Backing up a bit, suppose you were asked to design and deliver a class or training session that had to maximize educational outcome – meaning, it had to work as a learning tool more to the benefit of the students than the teacher – no holds are barred, and you knew of a technique that resulted in an 80% improvement over the traditional lecture method. Would you use that method? More to the point, could you justify not using it? Well that is what Deslauriers, Schelew and Wieman found (see Science article below) when they compared the lecture with a more interactive class they designed to teach physics. All things being equal, if you supplant the lecture with a presentation that is designed to work more in accord with how most people learn, test scores go from 41% for the garden-variety lecture class to 74% for the interactive class. Pretty impressive stuff.

So what is the nature of the design of the interactive class? Put simply, research in cognitive psychology suggests that learners will get better results if they use what they have just been given right away. The theme: Deliver new information, play with it, use it to solve problems, evaluate mastery of the skills and concepts, repeat as needed. Deslauriers, Schelew and Wieman’s physics students were hit repeatedly with questions during class that they had to answer with clickers. Students frequently worked in groups where they were challenged to use their new knowledge to solve problems. Lastly, the students were evaluated in part using two class tests rather than the traditional single mid-term exam.

Let’s make it clear, pouring the old wine in a new bottle does not make it sweeter. Content matters. Doing homework in class and listening to lectures at night is not “flipping the classroom.” Recording lectures and putting them on YouTube or iTunes U is no solution:

“A University of Maryland study of undergraduates found that after a physics lecture by a well-regarded professor, almost no students could provide a specific answer to the question, ‘What was the lecture you just heard about?’ A Kansas State University study found that after watching a video of a highly rated physics lecture, most students still incorrectly answered questions on the material.” — David H. Freeman, Discover Magazine

Even in the best cases of well-thought-out well-designed interactive classes some likely criticisms remain. There is an issue with the Hawthorne Effect that needs to be retired, but personal experience suggests that these findings are not surprising or unusual, at least in kind. Another question that surfaces is whether this kind of interactive class lends itself to subjects like literature, philosophy, history or political science. What are the limits of the approach?

Finally, we have to ask why if there is so much evidence and personal experience against lectures do we persist in giving them? The answer might well be wrapped in four prominent qualities of the practice: 1) lecturing is easy and cheap to do; 2) we have been taught to accept bad lectures as normal (for well over a thousand years!); 3), they (certainly the live version) create an illusion of interactivity between the presenter and audience that is not supported in actual observation (see D. Clark below); and 4), they stand as proof by the presenter and/or the institution that the material has been covered and “delivered” to the audience.

Pragmatically, and for the reasons above, lectures inherently favor the presenter and the institution. Lectures originated in a time when books and information were both scarce and expensive and colleges needed to solve a problem of distribution. Closer to the modern era lectures appear to be supported by tacit agreement with the dubious notion that teaching and telling are the same thing:

“The problem is not with the lecture but with the idea that receiving information is the key part of learning.” — Dominik Lukeš

The notion that the lecture’s time has come is finally reaching the Academy. Educators like Graham Gibbs (see below) have been questioning its value for over thirty years. More recently university professors like Stanford University’s (formerly) Sebastian Thrun have had their own epiphanies on the matter:

“Mr. Thrun told the crowd his move [away from Stanford] was motivated in part by teaching practices that evolved too slowly to be effective. During the era when universities were born, ‘the lecture was the most effective way to convey information. We had the industrialization, we had the invention of celluloid, of digital media, and, miraculously, professors today teach exactly the same way they taught a thousand years ago,’ he said.” — Nick DeSantis, Wired Campus

Dr Wieman likewise has his own concerns about his colleagues and the future of the lecture in science instruction. As recorded by David Freeman of Discover Magazine:

“But scientists who teach have proven reluctant to toss out the lecture, never mind the evidence that it doesn’t work. ‘They say this is the way it’s always been done, and it was good enough for them, so it’s good enough for their students,’ Wieman says. Were this attitude to hold in medicine we would still be bloodletting, in physics we would be trying to reach the moon with very large rubber bands, and in economics we would still be suffering major worldwide financial crashes. (Well, physics and medicine are advancing, anyway.)” — David H. Freeman, Discover Magazine

What seems certain is that we are on the foothills of a major shift in what happens in the classroom. What develops in terms of the effects on corporate, college and military training remains to be seen. After all, it might not result in a single universal one-size-fits-all form. How this upheaval in teaching feeds into distance learning and web-based training is another discussion that almost certainly has to rear its head. The resultant form of the instructional process is anybody’s guess, but what is certain is that whatever it evolves into, whatever we see as the best fit for our instructional purpose, teaching well will remain hard work.

References.
Freeman, David, H., Impatient Futurist: Science Finds a Better Way to Teach Science

http://homepage.ntu.edu.tw/~jiayang/me1005/2011f/2011%20Science-%20Improved%20learning%20in%20a%20large-entrollment%20Physics%20class.pdf

Gibbs, G., “Twenty Terrible Reasons for Lecturing,” SCED Occasional Paper No. 8, Birmingham. 1981.

Clark, Donald, “Don’t Lecture Me” – ALT-C 2010.

Clark, Donald, “Lectures selling students short: evidence from ‘Science’ “

Lukeš, Dominik, “Putting lectures in their place with cautious optimism“

DeSantis, Nick, “Tenured Professor Departs Stanford U., Hoping to Teach 500,000 Students at Online Start-Up“

Deslauriers, Loius, Schelew, Ellen and Wieman, Carl, “Improved Learning in a Large-Enrollment Physics Class” Science 13 May 2011: Vol. 332 no. 6031 pp. 862-864

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

“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

References.

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. (http://www.sciencedirect.com/science/article/B6VDC-5230FHR-1/2/0510a5a803281cf536a0b381dcd2052d)

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.

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.

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.