​The last decade has seen a huge upsurge in researcher interest in the consequences of pre-k education. That’s due, in part, to the steady increase over the last fifty years in the number of children enrolled in pre-k. In the last twenty years, that increase has been driven by children enrolled in public programs. 

The increase in publicly funded programs naturally enough sparks interest among policymakers as to whether these programs work.

That’s not an easy research question. It’s hard to track children in future years as their families move, it’s more difficult to construct reliable assessments for younger children, and there’s less agreement about what constitutes successful outcomes in pre-k than in higher grades.

Perhaps most troubling, it’s not obvious what the counterfactual is; in other words, you’d like to compare the outcome for a child if he goes to pre-k compared to the outcome for the same child if he doesn’t go to pre-k. That’s obviously impossible, so you’ll compare the child to another child who doesn’t go to pre-k….but what does that child do? Fifty years ago it was a good bet that “didn’t go to pre-k” meant the child was at home with his mother. Today, he might go to a home daycare, or be cared for by a relative. To what we should compare pre-k just isn’t so obvious.

In addition, the answers to these questions are clouded by political factors. Ones assessment of the efficacy of public pre-k programs has the potential to be influenced by one’s preconceptions of the efficacy of government programs more generally, and by preconceptions as to whether such programs ought to be within the role of government, whatever their efficacy.

Some researchers have countered the latter set of concerns by noting that effective pre-k programs can more than pay for themselves; children who attend effective pre-k programs will be less likely to drop out of school, more likely to end up in high paying jobs (and so pay more taxes), are less likely to be incarcerated, to need public assistance, and so on. Some researchers claim that pre-k programs return ten dollars or more for each dollar spent.

The pressing questions are: (1) are these claims accurate; (2) what are the characteristics of a “high quality” pre-k program; (3) can governments create and sustain pre-k programs with these features at scale?

Two groups of researchers recognized the need to bring together existing research and to provide policymakers with some answers that are, insofar as is possible, objective and devoid of political ax-grinding. This academic world is small enough that it was inevitable that each should learn about the other, and they chose to join forces. The result is a report, The Current State of Scientific Knowledge on Pre-Kindergarten Effects.

The heart of the report is a consensus statement. I offer four key conclusions from that statement here, verbatim, in red, with brief comments of my own after each conclusion. 

Studies of different groups of preschoolers often find greater improvement in learning at the end of the pre-k year for economically disadvantaged children and dual language learners than for more advantaged and English-proficient children.

That’s the counterfactual at work. Rich and poor kids would have different experiences if they were not in pre-k, with poor kids having fewer opportunities for an enriching environment than the wealthy kids.

Pre-k programs are not all equally effective. Several effectiveness factors may be at work in the most successful programs. One such factor supporting early learning is a well implemented, evidence-based curriculum. Coaching for teachers, as well as efforts to promote orderly but active classrooms, may also be helpful.

This is not a surprise…the curriculum matters, and providing training and direction to teachers helps. In the details of the report, curricular comparisons are pretty rough-cut: whole-child vs. skills-based (i.e., math, literacy or both). Whole-child curricula have not been successful in developing literacy, math, or socio-emotional skills…but it also sounds like a bit of a basket category.

Children’s early learning trajectories depend on the quality of their learning experiences not only before and during their pre-k year, but also following the pre-k year. Classroom experiences early in elementary school can serve as charging stations for sustaining and amplifying pre-k learning gains. One good bet for powering up later learning is elementary school classrooms that provide individualization and differentiation in instructional content and strategies.

This is one of the most important points. It’s saying that the oft-cited Perry and Abcederian preschool results are atypical. Absent continued intervention, you should expect fadeout of the pre-k benefit. I’ve blogged about relevant studies before, but the main point is intuitive. Academic and social outcomes are a product not just of school experiences, but also of home and other out-of-school experiences. If those out-of-school experiences are not especially enriching, children benefit (to a greater or lesser degree) from substituting pre-k experiences. The out-of-school experiences continue to matter after pre-k.

When you spell it out, the counter-assumption sounds a little strange: children may be behind their peers in important knowledge and skills by age four, but with a good year or two of pre-k they catch up, and can keep pace with their wealthier peers thereafter. This assumption makes sense if you ascribe an outsize importance to the first few years of development, e.g., as Freud did. But that assumption isn’t right. Outside-of-school experiences continue to matter after age six.

