Can Instructor Attractiveness lead to Higher Smile-Sheet Ratings? More Learning? A Research Brief.

In a recent research article, Tobias Wolbring and Patrick Riordan report the results of a study looking into the effects of instructor “beauty” on college course evaluations. What they found might surprise you — or worry you — depending on your views on vagaries of fairness in life.

Before I reveal the results, let me say that this is one study (two experiments), and that the findings were very weak in the sense that the effects were small.

Their first study used a large data set involving university students. Given that the data was previously collected through routine evaluation procedures, the researchers could not be sure of the quality of the actual teaching, nor the true “beauty” of the instructors (they had to rely on online images).

The second study was a laboratory study where they could precisely vary the level of beauty of the instructor and their gender, while keeping the actual instructional materials consistent. Unfortunately, “the instruction” consisted of an 11-minute audio lecture taught by relatively young instructors (young adults), so it’s not clear whether their results would generalize to more realistic instructional situations.

In both studies they relied on beauty as represented by facial beauty. While previous research shows that facial beauty is the primary way we rate each other on attractiveness, body beauty has also been found to have effects.

Their most compelling results:

1.

They found that ratings of attractiveness are very consistent across raters. People seem to know who is attractive and who is not. This confirms findings of many studies.

2.

Instructors who are more attractive, get better smile sheet ratings. Note that the effect was very small in both experiments. They confirmed what many other research studies have found, although their results were generally weaker than previous studies — probably due to the better controls utilized.

3.

They found that instructors who are better looking engender less absenteeism. That is, students were more likely to show up for class when their instructor was attractive.

4.

They found that it did not make a difference on the genders of the raters or instructors. It was hypothesized that female raters might respond differently to male and female instructors, and males would do the same. But this was not found. In previous studies there have been mixed results.

5.

In the second experiment, where they actually gave learners a test of what they’d learned, attractive instructors engendered higher scores on a difficult test, but not an easy test. The researchers hypothesize that learners engage more fully when their instructors are attractive.

6.

In the second experiment, they asked learners to either: (a) take a test first and then evaluate the course, or (b) do the evaluation first and then take the test. Did it matter? Yes! The researchers hypothesized that highly-attractive instructors would be penalized for giving a hard test more than their unattractive colleagues. This prediction was confirmed. When the difficult test came before the evaluation, better looking instructors were rated more poorly than less attractive instructors. Not much difference was found for the easy test.

Ramifications for Learning Professionals

First, let me caveat these thoughts with the reminder that this is just one study! Second, the study’s effects were relatively weak. Third, their results — even if valid — might not be relevant to your learners, your instructors, your organization, your situation, et cetera!

  1. If you’re a trainer, instructor, teacher, professor — get beautiful! Obviously, you can’t change your bone structure or symmetry, but you can do some things to make yourself more attractive. I drink raw spinach smoothies and climb telephone poles with my bare hands to strengthen my shoulders and give me that upside-down triangle attractiveness, while wearing the most expensive suits I can afford — $199 at Men’s Warehouse; all with the purpose of pushing myself above the threshold of … I can’t even say the word. You’ll have to find what works for you.
  2. If you refuse to sell your soul or put in time at the gym, you can always become a behind-the-scenes instructional designer or a research translator. As Clint said, “A man’s got to know his limitations.”
  3. Okay, I’ll be serious. We shouldn’t discount attractiveness entirely. It may make a small difference. On the other hand, we have more important, more leverageable actions we can take. I like the research-based findings that we all get judged primarily on two dimensions warmth/trust and competence. Be personable, authentically trustworthy, and work hard to do good work.
  4. The finding from the second experiment that better looking instructors might prompt more engagement and more learning — that I find intriguing. It may suggest, more generally, that the likability/attractiveness of our instructors or elearning narrators may be important in keeping our learners engaged. The research isn’t a slam dunk, but it may be suggestive.
  5. In terms of learning measurement, the results may suggest that evaluations come before difficult performance tests. I don’t know though how this relates to adults in workplace learning. They might be more thankful for instructional rigor if it helps them perform better in their jobs.
  6. More research is needed!

Research Reviewed

Wolbring, T., & Riordan, P. (2016). How beauty works. Theoretical mechanisms and two
empirical applications on students’ evaluation of teaching. Social Science Research, 57, 253-272.

Effects of Burnout on the Brain and on Cognitive Performance

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Great Article: Burnout and the Brain by Alexandra Michel, writing in The Observer, a publication of The Association for Psychological Science.

Article link is here.

Major Findings:

  • Stress may cause changes in the brain.
  • Stress may cause problems with:
    • attention
    • memory
    • creativity
    • problem-solving
    • working-memory problems in general

Will's Caveats:

  • Studies were mostly correlational, so not clear whether there is cause-and-effect relationship.

Defining Stress:

  • Stress is NOT caused just by working long hours. As the article says:

"a comprehensive report on psychosocial stress in the workplace published by the World Health Organization identified consistent evidence that 'high job demands, low control, and effort–reward imbalance are risk factors for mental and physical health problems.' Ultimately, burnout results when the balance of deadlines, demands, working hours, and other stressors outstrips rewards, recognition, and relaxation."

