Actor, author, comedian, film producer and behavioral scientist John Cleese offers his insights on how to foster creativity. Anyone who creates anything should see this talk.

Some of his tips include:

• Sleep on a problem
• Interruptions are dangerous
• Ideas come from our unconscious minds
• Get in the right “mood” to be creative

On how to get in the right “mood” to be creative:

• Create an “oasis” in which to be creative
• Create boundaries of space in which to work
• Create boundaries of time in which to “play”

One of Cleese’s gems:

“To know how good you are at something requires the same skills as it does to be good at that thing. Which means that if you are absolutely hopeless at something, you lack exactly the skills that you need to know that you’re absolutely hopeless at it. … It explains a great deal of life.”

See below or at YouTube.

Cleese, John, “The Importance of Creativity,” Creativity World Forum, 2008 (PDF).

“These findings cast doubt on a long-standing belief in education…. The belief in using concrete examples is very deeply ingrained, and hasn’t been questioned or tested.” – Vladimir Sloutsky, co-author

Ohio State’s Research Communications quotes Kaminski as saying:

“Teachers often use real-world examples in math class, the researchers said.  In some classrooms, for example, teachers may explain probability by pulling a marble out of a bag of red and blue marbles and determining how likely it will be one color or the other.

But students may learn better if teachers explain the concept as the probability of choosing one of n things from a larger set of m things.”

This research might help explain why so many people find word problems (and the semantic or linguistic use of mathematics) so daunting in mathematics and physics. In Kaminski’s words:

“The issue can also be seen in the story problems that math students are often given. For example, there is the classic problem of two trains that leave different cities heading toward each other at different speeds.  Students are asked to figure out when the two trains will meet.

The danger with teaching using this example is that many students only learn how to solve the problem with the trains.

If students are later given a problem using the same mathematical principles, but about rising water levels instead of trains, that knowledge just doesn’t seem to transfer.”

Sloutsky sees a role for word problems, however, just not as an instructional aid:

“It is very difficult to extract mathematical principles from story problems. Story problems could be an incredible instrument for testing what was learned.  But they are bad instruments for teaching.”

Kaminski’s and Sloutsky’s study should provide useful insight for those looking at ways to better teach subjects like mathematics, physics, signal analysis, algorithm design, dynamics, logic or economics. It should be noted that Kaminski and Sloutsky worked with Andrew Heckler of Ohio State’s Physics Department on parts of the study.

References.
Concrete Examples Don’t Help Students Learn Math, Study Finds
Students Learn Better When the Numbers Don’t Talk and Dance
Kaminski et al., LEARNING THEORY: The Advantage of Abstract Examples in Learning Math, Science 25 April 2008: 454-455, DOI: 10.1126/science.1154659.

Issues of memory, interest and higher cognitive processing aside, preliminary research at the University of Pennsylvania and Princeton University suggests that the retina transmits data to the brain at 10 million bits per second – the rate of a basic 10Base-T Ethernet connection. Tufte sets the stage for the discussion by noting that viewing a PowerPoint slide is vastly different from viewing the world:

“Looking around the world is easier than analyzing evidence displays, and there may also be within-brain impediments to handling vast amounts of abstract data, but at least the narrow-band choke point for information resolution should not be the display itself.

The average PP slide contains 40 words, which take less 10 seconds to read. Call that 1000 bits per second, which comes to 1/10,000 of the routine human retina-brain data capacity.

Also most of our evidence displays are in flatland, which is a easier than 3D perceptual tasks. On the other hand, many serious data displays are not in the familiar 4D space/time coordinate system that our eye-brain knows so well.

Memory problems can be partly handled by high-resolution displays, so that key comparisons are made adjacent in space within the common eyespan. Spatial adjacency greatly reduces the memory problems associated with making comparisons of small amounts of information stacked in time (PP slides, for example).