Convincing evidence shows that children attending a diverse array of state and school district pre-k programs are more ready for school at the end of their pre-k year than children who do not attend pre-k. Improvements in academic areas such as literacy and numeracy are most common; the smaller number of studies of social-emotional and self-regulatory development generally show more modest improvements in those areas.

There’s more than one way to do this sucessfully, and the public sector can get it right. There are probably three reasons (at least) that evidence is better for literacy and numeracy than for socio-emotional learning. First, we know better how to teach numbers and letters because we’ve been at it longer. Second, there’s less happening at home that might conflict with the learning happening at school when it comes to these skills.  Third, the relative contributions of environment vs. heritable factors (e.g., temperament) is probably larger for literacy and numeracy. 

In addition to the consensus statement, the report includes brief but meaty analyses of questions important to pre-k policy, for example:

• How can scale-up be improved? (Training and monitoring at a level of detail similar to that used during initial design.)
• Is the economic return really 10 dollars or more, per dollar spent? (That figure may apply to small scale programs operating in the 1960’s. Today, with a scaled up program expect more like 2-4 dollars per dollar spent).
• Should pre-k be targeted or universal? (It depends on the circumstances in the state or district—both are effective).

If you have even a passing interest in pre-k, I recommend this report to you. 

​As digital devices have decreased in price, they have become more available to more children. The impact of this availability on children’s social lives have been debated with vigor, often with gloomy foreboding. The concern centers on the possibility that online activities are absorbing so much of children’s time that little is left for other worthy pursuits, e.g., face-to-face conversations.

​But data to inform these opinions have been lacking. We know children who own digital devices (and now, that’s most of them) spend a great deal of time interacting with them—estimates for teens are around 10 hours per day or more. To some, it’s self-evident that must carry a cost, and the cost is assumed to be social. Kids are isolated by digital devices, kids no longer know how to speak face to face, and so on (e.g., here.)

But this was really punditry and speculation. Hard data were lacking, especially hard data to which causality could be ascribed. That is, we might see kids who were socially withdrawn who spent a lot of time in online social pursuits, but such a correlation would be hard to interpret. Were online social interactions replacing face-to-face interactions and causing social isolation, or would this child be withdrawn in any event, and online communication is actually a more social activity than this child would otherwise engage in?

Clearly, a true experiment would help clear the matter up: take 1,000 middle-schoolers, give 500 of them a computer, and see what happens over the course of a school year. Well, darned if someone didn’t do that (Fairlie & Kalil, 2017).

The experimenters administered a survey at the start and the end of the school year. They also had access to administrative data regarding school participation.

It should be noted that children in the control group did have access to computer time at school and elsewhere, and some families purchased computers on their own during the year. The researchers tracked these confounds as best they could. Children given the computers did indeed spend more computer time per week than control kids.

The results:
Friends: The results showed that kids given computers did not report communicating with or hanging out with their friends less…in fact, they reported spending more time with friends.
Social groups: Giving kids a computer had no impact on the probability that they would be part of a sports team, club, or music group.
School participation: There was also no effect of home computers on the number of days absent from school (or tardy), or days suspended.
Competing activities: Self-reported TV time, homework time and leisure reading were unaffected.
Social networking: Children with a home computer were more likely to have a social network page and reported spending more time on social networks. There was also a statistically nonsignificant increase in the probability of reporting cyberbullying, a result that is difficult to interpret because the overall mean was so low (less than 1%).

A few caveats of these conclusions should be borne in mind. First, the study only lasted for one school year. Second, having a smart phone, with the constant access it affords, may yield different results. Third, children were given a computer, but not Internet access. Some kids had it anyway, but the more profound effects may come from online access.

All that said, I am less frightened than some by the threat that digital technologies will eat children’s minds, or making them anti-social zombies. I wrote The Reading Mind before this study was published, but as I put together the data, I suggested that digital activities were not replacing reading, and that’s true for two reasons. First, reading provides a different sort of pleasure than gaming or social networking. If you like reading, that pleasure is only available by reading. Second, digital technology has not reduced reading for most kids because most kids don’t read anyway.

This later point is the most salient to me, and has most influenced how I raise my own kids. It’s not that most digital technologies are so terrible, but most of what my kids can do online is less preferable to me than what they can do offline. I’d rather they make something, take a bike ride, or read a book. But if they horse around on the computer, that’s no worse (or better) than watching Say Yes to the Dress, my ten-year-old's latest television infatuation.

