The HG2S Training Blog

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).

Fossil fish bridges the evolutionary gap between animals of land and sea. Credit: Zina Deretsky, National Science Foundation

I’m glad that someone has gathered the courage to say this out loud: Instructional design in the 21st Century is not about events, it’s about experiences. No doubt from the looks of things, instructional design (ID) is in the natural throes of shaking off the learning events metaphor imposed on it by the educational psychologists of the Industrial Revolution, but learning and development thinkers like Charles Jennings hope that we can hasten it along for the sakes of our students and ourselves. For Jennings the shift from working with the hands to working with the head is a key indicator that promotes the need to move from events toward processes:

“Undoubtedly instructional design is crucial if the mindset is learning events – modules, courses, programmes and curricula. However, if the mindset has stretched beyond event-based learning to where most learning occurs for workers, which is in the workplace at the point-of-need, where process-based learning serves best – and where learning through doing and learning as part of the work process happens, then ID takes on a whole new dimension.”

Jennings posits the notion of “learning” held by inhabitants of the 21st Century as moving from a habitat of “knowledge” to a new one of “behavior.” The medium is the message. It’s not about content anymore.

“For years we’ve been led to believe that ‘learning’ meant acquiring knowledge. If knowledge acquisition is the end-game, then the logical conclusion was to provide information that could be turned, whatever the magic employed, into knowledge in the recipient’s head. Believe me, the old idea that data becomes information which in turn becomes knowledge and finally transmogrifies into wisdom has been debunked years ago. We use our knowledge and experience to interpret data and information. Wisdom comes to a few only after years of experience.”

Jennings reminds us that Ebbinghaus and the Forgetting Curve aside, we need to observe learning in action to make intelligent assessments about its effectiveness. Experience and practice are the keys and, as such, instructional designers need to become interactivity designers.

“Good ID will result in the design of experiences that can build capability and learning far more quickly and effectively than by filling heads with information and ‘knowledge’ and then hoping that will lead to behavioural change.

We need designers who understand that learning comes from experience, practice, conversations and reflection, and are prepared to move away from massaging content into what they see as good instructional design. Designers need to get off the content bus and start thinking about, using, designing and exploiting learning environments full of experiences and interactivity.”

Further information about Charles Jennings and his work can be found here.

The Bandwagon

If you are thinking of using social media in a class to help build useful collaborative connections, retire the fears of shy students and introduce the same engagement you see in sites like Facebook, think again.  A recent study by the Lab for Social Computing at Rochester Institute of Technology suggests that the use of social media in classrooms might yield little effect in improved communications and enhanced connections between students. The study into the effects of social media was conducted as part of a course on the use of social media and tools. It included contributions from online learning and course management systems and discussion groups that were proposed to enhance instruction, improve communication and facilitate connections between the students and course content. The results indicate that poor social acumen in the face-to-face interactions might be mirrored in the (more) virtual social medium. What’s more, echoing teacher and educational social media researcher Michael Wesch, the RIT study suggests that the educational use of social media may have to be learned:

“…the educational use of social media may not counteract poor social connections that are seen in face-to-face communication or elicit the same impacts seen in the use of social media sites such as MySpace and FaceBook.”

Researcher and team leader Susan Barnes comments on the hopes and goals of social media in the educational environment relative to her team’s findings:

“Many social media advocates have argued that the use of these tools in classroom settings could greatly enhance interaction and learning and assist shyer, more reserved students in becoming more involved, as has been seen in other online environments. However, our findings show that the incorporation of social media had no measurable impact on social connections, to the point that students did not consider other members of the class to be part of their social network.”

The RIT research team plans to expand the study to consider different educational formats and additional social media applications in an effort to determine the effects and differences of social media from traditional classrooms. The intent is to help educational planners and instructional designers better use social media in course development and delivery.

“The issues surrounding poor social network construction within online educational environments points to greater opportunities to examine how technology and mediated software can be better designed to suit the types of communication and interactions desired by our students.”  – Christopher Egert, co-author

References.
Jacobs, Stephen, Egert, Christopher A., Barnes, Susan B., “Social Media Theory and Practice: Lessons Learned for a Pioneering Course,” 39th ASEE/IEEE Frontiers in Education Conference, T4J-1, October 18 – 21, 2009, San Antonio, TX.