– Edward Tufte, July 26, 2006″

The process from world to retina to brain seems sufficiently complex and multivariate that I am inclined to side with Tufte’s correspondent Niels Olson when he points out:

“While PowerPoint is surely a horrid way to transmit information, I’m not sure we can inject very abstract information into people at ethernet rates. 40 words in 10 seconds doesn’t translate to 1000 bits per second transmitted over the optic nerve, which connects the retina to the banks of the calcarine sulcus in the occipital lobe, via the optic chiasm and the lateral geniculate nucleus. At a minimum the data being transmitted would require an analysis of the typography’s geometry (edge detection being a basic function of the retina), the amount of the visual field taken up by the display, the location of the display’s image on the retina relative to the fovea, and the rates of change in the display and surrounding motion (the speaker, other audience members, etc).”

Interestingly Olsen picks up on a decidedly (Eric) McLuhanesque point when he comments on the 240-words-per-minute rate, a figure that roughly corresponds to both the average reading speed of sighted readers today (McLuhan) and the rate at which words in audio form (like podcasts) are transferred [Olsen comments on this in more detail in a later post]:

“Your guesstimate of 40 words in 10 seconds leads to a 240 word-per-minute reading speed. Like normal readers, braille readers can read at 200 to 400 words per minute. Is there any evidence that a person with an aquired partial nerve blindness also aquires an impaired ability to reason spatially? My classmates at Tulane Med found they preferred listening to the lecture audio I recorded at one-and-a-half speed, which also pushes close to 200 words per minute. Most people found twice-speed to be uncomfortably fast. This 200, 240, 400 word-per-minute rate may be a more accurate definition of the rate at which the human mind can receive and abstract information in word form, and this is likely driven by communication between Broca’s area and Wernicke’s area via the arcuate tract. Keep in mind, reading is a highly abstract function.”

The discussion has far from petered out. Combining the eye and the ear, The New York Times reported on research conducted at the University of California, San Diego, which calculated the average daily intake of data for a North American at 34 Gigabytes plus 100,000 words. What this means is that if you believe the estimate, our eyes and ears are busy handling that much data via all channels in a 24-hour period. According to the New York Times and the San Diego study the eye is still hard at work in the new media:

“Print media has declined consistently, but if you add up the amount of time people spend surfing the Web, they are actually reading more than ever.”

I leave it as an assignment to the interested reader to calculate the rate of information in Mbits/second of 34 Gigabytes per 24-hour period.

HMI Report/UC San Diego

References.

Penn researchers calculate how much the eye tells the brain

Kristin Koch, Judith McLean, Ronen Segev, Michael A. Freed, Michael J. Berry, Vijay Balasubramanian, Peter Sterling, “How Much the Eye Tells the Brain,” Current Biology 16 (July 25, 2006), 1428-1434.

The American Diet: 34 Gigabytes a Day

How Much Information?

“Distance learning was designed to provide learners with more opportunity and flexibility for learning. Distance learning allows the learner to overcome traditional barriers to learning such as location, disabilities, time constraints, and familial obligations. However, not every learner will be successful in a distance learning environment.”

Comparing demographic (age, gender, ethnicity, employment) and affective (personality, motivation) issues that might form barriers to learning, researcher Shawna Strickland looked at what makes some people successful at online learning while others drop out. Strickland cites some common barriers to successful online learning as:

• Lack of institutional support
• Lack of free time
• Family constraints
• Financial limitations
• Poor time management skills
• Isolation
• Anxiety and stress
• Limited prior experience
• Previous academic failure

Although no correlation with learning style was found (p. 35), Strickland notes that individual motivation and the degree to which the student accepts personal responsibility for his/her learning act as a prime factors in distinguishing the successful from the unsuccessful learners.

“…the major difference between the distance and traditional learner is the motivational level of the distance learner. A possible reason for this increased motivational level is that the learner has accepted more responsibility for the educational experience. Although the authors [see Simonson et al.] have provided rationale for this key difference, they further state that, when comparing the individual attributes of the two types of learners, they are ‘not generally different from each other.’ “

Strickland also sees communication as key to a successful outcome:

“The success of distance learning is dependent on communication between the learner, his or her peers and instructor. To encourage success within distance learning, it is necessary to evaluate each individual’s needs on a case-by-case basis. While successful learners tend to display certain traits, any adult learner with the proper motivation and preparedness could be successful in a distance learning program.”

References.

