If You Really Want To Learn Something, Intend To Teach It

evolution-ape-teachingNot content to take traditional folk wisdom at face value, upstart researchers from Washington University, St. Louis, the University of California, Los Angeles, and Williams College in Williamstown, MA, have challenged the old saw,

“If you really want to learn something, teach it.”

Their study employed a couple of reading and recall experiments given to two groups of students. One group was told that they were to be tested on certain passages in the text while the other was told that they would have to learn the passages in order to teach them to students who would in turn be tested. Sadly, perhaps, at the end of the trial everyone was tested and no one had an opportunity to teach.

The findings? From the study:

“Participants expecting to teach produced more complete and better organized free recall of the passage (Experiment 1) and, in general, correctly answered more questions about the passage than did participants expecting a test (Experiment 1), particularly questions covering main points (Experiment 2), consistent with their having engaged in more effective learning strategies.”

That’s quite a finding. Apparently just expecting to teach confers enough benefit to learning that it’s advantageous to adopt as a study tool. If you tell students that they will have to teach they will shift into a kind of turbo mode mentally and do a better job of curating and remembering the facts and organizing the information into a comprehensible whole.

Researchers Nestojko et al. hint at motives and goals as being central to the effect.

“Students, for example, typically have the goal of maximizing their performance on a later test when learning new material. In contrast, teachers presumably have the goal of being able to effectively communicate the new material they are learning to their students.”

Teaching leads to better learning, and students who expect to teach instinctively turn to the strategies of the teacher to prepare and structure information in a more effective manner than that utilized by the test taker. The authors of the study conclude that:

“Instilling an expectation to teach thus seems to be a simple, inexpensive intervention with the potential to increase learning efficiency at home and in the classroom.”

They hope that the finding encourages others to seek similar cost-effective techniques that readily enhance learning.


John F. Nestojko, Dung C. Bui, Nate Kornell, Elizabeth Ligon Bjork. Expecting to teach enhances learning and organization of knowledge in free recall of text passages. Memory & Cognition, 2014; DOI: 10.3758/s13421-014-0416-z

Washington University in St. Louis. “Expecting to teach enhances learning, recall.” ScienceDaily.

Gerry Everding. Expecting to teach enhances learning, recall – Student mindset has big impact on learning, study finds. Washington University in St. Louis Newsroom, 28 July 2014

Read the full study here (PDF)

Flatlined During Class

This published finding may not be scientifically significant (N=1) for some applications, but if nothing else it does provoke a chuckle (and a tendency to draw general conclusions without enough supporting data simply because – let’s face it – we’ve all been there).

A study published in the IEEE Transactions on Biomedical Engineering reports data from a wearable sensor attached to a student for a week. The portable sensor records the electrodermal activity of the wearer.

“Changes in skin conductance at the surface, referred to as electrodermal activity (EDA), reflect activity within the sympathetic axis of the Autonomic Nervous System (ANS) and provide a sensitive and convenient measure of assessing alterations in sympathetic arousal associated with emotion, cognition, and attention.”

Note the times at which the student is at class (yellow underscore).

No doubt this demonstration will be adopted by proponents of the Inverted Classroom and other related high-engagement learning techniques as an illustration of why the traditional lecture or classroom should be avoided. It is gratifying to note that activity levels during labs (yellow-green) and while doing homework and study (pink and red) are elevated. But then again, upon further inspection the sleep cycle is pretty impressive too.

One can hope this is followed by further investigations and discussions on the physiological and psychological meaning of the EDA waveforms given that the primary purpose of the paper is to report on Poh, Swenson and Picard’s work on sensor development.



Poh, M.Z., Swenson, N.C., Picard, R.W., “A Wearable Sensor for Unobtrusive, Long-term Assessment of Electrodermal Activity,” IEEE Transactions on Biomedical Engineering, vol.57, no.5, pp.1243-1252, May 2010. doi: 10.1109/TBME.2009.2038487

Download a PDF of the report here.