My real concern about digital technology use in teens is hard to quantify. When I was a teen I, like most, probably assigned too much value to the opinions of my peers.  They necessarily stopped influencing me when I got off the school bus, and I was influenced mostly by my parents and two sisters. I don’t relish the thought of children taking their peer groups home with them in their pockets, influencing them 24/7, and diminishing the impact of their families. 

Suppose a teacher asks students to read a text and he wants to be sure that children notice X, Y, and Z about the text. One strategy would be to pose questions before they read, the answers to which are X, Y, and Z. The problem with this strategy is that it increases the chances they will notice and remember X, Y, and Z, and decreases the chances they will notice everything else (e.g., Pressley et al, 1990).

The interpretation of this phenomenon is straightforward: posing questions keys the reader’s attention to certain content, and they pay less attention to everything else. Indeed, when researchers put in boldface type information in the text that was not prequestioned, but would later appear on the test, the disadvantage vanished (Richland et al, 2009). The boldface drew attention to the content that would have otherwise been skimmed.

In some cases, the teacher may view that as a worthwhile tradeoff, but that’s probably infrequent—she’d prefer the boost to X, Y, and Z occur without the cost. 

A new paper (Carpenter & Toftness, 2017) may suggest a strategy to avoid the problem, although the experiment actually tested memory for video content. 

The researchers tested 85 undergraduates, each of whom watched a video lasting about eight minutes. The video was divided into three segments describing the original settlement of Easter Island, religious practices there, and the arrival of outsiders. The researchers generated four questions about each segment. Half of the subjects were asked to guess at the answer to two randomly selected questions about the upcoming segment. The control group simply pushed a button to continue on with the video. At the end of the video, subjects in both groups answered all 12 questions about the video in random order.

​Just as in prior experiments using text rather than video, asking people about specific information before seeing the video made it more likely they would learn that information once they watched the video. (That’s the tallest bar at far left). 

More interesting is that the prequestioned group was also more likely to successfully answer questions for which they had not been precued. 

Why was the cost not observed? Carpenter & Toftness emphasize that a reader controls the pace of reading; the reader can skip over content that she deems less important, and read again content that matters more. The viewer does not control the pace of video; important content might pop up at any time, and once it’s past it cannot be reviewed. So the viewer is more likely to attend closely to the whole thing. 

The researchers note that this attention hypothesis can help explain other instances in the research literature where the prequestioning deficit for other content is not observed. For example, Pressely et al (1990) asked subjects to rate each paragraph for interest. Little & Bjork (2016) showed that non-prequestioned information got a boost if it was mentioned in a prequestion, although not the target to-be-learned information. 

So in the final analysis, can teachers pose prequestions in a way that boosts memory for targeted content but doesn’t incur a cost for everything else? 

​In principle, yes. With the right type of material (boldfaced, or video), you're good, and asking for interest judgments works too. But of course none of these may be practicable as the teacher envisions the lesson plan. 

This work suggests that a teacher could devise another strategy that uses prequestions without cost--a mental task that requires attention to all content, not just the prequestioned would do the trick. True in principle, but the bottom line on prequestions a the moment seems to be “proceed with caution.” 

It's been about ten years since college students on most campuses began to take notes on a laptop in lecture classes. So that's about 10 years of professors fretting about it.

It seems obvious that students are, at the least, themselves distracted during lectures--supporting anecdotal evidence can be collected by popping into the back of any large lecture hall and observing laptop content; you'll see plenty of social media websites open, students responding to messages or email, and a surprising number of people shopping.

​More insidious, some data indicate that using a laptop is distracting to students sitting behind the multi-tasking student.

But most data on the consequences of laptop use are correlational; for example, students who report taking notes on laptops earn lower grades than those who don't, and observational data show that students have non-course related software open during class (e.g., Kraushaar & Novak, 2010). The obvious problem is that laptop use may not be causing low grades; laptop use may be more appealing to those who would earn lower grades anyway. 

Two new studies using different methods suggest that laptop use does, indeed, incur a cost to students.

In one (Carter et al., 2017) students in sections of a required introductory economics course were randomly assigned to use a laptop or to refrain from doing so. In a clever twist, some classrooms were asked to take notes on tablets with the tablet lying flat, hence reducing the likelihood that the screen would distract neighboring students.

The final examination was the same across sections, so scores on the exam served as the outcome measure for the study. Laptop (or tablet) use was associated with lower scores, d = -.18 (controlling for demographic variables, GPA, and ACT scores at admission). It is noteworthy that all of the effect was carried by performance on the multiple choice and short-answer portion of the exam, with no effect on essay grade. The researchers noted that essay grading is not objective and professors might center their grades on student performance. 