Study Examines Use of Social Media in the Classroom

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.

There is an entertaining (and on-going) discussion at Edward Tufte‘s blog on the rate at which the human eye (specifically the retina) transfers information to the brain. The implications of the discussion point to the design of displays but the discussion has necessarily taken a turn in the direction of the likely question “What is the maximum amount of information (or data) that can be transferred from a PowerPoint slide to the brain?”

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?

Despite the fact that we are quick to assert that we live in The Information Age and are swimming in all kinds of media, data and sensory stimuli, it’s sobering to take a step back and reflect on the fact that information is not always where the focus of attention is. Marshall McLuhan was fond of saying that “We don’t know who discovered water, but we know it wasn’t the fish.” Information today is a little like that water and as teachers and instructional designers we have to pay attention to the differences between the medium and message if we want to be effective in what we do.

Right in keeping with this problem, the folks at MAYA Design have produced a really useful and (dare I say) informative animated short on the problem of distinguishing information from its presentational form. That is, in Gestalt terms, how to see the ground separate from the figure.

As an example of the problem of teasing information from its encapsulating medium, do you know what information is? Can you cite an example? What would you say if you were told that you can’t actually see or hear information? Would you be comfortable with the idea that neither the words on a page nor the numbers on a spreadsheet are information? In the words of MAYA Design, “Information has no form. It’s not made of atoms.”

So, what is information? In MAYA’s view:

“Information is what allows us to confidently make a selection from a set of given or implied alternatives.”

And what is our job then relative to information design? Our job is to give it form. We write it down, verbalize it, draw it and act it out. All with the intent of communicating it. Take a few minutes and look here or below and get reacquainted with the differences between medium and message.

Chip and Dan Heath, authors of the popular book Made to Stick: Why Some Ideas Survive and Others Die, have applied key principles of their stickiness theory to teaching. The resultant 13 page e-book is available in PDF format at their web site or on scribd.com as a free download.

Borrowed from their research, the brothers Heath apply six traits that make ideas (and teaching) stickier. Sticky ideas are:

SIMPLE.

“This process of prioritization is the heart of simplicity. It’s what we call ‘finding the core.’ Simplicity doesn’t mean dumbing down, it means choosing. Some concepts are more critical than others. And as the teacher, you’re the only one who can make that determination.”

UNEXPECTED.

“Piquing curiosity is the holy grail of teaching.” Cialdini said, “You’ve heard of the famous Ah ha! experience, right? Well, the Ah ha! experience is much more satisfying when it’s preceded by the Huh? experience.

So how do you create the ‘Huh?’ experience with your students? George Loewenstein, a behavioral economist, says that curiosity arises when we feel a gap in our knowledge. Loewenstein argues that gaps cause pain. When we want to know something but don’t, it’s like having an itch we need to scratch. To take away the pain, we need to fill the knowledge gap. We sit patiently through bad movies, even though they may be painful to watch, because it’s too painful not to know how they end.

Movies cause us to ask, What will happen? Mystery novels cause us to ask, Who did it? Sports contests cause us to ask, Who will win? Crossword puzzles cause us to ask, What is a 6-letter word for psychiatrist? Pokemon cards cause kids to wonder, Which characters am I missing?

One important implication of the ‘gap theory’ is that we need to open gaps before we close them. Our tendency is to tell students the facts. First, though, they must realize they need them.”

CONCRETE.

“Concreteness etches ideas into our brain—think of how much easier it is to remember a song than a credit card number—even though a song contains much more data!”

CREDIBLE.

“For an idea to stick, it needs to be credible. YouTube-era students don’t find it credible that hanging out outside, for a long period of time, alone, could be conducive to great thinking. So how do you combat their skepticism? You let them see for themselves. It’s like a taste test for ideas.”

EMOTION.

