This article first appeared on the RealClearEducation website on April 22, 2014.

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You often hear the phrase that small children are sponges, that they constantly learn. This sentiment is sometimes expressed in a way that makes it sound like the particulars don’t matter that much; as long as there is a lot to be learned in the environment, the child will learn it. A new study shows that for one core type of learning, it’s more complicated. Kids don’t learn important information that’s right in front of them, unless an adult is actively teaching them. 

The core type of learning is categorization. Understanding that objects can be categorized is essential for kids’ thinking. Kids constantly encounter novel objects; for example, each apple they see is an apple they’ve never encountered before. The child cannot experiment with each new object to figure out its properties; she must benefit from her prior experience with other apples, so that she can know, for example, that this object, since it’s an apple, must be edible.

But how can a child tell which properties of the apple are incidental (e.g., it has a long stem) and which properties are true of all apples (it’s edible)?  The child must ignore many incidental properties, and hold on to the important properties that are true of all apples.

Previous research shows that by age three or four, children are sensitive to linguistic cues about this matter. They appropriately attach significance to the difference between “This apple has a long stem” and “Apples are good for eating.” “This apple” signifies that the information provided applies only to this particular apple; “Apples” indicates a generalization about all apples.

A recent study (Butler & Markman, 2014) examined whether there are cues outside of language that guide children in resolving this problem. The researchers tested the possibility that children are sensitive to adults teaching them; if an adult deliberately highlights a property for the child’s benefit, that presumably is a property of some importance, and one that is characteristic  of all objects of this sort.

Children (aged 4-5) were shown a novel object and were told that it was a “spoodle.” There was a brief test to be sure that the child got the name right, and then another, irrelevant task. The experimenter began to clean the materials from this other task, and this is when the special property of the spoodle came into play; spoodles are magnetic.

In the pedagogical condition, the experimenter said “Look, watch this” and used the spoodle to pick up paperclips. In the intentional condition, the experimenter used the spoodle to pick up paperclips, but did not request the child’s attention or make eye contact. In the accidental condition, the experimenter feigned accidentally dropping the spoodle on the clips. In all of the conditions, the experimenter held the spoodle with the paper clips clinging to it and said “wow!”

Next, the child was presented 16 objects and was asked to say which were spoodles. Half were identical to the original spoodle, and half were another color. In addition, half of each color were magnetic and half were not.

So the question is which property kids think makes an object a spoodle: appearance (i.e., color) or function (i.e., magnetism). The data are shown here:

These children were clearly quite sensitive to the non-verbal teaching. Recall that in the Intentional condition, the adult used the spoodle’s function purposefully. The child could easily infer that magnetism is an important property of spoodles. But the children didn’t. Appearance made a spoodle a spoodle for these kids.

Yet when the adult did the exact same thing, but also made plain to the child her actions were for the child’s benefit, that she was teaching, then the child understood that magnetism held special significance for spoodle-hood.

I think this study has an interesting implication for differences in kids’ preparedness for schooling, associated with their home environment. We tend to focus on differences in the richness of experiences available to kids. That’s important, but this experiment provides a concrete example of small differences in parenting may have important consequences for children’s learning. “Little sponges” don’t learn certain types of information, even in a rich environment. They have to be taught.

Reference:

Butler, L. P., & Markman, E. M. (2014). Preschoolers use pedagogical cues to guide radical reorganization of category knowledge. Cognition, 130,  116-127.

This piece first appeared on RealClearEducation.com on April 16, 2014.
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A recent article in the Washington Post sounds a warning klaxon for our ability to read deeply. You’ve probably heard this argument elsewhere, made most forcefully by Nick Carr in the The Shallows: frequent users of the Web (i.e., most of us) are so in the habit of skittering from page to page, scanning for juicy bits of information but not really reading, that they have lost the ability to sit down and read prose from start to finish. I think the suggestion is probably wrong.