Learning-and-Performance Ramifications

  • If we want our organization's employees to work at their best, we can't put them under long-periods of stress.
  • We need to give them more control of their work, reward them appropriately especially with recognition and status (not necessarily with money), promote periods of rest and relaxation, and give employees input into their job environment.

Brain Based Learning and Neuroscience – What the Research Says!

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Updated on March 29, 2018.
Originally posted on January 5, 2016.
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The world of learning and development is on the cusp of change. One of the most promising—and prominent—paradigms comes from neuroscience. Go to any conference today in the workplace learning field and there are numerous sessions on neuroscience and brain-based learning. Vendors sing praises to neuroscience. Articles abound. Blog posts proliferate.

But where are we on the science? Have we gone too far? Is this us, the field of workplace learning, once again speeding headlong into a field of fad and fantasy? Or are we spot-on to see incredible promise in bringing neuroscience wisdom to bear on learning practice? In this article, I will describe where we are with neuroscience and learning—answering that question as it relates to this point in time—in March of 2018.

What We Believe

I’ve started doing a session in conferences and in local trade-association meetings I call The Learning Research Quiz Show. It’s a blast! I ask a series of questions and get audience members to vote on the answer choices. After each question, I briefly state the correct answer and cite research from top-tiered scientific journals. Sometimes I hand out candy to those who are all alone in getting an answer correct, or all alone in being incorrect. It’s a ton of fun! On the other hand, there’s often discomfort in the room to go with the sweet morsels. Some people’s eyes go wide and some people get troubled when their favorite learning approach gets deep-sixed.

The quiz show is a great way to convey a ton of important information, but audience responses are intriguing in and of themselves. The answers people give tell us about their thinking—and, by extension, when compiled over many audiences, people’s answers hint at the current thinking within the learning profession. Let me give you an example related to the topic of brain science.

Overwhelmingly, people in my audiences answer: “C. Research on brain-based learning and neuroscience.” In the workplace learning field, at this point in time, we are sold on neuroscience.

 

What do the Experts Say?

As you might expect, neuroscientists are generally optimistic about neuroscience. But when it comes to how neuroscience might help learning and education, scientists are more circumspect.

Noted author and neuroscientist John Medina, who happens to be a lovely gentleman as well, has said the following as recently as January 2018. I originally saw him say these things in June 2015:

  • “I don’t think brain science has anything to say for business practice.”
  • “We still don’t really know how the brain works.”
  • “The state of our knowledge [of the brain] is childlike.”

Dan Willingham, noted research psychologist, has been writing for many years about the poor track record of bringing neuroscience findings to learning practice.

In 2012 he wrote an article entitled: “Neuroscience Applied to Education: Mostly Unimpressive.” On the other hand, in 2014 he wrote a blog post where he said, “I’ve often written that it’s hard to bring neuroscientific data to bear on issues in education… Hard, but not impossible.” He then went on to discuss how a reading-disability issue related to deficits in the brain’s magnocellular system was informed by neuroscience.

In a 2015 scientific article in the journal Learning, Media and Technology, Harvard researchers Daniel Busso and Courtney Pollack reviewed the research on neuroscience and education and came to these conclusions:

  • “There is little doubt that our knowledge of the developing brain is poised to make important contributions to the lives of parents, educators and policymakers…”
  • “Some have voiced concerns about the viability of educational neuroscience, suggesting that neuroscience can inform education only indirectly…”
  • “Others insist that neuroscience is only one small component of a multi-pronged research strategy to address educational challenges, rather than a panacea…”

In a 2016 article in the world-renowned journal, Psychological Review, neuroscientist and cognitive psychologist Jeffrey Bowers concluded the following: “There are no examples of novel and useful suggestions for teaching based on neuroscience thus far.” Critiquing Bower’s conclusions, neuroscientists Paul Howard-Jones, Sashank Varma, Daniel Ansari, Brian Butterworth, Bert De Smedt, Usha Goswami, Diana Laurillard, and Michael S. C. Thomas wrote that, “Behavioral and neural data can inform our understanding of learning and so, in turn, [inform] choices in educational practice and the design of educational contexts…” and “Educational Neuroscience does not espouse a direct link from neural measurement to classroom practice.” Neuroscientist John Gabrieli added: “Educational neuroscience may be especially pertinent for the many children with brain differences that make educational progress difficult in the standard curriculum…” “It is less clear at present how educational neuroscience would translate for more typical students, with perhaps a contribution toward individualized learning.” In 2017, Gabrieli gave a keynote on how neuroscience is not ready for education.

Taken together, these conclusions are balanced between the promise of neuroscience and the healthy skepticism of scientists. Note however, that when these researchers talk about the benefits of neuroscience for learning, they see neuroscience applications as happening in the future (perhaps the near future), and augmenting traditional sources of research knowledge (those not based in neuroscience). They do NOT claim that neuroscience has already created a body of knowledge that is applicable to learning and education.

Stanford University researchers Dan Schwartz, Kristen Blair, and Jessica Tsang wrote in 2012 that the most common approach in educational neuroscience tends “to focus on the tails of the distribution; namely, children (and adults) with clinical problems or exceptional abilities.” This work is generally not relevant to workplace learning professionals—as we tend to be more interested in learners with normal cognitive functioning.