“That’s what Emotion does for an idea—it makes people care. It makes people feel something. In some science departments, during the lesson on ‘lab safety,’ the instructor will do something shocking: They’ll take some of the acid that the students will be handling and use it to dissolve a cow eyeball. A lot of students shudder when they see the demonstration. They feel something. Lab safety ‘dos and don’ts’ don’t grab you in the gut, but a dissolving eyeball sure does.”

STORY.

“The second surprise about stories is why stories, even boring stories, are so sticky. The answer starts with some fascinating research done on ‘mental simulation.’ Brain scans show that when people imagine a flashing light they activate the visual area of the brain; when they imagine someone tapping on their skin they activate tactile areas of the brain. The activity of mental simulation is not limited to the insides of our heads. People who imagine words that start with “b” or “p” can’t resist subtle lip movements, and people who imagine looking at the Eiffel Tower can’t resist moving their eyes upward. Mental simulation can even alter visceral physical responses: When people drink water but imagine it is lemon juice, they salivate more. Even more surprisingly, when people drink lemon juice but imagine it is water, they salivate less. … The takeaway is simple: Mental simulation is not as good as actually doing something—but it’s the next best thing. And, to circle back to the world of sticky ideas, what we’re suggesting is that the right kind of story is, effectively, a simulation. Stories are like flight simulators for the brain.“

The free booklet gives practical suggestions and examples of how to use “stickiness” to improve lessons and teaching. The authors are quick to remind readers that the principles are pragmatic design guidelines for better teaching not just theories for the way instructional design works. “Teaching that Sticks” is an entertaining and informative read for anyone who designs, writes or presents classes or educational material. A companion booklet “Making Presentations that Stick” is also available.

References.

http://www.madetostick.com/teachers/

Why you should train your people

In what might seem an unusual post by a leading business analyst and venture capitalist, Ben Horowitz, of Andreesen Horowitz, writes in businessinsider.com

“Almost everyone who builds a technology company knows that people are the most important asset. Properly run start-ups place a great deal of emphasis on recruiting and the interview process in order to build their talent base. Unfortunately, often the investment in people stops there.”

Horowitz’s own experience in training sounds all too familiar:

“When I first became a manager, I had mixed feelings about training. Logically, training for hi-tech companies made sense, but my personal experience with training programs at the companies where I had worked was underwhelming. The courses were taught by outside firms who didn’t really understand our business and were teaching things that weren’t relevant.”

A turning point in Horowitz’s perspective on training came through Andy Grove’s High Output Management, specifically the chapter titled “Why Training is the Boss’s Job.”

As Director of Product Management at Netscape, Horowitz decided to put his new found inspiration to work and produced a guide titled Good Product Manager/ Bad Product Manager in an attempt to educate his staff on how to bring value to product management.

“I was shocked by what happened next. The performance of my team instantly improved. Product managers that I previously thought were hopeless became effective. Pretty soon, I was managing the highest performing team in the company. Based on this experience, after starting Loudcloud, I heavily invested in training. I credit that investment with much of our eventual success. And the whole thing started with a simple decision to train my people and an even simpler training document.”

Horowitz sees four key benefits to well-designed well-delivered training:

• Productivity
• Performance Management
• Product Quality
• Employee Retention

On Productivity Horowitz credits Grove with doing the math for the amplification of benefits from training:

“Training is, quite simply, one of the highest-leverage activities a manger can perform. Consider for a moment the possibility of your putting on a series of four lectures for members of your department. Let’s count on three hours preparation for each hour of course time—twelve hours of work in total. Say that you have ten students in your class. Next year they will work a total of about twenty thousand hours for your organization. If your training efforts result in a 1 percent improvement in you subordinates’ performance, you company will gain the equivalent of two hundred hours of work as the result of the expenditure of your twelve hours.“

On Performance Management Horowitz sees training as laying the foundation in understanding between the manager and the employees in terms of job responsibilities and expectations:

“If you don’t train your people, you establish no basis for performance management. As a result, performance management in your company will be sloppy and inconsistent.”

