About six weeks ago, Amanda Ripley published an article suggesting that it be made more difficult to become a teacher. I'll add one story.

My colleague at the University of Virginia, Shige Oishi, is, without exaggeration, a brilliant and highly accomplished man. I recently learned that he briefly thought about a career in teaching in Japan. I asked why he didn't pursue it.

He told me "I wasn't sure I could do it. The entrance examination is very very difficult. I knew I would have to study at least a year, and then I wasn't sure I could pass."

So he took the easy route. He got a PhD and became a professor.

This piece originally appeared at RealClearEducation.com on June 17, 2014


Stick a few hundred kids together in a building for six hours and you can bet that a few are going to misbehave. How teachers and administrators should react to rule infractions—especially more serious ones—is perennial problem. A newly published report from the School Discipline Consensus Project, with over 700 experts contributing, offers the most comprehensive answer I’ve seen.

The reports starts with two grim facts. First, present practices are ineffective. Policies tend to focus on student removal--suspensions, expulsions and arrests—as a way to keep schools orderly and safe. But while they are removed the offenders fall behind in their schoolwork, and removal puts them at greater risk for dropping out or getting in trouble with the law. Second, present policies are poorly implemented. Students are often suspended for minor infractions such as cell phone use, and kids from some groups—those with disabilities, kids of color, and LGBT youth—are disproportionately disciplined.

What’s a better way? The overarching principle emphasized in the report is the creation of more positive environments in schools and classrooms, and more supportive relationships among students, teachers, and administration. Sounds great. How do we get there? Actions in schools and districts such as these:

1)      Collect (disaggregated) data on infractions and on factors that are predictive of misbehavior. Make these data transparent to all, consistent with privacy policies.

2)      Work on school climate, for example through social/emotional learning programs.

3)      Design a graduated system of responses to misconduct that keeps students in school whenever possible, addresses the harm caused, and considers the factors that may contribute to the problem, while encouraging students to take responsibility for changing their behavior.

4)      Have a place on school grounds to which disciplined students can be removed but still receive instruction and social or emotional support, as needed.

5)      Partner with police to ensure that officers understand school policies, to formalize cooperation in written documents that are periodically reviewed, and to ensure that officers on school grounds are committed to the school learning environment.

6)      Plan for ways to divert students from the juvenile justice system for minor infractions (if appropriate); plan for ways that students released from correctional facilities can transition smoothly back to community schools.

The six points listed above constitute an incomplete summary of the very broad goals that the report addresses. These broad goals are cashed out in over 60 more specific action recommendations. But even in a long report (some 400 pages) these inevitably end up as guidelines rather than specific blueprints, e.g., “Address physical facility conditions and school security procedures to ensure schools are safe and feel secure while also being welcoming and orderly.”

 

The value of the report lies not in the specificity of the recommendations, but in the breadth of its vision. It gives an administrator or legislator a view of just how broad the problem is, and emphasizes that attending to just one or two pieces of this complex puzzle will not be sufficient. And although it cannot serve as a policy guidebook, it does lay out big-picture conclusions based on solid research.

A natural reaction upon reading this report might be despair: sixty recommended actions, each of them formidable in its own right. The authors anticipate this reaction, and confirm that implementing all of the recommendations at once would be impossible, and further, that there is no right or wrong place to start. The important thing is to start.

 

Let’s hope this report provides some impetus to reform in discipline practices. There’s little evidence that current policies are serving students and schools well, and there is reason to think we can do better.

This column first appeared at RealClearEducation.com on June 12, 2014

How can we do a better job of teaching kids math? A different curriculum? New pedagogical strategies? Personalized instruction through technology? All these worthy ideas have their adherents, but another method—reducing math anxiety—may both improve performance and help kids enjoy math more. Sian Beilock and I recently reviewed the research literature on math anxiety with an eye towards remediation. Here are some of the highlights.

Math anxiety means, unsurprisingly, that one feels tension and apprehension in situations involving math. What is surprising is the frequency of the problem, and the young age at which it can start. Fully half of first and second graders feel moderate to severe math anxiety. And many children do not outgrow it; about 25% of students attending a four-year college suffer from math anxiety. Among community college students, the figure is 80%.

The consequences of math anxiety are also unsurprising; people avoid situations where they might have to do math. So as you’d predict, math anxiety is associated with having difficulty in math. Now you might think “Well, math anxiety actually sounds pretty logical. If you’re bad at math, then doing math would make you anxious.” But it’s not just appropriate anxiety about facing a tough challenge; the term “math anxiety” is used only in cases where the person would perform better at math if he or she were calmer.