Researchers Pedro De Bruyckere, Paul A. Kirschner, and Casper D. Hulshof in their book, Urban Myths about Learning and Education, concluded the following:

“In practice, at the moment it is only the insights of cognitive psychology [not neuropsychology] that can be effectively used in education, but even here care needs to be taken. Neurology has the potential to add value to education, but in general there are only two real conclusions we can make at present:

– For the time being, we do not really understand all that much about the brain.
– More importantly, it is difficult to generalize what we do know into a set of concrete precepts of behavior, never mind devise methods for influencing that behavior.”

The bottom line is that neuroscience does NOT, as of yet, have much guidance to provide for learning design in the workplace learning field. This may change in the future, but as of today, we cannot and should not rely on neuroscience claims to guide our learning designs!

 

Neuroscience Research Flawed

In 2016, researchers found a significant flaw in the software used in a large percentage of neuroscience research, calling the findings of neuroscience research into question (Eklund, Nichols, & Knuttson, 2016). Even as recently as February of 2018, it wasn’t clear whether neuroscience data was being properly processed (Han & Park, 2018).

Neuroscience is done using imaging techniques like fMRI, PET, SPECT, and EEG. Functional Magnetic Resonance Imagining (fMRI) is by far the most common method. Basically, fMRI is like taking a series of photos of brain activity by looking at blood flow. Because there tends to be “noise” in these images—that is false signals—software is used to ensure that brain activity is really in evidence where the signals say there is activity. Unfortunately, the software used before 2016 to differentiate between signal and noise was severally flawed, causing up to 70% false positives when 5% was expected (Eklund, Nichols, & Knuttson, 2016). As Wired Magazine wrote in a headline, “Bug in fMRI sofware calls 15 years of research into question.” Furthermore, it’s still not clear that corrective measures are being properly utilized (Han & Park, 2018).

The problems with neuroscience imaging were most provocatively illustrated in a 2010 article in the Journal of Serendipitous and Unexpected Results, that showed fMRI brain activation in a dead salmon—where none would be expected (obviously). This article was reviewed in a 2012 post on Scientific American.

 

Are We Drinking the Snake Oil?

Yes, many of us in the workplace learning field have already swallowed the neuroscience elixir. Some of us have gone further, washing down the snake oil with brain-science Kool-Aid—having become gullible adherents to the cult of neuroscience.

My Learning Research Quiz Show is just one piece of evidence of the pied-piper proliferation of brain- science messages. Conferences in the workplace learning field often have keynotes on neuroscience. Many have education sessions that focus on brain science. Articles, blog posts, and infographics balloon with neuroscience recommendations.

Here are some claims that have been made in the workplace learning field within the past few years:

  • “If you want people to learn, retain, and ultimately transfer knowledge to the workplace, it is essential that you understand the ergonomics of the brain.”
  • “The brain is our primary tool for learning. It’s seat of thought, memory, consciousness and emotion. So it only makes sense to match your eLearning design with how the learner’s brain functions.”
  • “Neuroscience changes everything. Neuroscience is exposing more and more about how our brains work. I find it fascinating, and exciting, because most of the theories our industry follows are based on the softer behavioral sciences. We now have researchers in the hard sciences uncovering the wonders of our neuroanatomy.”
  • “Neuroscience Facts You Need to Know: Human attention span – 8.25 seconds. Goldfish attention span – 9 seconds… Based on these facts (and a few others)… you can see why 25% of L&D professionals are integrating neuroscience.”

All of these claims are from vendors trying to get your business—and all of these claims were found near the top of a Google search. Fortunately for you, you’re probably not one of those who is susceptible to such hysterics.

Or are you?

Interestingly, researchers have actually done research on whether people are susceptible to claims based on neuroscience. In 2008, two separate studies showed how neuroscience information could influence people’s perceptions and decision making. McCabe and Castel (2008) found that adding neuroscience images to articles prompted readers to rate the scientific reasoning in those articles more highly than if a bar chart was added or if there was no image added. Weisberg, Keil, Goodstein, Rawson, and Gray (2008) found that adding extraneous neuroscience information to poorly-constructed explanations prompted novices and college students (in a neuroscience class) to rate the explanations as more satisfying than if there was no neuroscience information.

Over the years, the finding that neuroscience images lend credibility to learning materials has been called into question numerous times (Farah & Hook, 2013; Hook & Farah, 2013; Michael, Newman, Vuorre, Cumming, & Garry, 2013; Schweitzer, Baker, & Risko, 2013).

On the other hand, the finding that neuroscience information—in a written form—lends credibility has been supported many times (e.g., Rhodes, Rodriguez, & Shah, 2014; Weisberg, Taylor, & Hopkins, 2015; Fernandez-Duque, Evans, Christian, & Hodges, 2015).

In 2017, a research study found that adding both irrelevant neuroscience information and irrelevant brain images pushed learners to rate learning material as having more credibility (Im, Varna, & Varna, 2017).

As Busso and Pollack (2015) have concluded:

“Several highly cited studies have shown that superfluous neuroscience information may bias the judgement of non-experts…. However, the idea that neuroscience is uniquely persuasive has been met with little empirical support….”