The first thing to make clear is that anyone who comments on this issue (including me) is guessing. There are simply not any data that address it directly. We might predict, for example, that scores on standardized reading tests would have dropped in the last fifteen years or so (they haven’t) but such data are hardly definitive, as reading comprehension test scores are a product of many factors.

The Post article cites studies comparing reading on paper versus reading on screens, but that won’t address the issue, which concerns the long-term consequences of a particular type of reading. The Post also incorrectly says that paper is superior. Most studies indicate no difference between screens and paper for pleasure reading. For textbook reading, students take longer to read on screens, although comprehension is about the same. (Daniel & Willingham, 2012).

The article, like all the pieces I’ve seen on this topic, is short on data and long on individual’s impressions. For example, teachers aver that students can no longer read long novels. Well, if we’re swapping stories, I (and most of my classmates) had a hard time with Faulkner and Joyce back in the early ‘80’s, when I was an English major.

The truth is probably that the brain is simply not adaptable enough for such a radical change. Yes, the brain changes as a consequence of experience, but there are likely limits to this change, a point made by both Steve Pinker and Roger Schank when commenting on this issue. If our ability to deploy attention or to comprehend language processes were to undergo substantial change, the consequences would cascade through the entire cognitive system, and so the brain is probably too conservative for large-scale change.

For example, there’s a lot of overlap in the processes of reading and the processes used for understanding spoken speech—processes that assign syntactic roles to words. Do we see any evidence that people are having a harder time understanding spoken language? Or does the problem lie in the mental processes that build understanding of larger blocks of language, as when we’re comprehending a story? If so, habitual Web users should have a hard time understanding complex narratives not just when they read, but in television and movies. No one should have watched The Sopranos, with its complicated, interweaving plotlines.

A more plausible possibility is that we’re not less capable of reading complex prose, but less willing to put in the work. Our criterion for concluding “this is boring. This is not paying off” has been lowered because the Web makes it so easy to find something else to read, watch, or listen to. (I explore the possibility in some detail in my upcoming book, Raising a Reader in an Age of Distraction.) If I’m right, there’s good news and bad news. The good news is that our brains are not being deep-fried by the Web; we can still read deeply and think carefully. The bad news is that we don’t want to.

Reference

Daniel, D. B. & Willingham, D. T. (2012). Electronic textbooks: Why the rush? Science, 335, 1569-1571.

This blog posting first appeared on RealClearEducation on April 8, 2014.

The 2012 results for the brand-new PISA problem-solving test were released last week. News in various countries predictably focused on how well local students fared, whether they were American, British, Israeli, or Malaysian. The topic that should have been of greater interest  was what the test actually measures.

How do we know that a test measures what it purports to measure? There are a few ways to approach this problem.

One is when the content of the test seems, on the face of it, to represent what you’re trying to test. For example, math tests should require the solution of mathematical problems. History tests should require that test-takers display knowledge of history and the ability to use that knowledge as historians do. 

Things get trickier when you’re trying to measure a more abstract cognitive ability like intelligence. In contrast to math, where we can at least hope to specify what constitutes the body of knowledge and skills of the field, intelligence is not domain-specific. So we must devise other ways to validate the test. For example, we might say that people who score well on our test show their intelligence in other commonly accepted ways, like doing well in school and on the job.

Another strategy  is to define what the construct means—“here’s my definition of intelligence”—and then make a case for why your test items measure that construct as you’ve defined it.

So what approach does PISA take to problem-solving? It uses a combined strategy that ought to prompt serious reflection in education policymakers.

There is not any attempt to tie performance on the test to everyday measures of problem-solving. (At least, none have been offered so far, but there is more detail on the construction of the test to come, in an as-yet-unpublished technical report.)

From the scores report, it appears that the problem-solving test was motivated by a combination of the other two methods.

First, the OECD describes a conception of problem solving—what they think the mental processes look like. That includes the following processes:

·         Exploring and understanding

·         Representing and formulating

·         Planning and executing

·         Monitoring and reflecting

So we are to trust that the test measures problem solving ability because these are the constituent processes of problem solving, and we are to take it that the test authors could devise test items that tap these cognitive processes.