And that gives us a start on understanding the mechanism of the problem. Anxiety distracts. It’s hard to focus on the math because your mind is preoccupied with concern that you’ll fail, that you’ll look stupid, and so on. Every math problem is a multi-tasking situation, because all the while the person is trying to work the problem, he’s also preoccupied with anxious thoughts.

There seem to be two sources of math anxiety. First, there’s a vicious cycle at work; you’re anxious because the math is hard, the anxiety makes you avoid math, and that lack of practice makes the math still harder. We might guess, then, that the cycle might start with the very earliest math instruction, and indeed there is evidence that children who have trouble with basic numeric skills—counting, appreciating which of two numbers is the larger—are at greater risk for developing math anxiety.

The second source of math anxiety is social. Kids learn from adults—parents, teachers, figures in the media—that math is hard, and something to be feared. For example, students are at greater risk to develop math anxiety if their teacher is anxious about math. This social communication constitutes information for kids about how to think about their difficulty with math; it’s hard not because you’re inexperienced and need more practice, but because lots of people (maybe including you) just cant’ do it. They aren’t “math people.”

So what can be done?

The sources of math anxiety provide strong clues about how to address it. First, we can work hard to ensure that all children acquire basic skills. Math won’t provoke anxiety if kids know they can handle it. Second, teacher training can include information about how to talk to kids who do encounter difficulties; how to ensure that kids see their setbacks as a normal part of learning and problems that can be overcome, rather than as evidence that they are simply no good at math.

The testing of a third strategy is still in its infancy, but shows promise. Some studies show that giving students ten minutes to write about their emotions just before a math exam significantly boosts scores. The idea is that getting their thoughts on paper prompts students to reevaluate how anxious they really ought to be. The thought “it would be catastrophic to fail this test!” may race through your mind, but when you start to set it down on paper, you’re likely to think “well. . . .would failing really be a catastrophe?” Ten minutes of this writing reflection may put the upcoming confrontation with math in perspective, and so feelings of anxiety will not be consuming the student’s thoughts and attention during the exam.

Researchers and educators must continue to seek new pedagogies to improve math education. In parallel with these efforts, we should seek methods to address math anxiety. The payoff could be significant.

Reference

Beilock, S. & Willingham, D. T. (2014). Math anxiety: Can teachers help students reduce it? American Educator, Summer, 28-32,43.

This column first appeared at RealClearEducation.com on June 5, 2014.

We normally expect that a kindergarten classroom will be decorated with colorful posters, maps, and student artwork. But a recent study indicated that trimming out the classroom may distract students, and compromise learning.

Headlines in the press have been not equivocated. The Telegraph, Tampa Bay Times, and Smithsonian all ran stories along the lines of “wall displays distract from learning,” while the Daily Mail asked whether they should be banned altogether. My hunch is that most kindergarten teachers and parents of kindergarteners would not agree, but it’s worth looking at why this conclusion would be hasty.

The study confirms something you likely find intuitive; if there are bright, colorful things on the wall, kids will look at them, and will do so at times that the teacher hopes their attention will be focused on something else. Hence, kids might learn less about the lesson plan in such an environment.

The study (Fisher et al, 2014) took place in a laboratory reconfigured to look like a classroom. Kids first visited on five occasions to get to know the “teacher” (i.e., researcher) and to acquaint them with the paper-and-pencil measures that would be used in the experiment. Over the following two weeks, kids listened to six read-alouds on science topics (e.g., volcanoes, the solar system) in groups. Afterwards, children answered six questions about the lesson.

During the familiarization sessions, there were a moderate number of decorations on the walls. During the experimental sessions, the walls were either bare, or quite covered.

There were two outcomes of interest: first, how kids scored on the test in each environment, and second, the frequency with which kids were distracted. Children were videotaped during the story, and they were deemed to be thinking about something else if their eyes were not on the teacher.

The results were as you would expect. Kids spent a greater percentage of time looking away from the teacher in the decorated classroom (38% of time vs. 28% of time). And kids in the decorated classroom scored lower on the assessment (42% vs. 55%).

I can see two important reasons to be cautious when interpreting these results.

First, it seems likely to me that kids would habituate to the wall decorations in time. The authors address that possibility in their paper, citing data presented at a conference (but not yet published) showing that kids distraction was above baseline levels two weeks after the introduction of a distracting classroom environment. But the abstract of the talk also says that there was significantly less distraction in week 2 than in week 1. In other words, kids were getting used to the busy environment, and might have returned to baseline soon.