Based on the research to date, it would appear that we as learning professionals are not likely to be influenced by extraneous neuroscience images on their own, but we are likely to be influenced by neuroscience information—or any information that appears to be scientific. When extraneous neuroscience info is added to written materials, we are more likely to find those materials credible than if no neuroscience information had been added.

 

If the Snake Oil Tastes Good, Does it Matter in Practice?

If we learning professionals are subject to the same human tendencies as our fellow citizens, we’re likely to be susceptible to neuroscience information embedded in persuasive messages. The question then becomes, does this matter in practice? If neuroscience claims influence us, is this beneficial, benign, or dangerous?

Here are some recent quotes from researchers:

  • “Explanations of psychological phenomena seem to generate more public interest when they contain neuroscientific information. Even irrelevant neuroscience information in an explanation of a psychological phenomenon may interfere with people’s abilities to critically consider the underlying logic of this explanation.” (Weisberg, Keil, Goodstein, Rawson, & Gray, 2008).
  • “Given the popularity of neuroimaging and the attention it receives in the press, it is important to understand how people are weighting this evidence and how it may or may not affect people’s decisions. While the effect of neuroscience is small in cases of subjective evaluations, its effect on the mechanistic understanding of a phenomenon is compelling.” (Rhodes, Rodriguez, & Shah, 2014)
  • “Since some individuals may use the presence of neuroscience information as a marker of a good explanation…it is imperative to find ways to increase general awareness of the proper role for neuroscience information in explanations of psychological phenomena.” (Weisberg, Taylor, & Hopkins, 2015)
  • “For several decades, myths about the brain — neuromyths — have persisted in schools and colleges, often being used to justify ineffective approaches to teaching. Many of these myths are biased distortions of scientific fact. Cultural conditions, such as differences in terminology and language, have contributed to a ‘gap’ between neuroscience and education that has shielded these distortions from scrutiny.” (Howard-Jones, P. A., 2014).
  • “Powerful, often self-interested, commercial forces serve as mediators between research and practice, and this raises some pressing questions for future work in the field: what does responsible [research-to practice] translation look like?” (Busso and Pollock, 2015).

As these quotations make clear, researchers are concerned that neuroscience claims may push us to make poor learning-design decisions. And, they’re worried that unscrupulous people and enterprises may take advantage—and push poor learning approaches on the unsuspecting.

But is this concern warranted? Is there evidence that neuroscience claims are false, misleading, or irrelevant?

Yes! Neuroscience and brain-science claims are almost always deceptive in one way or another. Here’s a short list of issues:

  • Selling neuroscience and brain science as a panacea.
  • Selling neuroscience and brain science as proven and effective for learning.
  • Portraying standard learning research as neuroscience.
  • When cognitive psychologists portray themselves as neuroscientists.
  • Portraying neuroscience as having already developed a long-list of learning recommendations.
  • Portraying one’s products and/or services as based on neuroscience or brain-science.
  • Portraying personality diagnostics as based on neuroscience.
  • Portraying questionnaire data as diagnostic of neurophysiological functioning.

These neuroscience-for-learning deceptions lead to substantial problems:

  1. They push us away from more potent methods for learning design—methods that are actually proven by substantial scientific research.
  2. They make us believe that we are being effective, lessening our efforts to improve our learning interventions. This is an especially harmful problem in the learning field since rarely are we getting good feedback on our actual successes and failures.
  3. They encourage us to follow the recommendations of charlatans, increasing the likelihood that we are getting bad advice.
  4. They drive us to utilize “neurosciencey” diagnostics that are ineffective and unreliable.
  5. They enable vendors to provide us with poor learning designs—partly due to their own blind spots and partly due to intentional deceptions.

Here is a real-life example:

Over the past several years, a person with a cognitive psychology background has portrayed himself as a neuroscientist (which he is NOT). He has become very popular as a conference speaker—and offers his company’s product as the embodiment of neuropsychology principles. Unfortunately, the principles embodied in his product are NOT from neuroscience, but are from standard learning research. More importantly, the learning designs actually implemented with his product (even when designed by his own company) are ineffective and harmful—because they don’t take into account several other findings from the learning research.

Here is an example of one of the interactions from his company’s product:

This is very poor instructional design. It focuses on trivial information that is NOT related to the main learning points. Anybody who knows the learning research—even a little bit—should know that focusing on trivial information is (a) a waste of our learners’ limited attention, (b) a distraction away from the main points, and (c) potentially harmful in encouraging learners to process future learning material in a manner that guides their attention to details and away from more important ideas.

This is just one example of many that I might have used. Unfortunately, we in the learning field are seeing more and more misapplications of neuroscience.

 

Falsely Calling Learning Research Neuroscience

The biggest misappropriation of neuroscience in workplace learning is found in how vendors are relabeling standard learning research as neuroscience. The following graphic is a perfect example.

 

I’ve grayed out the detailed verbiage in the image above to avoid implicating the company who put this forward. My goal is not to finger one vendor, but to elucidate the broader problem. Indeed, this is just one example of hundreds that are easily available in our field.

Note how the vendor talks about brain science but then points to two research findings that were elucidated NOT by neuroscience, but by standard learning research. Both the spacing effect and the retrieval-practice effect have been long known – certainly before neuroscience became widely researched.