Now, this candidate taxonomy of processes that go into problem-solving seems reasonable at a glance, but I wouldn’t say that scientists are certain it’s right, or even that it’s the consensus best guess. Other researchers have suggested that different dimensions of problem solving are important—for example, well-defined problems vs. ill-defined problems. So pinning the validity of the PISA test on this particular taxonomy reflects a particular view of problem-solving.

But the OECD uses a second argument as well. They take an abstract cognitive process—problem solving—and vastly restrict its sweep by essentially saying “sure, it’s broad, but there is a limited way that we really care about how it’s implemented. So we just test those.”

That’s the strategy adopted by the National Adult Assessment of Literacy. Reading comprehension, like problem solving, is a cognitive process, and, like problem solving, it is intimately intertwined with domain knowledge. We’re better at reading about topics we already know something about. Likewise, we’re better at solving problems in domains we know something about. So in addition to (as best they could) requiring very little background knowledge for the test items, the designers of the NAAL wrote questions that they could argue reflect the kind reading people must do for basic citizenship. Things like reading a government-issued pamphlet about how to vote, and reading a bus schedule, and reading the instructions on prescription medicine.

The PISA problem solving test does something similar. They authors sought to present problems that students might really encounter, like figuring out how to work a new MP3 player, or finding the quickest route on a map, and or figuring out how to buy a subway ticket from an automated kiosk.

So with this justification, we don’t need to make a strong case that we really understand problem-solving at a psychological level at all. We just say “this is the kind of problem solving that people do, so we measured how well students do it.” 

This justification makes me nervous because the universe of possible activities we might agree represent “problem solving” seems so broad, much broader than what we would call activities for “citizenship reading.” A “problem” is usually defined as a situation in which you have a goal and you lack a ready process in memory that you’ve used before to solve the problem or one similar to it. That covers a lot of territory. So how do we know that the test fairly represents this territory?

The taxonomy is supposed to help with that problem. “Here’s the type of stuff that goes into problem solving, and look, we’ve got some problems for each type of stuff.” But I’ve already said that psychologists don’t have a firm enough grasp of problem-solving to advance a taxonomy with much confidence.

So the PISA 2012 is surely measuring something, and what it’s measuring is probably close to something I’d comfortably call “problem-solving.” But beyond that, I’m not sure what to say about it.

I probably shouldn’t get overwrought just yet—as I’ve mentioned, there is a technical report yet to come that will, I hope, leave all of us with a better idea of just what a score on this test means. Gaining that better idea will entail some hard work for education policymakers. The authors of the test have adopted a particular view of problem solving—that’s the taxonomy—and they have adopted a particular type of assessment—novel problems couched in everyday experiences. Education policymakers in each country must determine whether that view of problem solving syncs with theirs, and whether the type of assessment is suitable for their educational goals.

The way that people conceive of the other PISA subjects (math, science and reading) is almost surely more uniform than the way they conceive of problem-solving. Likewise, the goals for assessing those subjects is also more uniform. Thus, the problem of interpreting the problem-solving PISA scores is formidable compared to interpreting other scores. So no one should despair or rejoice over their country’s performance just yet.

This post first appeared at RealClearEducation on April 1, 2014.

Our scientific understanding is always evolving, changing. Thus, one of the ongoing puzzles in education research is how confident one must be in a set of findings before one concludes it ought to be the basis of educational practice. If the data show that X is true, but X seems really peculiar, do we assume X is probably true, or do we assume that we just don't understand things very well yet? A new study provides something of an object lesson in this problem; in this case "X" was "parents teaching reading at home doesn't help much after kindergarten."

Here's the background on that counterintuitive finding. The work was inspired by the home literacy model (Senechal & LeFevre, 2000). It posits two dimensions of home literacy experience: formal experiences are those in which the parent focuses the child's attention on print, for example by teaching letters of the alphabet, or pointing out that two words look the same, or that we read from left to right.