Strickland, Shawna L., “Understanding Successful Characteristics of Adult Learners,” Respiratory Care Education Annual Volume 16, Fall 2007, pp. 31-38.

Furst-Bowe, J., Dittman W., “Identifying needs of adult women in distance learning programs,” Int J Instr Media (2001) 28(4), pp. 405-413.

Mupinga, D. M., Nora, R. T., Yaw, D. C., “The learning styles, expectations and needs of on-line students,” College Teaching (2006) 54(1), pp. 185-189.

Simonson, M., Smaldino, S., Albright, M., Teaching and learning at a distance: Foundations of distance education 2nd ed., Merrill Prentice Hall (2003)

“Compared to the more traditional educational paradigm – the broadcast model, where knowledge is delivered from professor to student from on-high – e-learning turns teaching and learning into a shared endeavor.”

Citing a shift in dynamics between her online and brick-and-mortar classes, Haythornthwaite sees that online teaching offers more immediate and engaging interactions with the students:

“With the online classes, I interact with my students more frequently, dropping into asynchronous discussion daily for a half-hour or an hour. With my traditional classes, I might see them once a week for three hours. If there’s a news article I want my online students to read, I can post it and discussion can begin right away. With my classroom students, if I e-mail them an article on Tuesday and we meet for class on Friday, that’s one of many things we might discuss. The impact isn’t quite as immediate.”

In online instruction the roles of student and teacher are modified. The teacher moves from pundit to facilitator and the student is urged to assume a greater active role in his or her tuition.

“Since there’s an emphasis on more learner-centric activities than traditional lecture-based classroom learning, the teacher is more of a facilitator in an online classroom. Not only does that enhance the collaborative nature of online learning, it also motivates students to be much more engaged and to take more responsibility for what they’re learning.”

Haythornthwaite doubts that e-Learning will (or should) replace traditional classroom instruction, asserting instead that it is best used as a complement to lecture and demonstration. Noting the move to open source course materials at places like MIT, Haythornthwaite says:

“No one stopped going to class when all that material was posted. It simply changed the delivery method and broadened the scope of knowledge available.”

References.

Haythornthwaite’s Blog (includes many research papers)

E-Learning can have positive effect on classroom learning, scholar says

Cutting Class – Online vs. Classroom Learning

Since then I have had to learn (to some degree) about a dozen programming and scripting languages. Some were for application development, some were for web development, others were for database systems, but all were a hard-fought climb up a learning curve of an unnatural new literacy. Since I am not a real “computer person” I have had to learn to program for practical reasons such as building new tools or to complete a project. This is to say, I have had to start learning new languages from the position of a neophyte – someone without much formal knowledge or skill – who nonetheless had a practical goal or objective in mind.

Often when working around computer scientists and engineers who program for a living, I would ask how to best go about learning programming. Invariably I was told that the best (and only) way to learn to program was to program. I think this was the result of my colleagues early experience and education. They read books on the syntax and rudiments of the language in question and started in on cobbling together simple lines of code that eventually grew to more and complex routines until they achieved a modest proficiency in the language and it quirks. And so did I.

As things progressed, and I added more computer languages to my list of things to learn, I started to suspect that I could climb the learning curve a little faster if I read lots of programming examples to get a good sense of the everyday grammar of the language and learn some of the colloquial shortcuts employed by experienced users. In a sense I began to suspect that learning a programming language was much like any other foreign language.

It seems professionals in the field of computer science are having some of the same concerns. Professor Mark Guzdial, of the Georgia Institute of Technology, writing in the blog of the Communications of the ACM, lays it on the line in the title of his post:

“How We Teach Introductory Computer Science Is Wrong.”

Basing this conclusion not only on his own experience but also on results from several researchers, Guzdial questions whether extensive use of programming exercises are the best path to teaching programming to introductory learners. That is, is it best to teach problem solving by problem solving?

Guzdial starts his critique of computer science instruction by citing research in mathematics education by Sweller and Cooper (1985). In it, Sweller and Cooper compare two groups of students both of which are shown two worked examples in algebra. An experimental group is given eight more completely worked out examples in algebra. The control group gets the same eight problems to work out themselves. Not surprisingly the control group takes five times longer to complete their assignment. Next, both groups get a new set of problems to solve. Ready for the ta-da? Drum roll please….