Here is another example, also claiming that the spacing effect is a neuroscience finding:

Again, I’m not here to skewer the purveyors of these examples, although I do shake my head in dismay when they are portrayed as neuroscience findings. In general, they are not based on neuroscience, they are based on behavioral and cognitive research.

Below is a timeline that demonstrates that neuroscience was NOT the source for the findings related to the spacing effect or retrieval practice.

You’ll notice in the diagram that one of the key tools used by neuroscientists to study the intersection between learning and the brain wasn’t even utilized widely until the early 2000’s, whereas the research on retrieval practice and spacing was firmly established prior to 1990.

 

Conclusion

The field of workplace learning—and the wider education field—have fallen under the spell of neuroscience (aka brain-science) recommendations. Unfortunately, neuroscience has not yet created a body of proven recommendations. While offering great promise for the future, as of this writing—in January 2016—most learning professionals would be better off relying on proven learning recommendations from sources like Brown, Roediger, and McDaniel’s book Make It Stick; by Benedict Carey’s book How We Learn; and by Julie Dirksen’s book Design for How People Learn.

As learning professionals, we must be more skeptical of neuroscience claims. As research and real-world experience has shown, such claims can persuade us toward ineffective learning designs and unscrupulous vendors and consultants.

Our trade associations and industry thought leaders need to take a stand as well. Instead of promoting neuroscience claims, they ought to voice a healthy skepticism.

 

Post Script

This article took a substantial amount of time to research and write. It has been provided for free as a public service. If you’d like to support the author, please consider hiring him as a consultant or speaker. Dr. Will Thalheimer is available at info@worklearning.com and at 617-718-0767.

 

Also of Interest

 

Research Citations

Bennett, C. M., Baird, A. A., Miller, M. B., Wolford, G. L. (2010) “Neural correlates of interspecies perspective taking in the post-mortem atlantic salmon: An argument for multiple comparisons correction,” Journal of Serendipitous and Unexpected Results, 1 (1), 1-5.

Bjork, R. A. (1988). Retrieval practice and the maintenance of knowledge. In M. M. Gruneberg, P. E. Morris, & R. N. Sykes (Eds.), Practical aspects of memory: Current research and issues, Vol. 1. Memory in everyday life (pp. 396-401). Oxford, England: John Wiley.

Bowers, J. S. (2016). The practical and principled problems with educational neuroscience. Psychological Review, 123(5), 600-612.

Bruce, D., & Bahrick, H. P. (1992). Perceptions of past research. American Psychologist, 47(2), 319-328.

Busso, D. S., & Pollack, C. (2015). No brain left behind: Consequences of neuroscience discourse for education. Learning, Media and Technology, 40(2), 168-186.

Eklund A., Nichols T. E., Knutsson H. (2016). Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Science, 113, 7900–7905.

Farah, M. J., & Hook, C. J. (2013). The seductive allure of “seductive allure”. Perspectives on Psychological Science, 8(1), 88-90. http://dx.doi.org/10.1177/1745691612469035

Fernandez-Duque, D., Evans, J., Christian, C., & Hodges, S. D. (2015). Superfluous neuroscience information makes explanations of psychological phenomena more appealing. Journal of Cognitive Neuroscience, 27(5), 926-944. http://dx.doi.org/10.1162/jocn_a_00750

Gabrieli, J. D. E. (2016). The promise of educational neuroscience: Comment on Bowers (2016). Psychological Review, 123(5), 613-619.

Gordon, K. (1925). Class results with spaced and unspaced memorizing. Journal of Experimental Psychology, 8, 337-343.

Gotz, A., & Jacoby, L. L. (1974). Encoding and retrieval processes in long-term retention. Journal of Experimental Psychology, 102(2), 291-297.

Han, H., & Park, J. (2018). Using SPM 12’s Second-level Bayesian Inference Procedure for fMRI Analysis: Practical Guidelines for End Users. Frontiers in Neuroinfomatics, 12, February 2.

Hook, C. J., & Farah, M. J. (2013). Look again: Effects of brain images and mind–brain dualism on lay evaluations of research. Journal of Cognitive Neuroscience, 25(9), 1397-1405. http://dx.doi.org/10.1162/jocn_a_00407

Howard-Jones, P.A. (2014). Neuroscience and education: myths and messages. Nature Reviews Neuroscience, 15, 817-824. Available at: http://www.nature.com/nrn/journal/v15/n12/full/nrn3817.html.

Howard-Jones, P. A., Varma, S., Ansari, D., Butterworth, B., De Smedt, B., Goswami, U.,  Laurillard, D., & Thomas, M. S. C. (2016). The principles and practices of educational neuroscience: Comment on Bowers (2016). Psychological Review, 123(5), 620-627.

Im, S-h., Varma, K., Varma, S. (2017). Extending the seductive allure of neuroscience explanations effect to popular articles about educational topics. British Journal of Educational Psychology, 87, 518–534.

Jones, H. E. (1923-1924). Experimental studies of college teaching: The effect of examination on permanence of learning. Archives of Psychology, 10, 1-70.