Informal experiences are those for which print is present, but is not the focus of attention; reading aloud to one's child would be an example. Children usually look at pictures, not print, during a read-aloud.

Previous research from this research team, and others, has shown that formal and informal experiences have different effects. Formal experiences are associated with early literacy skills like knowing letters, and later, with word reading. Informal experiences, in contrast, are associated with growth in vocabulary and general knowledge.

But data supporting the home literacy model have usually been concurrent, not predictive, and have been limited to preschool, kindergarten, and early 1st grade. That is, the research shows an association between the relevant factors measured now, as opposed to showing that the home factors at, say, kindergarten, predict growth in reading outcomes for the 1st grade and beyond. That's peculiar.

There are at least two possible reasons. One is that the home literacy environment does have an impact on literacy growth, but researchers have been looking for the effect in 1st grade - just at the time that school instruction is so heavy. So perhaps the impact of home literacy environment on literacy growth is overwhelmed by the effect of school instruction. A second possible reason is that the home literacy environment may change as a consequence of how the parents perceive their child is doing in school.

A new study (Senechal & LeFevre, 2014) used a clever design to examine both possibilities. Subjects were 84 children in Quebec who spoke English at home, but for whom the language of instruction at school was French. So researchers could test progress in English, and thereby examine the impact of the home literacy environment independent of schooling. The research measured various aspects of children's literacy -- reading and oral language -- from kindergarten until spring of second grade. In addition, researchers used a number of measures to characterize their formal and informal literacy experiences at home.

The results provided strong support for the Home Literacy Model. Formal literacy activities at home were linked not only to performance in reading English, but, in contrast to prior work, a relationship was observed with growth in reading English from kindergarten to 1st Grade. Thus, there is some support of the idea that previous studies failed to observe the relationship because the experiences at school overwhelmed any effect that home experiences might have had.

But that can't be the whole story, because the relationship was no longer observed in 2nd grade. This is where parental responsiveness comes in. English instruction, one hour daily, began in 2nd grade, and so parents began to get feedback from schools about their child's English reading at that time.

Researchers found that the degree to which parents taught their children English at home was positively associated with student outcomes in kindergarten and 1st grade. But there was a negative association in 2nd grade. A straightforward interpretation is that many parents engaged in some English teaching at home during kindergarten and 1st grade, and the more of it they did, the better for their kids. Then in 2nd grade, parents get feedback from the school about their child's reading in English. If their child is doing well, parents ease off on the teaching at home. If their child is doing poorly, they increase reading. Indeed, researchers found that most parents -- 76 percent -- changed their formal literacy practices in response to their child's reading performance in 2nd grade. So you end up with a negative correlation of parental instruction and child performance in 2nd grade. The kids who are doing the worst in reading are the ones whose parents are teaching them the most.

The impact of informal literacy activities like read-alouds did not change; they were consistently linked to growth in vocabulary and other measures of oral language from kindergarten through second grade.

It should be noted that the parents in this study had greater than average education - more than half had a university degree. It's a good bet then, that the baseline home literacy environment was atypically high and that these parents may have been more responsive to their child's literacy outcomes than others would have been. We should not generalize these findings broadly.

Still, in this case, "X" turned out to be explicable and sensible. It appeared that parents teaching literacy at home did not help children's literacy because other variables had gone uncontrolled. This study doesn't solve the broader problem - we never know if our understanding of an issue is incomplete to the point of inaccuracy - but that's one issue on which we are at least closer to the truth. 

References:

Senechal, M., & LeFevre, J. (2002). Parental involvement in the development of children’s reading skill: A 5-year longitudinal study. Child Development, 73, 445–460.

Senechal M. & Lefevre, J. (in press). Continuity and change in the home literacy environment as predictors of growth in vocabulary and reading. Child Development.

This piece first appeared on RealClearEducation.com on March 26.

