For decades, the US Food and Drug Administration (FDA) has been touting schemes to attempt—at least ostensibly—to help Americans eat more healthfully. From the 4-food-group concept to the current food pyramid, the idea has been to help us think more intelligently about what we eat. The development of these formulations have always been under influence by industry groups representing food companies and health advocates, and it has never been clear that the resulting compromises have been effective. Note the current obesity epidemic as evidence.

But now, the Hannaford Brothers supermarket chain is taking matters into their own hands. Hannaford’s nutritionists have developed a simple coding system to let shoppers know the relative health value of foods. Three stars is most healthy. Zero stars is least healthy. And while the coding system is simple, the underlying algorithm—the algorithm that assigns the ratings—is complex.

Listen to this: 77% of all the items on Hannaford’s shelves rated zero stars. Even most of the Hannaford Store brand items get zero stars. Our choices are not very nutritious!!

As you might imagine, some manufacturers are not happy.

Only time will tell whether shoppers will change their eating habits—or whether food manufacturers will change the formulas for their processed foods to get a better star rating, and Hannaford will be able to track this data very easily. It’s hard to tell whether the food pyramid has had an effect. Hannaford’s Guiding Star system will be much easier to assess. I hope they’ve got some control-group stores to make comparisons.

I love the simplicity of the system. It’s like a job aid on steroids. It’s simple. It’s provided exactly when needed (as shoppers make their selections), and it’s based on proven nutritional recommendations.

Those of us in the learning-and-performance field can learn a lot from this design.

  1. Ensure that learning designs impact performance situations.
  2. Simplify, to increase how much your learners/performers actually use your stuff.
  3. Base learning designs on proven content. For example, is the stuff you teach in your leadership classes really been proven to improve management performance?
  4. Utilize systems to track your success, so that you can make

To read the excellent NY Times article, click here.

I recently attended the eLearning Guild’s DevLearn06 conference, dedicated to e-learning developers. I gave a preconference workshop on the learning research and two conference sessions, one on the spacing effect and one on assessments (especially as they are illuminated by the learning research).

Here’s some quick highlights from the conference:

1. 50 developers showed off their latest and greatest e-learning interventions in the eLearning Guild’s Demo Fest. This 2.5 hour opportunity is a great addition to the typical conference fare. I noticed two things:

    • The production values and aesthetic quality of the entries were remarkable. Beautiful work. So, if you’re developing e-learning, you better start hiring talented graphic designers and managers who have an aesthetic sensibility.
    • The designs were generally much better than page turners, but they still weren’t completely consistent with research-based practices. There were good interactions and lots of decision scenarios, but still there were too many missed opportunities for authentic practice and decision-making.

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2. The eLearning Guild’s Research Posse, led by the ingenious and tireless Steve Wexler, has developed an amazing data-gathering tool, that enables eLearning Guild members to find out the market penetration of all kinds of things, including authoring tools, simulation practices, m-Learning, etc., etc. With this foray into industry research, the Guild easily leapfrog’s the feeble attempts by ASTD and Bersin, both of which provide only broad strokes analysis of industry data.

3. Great session by Judy Brown on m-Learning, who showed (a) an amazing installed base of m-Learning tools that makes it clear that m-learning tsunami is on its way here, and (b) some interesting statistics showing that the US is way behind Asia and Western Europe in the amount of data that is utilized on cell phones (the more data, the more learning programs).

Recently, the New York Times Public Editor wrote an article on polling. The article skewered many current practices. It also was educational for any of us who want to truly understand the polling data we may hear in the news.

Sadly, the article made me think of the pathetic data that runs around our field—the training, learning, development, e-learning field. I’ve already mentioned some of the data allegedly posing as learning research.