Ito, Joi, “A week of a student’s electrodermal activity”



The Eye of the Beholder – Why We Prefer Rounded Corners Over Sharp Edges

Rounded rectangles are everywhere. You might think the reason they are so ubiquitous is because web and product designers’ minds are being controlled by an alien graphical design style ray that shows little chance of letting go. Or, maybe not. Beauty, in the case of the rounded rectangle, might be in the eye of the beholder – literally.

Apparently the visual system favors rectangles with rounded corners, making layouts, interfaces and presentation graphics easier to view and take in. Having a hard time believing that rounded corners make a difference, try this. Look at the images below. Which is easier to look at?


The reason the circle appears more agreeable is because we are wired to prefer round to sharp edges (and by extension round to sharp things). Keith Lang at UI&Us quotes researcher Jürg Nänni on the eye-brain’s peculiar penchant for roundness:

A rectangle with sharp edges takes indeed a little bit more cognitive visible effort than for example an ellipse of the same size. Our ‘fovea-eye’ is even faster in recording a circle. Edges involve additional neuronal image tools. The process is therefore slowed down. – Professor Jürg Nänni as quoted by Keith Lang (see below)

Anthony Tseng at UX Movement presents two other examples where rounded corners aid and abet the perception of graphical information. The box diagram is a common graphical type used in organization charts and process diagrams. Note the differences between the rectangular and rounded lines. The curves add flow to the procession through the diagram.

Attribution: FMC Visualization Guidelines

In a second example Anthony Tseng shows how rounded corners not only guide the eyes but also act on the attention of the viewer. In what might be a great tip for instructional designers and artists notice how the use of the corner radius acts to focus attention on what is inside the boxes.


Attribution: Anthony Tseng

Rounded corners also make effective content containers. This is because the rounded corners point inward towards the center of the rectangle. This puts the focus on the contents inside the rectangle. – Anthony Tseng at


Still wondering why we see so many rounded rectangles in objects around us?

Attribution: UI&Us



Tseng, Anthony, “Why Rounded Corners are Easier on the Eyes

Lang, Keith, “Realizations of Rounded Rectangles

FMC Visualization Guidelines

The Face in the Mirror – Online Avatars Affect Outcomes

According to a study at North Carolina State University, the effectiveness of online training might be enhanced if online educational helpers, or avatars, closely match the student. Researchers Tara S. Behrend and Lori F. Thompson designed instructional avatars using a program called People Putty to match or contradict gender, race and teaching styles of 257 test subjects involved in an online training course. For example, subjects were asked “If you were teaching this course would you give specific directions on what to do or offer general suggestions?” Similarly, “Would you rate an individual’s performance based on how far a participant improved compared to where he or she started or relative to the performance of the entire class?” The avatars where then set in motion on the course, advising, guiding and assisting the learners according to their collected attributes. What the researchers found was a mixed bag of somewhat counter intuitive results.

“We know from existing research on human interaction that we like people who are like us. We wanted to see whether that held true for these training agents.” – Dr. Lori Foster Thompson

Measurements of enjoyment, engagement and effectiveness of the training suggest that each element has a different cause. Subjects reported being more engaged in the program when the avatar matched their race and gender. Learning, on the other hand, was enhanced when the online helper employed feedback and teaching styles more akin to that of the student. Whether this predisposition is strong enough to constitute an outright learning style remains to be seen. According the researcher Thompson:

“We found that people liked the helper more, were more engaged and viewed the program more favorably when they perceived the helper agent as having a feedback style similar to their own – regardless of whether that was actually true.”

Interestingly researchers found no link between enjoyment or overall success of educational outcome based on gender or race. Matching teaching style did, however, have a pronounced effect on performance on quizzes. What might come as the greatest surprise concerns the dominant factor affecting participants’ ratings of overall effectiveness and enjoyment. As it turns out the “perceived” similarity of the avatar is more important than the reality underlying its design.