We can have greater confidence that laptop use is really causal here, given that class sections were randomly assigned to use them or not. But there are some other caveats of interpretation. It's possible that teachers teach differently when they know their students are taking notes on devices. We should also be cautious in generalizing beyond the institution (West Point) and the class (economics). For example, perhaps economics requires the drawing of many figures and graphs, a process that's clumsy on a device. Perhaps the same experiment conducted in an English literature class would show no effect. Or maybe laptop use offers a small benefit to most students, but there's a minority for whom the forced use of a laptop incurs a big cost.

The second study (Patterson & Patterson, 2017) uses a different, complimentary method that avoids these problems (but has others).

​The researchers noted that, at their institution, different classes required laptops (15%), forbade them (2%), or made them optional (83%). The researchers reasoned that a student who had a laptop-optional class might be nudged into using one if she had a laptop-required class the same day. After all, she would already have the laptop with her, so why not use it? And indeed, their survey showed that students were about 20% more likely to use a laptop under those circumstances.

And students were 48% less likely to use laptop in a laptop-optional class if they had another class the same day that forbade laptop use. 

So researchers compared grades in a class where some students are biased to use laptops and others were biased not to use them--biased, not forced, so if a student believes he really takes better notes on a laptop, he can use it. And both the laptop-user and non-user are in the same class, with the same opportunities to be distracted by other students, and experiencing the same lecture from the professor. 

Researchers analyzed about 5,500 grades in lap-top optional courses from undergraduate and masters students over the course of six semesters. All were enrolled at the same liberal arts college. 

The effect of having a laptop-required course at another time was d = -.32 to -.46.  Having a laptop-prohibited course the same day was associated with a positive effect, d = .14 to .25. (These effects controlled for gender, ethnicity, age, course load, course schedule difficulty, major, and GPA.)

Patterson & Patterson found that the negative effect was larger for weaker students--in fact, there was little or no effect for stronger students. They also found that laptop use (or more precisely, having a laptop-required course the same day) had a larger negative effective in quantitative classes. 
​
So we have two studies--a randomized control trial, and a quasi-experimental design--to add to the correlational studies showing that students are better off taking notes by hand than doing so on a laptop. None is perfect, but different designs have different flaws. This desirable state of affairs is referred to as converging operations--different types of experiments all point to the same conclusion. ​

In closing, it's worth remembering that this study does not concern all classroom laptop use, but rather one function: taking notes in a lecture course. Still, considering that most time in introductory college courses is spent listening to lectures, the impact of this work ought to be consequential. 

Once children are fluent decoders, the most frequent problem in reading is poor comprehension due to a failure to make inferences. Even seemingly straightforward anaphoric inferences can elude these students: they might read “Bob gave Tamisa some of his snack because she was hungry” and still be unsure of the referent for “she.” Other inferences require bridging information from long term memory, and these are still more challenging. For example, “Kevin said he was cold. Zeke gave him his coat.” Even if these two sentences are understood, each on its own, deeper comprehension entails making the (probably accurate) inference that Zeke gave Kevin the coat because Kevin said he was cold, which requires knowing that putting on a coat is something one does when cold.

Educators have sought to improve inferencing. In some cases they can teach students reading comprehension strategies: create a summary, for example, or create a graphic organizer. The task provides some structure that will prompt the student to make the necessary inferences. Alternatively, students might be taught more directly to make inferences, usually by instruction to elaborate on what they read in the text,  to use cues in the text that provide clues to inferencing (e.g., words like “because,” or “so,”), and to monitor their comprehension.

​A new meta-analysis of inference instruction (Elleman, 2017) shows that it’s quite effective, but carries some important caveats…ones that I’ve mentioned before.

The overall mean effect size on comprehension of inference instruction was a healthy g = .58. Elleman also evaluated separately comprehension due to inferences, and literal comprehension, and observed a difference moderated by ability. 

As the figure shows, both skilled and less skilled readers improve in inference-making ability, but inference instruction provides a boost to understanding of things stated explicitly in the text only for less skilled readers.
​
Still more interesting to me was the report that the amount of instructional time had no impact on the effectiveness of the intervention, which I’ve shown in the graph below, compiled from a data table in the paper.