How do you know that whether a book is at the right level of difficulty for a particular child? Or when thinking about learning standards for a state or district, how do we make a judgment about the text difficulty that, say, a sixth-grader ought to be able to handle?

It would seem obvious that an experienced teacher would use her judgment to make such decisions. But naturally such judgments will vary from individual to individual. Hence the apparent need for something more objective. Readability formulas are intended as just such a solution. You plug some characteristics of a text into a formula and it combines them into a number, a point on a reading difficulty scale. Sounds like an easy way to set grade-level standards and to pick appropriate texts for kids.

Of course, we’d like to know that the numbers generated are meaningful, that they really reflect “difficulty.”

Educators are often uneasy with readability formulas; the text characteristics are things like “words per sentence,” and “word frequency” (i.e., how many rare words are in the text). These seem far removed from the comprehension processes that would actually make a text more appropriate for third grade rather than fourth.

To put it another way, there’s more to reading than simple properties of words and sentences. There’s building meaning across sentences, and connecting meaning of whole paragraphs into arguments, and into themes. Readability formulas represent a gamble. The gamble is that the word- and sentence-level metrics will be highly correlated with the other, more important characteristics.

It’s not a crazy gamble, but a new study (Begeny & Greene, 2014) offers discouraging data to those who have been banking on it.

The authors evaluated 9 metrics, summarized in this table:

The dependent measure was student oral reading fluency, which boils down to number of words correctly read per minute. Oral fluency is sometimes used as a convenient proxy for overall reading skill. Although it obviously depends heavily on decoding fluency, there is also a contribution from higher-level meaning processing; if you are understanding what you’re reading, that primes expectations as you read, which makes reading more fluent.

In this experiment, second, third, fourth, and fifth graders each read six passages taken from the DIBELS test: two passages each from below, at, and above their grade level, for a total of six passages. 

Previous research has shown that the various readability formulas actually disagree about grade levels (e.g., Ardoin et al, 2005). In this experiment, oral reading fluency was to referee the disagreement. Suppose that according to PSK, passage A is appropriate for second graders and passage B is appropriate for third graders. Meanwhile Spache says both are third-grade passages. If oral reading fluency is better for passage A than passage B, that supports the PSK. (“Faster” was not evaluated only in absolute terms, but accounted for the standard error of the mean).

The researchers used an analytic scheme to evaluate how good a job each metric did of predicting the patterns of student oral reading fluency. Each prediction was considered binary: the grade level assignment predicted that there should be a difference (or not) in oral reading fluency: was a difference observed?  Chance, therefore, would be 50%. The data are summarized in the Table

All of the readability formulas were more accurate for higher ability than lower ability students. But only one—the Dale-Chall—was consistently above chance.

So (excepting the Dale-Chall), this study offers no evidence that standard readability formulas provide reliable information for teachers as they select appropriate texts for their students. As always, one study is not definitive, least of all for a broad and complex issue. This work ought to be replicated with other students, and with outcome measures other than fluency. Still, it contributes to what is, overall, a discouraging picture.

References

Ardoin, S. P., Suldo, S. M., Witt, J., Aldrich, S., & McDonald, E. (2005). Accuracy of readability estimates’ predictions of CBM performance. School Psychology Quarterly, 20, 1 – 22.

Begeny, J. C., & Greene, D. J. (2014). Can readability formuas be used to successfully gauge difficulty of reading materials? Psychology in the Schools, 51(2), 198-215.

This piece originally appeared at RealClearEducation.com on March 18, 2014

Because I teach in higher education but spend a lot of time thinking about K-12 education, the differences between the two naturally stand out to me. Perhaps most striking is the extent to which college students are responsible for their own education. Not only do they pick their classes and major (with few restrictions), they are fully responsible for regulating their own study time, and for showing up to class. At most colleges no one is aware that a student is experiencing academic trouble until things get pretty bad.