Second, even if we accept that classroom decoration brings a cost to learning, we should remember that teachers have other reasons for brightening their rooms; they want the classroom to be inviting, to feel like a social environment. Would it be more difficult to build a sense of classroom community in the sterile environment? That’s at least plausible. In other words, there may be a cost to outcomes that were not evaluated in this study.

Or to put it another way, the sterile environment is the wrong control condition. How about comparing the busy environment with something that looks homier, but less stimulating:  a serene painting or two on each wall, perhaps a vase of dried flowers on a table, that sort of thing.

I think there’s a useful point for lower elementary teachers to consider here. As the lead researcher mentions in a video about the study, it seems ironic that classrooms become less decorated as kids get older, given that it’s the younger kids who have more difficulty regulating attention. But I don’t think bare walls are the way to go.

Fisher, A. V., Godwin, K. E., & Seltman, H. (2014). Visual Environment, Attention Allocation, and Learning in Young Children When Too Much of a Good Thing May Be Bad. Psychological science, 0956797614533801.

This column originally appeared at RealClearEducation on May 27, 2014.
Is college worth the high cost? After all, everyone knows a college degree doesn’t guarantee a good job—English majors all work at McDonalds, right? This lore is gaining acceptance (see discussion of the “college bubble” here and here, for example) but a new review in Science (Autor, 2014) marshals data suggesting that it’s dead wrong. 

College graduates not only continue to make more than non-graduates, the education-wage premium is increasing. It has risen in most advanced economies, and nowhere more than the US. In fact the difference between the median incomes of high school graduates and college graduates in 1979 was about $17,400 (in 2012 dollars). In 2012 it had doubled to around $35,000. (Autor doesn’t cite these data, but a 2013 OECD report also suggested that unemployment rose much less for college graduates during the recent recession.)

The income difference is not an artifact of the wild increases among the top 1% of earners. As Autor emphasizes, wage inequality has increased across the spectrum of incomes and is well represented among the “other 99%.” He cites various studies suggesting that 60% or so of the increasing difference in US wages is due to the increase in the education-wage premium. Other factors contributing to the difference include the decline (in real dollars) of income for high school graduates, the declining influence of trade unions, the rise in automation and subsequent loss of jobs calling for minimal cognitive skills, and competition for those jobs in the developing world.

Autor makes the case that these seemingly disparate factors can come together into a relatively simple model of supply and demand. Since the 1980’s the number of people getting a college education has increased, but the rate of increase has slowed, relative to earlier decades. Hence, the supply of people with the cognitive skill set afforded by education was expanding, but at a slow pace. Meanwhile, the percentage of jobs requiring that skill set was increasing at a much faster pace, as low skill jobs were exported or automated. The market responded to supply and demand: wages for college graduates skyrocketed because there were not enough of them; wages for high school graduates declined (in real dollars) because there was a glut of such workers, relative to the need.

As Richard Murnane (who often collaborates with Autor) has argued for decades, there is every reason to think that these employment trends will continue; jobs that require modest cognitive skill yet pay living wages will not return to the United States economy.

So yes, college is worth it—or better, obtaining cognitive skill that is demonstrable to employers is worth it. The promise of obtaining them outside of the usual educational route (via MOOCs, for example) is still a work in progress.

For educators and education researchers, this conclusion doesn’t change much of what we’ve been trying to do, as we already believed in the importance of education for individual economic promise. Autor’s analysis offers data should we care to take on skeptics, and adds urgency (if any was needed) to the work.

This column was originally published on RealClearEducation.com on May 13, 2014.

Last week I discussed the case Carl Wieman made that education research and physics have more in common than people commonly appreciate. I mentioned in passing Wieman’s suggestion that random control trials—often referred to as the “gold standard” of evidence—are not the be-all and end-all of research, and that qualitative research contributes too. But I didn’t say how it contributes, and neither did Wieman (at least in the paper I discussed). Here, I offer a suggestion.

Qualitative research is usually contrasted with quantitative research. In quantitative research the dependent variable—that is, the outcome—is measured as a quantity, usually using a measurement that is purportedly objective. In qualitative research, the outcome is not measured as a quantity, but as a quality.

For example, suppose I’m curious to know what students think about their school’s use of technology. In a quantitative study, I might develop a questionnaire that solicits student’s opinions about, say, the math and reading software they’ve been using, and also asks students to compare them to the paper versions they used last year. I’d probably try to get most or all of the students in the school to respond. I would end up with ratings which I can treat as quantitative data.  