Each dot represents a study condition. Two things are notable: first, most studies entail rather little practice, but nevertheless show a large effect. Second, more practice does not lead to a larger effect.

​This finding is important because it provides an important clue to the mechanism by which this instruction helps. Practice usually helps, especially early in training. The curve looks like this

Performance improves with practice, and the curve is negatively accelerating. You get the most bang for the buck from practice early in training. The data from this meta-analysis don’t show either effect. It’s true that the range is relatively small…most studies use very little practice, so it’s harder to observe any effect of practice. That still doesn’t explain why you get such a big effect with very little practice.

​This failure to observe a practice effect is more understandable if the relationship of practice and performance for inference generation look more like this: 

The plot would look like this if what kids learn during inference instruction is easy to learn, and easy to implement, but carries a one-time benefit. For example, this instruction might prompt children to better understand the importance of making the effort to coordinate meaning across sentences. It might teach them to look for cues to inference possibilities like the word “because,” or time cues like “later.”

As Elleman notes, theories of reading have centered on the knowledge from long-term memory needed to make inferences (Kintsch, 1998) and/or the working memory capacity needed to hold information in mind simultaneously so meanings can be compared (Daneman & Carpenter, 1980). Neither of these is going to change over the course of 10 hours of instruction.

And that’s the practical implication of this meta-analysis. There’s a big benefit to inference instruction, but all of the benefit accrues rapidly. There seems to be no point in spending extended classroom time on the practice.

Similar results have been observed in meta-analyses of studies examining the impact of reading comprehension strategy instruction. Gail Lovette and I (2014) reported on nine meta-analyses of typically developing readers and of readers either identified with a reading disability, or at risk. In each case, the pattern was the same: a large effect with few hours of instruction, and no benefit to more instruction. (This piece was published as a commentary in Teachers College Record and seems to have disappeared from the website. Email me if you’d like a copy.)

This interpretation is also consistent with theories of reading comprehension. Inferences are situation specific: you can’t really teach how to make inferences because the inference to be made depends on the content of the text. Rather, you can teach them to seek and use some cues (probably a causal connection here) and you can teach them that it’s important to make inferences. But making them requires broad background knowledge in long term memory.

That is an essential goal to improve reading comprehension, but it is a goal requiring years of planning, not hours. 

Tomorrow, March 18, is Trisomy-18 awareness day. It’s important to me because one of my daughters, Esprit, has Trisomy-18. In the spirit of the day I’m going to offer just a little background for those who are unfamiliar with it, but focus mostly on one interaction small children typically have with Esprit—staring at her.

By way of background, Trisomy-18 is a chromosome disorder. Each cell in the human body has 23 pairs of chromosomes, strands of DNA. Trisomy means that there are three copies, not two, of one of the pairs. Three copies of the 21st chromosome is Trisomy-21, also called Downs Syndrome. Three copies of the 18th chromosome give you Trisomy-18, also called Edwards Syndrome. (So now you know why they picked the eighteenth of March—3/18—as Trisomy-18 awareness day. )

There’s no particular cause—it’s a fluke. There’s also no cure, and over 90% of the children born with Trisomy-18 die within the first year.

Esprit is unusual for still being here at age 13, but her profile is typical of Trisomy-18 in other ways. She cannot walk or speak, and she learned to sit upright about a year ago. Cognitively, she’s like a one-year old on many dimensions. 

My goal here is not to raise awareness about the medical side of Trisomy-18—if you’ve read to this point, you’ve come close to the end of my knowledge—but rather to consider the manner in which you are most likely to encounter a child with Trisomy-18; out and about with your own child. Older kids (and their parents) will sneak a surreptitious glance at Esprit. Many adults will smile and some will approach her, always with warmth.

But kids aged two to six are generally flummoxed and show it. Parents are usually not prepared to respond to their child’s curiosity and bafflement.

​Your five-year-old will notice that Esprit doesn’t look like other children. She has facial features typical of Trisomy-18 kids. Her head is small, her ears low-set, her chin recedes, and her eyelids droop, so she usually looks sleepy. 

But your child wouldn’t be paying attention to these facial features, because others are much more noticeable. Esprit is tiny for her age, weighing just fifty pounds, and she’s usually in a large wheelchair that provides support for her back. She also wears a TLSO around her midsection, and orthotics on her feet.

To a five-year old, this is a sight.