“We found that people liked the helper more, were more engaged and viewed the program more favorably when they perceived the helper agent as having a feedback style similar to their own – regardless of whether that was actually true.” – Lori F. Thompson

What the study suggests is that perception might be more important than reality where avatar design and success of online training are concerned. In essence, if a learner believes that a particular online helper has been designed “specifically for people like you,” its effects will likely be beneficial to the outcome of the training. Regrettably from the point of view of the instructional designer and developer of the training, one-size-fits-all might be out the window:

“It is important that the people who design online training programs understand that one size does not fit all. Efforts to program helper agents that may be tailored to individuals can yield very positive results for the people taking the training.” – Lori F. Thompson


Tara S. Behrend, Lori Foster Thompson, Similarity effects in online training: Effects with computerized trainer agents, Computers in Human Behavior, Volume 27, Issue 3, Group Awareness in CSCL Environments, May 2011, Pages 1201-1206, ISSN 0747-5632, DOI: 10.1016/j.chb.2010.12.016. (

Participation in Pedagogical Agent Design: Effects on Training Outcomes, Tara S. Behrend, A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Psychology, Raleigh, North Carolina, 2009.

Make Mine Comic Sans – Bad Fonts Aid Learning

Ransom note typography pays off in learningIf you are the kind of designer who cannot tell the difference between Times and Helvetica, you’re in luck. A recent study by a team from Princeton and Indiana Universities shows that educational presentations that are hard for students to read may lead to improved memory performance. In the technical jargon of cognitive psychology the reason for this counter-intuitive result is due to the heightened “disfluency” caused by poor typography that leads to deeper processing (or encoding) in the brain.

Many classroom instructors and and instructional designers assume that clearer, easier to read, media reduce the “friction” of learning and act to promote and accelerate the transmission of new ideas and skills. Not so, say Connor Diemand-Yauman, Daniel Oppenhiemer and Erikka Vaughan who penned the study soon to be published in the journal Cognition. In some cases, they assert, making material harder to learn actually improves long-term memory. What’s worse, they have the control group data to prove it.

“Many educators believe that their ability to teach effectively relies on instinct and experience. However, research has shown that instinct can be deceiving and lead to educational strategies that are detrimental to learners.” – Diemand-Yauman, et al.

Two studies were undertaken to test the hypothesis that “desirable difficulties” can lead to enhanced learning. In the first, twenty-eight participants ranging in age from 18 to 40 were asked to learn fictional taxonomic data similar to that found in biology classes. The disfluent media presented the material in 12-point Comic Sans rendered in 60% grayscale or 12-point Bodoni MT also in 60% grayscale. The fluent media used 16-point Arial rendered in plain black. (It should be noted that the author knows more than one professional designer who considers Arial to be at least as disfluent as Comic Sans, grayscale notwithstanding.)

Participants were given 90 seconds to memorize their fictional taxonomic data. For example:

The norgletti

  • Two feet tall
  • Eats flower petals and pollen
  • Has brown eyes

Each data set like the above was composed of three species of aliens, each with seven features, for a total of 21 items to be learned. After 90 seconds of study the participants were distracted for 15 minutes with another task after which their recall was tested (“What is the diet of the norgletti?”).

The results? Fluent learners successfully recalled 72.8% of their data. Disfluent learners scored higher: 86.5%! What’s more, differences between the two disfluent fonts were not found (probably because ugly is ugly).

“Similarly, many education researchers and practitioners believe that reducing extraneous cognitive load is always beneficial for the learner. In other words, if a student has a relatively easy time learning a new lesson or concept, both the student and instructor are likely to label the session as successful even if the student is unable to retrieve the information at a later time.” – Diemand-Yauman, et al.

Not wishing to hastily generalize their preliminary results to classroom conditions, Diemand-Yauman, Oppenhiemer and Vaughan arranged a study with 222 Ohio high school students (ages 15-18). In the high school study teacher-prepared instructional content (Powerpoint and worksheets) were reformatted (but not edited) using disfluent fonts or left unchanged. Different sections of the classes were randomly assigned to a disfluent or control group. Teachers were told that the study focused on the effects of different fonts in presentations to counteract the Pygmalion Effect. After the classes were presented in normal fashion exams were given along with a survey to assess whether disfluency affects motivation.

