which is a result of her ADHD. Accordingly, the support system that each needs is also different.

A crucial step toward accurately qualitatively assessing children’s EF, especially in schools, is therefore to attend to the interactions between the person and the world within which a child is situated. Before we call in the specialist, before we assign remediation, before we purchase the neurotraining software, let’s ask such questions as what are the social rules, values, and stressors that this child is navigating among?

Because for some children, a workbook of puzzles and concentration exercises ordered from Amazon may be enormously helpful. But for others, a consideration of context, resources, and resiliency is the better route.

1. Best, J.R. & Miller, P.H. (2010). A developmental perspective on executive function. Child Development, 81(6), 1641-1660.
2. Blair, C. & Razza, R.P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78(2), 647-663.
3. Brydges, C.R., Reid, C.L., Fox, A.M. & Anderson, M. (2012). A unitary executive function predicts intelligence in children. Intelligence, 40(5), 458-469
4. Toll, S.W.M, Van der Ven, S.H.G, Kroesbergen, E.H. & Van Luit, J.E.H. (2011). Executive functions as predictors of math learning disabilities. Journal of Learning Disabilities, 44(6), 521-532.
5. Woltering, S., Lishak, V., Hodgson, N., Granic, I. & Zelazo, P.D. (2016). Executive function in children with externalizing and comorbid internalizing behavior problems. Journal of Child Psychology and Psychiatry, 57(1), 30-38.
6. Deater-Deckard, K., Wang, Z., Chen, N. & Ann Bell, M. (2012). Maternal executive function, harsh parenting, and child conduct problems. The Journal of Child Psychology and Psychiatry, 53(10), 1084-1091.
7. Craig, F., Margari, F., Legrottaglie, A.R., Palumbi, R., de Giambattista, C. & Margari, L. (2016). A review of executive function deficits in autism spectrum disorder and attention-deficit/hyperactivity disorder. Neuropsychiatric Disease and Treatment,12, 1191-1202.
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