Students arrive at college with different levels of preparation to handle these responsibilities. Unsurprisingly, family background makes a difference. Students who are the first in their families to attend college (first-generation students) earn lower grades and drop out at higher rates than students with at least one parent who attended college (continuing-generation students), controlling for high school GPA (Pascarella et al 2004). (Otherwise-successful charter schools are struggling with this social-class achievement gap.)

What fuels the gap? Partly the access that continuing-generation students have to advice from parents on how best to navigate college—access that first-generation students obviously lack. Colleges try to make up for this difference by offering programs to aid first-generation students; programs that offer advice on how to select a major, how to manage one’s time, and so on.

But first-generation students don’t take colleges up on their offers of help. They are less likely to take advantage of college services than continuing-generation students.

That may be because first-generation students are unsure whether or not they really belong at college, whether they can succeed.

Taking a cue from similar studies examining race (e.g., Walton & Cohen, 2011) a new study Stephens et al, in press) sought to change how freshmen students thought about their family backgrounds by exposing them to stories of successful upper-class students, who described how their family backgrounds could be a source of challenges and of strength.

First-generation (N=66) and continuing-generation (N=81) freshman students attended a panel discussion by college seniors on college adjustment.

For half of the freshman, the panelists’ answers were linked to their family backgrounds. For example, a first-generation panelist pointed out that his parents couldn’t provide much advice about selecting classes, so he learned that he had to rely on his advisor more than other students. The continuing-generation students highlighted that they faced challenges as well: a panelist mentioned that she had attended a small private school that offered a lot of one-on-one attention, and that she felt lost in large lecture classes.

The other half of the freshman served as the control condition: they attended a different panel discussion in which challenges of college life and how to address them were discussed, but the answers were not directly linked to family background.

At the end of the year, this brief, one-time intervention had significantly reduced the social-class gap, as measured by cumulative GPA.

End-of-year surveys also indicated that the intervention had reduced student anxiety and led to better adjustment to college life. (Note: these outcomes were observed for both first-generation and continuing-generation students.)

Anytime you hear about a one-hour intervention that has such a profound and long-lasting effect, it’s natural to be suspicious. Certainly, we’d like to see this effect replicated, but there is at least a plausible explanation for the profound effect; the intervention provides a new way for students to think about difficulties. Instead of evidence that they don’t really belong at college, set-backs become a normal part of college life, and one that can be addressed.

References

Pascarella, E., Pierson, C., Wolniak, G., & Terenzini, P. (2004).  First-generation college students: Additional evidence  on college experiences and outcomes. Journal of Higher  Education, 75, 249–284.

Stephens, N. M., Hamedani, M. G., & Destin, M. (in press). Closing the Social-Class Achievement Gap A Difference-Education Intervention Improves First-Generation Students’ Academic Performance and All Students’ College Transition. Psychological science.

Walton, G. M., & Cohen, G. L. A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331, 1447-1451.

One of the controversies of the Common Core State Standards (CCSS) concerns the difficulty of the content, especially for early elementary grades. Some critics have suggested that the standards are too difficult; first grade children are simply not ready to learn about Mesopotamian civilizations, for example. But a new experiment shows that first graders can understand a scientific topic usually reserved for older grades--natural selection.

Even before the CCSS, key ideas from some content areas were left to later grades, presumably because students wouldn’t understand them earlier. For example, evolution has usually been taught in high school, even though it’s a foundational idea biology that, if students had under their belts, would likely make learning other concepts easier. The latest standards from Achieve, the National Research Council, and AAAS all take that tack.

It may seem foolish to suggest that students could tackle evolutionary ideas earlier, given that they frequently don’t understand them now. High schoolers usually understand the general idea of adaptation, but they focus on individuals, rather than populations. For example, they think that an individual’s efforts over a lifetime are influential in shaping its fitness, rather than random variation making some animals more fit, and thus more likely to survive and reproduce.

But the history of developmental psychology shows that the age at which children can reach cognitive milestones depends in no small part on the cleverness of the methods used to measure their ability. Perhaps younger students could understand evolution under the right circumstances. A new study (Kelemen et al, 2014) indicates that’s so.