“The experimental group solves the problems in half the time and with fewer errors than the control group.” – Guzdial, 2009

In other words, the work-it-out-for-yourself problem solving approach was less effective by a long shot. And, as an aside, it should be said that this approach to instruction is common not only in computer science courses but also in subjects like mathematics, physics, chemistry and engineering.

Other work by researchers Kirschner, Sweller and Clark (2006) and Kalyuga, Chandler, Tuovinen and Sweller (2001) comment on this effect and help explain where and when problem solving is superior to worked examples. Guzdial quotes Kirschner (1992) in summarizing the state of the problem:

“After a half-century of advocacy associated with instruction using minimal guidance, it appears that there is no body of research supporting the technique. In so far as there is any evidence from controlled studies, it almost uniformly supports direct, strong instructional guidance rather than constructivist-based minimal guidance during the instruction of novice to intermediate learners.”

Does this mean, as Marshall McLuhan was fond of saying, that “the whole fallacy is wrong?” Have we been sold down the river educationally where training in computer science, physical sciences, mathematics and engineering are concerned? Perhaps not. What the studies do suggest is that relying primarily on learn-programming-by-programming, work-it-out-for-yourself, minimal guidance methods are not well suited to introductory learners. These methods are, however, better suited to learners who have already acquired some background knowledge and are therefore a better fit to intermediate and advanced courses.

“What’s striking is that no one challenges [Kirschner, Sweller and Clark] on the basic premise, that putting introductory students in the position of discovering information for themselves is a bad idea!”  – Guzdial, 2009

That is not to say “never” of course. What the data are saying is that it’s not the best principal approach for beginners.

In hindsight the findings make perfect sense. My original intuition that learning a computer language is like learning a foreign language was not far off the mark.

The data suggest that for a beginner, learning to read before learning to write is a more effective approach.

References.

Kalyuga, S., Chandler, P., Tuovinen, J., Sweller, J. (2001), “When Problem Solving Is Superior to Studying Worked Examples,” Journal of Educational Psychology, 93(3), 579-588.

Kirschner, P. A. (1992), “Epistemology, practical work and academic skills in science education.” Science and Education, 1, 273-299.

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.

Sweller, J., Cooper, G. A., (1985). “The use of worked examples as a substitute for problem solving in learning algebra.” Cognition and Instruction, 2, 59-89.

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.

…

References.

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.

Measured “return on investment” and “training effectiveness” are two of the business metrics commonly used to yoke trainers and developers in business and government training centers around the globe. “Is the training effective?” and “Is it worth the cost?” are standard queries at development meetings and design reviews. Knowledgeable designers and managers invoke Bloom, Kirkpatrick and things like ADDIE to promote development of effective training, little knowing that Pygmalion might provide the help they need.

A little over 40 years ago, Robert Rosenthal and Lenore Jacobson performed a simple and ingenious experiment in a California school that jolted educational psychology. Dubbed the Pygmalion Effect (after the play by George Bernard Shaw; later the musical and movie My Fair Lady) the experiment showed that the effectiveness of teaching was largely determined by the belief of the teacher in the students. That is, all things being equal, if a teacher believes the students are exceptional, they will tend to match the expectation. Surprisingly perhaps, this “effect” has been replicated many times since its inception and has garnered support from similar studies done in colleges, industry and the military. What Pygmalion describes might be taken as the equivalent of the Placebo Effect in education, but it might just as well be a re-coining of the psychotherapeutic expression “you have to believe in the Process” directed toward the classroom.

What Rosenthal and Jacobson did in their study was give teachers false information about their students based on what they said was an advanced test to determine future performance and achievement. In reality they administered a standard IQ test, randomly selected a group of students without regard to the test results, told the teachers these students were going to bloom in achievement and sat back and noted the results. At the end of the school year the students were tested and the results showed that a significant number of the “bloomers” had in fact made unexpected gains in academic performance and behavior. In fact, tests of the same students two years later showed that they carried and maintained this advantage over that time.