Or I could do a qualitative study, in which I use focus groups to solicit their opinions. The result of this research is not numeric ratings, but transcripts of what people said. I’d try to find common themes in what was said. Because focus groups are time-consuming, I’d probably talk to just a handful of students.

People who criticize qualitative research treat it as a weak version of quantitative methods. For example, a critic would point out that you can’t trust the results of the focus groups, because I might have, by chance, interviewed students with idiosyncratic views. Another problem is that focus groups are not very objective; responses are a product of the dynamic between the interviewer and the subjects, and among the subjects themselves.

These complaints are valid, or would be if we hoped to treat the focus group results the way we treat the outcomes of other experiments. I suggest we should not.

Here’s a simple graphic I used in a book to describe how science works. We begin with observations about the world. These can come from our casual, everyday observations or from previous experiments. We then try to synthesize those observations into a theory; we try to find simplifying rules that describe the many observations we’ve made. This theory can be used to generate a prediction, something we haven’t yet observed, but ought to. Then we test the prediction, and that test leads to a new observation, a new fact about the world. And we continue around the circle, always seeking to refine the theory.

The criticism of qualitative research is that it does not provide a very reliable test of a theory. But I think it’s better viewed as providing a new observation of the world.

An advantage of qualitative over quantitative data is the flexibility in how its collected. If I want to know what students think of the new technology instruction and I create a quantitative scale to measure it, I’m really guessing that I know the important dimensions of student opinion. I may try to capture their views on effectiveness and ease-of-use, for example, but what students really care about is the fact that so many websites are blocked.

Qualitative studies allow the subject to tell you what he thinks is important. For example in this qualitative study, students suggested that the schools policy on blocking websites affected their relationships with teachers—they felt untrusted. Maybe that’s the kind of thing a researcher would have been looking for, but I doubt it.

In addition, qualitative data tend to be richer. I’m letting the subject describe in his or her own words what she thinks, rather than asking her to select from reactions that I’ve framed.

Naturally, either type of research—qualitative or quantitative--can be poorly conducted. Each has its own rules of the road. It’s important to judge the quality of research by its own set of best-practice rules, and to judge it by how well it fulfils the purpose to which it is suited.

That’s why I believe qualitative research has an undeserved bad reputation. It is judged by standards of quantitative research, and deemed unable to serve purposes that ought to be served by quantitative research. But I agree with Wieman that qualitative research, well-conducted, makes a valuable contribution, one to which quantitative research is ill-suited.

I have a new article (with Sian Beilock) on math anxiety in the new American Educator. Free download here:
http://www.aft.org/pdfs/americaneducator/summer2014/Beilock.pdf

This post first appeared at RealClearEducation.com on May 6, 2014

As someone who spends most of their time thinking about the application of scientific findings to education, I encounter plenty of opinions about the scientific status of such efforts. Almost always, a comparison is made between the rigor of “hard” sciences and education. What varies is the accompanying judgment: derision for education researchers or despondency about the difficulty of the enterprise. In a recent article, physicist Carl Wieman (2014) offers a different perspective on the issue, suggesting that the difference between research in education and in physics is smaller than you might guess.

In education, Wieman is best known for refining and popularizing techniques to have college students better engage in large lecture courses. He started his career as a physicist, producing work that culminated in a Nobel prize. So he has some credentials in talking about the work of “hard” scientists.

Wieman begins by making clear what he takes to be the outcome of good science: predictive power. Can you use the results of your research to predict with some accuracy what will happen in a new situation? A common mistake is to believe that in education one ought to be able to predict outcomes for individual students; not necessarily so, any more than a physicist must be able to predict the behavior of each atom. Prediction in aggregate—a liter of gas or a school of children—is still an advance.

Wieman’s other points follow from his strong emphasis on prediction.

First, it follows that “rigorous” methods are any that contribute to better prediction. You don’t state in the absolute that randomized controlled trials are better than qualitative research. They provide different types of information in a larger effort to allow prediction.

Second, the emphasis on prediction frames the way one thinks about the messiness of research. Education research is often portrayed as inherently messy because there are so many variables at play. Physics research, in contrast, is portrayed as better controlled and more precise because there are many fewer variables that matter. Wieman argues this view is a misconception.

Physics seems tidy because you’ve probably only studied physics in textbooks, where everything is worked out: in other words, where no extraneous variables are discussed as possibly mattering. When the work that you study was first being conducted, it was plenty messy: false leads were pursued, ideas that (in retrospect) are self-contradictory were taken seriously, and so on. The same is true today: the frontiers of physics research is messy. “Messy” means that you don’t have a very good idea of which variables are important in gaining the predictive power that characterizes good science.