On Product Quality, Horowitz cites a common instance of where a push to cater to an urgent demand forces training out of the process leading only to an unnecessary and expensive reinvention of the wheel:

“As success drives the need to hire new engineers at a rapid rate, companies neglect to train the new engineers properly. As the engineers are assigned tasks, they figure out how to complete them as best they can. Often this means replicating existing facilities in the architecture, which lead to inconsistencies in the user experience, performance problems, and a general mess. And you thought training was expensive.”

Last but not least, Horowitz speaks to the issue of Employee Retention. Using his own experience at Netscape as a real-life example, Horowitz recounts an instance where he analyzed exit interviews to determine why people were leaving:

“1. They hated their manager – generally the employees were appalled by the lack of guidance, career development and feedback they were receiving.
2. They weren’t learning anything – the company wasn’t investing in the employees.”

“As Andy Grove writes, there are only two ways for a manager to improve the output of an employee: motivation and training. Therefore, training should be the most basic requirement for all managers in your organization. …Managing the company is the CEO’s job. While you won’t have time to teach all of the management courses yourself, you should teach the course on management expectations, because they are, after all, your expectations.“

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)

The archives collection  at Wired.com has a transcribed discussion between computer gurus Alan Kay and Danny Hillis that, surprisingly perhaps, includes a few comments about learning and education. It’s a worthwhile read in many respects but I’m plucking a couple quotes from it that relate specifically to knowledge, learning and pedagogy:

“There’s this interesting interplay between what you might call talent and how much of a meta-system we can put down on top of meager talents to learn how to do things. Two recent tennis champions, Ivan Lendl and Chris Evert, were not actual athletes. They were people who just learned how to play tennis. Some of the most natural tennis players, like Nastasi and Agassi, only do well when things are going well – they don’t have learned skills to drop back on. So in any given population maybe 5 to 20 percent have a natural hacker sort of talent; they are often not helped by pedagogy. Pedagogy is about getting the other 80 percent of people within hailing distance. So I’ve been very interested in taking some very important ideas and wondering how you get them in a state where the 80 percent can actually learn them in an operational way. And that’s why I keep coming back to computers.”

Interestingly the conversation concludes with:

“DH:

The question that I keep asking myself is, where is the next frontier? Where is that place that a new world is being constructed? Do you know any candidates?

AK:

I think the frontier has to do with human learning. Knowledge is not completely relative. There are a hundred or so powerful ideas that basically mean the difference between life and death, and I think one of our major jobs should always be to be true and get as many people enfranchised into them as possible.

DH:

But in fact, if you look at what’s happening, it seems just the opposite. We’re very much heading toward a two-class society, where either you’re somebody who sort of knows about, or feels empowered to deal with all of the complexity in society, or you’re one of the people that is a victim of it and is just on the receiving end of it all.

AK:

And I think the gap actually gets bigger as the leading edge of knowledge gets less intuitive.”

The full transcript by Steven Levy and Kevin Kelly can be viewed here.

Weighing in on the side of blended learning, Dr. Caroline Haythornthwaite of the Graduate School of Library and Information Sciences, University of Illinois at Urbana-Champaign, states that e-Learning may be at its best when used as a tonic to the traditional classroom.

“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

When should simulations be used in class? Do they matter or are they eye candy, empty demonstrations and a waste of time? A recent study published in Medical Teacher suggests that medical students benefit significantly in both learning and retention when high fidelity simulations are used in training. Authors Corey Heitz, Ashley Brown, James E. Johnson & Michael T. Fitch of Wright State University and Wake Forest University School of Medicine, compared the educational effects of a 90-minute live simulation to a traditional lecture.

A team of physicians assisted the in the presentation by acting the roles emergency medical staff, nurses and even family members. A computerized Laerdal SimMan(tm) was programmed to represent the patient who displayed symptoms like nausea, mental confusion and vomiting. As reported in Heitz, et al. (2009) the students were immersed in a theatrical enactment of the medial crisis:

“A Laerdal SimManTM was transported from the simulation center to the medical school lecture hall where a prerecorded EMS radio call announced the arrival of the fully clothed simulation mannequin. Resident physician actors portrayed EMS provi- ders, nurses, and family members. Student volunteers ran the case as emergency physicians and patient management decisions were guided by class input. The clinical scenario was enhanced with group discussion of the relevant basic science mechanisms underlying the autonomic nervous system, neurotransmitters, receptors, and neuropharmacology.” – Heitz et al. (2009)

The authors note that one of the key differences in this trial was the size of the group – 112 students in two groups. The live simulation was based on a clinical scenario designed to bring out basic concepts in neuroscience already presented in a lecture several days earlier by a participant who was unaware of the study.