Interestingly, while accounts of the first study did not include details of what went on in the classroom while the study was underway, written reports by the teachers themselves indicate that no special measures, programs or materials were provided to assist the “bloomers” in learning or to enhance the classroom experience. What Rosenthal and Jacobson concluded the “bloomers” got that the control group missed were clear signs of approval, more chances to interact with the teacher and patient acceptance, all moderated unconsciously by of the beliefs of the teacher.

Over the years the Pygmalion Effect has come under scrutiny by many researchers and has been criticized for its original experimental design and the general meaning of its results. But, all in all, it remains steadfastly rooted in the literature of educational psychology and provides a lasting contribution to the field.

References.

Rosenthal, R., and Jacobson, L. (1968). Pygmalion in the classroom: Teacher expectation and pupils’ intellectual development’. New York: Rinehart and Winston. (Newly updated edition, 2003)

Rosenthal, R., and Jacobson, L. (1966). Teachers’ expectancies: Determinates of pupils’ IQ gains. Psychological Reports, 19, 115-118.

Rosenthal, R. (1965). Clever Hans: A case study of scientific method. Introduction to Oskar Pfungst, Clever Hans (translated by Rahn, C. L., 1911). New York: Bolt, Rinehart and Winston, pp. ix-xiii.

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.

References.

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

“Every year, the services spend millions of dollars teaching our people how to think. We invest in everything from war colleges to noncommissioned officer schools. Our senior schools in particular expose our leaders to broad issues and historical insights in an attempt to expose the complex and interactive nature of many of the decisions they will make.

Unfortunately, as soon as they graduate, our people return to a world driven by a tool that is the antithesis of thinking: PowerPoint. Make no mistake, PowerPoint is not a neutral tool — it is actively hostile to thoughtful decision-making. It has fundamentally changed our culture by altering the expectations of who makes decisions, what decisions they make and how they make them. While this may seem to be a sweeping generalization, I think a brief examination of the impact of PowerPoint will support this statement.”

Others have voiced concern over the nature and limitations of this tool and its ilk. Edward Tufte for example penned the monograph “The Cognitive Style of PowerPoint: Pitching Out Corrupts Within” in an attempt to illustrate the common problems with the medium and offer suggestions on how to rectify them. Designers, illustrators and even cognitive scientists join the chorus in an effort to stem the plague of needlessly ineffective slide shows.

PowerPoint and its cousins have their genetic roots in presentation packages designed for selling, which is why PowerPoint still has a strong tendency to reduce everything it touches to a sales pitch. Hammes lights on this when he mentions how language and communication are bent to that of the Ad Man:

“Let’s start by examining the impact on staff work. Rather than the intellectually demanding work of condensing a complex issue to two pages of clear text, the staff instead works to create 20 to 60 slides. Time is wasted on which pictures to put on the slides, how to build complex illustrations and what bullets should be included. I have even heard conversations about what font to use and what colors. Most damaging is the reduction of complex issues to bullet points. Obviously, bullets are not the same as complete sentences, which require developing coherent thoughts. Instead of forcing officers to learn the art of summarizing complex issues into coherent arguments, staff work now places a premium on slide building. Slide-ology has become an art in itself, while thinking is often relegated to producing bullets.”

In PowerPoint language is reduced to a staccato burst of one-liners. Complete sentences are not at home in the medium. Language and rhetoric are reduced to a fractured mosaic of bullets, images and partial thoughts that serve as placeholders for information and ideas. The inherent bandwidth limitation of the medium is fine for sales presentations but falls flat when content and depth are required. Users struggle, perhaps unknowingly, to compensate for the inherent bias of the medium:

“Our personnel clearly understand the lack of clarity and depth inherent in the half-formed thoughts of the bullet format. In an apparent effort to overcome the obvious deficiency of bullets, some briefers put entire paragraphs on each briefing slide. (Of course, they still include the bullet point in front of each paragraph.) Some briefs consist of a series of slides with paragraphs on them. In short, people are attempting to provide the audience with complete, coherent thoughts while adhering to the PowerPoint format. While writing full paragraphs does force the briefer to think through his position more clearly, this effort is doomed to failure.”