So usually he stares. (I'll call the child "he" to keep pronouns unambiguous, cf Esprit.) That makes the parent uncomfortable, and they try to call the child away, distract him, so he’ll unlock his gaze. The parent doesn’t say “don’t stare;” doing so would acknowledge that he’s staring, and that there’s something to stare at. Eventually, the parent might drag or chivy the child away, with the child looking back the whole time, staring.

I appreciate that you don’t want your child to stare, but ignoring his interest or trying to get the hell away doesn’t work well and sends your child the message that there’s something wrong here. And admonishing the child “it’s not nice to stare” once out of earshot will not make him accept a disabled kid as part of the shopping mall crowd next time he sees one. Kids this age are too young for that. (If there's any effect, it will be to make him a sneakier starer.)

Here’s an alternative. Encourage your child to add a social element to the staring. It’s natural and unobjectionable that he’s curious about a child who looks different. Staring feels wrong because interaction with another person demands some outward acknowledgement that there is a fellow human in front of you. You can gape at a skyscraper or a sunset, but no matter how interesting another person is to behold and for whatever reason, you must give social signals that you recognize that they are not an object.

Once you add social signals, staring doesn’t feel like staring. Staring while smiling, for example, seems perfectly appropriate. Sure, a five-year old’s voluntary smile is comically phony, but who cares? It’s the thought that counts. Or encourage your child to say “hi,” or to wave. Any of these changes the dynamic from “observation of a spectacle” to a bid for social interaction. (It will also thrill Esprit.)

More outgoing kids will ask questions, usually of my wife or I rather than Esprit. Please don’t shush them, and please don’t worry about what they will ask. Parents (and strangers) don’t expect social graces at this age, so we know we’ll hear “what’s wrong with her?” or “what’s that? (pointing to her orthotic). We’re very used to talking with children about Esprit’s disability. Questions are a way of initiating social interaction. They’re great.

​Every family’s experience is different, of course, and I’d never claim to speak for all parents of disabled children. But the next time your little one stares at someone different, give the social signal strategy a try. Let me know how it goes. 

​Everyone knows that it’s dangerous to use a cell phone while driving. The danger lies in distraction; even with a hands-free phone, the conversation saps attentional resources that should be devoted to the road.

But what if you’re not driving? Suppose you’re a student working a multi-step math problem and an announcement comes in over the school PA system. 

Lab studies provide some disquieting answers. Coming back to your main task after an interruption carries a cost both in time lag and in error rates…in one field study errors were observed where the main task was the administration of medication.

In a new study Erik Altmann and his colleagues investigated the effect of the duration of the interruption. They were especially interested in duration because delay is known to have a robust effect on memory, but is likely to have an effect on attention. (The importance of delay is explained below.)

​In this experiment, the multi-step main task involved a display of a digit and a letter, about which subjects had to make seven judgments, in a particular sequence. The graphic below explains the task. 

​Interruptions occurred randomly, on average every 6 trials (but with a lag of at least three trials). Subjects saw a letter string that they were to type on the computer keyboard. Duration of the interruption was controlled by the number of strings and the number of characters in each string. 

Let’s return to the importance of the interruption delay. Suppose you’re executing a multi-step procedure and you’re interrupted. When you return to your main task any problems you have might be due to memory or attention. If the problem is one of memory (but your attention is fully back on task) we’d expect that you might forget where you are in the sequence, e.g., you were supposed to the “R” task in the UNRAVEL sequence, but you forget and repeat the “N” task. But we wouldn’t expect you’d necessarily make a mistake when answering the “N” task, because attention is fully back on task.

But if the problem is that your distracted, we’d think that both types of problems would be more likely—an interruption would make you lose your place in the sequence (sequence errors) and make you more prone to mistakes in when answering (answer errors).

​The results were quite clear, as shown in the figure

The interruption made subjects forget where they were in the sequence, but it did not seem to affect attention much once they returned to the task, because the interruption didn’t make them more error prone.

​As in other experiments, this one showed that an interruption incurred a cost to response time upon return to the main task. In this experiment, this cost increased with delay, and in light of the error data, we’d interpret this effect as being due to subjects struggling to remember where they were in the sequence. 

This memory-based account of the effect of interruptions on sequential tasks is consistent with decades of experimental work showing that the contents of short term memory is compromised by delay.  These results don’t mean attention is not a relevant factor in interruptions, but they do speak to the relative roles of memory and attention in sequenced tasks.

So administrators...if you haven't set a policy that the PA system is silent during class, think about doing so!