The results? Once again the disfluent group scored higher (m=0.164, sd=1.03; m=-0.295, sd=1.03; using Z-scores) and there was no difference between ugly fonts. Further, the survey revealed no motivational differences between fluent and disfluent presentations.

The authors warn that interpretation of the results and their subsequent application in the classroom be cautiously undertaken. First, the novelty and distinctiveness of the disfluent fonts might be a factor enhancing their “desirable difficulty.” Another issue is that the point at which a typeface changes from “desirably difficult” to “illegible” is not known.

The authors concede that there is a point at which “disfluent” pushed to its extreme becomes “impossible,” hindering learning altogether.

At present it seems as though the tonic effects of disfluency probably follow a U-shaped curve and that the exact parameters that affect the shape have to be teased out through further experiment.

Another question is whether this disfluent effect will be seen with other media as well. The authors of this study only considered typographic media, but one has to wonder if it is possible to obtain similar results with audio and video.


Diemand-Yauman, C., et al. Fortune favors the Bold (and the Italicized): Effects of disfluency on educational outcomes. Cognition (2010), doi:10.1016/j.cognition.2010.09.012

McDaniel, M. A., Hines, R., & Guynn, M. (2000). When text difficulty benefits less-skilled readers. Journal of Memory and Language, 46(3), 544–561.

McNamara, D. S., Kintsch, E., Butler-Songer, N., & Kintsch, W. (1996). Are good texts always better? Text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14, 1–43.

Oppenheimer, D. M. (2008). The secret life of fluency. Trends in Cognitive Sciences, 12(6), 237–241.

Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and Instruction, 12(3), 185–233.

Should We Teach to Learning Styles?

Learning styles, it seems, are part of education. How exactly they got there I am not sure, but I don’t recall a time when I did not know (or suspect I knew) my dominant learning style. In fact, I suspect more educators know their learning styles than blood types. That said, after all this time, I’ve begun to readdress my thinking concerning learning styles and the role they should play in teaching and instructional design.

I’ve been reading Daniel Willingham’s book Why Don’t Students Like School? which has a chapter titled “How Should I Adjust My Teaching for Different Types of Learners?” A note to auditory learners: If you go over to the Future of Education web site or look at the Episode 90 interview at the Psych Files web site you can listen to two interviews with Dr Willingham that address the topic in light of the current research in cognitive psychology. But be warned: you might not like what you find.

To start, it’s a good idea to distinguish between learning style and ability. Willingham points out that there are scores of various learning styles that have been put forth over the years. A short list might include:

  • Analytic/nonanalytic
  • Field dependent/field independent
  • Impulsive/reflective
  • Convergent/divergent
  • Serialist/holist
  • Adaptor/innovator
  • Reasoning/intuitive
  • Visualizer/verbalizer
  • Visual/auditory/kinesthetic

Style should be distinguished from ability in that style implies a “manner of doing something” whereas ability suggests “a capacity for doing something,” leading even to notions of talent. That is, two equally adept (able) students might think about a subject in different ways (sequentially vs. holistically, for example). As Willingham says:

“Abilities are how we deal with content (for example, math or language arts) and they reflect the level (that is, the quantity) of what we know and can do. Styles are how we prefer to think and learn. We consider having more ability as better than having less ability, but we do not consider one style as better than any other style.”

Teachers and instructional designers no doubt note the differences between individuals (in personality, motivation, and interest) and may account for the inherent advantages of certain cognitive styles for a particular lesson or task, but Willingham is quick to remind us that after nearly seventy years of research, no evidence exists to support the notion that learning styles, as described by learning style theorists, exist. Simply put:

Teaching to an individual’s purported dominant learning style offers no advantage in terms of how much that individual learns.

In fact, in Why Don’t Students Like School? (2009), page 113, Willingham presents a positive spin on this finding when he writes:

“Children are more alike than different in terms of how they think and learn.”

This is far from the end of the conversation where learning styles are concerned however. The teacher and instructional designer can still benefit from a knowledge of learning styles if they flip their application over and apply them to the instruction rather than to the instructed. That is,

…differentiate instruction based on the meaning of the lesson to be conveyed. Match the content (or meaning of the lesson) to the style of the presentation not to the presumed “learning style” of the students.

