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. 

A prominent theory of the difference in cognition between those with Autism Spectrum Disorder and typical adults has it that the former have difficulty understanding and predicting what others think. This ability is often called Theory of Mind. A recent experiment indicated that, at least in one task, this process is not difficult for adults on the Autism spectrum (Pantelis & Kennedy, 2017).

The authors used a task called the Beauty Contest game. It’s deceptively simple. You’re told that a large group of people will each pick one number from 1 to 100. Whoever picks the number closest to 2/3 the value of the mean guess is the winner.

So consider why this is a theory of mind task.

• One possibility is that a player will say to himself “this is complicated, I have no idea how to be strategic, so I’ll pick a random number between 1 and 100. Call this a “0th order” player.
• A 1st order player assumes that others will be 0th order players. They will choose randomly, so the mean of their choices will be 50, so the 1st order player chooses a value of 2/3*50 or 33.
• A 2nd order player assumes that some of the other players will be 0th order, but some will figure out how to be 1st order players, and will choose numbers accordingly. The 2nd order players chooses her number according to how many 1st order and how many 0th order players she expects to be among her opponents.
• A 3rd order player assumes some other players will be 2nd order, as well as 1st and 0th order…and in principle it can continue from there.

You can see how this theory of mind task is recursive. You might start by asking “what will my opponent think?” They you can ask “will my opponent guess what I’m thinking he’ll think?” and so on. You can also see that thinking through what others might do leads you to pick a lower number.

(It’s called the Beauty Contest because the idea was first forwarded by John Maynard Keynes in the form of a newspaper beauty contest, where entrants were to select the six most attractive faces from 100 pictures, with the best pickers getting a prize. Keynes pointed out that entrants might simply pick the ones they thought most attractive, but their choices might also be informed by what they thought others would do.)

Pantelis & Kennedy first gave this task to 250 undergraduates; each was also asked to complete the Autism-Spectrum Quotient questionnaire (AQ), which is meant to measure sub-clinical personality traits consistent with autism. There was no correlation between guesses and scores on the AQ.

There is also a method of estimating the “depth” of recursive thinking in a large group of subjects. The experimenters separated the 250 subjects into those scoring relatively high on the AQ and those scoring relatively low. They observed no difference in the depth of recursive thinking in the two groups.

More telling, experiment 2 tested 30 adults with autism or Asperger’s, all of whom were described as high-functioning and of typical to high intelligence (as measured by a standard IQ test). The researchers observed no difference between this group and typical controls in the mean number guessed, nor in the model’s estimate of the depth of theory-of-mind thinking.

The researchers are appropriately cautious in drawing conclusions from this research. As they note, theory of mind is complex and it’s easy to believe that it has multiple components. The beauty contest likely taps just one. Then too, autism spectrum disorder is likely complex, with a constellation of abilities and challenges which may vary considerably from person to person. And that is perhaps the main point of this experiment: the simple characterization: “autism is mainly a theory of mind problem” won’t do. 

Students come to school to learn, but that doesn’t mean they begin school devoid of knowledge. As much as policymakers have focused in recent years on the potential richness of the home environment (and the edge that may give children) it’s equally true that children come to school with erroneous beliefs—beliefs that teachers have long recognized can be an obstacle to learning.

That’s especially true in some areas of science. Humans must know how to interact with their world, and that necessity appears to have led to a set of intrinsic, intuitive beliefs about the nature of objects in the world which have some utility for individuals, but also conflict with important principles of biology. Researchers have, in the last ten years or so, documented some of these. A new report (Coley et al, 2017) is perhaps the most thorough in doing so.

Researchers administered a series of measures to three groups of subjects (total N = 211) : 8th grade students, college biology majors, and college non-biology majors. The measures focused three intuitive beliefs:

​Anthropocentric thinking: Using humans as the basis for reasoning about other biological species (i.e., thinking of humans as the norm to which others are compared) and seeing humans as biologically unique and discontinuous compared to other species. For example, adults are slower to confirm that plants are living things, consistent with the idea that organisms that seem more distant from humans must not share other qualities with humans.

Teleological thinking: believing that the apparent goal or function of an object or event is the cause of the event. Hence, a young child might reason that lions exist so that they can be in zoos for us to see. An older child might reason that it rains in order that plants grow.

Essentialist thinking: the belief that objects have a fundamental essence that specifies their category membership, and that all members of this category share this essence. For example, an animal is either a bird or not a bird—there’s no degree of “birdiness” in the final analysis, and all birds share the bird essence. Young children believe that parents and their offspring tend to share not only physical characteristics (e.g., eye color) but also preferences (e.g., liking sweets).  
​
The researchers wanted to examine (1) whether these intuitive beliefs change in late adolescence and (2) whether training in biology affected the prevalence or depth of these beliefs. There was an effect for both factors, but in both cases, smaller than the researchers expected. The graphs below show results for one question type from each category—the final analysis used a composite of questions from each category, but these are representative. 

At left, responses to a anthropocentric question. Students were given a number of organisms and asked whether each shared a common ancestor and any point in evolution with humans. Higher bars reflect better performance.
At center, responses to a teleological question. Students read an "explanation" of a biological phenomenon that appealed to causality (e.g., "worms exist to aerate the soil, which benefit plants"). Shorter bars reflect better performance.
At right, responses to an essentialist question. Students were asked questions about absolute category membership (e.g., something is either a mammal or it isn't, and there's never an in-between). Shorter bars reflect better performance. 

All in all there are effects of age (and associated effects like further general education) as well as effects of training in biology. But all three groups show the same trend for anthropocentric and teleological thinking. Curiously, essentialist thinking grows stronger with development. The authors suggest a few possible interpretations of this finding the most plausible of which is that it is broadly consistent with how biology is frequently taught.

The core finding---that these intuitive ways of thinking are quite resistant to education—matches the findings regarding naïve  physics (e.g., Potvin et al., 2015) and highlights the challenges science teachers face.