I’m leery of value-added measures as a metric of individual teacher quality. Aside from the straight psychometric challenges, I’ve always worried that there’s too much that teachers give students that a VAM would miss. For example, I think of a LAUSD high school teacher who told me with considerable excitement that he had managed to get one of his kids who was near to dropping out to come to his class for five days in a row. The student wasn’t really participating in any way yet, but the teacher had a plan in mind for how to try to coax this student back to trying to engage with math just one more time, after many years of difficulty and frustration.

Who would tell this teacher “that child is a bad investment of your time?” Yet where is the VAM that will measure the value the teacher is adding to this student?

A recent paper (Master, Loeb & Wyckoff, 2017; preprint here) tried to shed light on this problem. The authors note that the short-term effects of ELA teachers are usually smaller than those of math teachers (as measured by VAM). This outcome is easy to understand from a cognitive perspective—students learn more outside of school that can be applicable to ELA tests (compared to math), and thus the contribution of a teacher and school will be relatively smaller. But the contribution of ELA and math teachers to long-term outcomes (e.g., graduation) is equivalent. Why?

One possibility is that students learn different kinds of things from each. They may learn more subject-specific content from math teachers that persist to math performance next year. But a good ELA teacher may be more likely to impart different, more persistent skills to students—they may improve their self-image as students, for example.

To examine this possibility the authors compared VAMs across years in ELA and math both within subjects and between them. In other words, if an ELA teacher was really effective, we know that effect will be observable in English class the next year. Will it be observable in math class as well?

The authors had two enormous data sets with which to investigate this question: standardized test scores from 3rd through 8th students in New York City and Miami-Dade County from 2003/04 to 2011/12.

This study, like previous studies, found that about 25 or 30% of VAM persists into the next year. In this study, those quantities were similar in math and in ELA. The startling result came when investigators used VAM in one subject to predict VAM in the other subject in subsequent years. (More precisely, they examined student achievement in year 2 in subject A, accounting for achievement in year 1 in subjects A and B, and the VAM estimates of the teachers in subjects A and B in year 1.)

As noted, about ¼ to 1/3 of the VAM in ELA from one year carried over to ELA achievement the following year. And about 46% of that effect also carried over to math achievement in Miami-Dade. In New York City, it was 70%.

But having a really effective math teacher had very little impact on ELA achievement the following year. In both districts, this carryover was around 5%.

There was good evidence that these effects persist into a third year in New York City, but in Miami-Dade the results were inconclusive, according to the researchers because measurements there were less precise.

The results point to three conclusions, given the caveats typical to this work (and often forgotten): subjects other than ELA and math were not measured, and students were not randomly assigned to teachers (and in fact, there is likely systematic bias in assignment)

First, these results help us to understand why ELA short-term VAMs are smaller than math short-term VAMs, yet the predictive value for long-run outcomes (like graduation) is the same. The ELA short-term VAMs may typically be smaller, but they contribute across subjects (which the math do not). And these cross-subject effects may last years.

Second, the authors don’t speculate much on the mechanism of transfer, but at least two routes seem plausible. First, ELA teachers may, on average, provide a bigger boost to what are usually called non-cognitive skills: self-regulation, persistence, seeing oneself as belonging in school, and so on. Second, better ELA skills—especially better reading skills like decoding, fluency, deployment of comprehension strategies, and self-monitoring of comprehension—seem likely to pay dividends in many subjects.

​Third, when it comes to policy, I’ll leave the conclusion to the authors: “educators and policymakers may miss valuable information if they rely only on short-term within-subject student learning to evaluate teachers’ “value added” to student achievement.” Student achievement gains prompted by teacher X could easily be misattributed to another teacher in another subject, years later.  

Over the weekend I posted a link to a new study (Singer & Alexander, 2017) comparing reading comprehension when reading from a screen and reading from paper. Ninety undergraduates read four texts each: two book excerpts and two newspaper articles, all on various topics concerning childhood ailments. Two were read digitally, and two on paper. The results showed that subjects reading from a screen or from paper were equally proficient in identifying the main idea, but subjects reading paper did a better job when asked to list key points of the text, and to describe how it related to the main idea. Despite this pattern, 69% of subjects thought they performed better on the screen-based texts, and just 18% thought they had done better with paper.

I didn’t think all that much about this study because there have been lots of comparable studies. But my tweet garnered more comments and retweets than mine typically do, so I figured this finding must be news to some of my Twitter followers.

That’s when I decided to write a blog post flagging some of the relevant studies.