At first glance this is ingenious but a few likely examples reveal its necessary utility.

Consider that you need to present some lessons on tying knots for a class on mountaineering. Can you imagine that your students would actually master how to tie complicated knots if they did not have a chance to kinesthetically learn the knots by practicing with rope? Would a language course in Chinese be well designed if it did not offer its students an auditory portion wherein they could listen to proper pronunciation by native speakers? Would you try to teach geography by describing countries by the contours of their borders rather than using a visual presentation of the land areas and their features?

These are examples of how to match the (learning) style of the presentation to the meaning (or inherent goal) of the lesson.

Although predictions from individual learning styles theories might not be supported by experimental evidence, learning styles themselves are nonetheless persistent memes in education. Willingham estimates that 90% of his students at the University of Virginia believe in them although he is unable to find mention of learning styles in popular education texts. In addition, many professional training seminars promise to help practitioners in education and business master the application of learning styles for problems in the classroom and the workplace. But still they elude the researcher. Maybe they do exist but we have yet to design the correct experiments to measure them? Or maybe the lesson of learning styles is just that we have to understand them differently and approach them more as guides for connecting meaning to the content and the style of presentations we fashion for our students.


Daniel T. Willingham

DIFFERENT STROKES FOR DIFFERENT FOLKS? A Critique of Learning Styles,” Steven Stahl, American Educator, Fall 1999.

Learning Styles: Concepts and Evidence,” Psychological Science in the PUBLIC INTEREST, Harold Pashler, Mark McDaniel, Doug Rohrer, and Robert Bjork, Volume 9 Number 3, December 2008.

Advances in Applying the Science of Learning and Instruction to Education,” Psychological Science in the PUBLIC INTEREST, Richard E. Mayer, Volume 9, Number 3, 2009.

Mind myth 7: Learning styles and multiple intelligences

Professor pans ‘learning style’ teaching method

Reframing the Mind – Howard Gardner and the theory of multiple intelligence,” Daniel Willingham, EducationNext, Summer 2004 / Vol. 4, No. 3.

New Science Points To New Classrooms


In a note that could have been taken from one of Maria Montessori’s books, researchers in neuroscience, machine learning, education and psychology have convened to show that findings from a joint study suggest that “the prepared environment” might be supported by new scientific data.

The ‘prepared environment‘ is Maria Montessori’s concept that the environment can be designed to facilitate maximum independent learning and exploration by the child.”

Terrence J. Sejnowski, Ph.D, researcher at the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies and co-director of the Temporal Dynamics of Learning Center (TDLC) at the University of California, San Diego, echoes Montessori in his team’s findings. As quoted in Science Daily:

“To understand how children learn and improve our educational system, we need to understand what all of these fields [neurobiology, psychology, education, machine learning] can contribute. Our brains have evolved to learn and adapt to new environments; if we can create the right environment for a child, magic happens.”

The cross-disciplinary research points to a new science of learning that might influence the way classrooms are organized and run in the future. In particular, three guiding principles (or concurrent processes) emerge from the study:

  1. Learning is computational
  2. Learning is social
  3. Learning is supported by neurological (perception-action) circuits

Research in machine learning and developmental psychology illuminate the computational complexity employed by learners who use statistical patterns and probabilistic models to infer rules of logic, relationships between words, syntax, and causal dependence between objects in the physical world.

Evidence that the three component processes happen concurrently is supported by the fact that learners do not calculate and compile statistical models of the environment
indiscriminately but throttle the process using social cues from the people around them. Further, animal studies point to the presence of certain neurosteroids secreted during social interaction that promote learning.

Imitation also comes into play as a key factor:

“Imitation [presumably from others in the environment] accelerates learning and multiplies learning opportunities. It is faster than individual discovery and safer than trial-and-error learning.”

In essence, a social context fosters learning.