Michael, R. B., Newman, E. J., Vuorre, M., Cumming, G., & Garry, M. (2013). On the (non)persuasive power of a brain image. Psychonomic Bulletin & Review, 20(4), 720-725.

Rhodes, R. E., Rodriguez, F., & Shah, P. (2014). Explaining the alluring influence of neuroscience information on scientific reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(5), 1432-1440. http://dx.doi.org/10.1037/a0036844

Ruch, T. C. (1928). Factors influencing the relative economy of massed and distributed practice in learning. Psychological Review, 35, 19-45.

Schweitzer, N. J., Baker, D. A., & Risko, E. F. (2013). Fooled by the brain: Re-examining the influence of neuroimages. Cognition, 129(3), 501-511. http://dx.doi.org/10.1016/j.cognition.2013.08.009

Weisberg, D. S., Taylor, J. C. V., & Hopkins, E. J. (2015). Deconstructing the seductive allure of neuroscience explanations. Judgment and Decision Making, 10(5), 429-441.

Weisberg, Deena Skolnick; Keil, Frank C.; Goodstein, Joshua; Rawson, Elizabeth; Gray, Jeremy R. (2008). The seductive allure of neuroscience explanations. Weisberg, D. S., Keil, F. C., Goodstein, J., Rawson, E., & Gray, J. R. (2008). The seductive allure of neuroscience explanations. Journal of Cognitive Neuroscience, 20(3), 470-477.

Zhao, X., Wang, C., Liu, Q., Xiao, X., Jiang, T., Chen, C., & Xue, G. (2015). Neural mechanisms of the spacing effect in episodic memory: A parallel EEG and fMRI study. Cortex: A Journal Devoted to the Study of the Nervous System and Behavior, 69, 76-92. http://dx.doi.org/10.1016/j.cortex.2015.04.002

Research finds a Gender Bias in Perceptions of Creativity

An article in the most recent issue of Psychological Science finds that people tend to perceive men as more creative than women.

Here's a quote from the article:

"Five studies provide converging evidence that lay conceptions of creative cognition (i.e., beliefs regarding what it takes to “think creatively”) overlap substantially with the unique content of male stereotypes, engendering systematic bias in the way that men’s and women’s creativity is evaluated. We found that creativity is strongly associated with stereotypically masculine-agentic qualities (Study 1), and both experimental and archival data indicated that men are judged as more creative than women (Studies 2–4). Finally, we found that attributions of agency mediate differential judgments of men’s and women’s creativity (Study 5)."  (page 1759)

They did NOT find that men are more creative than women. Indeed, they cite other research that has found no difference in creativity between the sexes.

This research adds further emphases regarding gender bias — highlighting that creative potency may be inaccurately judged based on stereotypes that work at an unconscious level. We must all beware of bias in hiring, recruiting, promotions, rewards, and task assignments.

Research on Smile Sheets — New Video

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I created a video to help organizations fully understand the meaning of their smile sheets.

 

You can also view this directly on YouTube: https://youtu.be/QucqCxM2qW4

New Scientific Review of Learning Styles

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Just last month at the Debunker Club, we debunked the learning-styles approach to learning design based on our previous compilation of learning-styles debunking resources.

Now, there’s a new research review by Daniel Willingham, debunker extraordinaire, and colleagues.

Willingham, D. T., Hughes, E. M., & Dobolyi, D. G. (2015). The scientific status of learning styles theories. Teaching of Psychology, 42(3), 266-271. http://dx.doi.org/10.1177/0098628315589505

Here’s what they tried to do in the article, in their own words:

“The purpose of this article is to (a) clarify what learning styles theories claim and distinguish them from theories of ability, (b) summarize empirical research pertaining to learning styles, and (c) provide suggestions for practice and implications supported by empirical research.”

The distinction between abilities and styles is important to the authors:

“The two are often confused, but the distinction is important. It is relatively uncontroversial that cognitive ability is multifaceted (e.g., verbal ability and facility with space have distinct cognitive bases), and it is uncontroversial that individuals vary in these abilities. For ‘‘styles’’ to add any value to an account of human cognition and learning, it must mean something other than what ability means. While styles refer to how one does things, abilities concern how well one does them.”

Predictions from learning-styles theory:

“Learning styles theories make two straightforward predictions. First, a learning style is proposed to be a consistent attribute of an individual, thus, a person’s learning style should be constant across situations. Consequently, someone considered an auditory learner would learn best through auditory processes regardless of the subject matter (e.g., science, literature, or mathematics) or setting (e.g., school, sports practice, or work). Second,  cognitive function should be more effective when it is consistent with a person’s preferred style; thus, the visual learner should remember better (or problem-solve better, or attend better) with visual materials than with other materials.”

Results: Are these learning-styles predictions validated by the research?:

“No. Several reviews that span decades have evaluated the literature on learning styles (e.g., Arter & Jenkins, 1979; Kampwirth & Bates, 1980; Kavale & Forness, 1987; Kavale, Hirshoren, & Forness, 1998; Pashler et al., 2009; Snider, 1992; Stahl, 1999; Tarver & Dawson, 1978), and each has drawn the conclusion that there is no viable evidence to support the theory. Even a recent review intended to be friendly to theories of learning styles (Kozhevnikov, Evans, & Kosslyn, 2014) failed to claim that this prediction of the theory has empirical support. The lack of supporting evidence is especially unsurprising in light of the unreliability of most instruments used to identify learners’ styles (for a review, see Coffield et al., 2004).”