Researchers tested children aged 5 through 8. Kids heard a story about pilosas, fictional animals whose survival was threatened when their food source, insects, started to live below ground in deep, narrow tunnels. Pilosas have trunks which might be wide or narrow. The story went on to explain that in successive generations, trunks became less variable, as pilosas with narrow trunks survived and had young, whereas pilosas with wide trunks could not get enough to eat and did not reproduce.

Researchers tested comprehension of the story and children’s ability to generalize the biological principle to a new case. They were tested immediately and after three months. Each test included ten questions in all (five open, five closed) which probed understanding of different aspects of natural selection such as differential survival, differential reproduction, and the passing on of traits between generations.

7 and 8 year-old children showed good comprehension of the story, with nearly half showing an understanding of the natural selection in one generation and 91% showing at least a partial understanding. Remarkably, 3 months later, this knowledge transferred more or less intact to a story about a new species.

A second experiment replicated the first AND added the idea of trait constancy within an individual; what you’re born with, you retain. This extra detail seemed to help, with still higher percentages of children showing complete understanding and transfer to a new case.

No one would claim that these children have a complete understanding of natural selection. But they got much farther along in their understanding than I think most would have guessed.

The authors speculate that children did so well because the explanation capitalized “on young children’s drive for coherent explanation, factual knowledge, and interest in trait function, along with their affinity for picture storybooks.”

They further speculate that explaining natural selection at a younger age may have worked out so well because they were not old enough to have developed naïve theories of species change; ideas that would become entrenched and potentially make it more difficult to understand natural selection properly.

The practical implication of this result is obvious; students may be ready to learn concepts of evolution much earlier than most have thought. It also invites the question of whether we do students a disservice if we are too quick to dismiss content as “developmentally inappropriate.”

Reference:

Kelemen, D., Emmons, N. A., Schillaci, R. S., Ganea, P. A., Lillard, A., Rottman, J., & Smith, H. Young (in press). Children Can Be Taught Basic Natural Selection Using A Picture Storybook Intervention. Psychological Science.

Dear blog readers
I will now be blogging at RealClearEducation.com.

RealClearEducation is an offshoot of RealClearPolitics, which has earned a good reputation as a clearinghouse for thoughtful commentary without partisan invective.

We need such a clearinghouse in education.

The fact that Andy Rotherham is heading RealClearEducation as Executive Editor gives me confidence that there is a steady hand at the wheel. I have great respect for Andy as someone who is able to make his political views plain, but actually listen to differing opinions, and to change his views when it makes sense.

I hope that you'll continue to be interested in what I write about science and how it applies to education, and that you'll find RealClearEducation a valuable site.

Here's my first post at RealClearEducation: Do We Underestimate our Youngest Learners?

~Dan Willingham

Researchers emphasize there are very few circumstances in which you can do two things at once without cost (relative to doing each on its own). Yet some drivers sneak a look at their phone while on the road, and some students have the television playing while they complete an assignment.

Why? One possibility is that they don't understand the cost of multi-tasking very well. A new study (Finley, Benjamin, and McCarley, 2014) investigated that possibility.

Subjects initially practiced a tracking task: a small target moved erratically on a computer screen and the subject was to try to keep a mouse cursor atop it.

Interleaved with practice on the tracking task, subjects practiced a standard auditory N-back task: they heard a series of digits (one every 2.4 seconds) and were asked to say whether the digits matched the one spoken 2 digits earlier (or in other versions of the task, 1 digit or 3 digits earlier).

After a total of 3 phases of practice for each task, subjects were told that they would try to do both tasks at the same time. They were told to prioritize the tracking task; just as a driver must keep the car in the lane, they should do their best to keep the cursor near the target, but they should do their best on the N-back task.

Then subjects got feedback on their performance on the three phases of tracking task (expressed as percent time they had the cursor on the target) and they were asked to predict their performance on the tracking task when simultaneously doing the N-back task.