Brain circuits that support both actions and perceptions are directly involved with learning. As seen in language learning, for example, there is a complex mix of imitative, computational and articulatory processes that come into play as learning proceeds that might be further facilitated or enhanced at specific developmental periods. In general, neuroscientists have determined that there is considerable overlap in the systems brought into play during learning that support both perception and action. From Science:

“For example, in human adults there is neuronal activation when observing articulatory movements in the cortical areas responsible for producing those articulations. Social learning, imitation, and sensorimotor experience may initially generate, as well as modify and refine, shared neural circuitry for perception and action.”

Finally, experts in machine learning and artificial intelligence are taking advantage of the recent findings in social learning, computational modeling and the plasticity of the brain to design software that monitors and uses social cues and environmental factors to enhance learning. In the future this software may be used in tutorial programs or embedded in instructional robots that are specifically “tuned” to enhance teaching practices in classrooms.


New Science Of Learning Offers Preview Of Tomorrow’s Classroom

Foundations for a New Science of Learning

New science of learning offers preview of tomorrow

From baby scientists to a science of social learning

Teaching Adults – We’re Not Just Big Kids


Adult and continuing education is a growing and common feature of modern life. More and more people are getting involved in taking classes and online courses as part of an ongoing need to keep pace with developments in their professions, move into new careers, and to further recreational pursuits. Government, military and corporate employers see the same need to offer continuing education to their workers. And, not to be forgotten, education among retired people is also growing as seniors renew old and embrace new interests and skills.

Teaching adults has always been a different matter than teaching children. Typically, adults come to class with objectives in mind, posses some background information, require relevance to be part of the training and have a better self-awareness of their strengths and learning styles (that they intend to capitalize on in the learning process). Many other issues enter the adult learning sphere and it is worth the time to consider these when presented with the task of developing or delivering training to an adult audience.

One particularly useful and concise review of the main issues that affect the education of adults was given by Dr. Karen Jarrett Thoms of St. Cloud State University as part of the Proceedings of the Sixth Annual Mid-South Instructional Technology Conference. A reprint of the talk can be found here.

Right off the bat, Dr. Jarret Thoms reminds us that distinct differences exist between between andragogy and pedagogy. That is, adult learning:

  • is problem-centered rather than content-centered.
  • incorporates experiential activities.
  • prompts redesign and new learning activities based on evaluation.
  • is based on an evaluation agreement.
  • permits and encourages active participation.
  • encourages past experiences.
  • is collaborative between instructor-student and student-student.
  • is based on planning between the teacher and the learner.

As such, adult learners expect (and need) to be involved, like to connect to past knowledge and experience, and see the teacher as a collaborator and guide. Doesn’t this sound like a partial description of the modern Web 2.0 learner we read so much about? Maybe there is a shift among certain high school and college age learners toward what we have formerly considered “adult” learning styles? But I digress….

According to Dr. Jarret Thoms the Sage on the Stage is out:

Andragogic sessions vary significantly from pedagogic classes. While there continues to be an increase in the number and degrees of active learning activities taking place in K-12, the college and training arenas may far surpass the learners’ understandings of what may and may not be negotiated as far as objectives, activities, etc. According to Laird (p. 126), andragogy raises interesting questions about the role of the instructor. As stated previously, in andragogy, the role of the instructor is to manage the processes, but not to manage the content. Two-way communication and feedback is critical. Instructors may serve as facilitators rather than lecturers. They may routinely switch between teaching strategies. For instructors, this change to the andragogic level of teaching may require a major adjustment to their teaching strategies.

Accordingly, the modern instructor or instructional designer has to be able to switch gears to meet the attitude, aptitude, learning style and experience of the adult learner.

Dr. Jarret Thoms sees the essence of adult learning represented in 12 basic precepts:

  • present information in a manner that permits mastery.
  • present new information if it is meaningful and practical.
  • present only one idea or concept at a time.
  • use feedback/frequent summarization.
  • practice learning as a self-activity.
  • accept that people learn at different rates.
  • recognize that learning is continuous/continual.
  • believe that learning results from stimulation.
  • enhance learning through positive reinforcement.
  • follow the concept that people learn by doing.
  • desires the “whole-part-whole” learning strategy.supports the team environment to improve learning.
  • knows that training/education must be properly timed.