 

 

Research on Mathematics Education for U.S. First Graders

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A recent research review (by Paul L. Morgan, George Farkas, and Steve Maczuga) finds that teacher-directed mathematics instruction in first grade is superior to other methods for students with “math difficulties.” Specifically, routine practice and drill was more effective than the use of manipulatives, calculators, music, or movement for students with math difficulties.

For students without math difficulties, teacher-directed and student-centered approaches performed about the same.

In the words of the researchers:

In sum, teacher-directed activities were associated with greater achievement by both MD and non-MD students, and student-centered activities were associated with greater achievement only by non-MD students. Activities emphasizing manipulatives/calculators or movement/music to learn mathematics had no observed positive association with mathematics achievement.

For students without MD, more frequent use of either teacher-directed or student-centered instructional practices was associated with achievement gains. In contrast, more frequent use of manipulatives/calculator or movement/music activities was not associated with significant gains for any of the groups.

Interestingly, classes with higher proportions of students with math difficulties were actually less likely to be taught with teacher-directed methods — the very methods that would be most helpful!

Will’s Reflection (for both Education and Training)

These findings fit in with a substantial body of research that shows that learners who are novices in a topic area will benefit most from highly-directed instructional activities. They will NOT benefit from discovery learning, problem-based learning, and similar non-directive learning events.

See for example:

  • Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching. Educational Psychologist, 41(2), 75-86.
  • Mayer, R. E. (2004). Should There Be a Three-Strikes Rule Against Pure Discovery Learning? American Psychologist, 59(1), 14-19.

As a research translator, I look for ways to make complicated research findings usable for practitioners. One model that seems to be helpful is to divide learning activities into two phases:

  1. Early in Learning (When learners are new to a topic, or the topic is very complex)
    The goal here is to help the learners UNDERSTAND the content. Here we provide lots of learning support, including repetitions, useful metaphors, worked examples, immediate feedback.
  2. Later in Learning (When learners are experienced with a topic, or when the topic is simple)
    The goal here is to help the learners REMEMBER the content or DEEPEN they’re learning. To support remembering, we provide lots of retrieval practice, preferably set in realistic situations the learners will likely encounter — where they can use what they learned. We provide delayed feedback. We space repetitions over time, varying the background context while keeping the learning nugget the same. To deepen learning, we engage contingencies, we enable learners to explore the topic space on their own, we add additional knowledge.

What Elementary Mathematics Teachers Should Stop Doing

Elementary-school teachers should stop assuming that drill-and-practice is counterproductive. They should create lesson plans that guide their learners in understanding the concepts to be learned. They should limit the use of manipulatives, calculators, music, and movement. Ideas about “arts integration” should be pushed to the back burner. This doesn’t mean that teachers should NEVER use these other methods, but they should be used to create occasional, short, and rare moments of variety. Spending hours using manipulatives, for example, is certainly harmful in comparison with more teacher-directed activities.

 

Why the World Needs Research Translators

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Research translators are people who read research articles from scientific refereed journals and distill the wisdom from those articles into practical recommendations for practitioners. Sometimes research translators translate one article at a time (or a few), compiling the main points from the article and transforming those main points into recommendations for practice.

More effectively, research translators read many articles about a particular topic and then — based on years of immersion in the research and years of experience with practitioners — make sense of the topic findings in relation to a wider body of research and the needs of practitioners. After developing a comprehensive and practical understanding of the research findings, research translators create simple elegant models and metaphors to help practitioners deeply understand the research findings, while ensuring that recommendations are clear, leverageable, and potent.

Research translators add value because they bridge the gap between the worlds of research and practice — between groups who speak different languages.

Some researchers are brilliant in translating research into practice. Most are not. We shouldn’t blame them for their deficiencies. The world they inhabit pushes against research translation in myriad ways. Researchers are not incentivized to do research translation. Indeed, those who write popular books are often scorned by other researchers. Researchers don’t have time to hang out with practitioners to learn their language, to deeply understand their needs, to see how research gets understood/misunderstood and applied, to see what obstacles are faced. Researchers’ language pool so controls their own thinking and verbal output that they can’t help themselves in using jargon that then overwhelms the working-memory capacity of their readers and listeners.

At a minimum, here is what research translation requires:

  1. Deep and current understanding of a wide body of research.
  2. Deep and current understanding of the practitioner ecosystem, language, motivations, incentive systems, body of knowledge, blind spots and misconceptions, organizational influences, etc.
  3. Ability to compile research into practical wisdom, utilize metaphor to support comprehension, create models that balance simplicity with precision, craft recommendations that propel appropriate applications of the research while avoiding misapplication, etc.
  4. Ability to reach a wide swath of practitioners to ensure that the research-based messages are heard.
  5. Ability to craft messaging that ensures that research-based messages are understood, remembered, and found compelling enough to generate actual attempts to be used.
  6. Ability to provide corrective feedback and encouragement as practitioners attempt to utilize research-based messages.

