Ranting & Raving on Instructional Design, Education & Technical Training

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?

Attribution: uxmovement.com

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 uxmovement.com

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

Attribution: UI&Us

References

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

Lang, Keith, “Realizations of Rounded Rectangles“

FMC Visualization Guidelines

If 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.

References.

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.

What’s the best way to teach math? It’s a big question, but research at Ohio State University’s Center for Cognitive Science challenges a commonly held (though perhaps informal) notion in instructional design that concrete examples aid the learning and application of mathematics more than abstract proofs and representations. The idea that mastery of abstract quantities and concepts actually provides the learner with a better, i.e., more practical, set of tools for problem solving seems counter-intuitive, but researcher Jennifer Kaminski and her team believe they have proof. Kaminski et al. looked at whether students who received instruction using concrete examples performed differently from those who were encouraged to master the concepts abstractly. What they found was that the group who were instructed in more concrete terms and examples were less able to apply the knowledge to new situations.

“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.

Although distance and online learning have become staples in today’s colleges and corporate classrooms, they are not regarded as approaches without problems. Statistics for completion of online courses are typically quoted at around 30%, leading many to conclude that the means and methods of online instruction are unappealing to the learner and less than effective for the teacher. Furthering concerns about the overall effectiveness of online instruction, a 2007 study at the University of Missouri suggests that online learning (or e-learning) may not be a good match for some learners.

“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)

I can’t say whether the only course I’ve taken in programming was taught well. This is partially the case because it was so long ago and looking back on it it’s doubtful that anyone had an idea about how to teach such a new subject. It seems in retrospect that the professors and graduate students of that era were trying to figure out how to program themselves, let alone teach programming to undergraduates. To give you an idea, the language I learned in class was something called FORTRAN.

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.

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 an article that reads surprisingly like a case study from a course on McLuhans’ Laws of Media, T. X. Hammes writes in the Armed Forces Journal on the pernicious effects of pushing PowerPoint too far in the presentation culture of the Pentagon. Apparently keenly aware of the implicit bias of media, Hammes observes:

“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.”