Dr. Jarret Thoms goes on to suggest that six resultant issues surface that affect the development of successful adult training programs:

Learning is not its own reward. Children and adults learn for different reasons. Adults are not impressed or motivated by gold stars and good report cards. Instead, they want a learning outcome which can be put to use immediately, in concrete, practical, and self-benefiting terms. Adult learners want practical, hands-on training sessions over general, theory-oriented classes. For example, the best way to motivate adults to learn a spreadsheet software package is to show them how they can use it in their own environment.

Adult learning is integrative. The adult learner brings a breadth of knowledge and a vast array of experiences to the learning situation. Adults learn best when they use what they already know and integrate new knowledges and skills into this bank of knowledge. In the event this new knowledge or skill is in direct opposition to what the learner already knows or believes, there is a possibility of conflict, which must be addressed immediately.

Value adjustment. Because training changes how work is processed, the adult learner must understand why the learning is useful and why these new skills must be mastered. Value adjustment means understanding why work that has been done a particular way in the past will not be performed in the same way in the future. Adult learners must be convinced this change is for the betterment of the organization.

Control. Adult learners want control over their learning experiences. In K-12 learning, the teacher tells the students what to do, being very specific about assignments and expectations. Adult learning encourages collaboration with trainees about the pace and the content of the training curriculum. Adult learners in a college classroom can frequently be given more flexibility in determining their assignments, with the understanding that the basic criteria for the assignment must be met.

Practice must be meaningful. Repetition for the sake of repetition just does not “cut it” with adult learners, and it is unlikely that learning will take place. If repetition, however, does have meaningful results, then learning will take place. Adults frequently tend to be slower in some physical, psychomotor tasks than children. The adults are also less willing to make mistakes (someone might see them make this mistake), and they often compenstate by being more exact. In other words, they may take less “chances” with trial-and-error activities, thus making few mistakes. Send these adult learners home to their work station or with an assignment that will parallel what they have just learned. Because the adult learner does not want to make mistakes, especially on an assignment, might explain why adult learners tend to ask for clarification on assignments more often than traditional learners.

Self-pacing. Because adult learners acquire psychomotor skills more slowly than younger students, adults should be given the opportunity to proceed at their own pace, often in a self-paced learning package. Can self-paced activities always be integrated into the curriculum? No, and this is definitely a challenge to an instructor where there is a mix of adult and traditional learners.

Finally, the characteristics of an optimal instructor emerge. The effective (motivating) instructor:

offers expertise, both in knowledge and preparation.

has empathy, which includes understanding and consideration.

shows enthusiasm, for the course, content, students, and profession of teaching.

demonstrates clarity, whether it be in classroom teaching, explanation of assignments, or classroom discussion.

For further reading, the Proceedings of the Sixth Annual Mid-South Instructional Technology Conference can be found here.


Arnold, W. and L. McClure. (1995) Communication Training & Development. New York: Harper & Row.

Creating Dynamic Adult Learning Experiences. (1987) San Francisco: Jossey-Bass. Sound recording. Stephen Brookfield interviews Malcolm S. Knowles, Raymond J. Wlodkowski, Alan B. Knox, and Leonard Nadler.

Gilley, J. and Eggland, S. (1989) Principles of Human Resource Development. Reading, MA: Addison-Wesley.

Jarvis, P., J. Holford, & C. Griffin. (1998) The Theory of Practice and Learning. London & Sterling, VA: Kogan Page/Stylus.

Knowles, Malcom. (1998) The Adult Learner : the Definitive Classic in Adult Education and Human Resource Development (5th ed.). Houston: Gulf Pub. Co. Malcolm S. Knowles, Elwood F. Holton III, Richard A. Swanson.

Knowles, Malcolm. (1984) The Adult Learner: A Neglected Species. (3rd ed.) Houston: Gulf Pub. Co.

Knowles, Malcolm. (1984) Andragogy in Action. San Francisco: Jossey-Bass.

Laird, D. (1985) Approaches to Training and Development (2nd). Reading, MA: Addison-Wesley.