Researchers’ Biggest Blind Spot

In my experience, most researcher’s biggest blind spot is that they just can’t communicate without the use of jargon and big words that overwhelm the working-memory capacity of those they are attempting to reach. Even when they try to communicate plainly to practitioners they just can’t do it.

Here is an example from a recent book that that authors claim is written to be accessible to practitioners. I won’t “out” the researchers here because I love their book and want it to do well.

My yellow highlights indicate jargon that is likely to overload working memory.

More than 50% of the paragraph is jargon, rendering the paragraph virtually indecipherable.

The Tragedy of the Uncommon

There are very few research translators in my field, the learning field. Ruth Clark recently retired, leaving a gaping hole. There’s simply no place for us — that is, there’s no place to earn a living as a research translator. The academy wants researchers, not research translators. Industry wants practitioners, not research translators. Those of us who try to carve out a niche as research translators find that research translation hardly pays a penny, that we must be consultants first. In some ways, this is great because it keeps us close to practitioners — and we get to see on a daily basis how research can be used to make learning more effective. In other ways, being a consultant doesn’t really give us enough time to do the research.

It is said that a successful consultant will allocate time as follows:

  • 3 days a week to paid work.
  • 1 day a week to marketing.
  • 1 day a week to administrative tasks.

For those translating research we can add:

  • 2 days a week compiling research.
  • 2 days a week crafting communications to share the research.

Ruth Clark once told me that surviving as a research translator was “really hard.” And, of course, she is the most successful full-time research translator in the history of our field.

It doesn’t make sense to wish away the realities faced, to hope the academy would make room for research translators, to hope that industry would have at least a few positions open. It’s just not going to happen anytime soon.

There is a window however. Perseverance, perhaps? But more importantly, innovating new business models that make research translation a sustainable option.

Summary

Research translation ain’t easy, but it’s a vital part of the research-to-practice ecosystem.

 

 

 

 

Spacing Learning Over Time — Research Report

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The spacing effect is one of the most potent learning factors there is–because it helps minimize forgetting.

Here’s a research-to-practice report on the subject, backed by over 100 research studies from scientific refereed journals, plus examples. Originally published in 2006, the recommendations are still valid today.

Click to download the research-to-practice report on spacing.   It’s a classic!

 

And here’s some more recent research and exploration.

Airforce finds Interesting Results with Ability Grouping–Plus Other Research on Ability Grouping

NPR's Morning Edition produced a five-minute radio piece on the U.S. Airforce Academy's attempt at improving learning results by modifying the ability-grouping of their cadets.

Shankar Vedantam

According to the piece, reported by Shankar Vedantam, based on research by Dartmouth researcher Bruce Sacerdote and colleagues:

  • Weaker students did better when in squadrons with stronger students (but note caveats below).
  • However, when researchers intentionally created squadrons with only the strongest and weakest students (that is, the middle students were removed), the weaker students did worse than they otherwise would have. The researchers argue that this was caused by the splintering of the squadron into groups of strong students and groups of weak students.
  • Middle students did better when they didn't have weaker and stronger students in their squadrons.
  • It appears that the middle students acted as a glue in the mixed-ability squadrons–and specifically, they helped the squadron to avoid splitting into groups.

Of course, one study should not be taken without some skepticism. Indeed, there is a long history of research on academic ability grouping. For example see the review article:

Schofield, J. W. (2010). International evidence on ability grouping with curriculum differentiation and the achievement gap in secondary schools. Teachers College Record, 112(5), 1492-1528.

As Schofield reports:

International research supports the conclusion that having high-ability/high-achieving schoolmates/classmates is associated with increased achievement. It also suggests that ability grouping with curriculum differentiation increases the achievement gap. For example, attending a high-tier school in a tiered system is linked with increased achievement, whereas attending a low-tier school is linked with decreased achievement, controlling for initial achievement. Furthermore, there is a stronger link between students’ social backgrounds and their achievement in educational systems with more curriculum differentiation and in those with earlier placement in differentiated educational programs as compared with others.

But she also warns:

However, numerous methodological issues remain in this research, which suggests both the need for caution in interpreting such relationships and the value of additional research on mechanisms that may account for such relationships.

In addition, social effects are probably not the only effects in play. For example, the research tells us that learners do better when they are presented with information and given instructional supports targeted specifically to their cognitive needs. So for example, this could be why the middle-ability students did better when they were grouped together.

Also interesting is that neither the NPR piece or Shofield's abstract reports specifically on how the mixed groupings affect the stronger learners.

Indeed, other researchers have advocated that gifted students should not be so ignored. See for example the following review article:

Subotnik, R. F., Olszewski-Kubilius, P., & Worrell, F. C. (2012). A proposed direction forward for gifted education based on psychological science. Gifted Child Quarterly, 56(4), 176-188.

Here's what these authors recommend:

In spite of concerns for the future of innovation in the United States, the education research and policy communities have been generally resistant to addressing academic giftedness in research, policy, and practice. The resistance is derived from the assumption that academically gifted children will be successful no matter what educational environment they are placed in, and because their families are believed to be more highly educated and hold above-average access to human capital wealth. These arguments run counter to psychological science indicating the need for all students to be challenged in their schoolwork and that effort and appropriate educational programing, training and support are required to develop a student’s talents and abilities.