Results of the training were measured using four multiple-choice pre-tests and post-tests.

“The primary study outcome was this comparison of student performance on a pretest compared to a posttest administered immediately after the simulation session, and participants were significantly more likely to get all four posttest questions correct after experiencing the simulation.” Heitz et al. (2009)

A follow-up post-test was given to students eleven days later to assess retention. The researchers suspect that immersion in the simulation contributed to recall:

“The concepts presented during our simulation session improved student testing immediately and may have facilitated performance on an examination 11 days later.”  - Heitz et al. (2009)

The authors conclude that the use of simulations of this kind can be valuable in medical education:

“The students not only felt the simulation experience correlated well with basic science concepts, but also showed statistically significant improvement on the pre- and posttest examinations. Our results show that this type of learning exercise may provide an alternative for ‘‘typical’’ lecture-style education.” – Heitz et al. (2009)

References.

Wake Forest University Baptist Medical Center (2009, July 14). “Simulating Medical Situations Helps Students Learn, Retain Basic Science Concepts”. ScienceDaily. Retrieved April 22, 2010, from http://www.sciencedaily.com/releases/2009/07/090714085822.htm

Heitz, Corey , Brown, Ashley , Johnson, James E. and Fitch, Michael T.(2009) “Large group high-fidelity simulation enhances medical student learning”, Medical Teacher, 31: 5, e206 — e210

Emergency Simulations at Wake Forest University School of Medicine

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

…

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.

THE WHITE HOUSE Office of the Press Secretary
FOR IMMEDIATE RELEASE, March 5, 2010:

“President Obama Announces More Key Administration Posts

Edward Tufte, Appointee for Member, Recovery Independent Advisory Panel
Edward Tufte is Professor Emeritus of Political Science, Statistics, and Computer Science at Yale University. He wrote, designed, and self-published The Visual Display of Quantitative Information, Envisioning Information, Visual Explanations, and Beautiful Evidence, which have received 40 awards for content and design. He is a Fellow of the American Academy of Arts and Sciences, the Guggenheim Foundation, the Center for Advanced Study in the Behavioral Sciences, the Society for Technical Communication, and the American Statistical Association. He received his PhD in political Science from Yale University and BS and MS in statistics from Stanford University.”

From ET:

“I will be serving on the Recovery Independent Advisory Panel. This Panel advises The Recovery Accountability and Transparency Board, whose job is to track and explain $787 billion in recovery stimulus funds:

‘The Recovery Accountability and Transparency Board was created by the American Recovery and Reinvestment Act of 2009 with two goals:To provide transparency in relation to the use of Recovery-related funds.
To prevent and detect fraud, waste, and mismanagement.
Earl E. Devaney was appointed by President Obama to serve as chairman of the Recovery Board. Twelve Inspectors General from various federal agencies serve with Chairman Devaney. The Board issues quarterly and annual reports to the President and Congress and, if necessary, “flash reports” on matters that require immediate attention. In addition, the Board maintains the Recovery.gov website so the American people can see how Recovery money is being distributed by federal agencies and how the funds are being used by the recipients.

Mission statement: To promote accountability by coordinating and conducting oversight of Recovery funds to prevent fraud, waste, and abuse and to foster transparency on Recovery spending by providing the public with accurate, user-friendly information.’

I’m doing this because I like accountability and transparency, and I believe in public service. And it is the complete opposite of everything else I do. Maybe I’ll learn something. The practical consequence is that I will probably go to Washington several days each month, in addition to whatever homework and phone meetings are necessary.”

http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0003e0&topic_id=1#