Compounding the problem, (post-literate) reading speeds and the need to digest detailed and complex data fly in the face of the easy sales pitch proffered by the slide deck:

“People need time to think about, even perhaps reread, material about complex issues. Instead, they are under pressure to finish reading the slides before the boss apparently does. Compounding the problem, the briefer often reads these slides aloud while the audience is trying to read the other information on the slide. Since most people read at least twice as fast as most people can talk, he is wasting half of his listeners’ time and simultaneously reducing comprehension of the material. The alternative, letting the audience read the slide themselves, is also ineffective. Instead of reading for comprehension, everyone races through the slide to be sure they are finished before the senior person at the brief. Thus even presenting full paragraphs on each slide cannot overcome the fundamental weakness of PowerPoint as a tool for presenting complex issues.”

Hammes notes other signs of users’ struggle against the flow of the medium in mentioning the “quad chart” and slides crammed with so much information they cannot be processed by the viewer’s visual system, let alone addressed by the speaker. This is simply a low-bandwidth medium with rigid boundaries.

An Example Quad Chart

“The next major impact of slide-ology has been the pernicious growth in the amount of information portrayed on each slide. A friend with multiple tours in the Pentagon said a good rule of thumb in preparing a brief is to assume one slide per minute of briefing. Surprisingly, it seems to be true. Yet, even before the onslaught of the dreaded quad chart, I saw slides with up to 90 pieces of information. Presumably, some thought went into the bullets, charts, pictures and emblems portrayed on that slide, yet the vast majority of the information was completely wasted. The briefer never spoke about most of the information, and the slide was on screen for a little more than a minute. While this slide was an aberration, charts with 20 items of information portrayed in complex graphics are all too common. This gives the audience an average of three seconds to see and absorb each item of information. As if this weren’t sufficient to block the transfer of information, some PowerPoint Ranger invented quad charts. For those unfamiliar with a quad chart, it is simply a Power Point slide divided into four equal quadrants and then a full slide is placed in each quadrant. If the briefer clicks on any of the four slides, it can become a full-sized slide. Why this is a good idea escapes me.”

Hammes further notes that PowerPoint, like every technology, creates or alters the environment of the user. Interestingly, Hammes cites the effect PowerPoint has on time and events:

“PowerPoint has clearly decreased the quality of the information provided to the decision-maker, but the damage doesn’t end there. It has also changed the culture of decision-making. In my experience, pre-PowerPoint staffs prepared two to four decision papers a day because that’s as many as most bosses would accept. These would be prepared and sent home with the decision-maker and each staff member that would participate in the subsequent discussion. Because of the tempo, most decision-makers did not take on more than three or four a day simply because of the requirement to read, absorb, think about and then be prepared to discuss the issue the following day. As an added benefit for most important decisions, they ‘slept on it.’

PowerPoint has changed that. Key decision-makers’ days are now broken down into one-hour and even 30-minute segments that are allocated for briefs. Of particular concern, many of these briefs are decision briefs. Thus senior decision-makers are making more decisions with less preparation and less time for thought. Why we press for quick decisions when those decisions will take weeks or even months to simply work their way through the bureaucracy at the top puzzles me.”

Hammes does not miss the effect the indiscriminate use of the tool has on understanding and thought processes (“We shape our tools and then our tools shape us.” – McLuhan):

“Unfortunately, by using PowerPoint inappropriately, we have created a thought process centered on bullets and complex charts. This has a number of impacts. First, it reduces clarity since a bullet is essentially an outline for a sentence and a series of bullets outline a paragraph. They fail to provide the details essential to understanding the ideas being expressed. While this helps immensely with compromise, since the readers can create their own narrative paragraphs from the bullets, it creates problems when people discover what they agreed to is not what they thought they had agreed to. Worse, it creates a belief that complex issues can, and should, be reduced to bullets. It has reached the point where some decision-makers actually refuse to read a two-page briefing paper and instead insist PowerPoint be used.”

In closing Hammes concedes that there are appropriate uses for PowerPoint but these tend to be presentations that are closer to its origin: “primarily, information briefs rather than decision briefs.” As depth and complexity increase, the appropriateness of PowerPoint falls away. As Hammes says, “There is a reason students cannot submit a thesis in PowerPoint format.”