The following studies compared reading comprehension from a screen and paper, and concluded paper is better:

• Chen et al, 2014;
• Jeong, 2012;
• Lauterman & Ackerman (2014);
• Kim & Kim, 2013;
• Mangen et al, 2013; 
• Rasmusson et al , 2015;

The following studies reported no difference in comprehension, but a difference in reading time. The common-sense interpretation is that if comprehension is more difficult on screen, the reader has the option to trade time for accuracy, and that’s’ what these readers; 

• Ackerman & Lauterman, 2012;
• Connell et al, 2012;
• Daniel & Woody, 2013;

That makes it more difficult to interpret the following three studies. The experimenters report no difference in reading comprehension, but they did not measure reading time:

• Margolin et al 2013;
• Rockinson-Szapkiw, 2013; 
• Subrahmanyam et al, 2013;  

One exception is Porion et al 2016 who report no difference in comprehension, but did restrict reading time.

So ten studies report that paper is superior and four call it a draw, but three of these did not measure reading time. Other researchers reviewing the literature draw same conclusion that I do: comprehension is better when reading from paper: Tees, 2010; Sidi 2016; Zucker et al, 2009;  Walsh, 2016 concludes that reading from paper is better only for complex documents.

I doubt I’ve captured all of the extant literature. These studies tend to be published in far-flung places, and quite honestly, not always in top-drawer journals. I think that’s because it seems like the same question is being posed over and over…but actually there are a lot of potentially important modifying variables: four obvious ones would be subject matter knowledge, reader age, the purpose of reading (textbook vs leisure, for example) and experience with e-readers. So when you read any one of these articles, you can’t help but think “hmm. How generalizable is this?”

The other caveat to this conclusion is that it’s almost certainly a moving target. The fifth important variable is the interface used by the e-book. It’s my hunch that that’s the vital factor in this (small) screen decrement. Software engineers are working on it, as hardware engineers have made great improvements in the brightness and contrast of screens. I think it’s just a matter of time before ereaders are as easy to read as paper.

​​That college grades have risen over the last fifty years is not in dispute. The average course grade in college was 2.5 in 1960 (on a 4.0 grading scale) and had risen to 3.1 in 2006.

I often hear two reasons offered for the increase. During the 1960s, Professors ​were eager to help students stay in the school, so as to avoid the draft and the Vietnam war. (The lax standards were extended to female students for verisimilitude.)

In more recent years, increases in grades are attributed to the consumer era of higher education. Students see college courses as a commodity they purchase, and professors, hoping for high enrollements and good course evaluations to boost their tenure prospects, hand out the A's.

This student-as-consumer account seems to be bolstered by the fact that grade inflation has been higher at private colleges and universities than at public ones. 

Both accounts propose that grade inflation is solely due to changes in professors' grading policies, and that students have not changed. A recent paper challenges that assumption, and offers some persuasive data. 

​Rey Hernández-Julián and Adam Looney brought economic models of inflation to bear on the question. As they point out, higher prices don't always signal inflation. For example, price may increase as quality increases. 

They examined 20 years of transcript data from 90,000 students at Clemson University over the years 1982-2001. During that time grades increased from 2.67 to 2.99, comparable to the increase observed elsewhere. They also had data on the characteristics of these students. 

The researchers observed shifts in student characteristics and course-taking behavior that accounted for a little more than half of the increase in grades.

First, the students coming to Clemson were better prepared, as measured by the SAT. Math scores increased by 29 points over the period, and Verbal scores by 15 points.

Second, student enrollment drifted towards departments that tend to assign higher grades. More students took courses in Speech and Communication, Spanish, Graphic Communications, and yes, Education; all these department assign average grades above the means of other departments. Tough-grading departments--Math, Accounting, Physics, Mechanical Engineering--saw drops in enrollment.

Third, the percentage of women at Clemson increased, and women earn higher grades than men do. This increase was modest (4% points) and accounts for little of the grade increase, but the authors point out that the increase in women enrollment is high nationwide (from 41% to 56% between 1970 and 2000) and so may account for more of the grade increase at other institutions. 

The upshot is that a little less than half of the increase in grades is attributable to grade inflation. The authors identify possible contributors to grade increase, and whatever they cannot account for is labeled "inflation." They point out that the actual value of grade inflation could be lower.

I would add that the actual value could, in theory, also be higher. Unobserved variables like course difficulty and student effort could be decreasing. Obviously measuring such variables at broad scale is difficult if not impossible.