Third, Wieman suggests that bad research is the same in physics and education. Research is bad when the researcher has failed to account for factors that, based on prior research, he or she should have known to include. There is plenty of bad research in the hard sciences. People aren’t stupid; it’s just that science is hard.

I agree with Wieman that differences in “hardness” are mostly illusory. (That’s why I’ve been putting the term in quotation marks.) The fundamentals of the scientific method don’t differ much wherever they are applied. I also agree that people (usually people uninvolved in research) are too quick to conclude that, compared to other fields, a higher proportion of education research is low-quality. Come to a meeting of the Society for Neuroscience and I’ll show you plenty of studies that were poorly conceived, poorly controlled, or were simply wheel-spinning and will be ignored.

Wieman does ignore a difference between physics and education that I take to have important consequences: physics (and other basic sciences) strive to describe the world as it is, and so strive to be value-neutral. Education is an applied science; it is in the business of changing the world, making it more like it ought to be. As such, education is inevitably saturated with values.

Education policy would, I think, benefit, with a greater focus on the true differences between education and other varieties or research, and a reduced focus on the phantom differences in rigor.

Reference:

Wieman, C. E. (2014). The similarities between research in education and research in the hard sciences. Educational Researcher, 43, 12-14.  

This article first appeared at RealClearEducation.com on April 29, 2014.


Do children learn to read by translating letters into sound, or by perceiving the spelling of the word? The answer has an indirect bearing on teaching; it would presumably be best to instruct kids in a way consonant with how most perform the task. The last fifteen years has seen an increasing consensus among researchers: children initially learn via the letter-sound translation mechanism. As they gain reading practice, they acquire the spelling mechanism as well, although the letter-sound translation method continues to make a contribution to reading. Now a new study of 284 French children in grades 1 through 5 offers support to this model (Ziegler et al, 2014).

From the child’s perspective, the experimental task was simple. They sat before a computer screen. An asterisk appeared at the center for one second, and then a string of letters replaced the asterisk (I’ll call this the “response letter string.”) Children were to push one button if the response letter string formed a word, and another if it did not.

What the kids were not told was that another letter string actually appeared between the asterisk’s disappearance and the appearance of the response letter string. This letter string (called the prime) appeared for just .07 seconds, so the children didn’t consciously see it—if anything, they might have thought the screen flickered.

Even though you’re unaware of it, the prime can influence your response. If the prime is “MOP” and then the response letter string is also “MOP,” you’re faster to verify that “MOP” is indeed a word, compared to how fast you respond if the prime were a non-matching word, say, “DOG.” Even though you were unaware of the prime, you read it, and so you’re a bit faster to read it a second time.

But what if the prime were “MAWP?” Would you still be faster to verify that “MOP?” is a word when it appears? If you think that people read via letter-sound translation, the answer ought to be yes. When “MAWP” appears, you read it and generate the right sound, and if sound is the basis of reading, you should get the advantage when “MOP” appears.

Using a comparable method, the researchers tested whether kids read via spelling. They used a prime with nearly the same spelling as the response letter string: for example “TALBE” followed by “TABLE.” Other work has shown the readers are pretty resistant to spelling errors like this one, where letters are off by just one position (McCusker et al, 1981). So if you’re using the spelling of a word to identify it, we can expect that you’ll be faster to verify that “TABLE” is a word if the prime was “TALBE,” compared to a prime like “CAIRH.” 

So take a moment and guess. Do first graders read mostly by sound or by spelling? How about fifth graders?

The data indicated that first graders read by sound. With each successive year, kids showed more and more evidence of using the spelling of words in their reading. BUT there was no diminution of the influence of sound. Experienced readers use both the sound and the spelling mechanisms.

This result fits with the following view of reading: most kids will learn to read by learning to sound out words. With practice over the course months and years, they develop an increasing number (and increasingly robust) mental representations that allow them to identify words by their appearance, i.e., by their spelling. These representations form as a consequence of reading practice and don’t require any special instruction. This general view accords with other behavioral data showing that methods of reading instruction that emphasize phonics have an edge over other methods.

References:

McCusker, L. X., Gough, P. B. & Bias, R. G. (1981). Word recognition inside out and outside in. Journal of Experimental Psychology: Human Perception and Performance, 7, 538-551.

Ziegler, J. C., Bertrand, D., Lété, B., & Grainger, J. (2014). Orthographic and phonological contributions to reading development: Tracking developmental trajectories using masked priming. Developmental Psychology, 50, 1026-1036.