The results showed a significant drop in tracking performance when subjects had to do the N-back task at the same time. What did subjects predict?

Subjects did predict a decrement. What they could not do was predict the size.

The graph shows the correlation between the predicted decrement in tracking performance and the actual decrement.

Subjects were not just wildly guessing. Their predicted performance in the dual task situation was related to their performance in the single-task situation, as shown here:

So to make the judgment "how much will it hurt my tracking performance to add a second task?" subjects take their single-task tracking performance and subtract something. . . but the "something" is not accurate.

The analogy to typical dual-task situations is not that great. In this case, I have never performed the two tasks simultaneously and am asked to guess at performance when I do. When a student decides to watch television while completing an assignment, he very likely has completed those tasks in a dual-task situation.

This means he has two ways of predicting his performance: one would be guessing at the dual-task cost, and this experiment shows that although subjects know there is some cost, they are terrible at predicting its size.

The second way students could predict what will happen if they multitask while working is based on their memory of similar situations. But the feedback students get in this situation is unclear. First, the feedback is significantly delayed, relative to when the work is completed. Second, every assignment varies (and so do tv programs) so the student might attribute bad performance to one of those variables (although I don't know of any study showing no cost to background television).

But there is another interpretation of students' choice to multitask.  They know their performance will suffer, they know they don't know how much it will suffer, and they don't care.


Reference:
Finley, J. R., Benjamin, A. S., & McCarley, J. S. (2014). Metacognition of multitasking: How well do we predict the costs of divided attention? Journal of Experimental Psychology: Applied, in press.

In today's New York Times Thomas Friedman reports on purported hiring practices at Google, as represented by Laszlo Bock, a senior vice president there.

Bock is an amateur psychometrician. He maintains that "GPA's are worthless as a criteria for hiring, and test scores are worthless."  Rather, they are looking for "general cognitive ability, and it's not IQ. It's learning ability."

Bock is similarly unimpressed by "expertise." According to Bock, someone with high cognitive ability will come up with the same answer as the person with expertise anyway.

They also value "emergent leadership," which means what it sounds like, and "humility and ownership," which sounds like being a responsible employee; shouldering blame when blame is yours, and trying to learn from your failures.

Everything Bock says is probably not true, and if it were true, it would not work well in organizations other than Google.

• Decades of research shows that job performance in many careers is pretty well predicted by standard IQ tests.
• "Learning to learn" is nebulous because it's domain-specific, and it's domain-specific because the ability to learn new things depends on what you already know.
• "Emergent leadership" and "humility and ownership" are qualities many organizations prize and would dearly love to reliably predict at hiring time. Maybe Bock has something to teach them about this. I kinda doubt it, but you never know.
  
• The idea that smart people can pretty well figure anything out without expertise? Even though IQ predicts job performance (not "learning to learn") experience still matters to performance.
  

Friedman adds the critical caveat in the last paragraph:
Google attracts so much talent it can afford to look beyond traditional metrics, like G.P.A.

Yes. It reminds me of a conversation I had with a Harvard admissions office who told me "Look, we could fill the freshman class with students who got 800,800 on the SAT. Literally. Every single freshman, 800,800. We're just not interested in doing that."

That doesn't mean that the SAT was irrelevant; you didn't meet many Harvard students with crummy SAT scores. It means that once you're in the 750 range, Harvard figured you're damn smart and whatever "edge" might be represented in the difference between 750 and 800 didn't matter much. They started to look at other qualities. Harvard admissions officers (at least as represented by my friend) were also quite serious in how they tried to do it, and quite humble about their ability to assess them.

Likewise, Google is, I'm willing to guess, selecting from tremendously capable people--capable as defined in standard ways--so it makes perfect sense that further selection is based on other qualities. It doesn't mean that standard metrics are rendered irrelevant.

Friedman is right when (in the last paragraph) he offers this advice: For most young people, though, going to college and doing well is still the best way to master the tools needed for many careers. I would add that I would expect Google's offbeat hiring practices wouldn't work in most places.