Loden, M., and J. B. Rosener. (1991) Workforce America! Managing Employee Diversity as a Vital Resource. Homewood, IL: Business One Irwin.

Nadler, L. and Z. Nadler. (1994) Designing Training Programs: The Critical Events Model (2nd). Houston: Gulf Pub. Co.

O’Connor, B., M. Bronner, & C. Delaney. (2002) Cincinnati: Delmar/South-Western Thomson Learning.

Vella, J. (1994) Learning to Listen, Learning to Teach: the Power of Dialogue in Educating Adults. San Francisco: Jossey-Bass.

Wlodkowski, R. (1993) Enhancing Adult Motivation to Learn: A Guide to Improving Instruction and Increasing Learner Achievement. San Francisco: Jossey-Bass.

Wlodkowski, R. J., and M. B. Ginsberg. (1995) Diversity and Motivation: Culturally Responsive Teaching. San Francisco: Jossey-Bass.

I Like to Watch – Passive Learning Works


Adding to a growing body of research in learning that people can acquire new motor skills by watching alone, research at Dartmouth College shows that both viewing and doing are effective in learning new skills. In fact, according to the authors of the study “Human motor skills can be acquired by observation without the benefit of immediate physical practice.” [1] Furthermore, as reported in Science News:

“It’s been established in previous research that there are correlations in behavioral performance between active and passive learning, but in this study we were surprised by the remarkable similarity in brain activation when our research participants observed dance sequences that were actively or passively experienced,” says Emily Cross, the principal investigator and PhD student at Dartmouth.

Cross et al. tested the hypothesis using a video game to teach a series of dance steps after which they compared performances of both actively (rehearsed) and passively (observed) routines. Interestingly,

“We collected fMRI data before and after five days of both visual and physical training,” says Cross, “and there was common AON [Action Observance Network] activity when watching the practiced and observed dance sequences.” [2]

Further information on the study can be gotten here. Correspondence should be directed to researcher Scott T. Grafton.

[1] Cerebral Cortex 2009 19(2):315-326; doi:10.1093/cercor/bhn083

[2] Dartmouth College (2008, July 15). Passive Learning Imprints On The Brain Just Like Active Learning. ScienceDaily. Retrieved June 29, 2009, from­ /releases/2008/07/080714111425.htm

Do You Really Want To Know?

Triple Self Portrait
Triple Self Portrait

One time honored technique of train-the-trainer programs is to video tape a trainer’s presentation for critique and analysis. Often painful for the new trainer, seeing and hearing oneself from the point of view of the student or attendee can stimulate a shift in perception and lead to improved presentation skills if the trainer can recover from the initial shock.

One reason this techniques might not work is reported at the British Psychological Society’s Research Digest blog. Apparently we have a blind spot concerning our ability to read our own body language.

“A fascinating study has shown that we’re unable to read insights into ourselves from watching a video of our own body language. It’s as if we have an egocentric blind spot. Outside observers, by contrast, can watch the same video and make revealing insights into our personality.”

Perhaps the fact that outside observers can read things about us that we are blind to goes to support the old addage that “The first thing you teach in any course is who you are.” (Once again, the Medium is the Message.)

The reviewer at the BPS suggests the answer to this is rooted in cognitive dissonance and the role it plays in hampering (in this case) self-perception.

What was going on? Why can’t we use a video of ourselves to improve the accuracy of our self-perception? One answer could lie in cognitive dissonance – the need for us to hold consistent beliefs about ourselves. People may well be extremely reluctant to revise their self-perceptions, even in the face of powerful objective evidence. A detail in the final experiment supports this idea. Participants seemed able to use the videos to inform their ratings of their “state” anxiety (their anxiety “in the moment”) even while leaving their scores for their “trait” anxiety unchanged.

Details of the study can be found here: “We’re unable to read our own body language.”

Hofmann, W., Gschwendner, T., & Schmitt, M. (2009). The road to the unconscious self not taken: Discrepancies between self- and observer-inferences about implicit dispositions from nonverbal behavioural cues. European Journal of Personality, 23 (4), 343-366 DOI: 10.1002/per.722