But there is another wide swath of data that we should be very skeptical about—the data some "research" firms and trade organizations are peddling as industry data. The data is typically gathered by sending out surveys to an unrepresentative sample of companies, by having only a fraction of respondants complete the data, by gathering opinions, and by boisterously proclaiming that the data tell us what the industry best-practices are. Here are some of the problems with this farce:

  1. Biased sampling of organizations.
  2. No control for the biasing effects of non-respondants.
  3. Assuming that opinion is fact.
  4. Relying on the averaging of opinions.
  5. Assuming that the average respondants have the best insights.
  6. The arrogance and lack of caveats in the reporting.
  7. Year-to-Year comparisons with different companies in each year’s sample.
  8. Additional biasing due to fraud and corruption, as when these "research" organizations tilt the best-practice results to their paid customers.

CLO magazine (Chief Learning Officer) has just finished accepting nominations for it’s 2006 "Learning In Practice Awards."

Awards are a great thing of course, because theoretically they can reward outstanding achievements and highlight for the industry the leading-edge thinkers and product/service implementations.

But award selections are not easy to administer. They involve a great deal of investment by the sponsoring organization. Sponsoring organizations almost always get payback from giving awards because their name becomes synonomous with power and credibility.

Unfortunately, the cost of these awards programs almost always push awards providers to ask for an entry fee of some sort. For example, the CLO awards ask for $149 per entry. The Brandon-Hall awards have an entry fee as well.

These entrance fees completely bias the results, and should make all of us suspicious about whether the award winners truly represent the best in our industry. All we need to do is ask ourselves this question, "Am I likely to spend $150 if I have no likelihood of personal, social, or financial gain?" Even allowing for the occasional radical altruist or rich benefactor, most of us want to get something in return for spending $20, let alone $150. So why do award nominations get submitted? For personal or business gain. And what kind of organizations are most likely to be nominated? Organizations with lots of money and marketing clout.

What kind of products/services and people get overlooked?

  • Innovators
  • Individual contributors
  • Small businesses
  • Those who worry about associating themselves with phony awards

One solution to this problem is to use a sliding scale for entrance fees or to offer scholarships for organizations or individuals without the financing of our largest vendors.

In the meantime, when we see an award, we ought to be aware that somebody else was probably more deserving.

Problem-Based Learning is an intriguing, if not proven, method of facilitating learning. A new journal has been launched to gather research related to problem-based learning. The first issue is available free online.

In that first issue, John R. Savery provides a nice overview of Problem-Based Learning (PBL). In it he acknowledges that the research has not shown that PBL is more effective than traditional learning approaches.

In thinking about PBL, please keep in mind that, like many learning approaches, PBL is really a compendium of many separate learning factors (for example, problems should be ill-structured, collaboration is essential, etc.). It could be that many of the components have value, but one or another of PBL’s components may actually suppress learning. As Savery notes, more research is needed.

Unless you’ve been a hermit since birth, you should have noticed that we human beings are drawn to celebrity. From peasants to the so-called intellectual elite, we are vastly more likely to pay attention to celebrities (and their activities) than to anyone else.

The evidence is everywhere.

  • Movie producers hire big name actors to draw people to theaters.
  • Yahoo’s home page almost always leads with a celebrity.
  • Google gets more searches for Johnny Depp than for any of your neighbors.
  • Advertisers pay big bucks for celebrity endorsements.
  • Celebrities get the largest book advances (and sales).
  • Conferences pay big bucks to hire celebrities, even if they have little to say.
  • Much of our idle chatter involves talk of celebrities.
  • Tournament organizers groan when the big names get ousted or withdrawal due to injury.

I have to admit it myself. While browsing the web, I often find myself clicking through to a story about a famous person. It’s almost an unconscious response. I seem to be drawn to want to know what’s going on with them.

The New York Times has a nice piece about the power of celebrities to draw interest on the internet.

How Can We Use Celebrity to Increase Learning?

First, we have to distinguish between drawing attention and creating distraction. Using celebrity is a good method for getting more of our learners to initially access the learning message. It might also be useful to maintain attention longer than normal. But we have to watch out for the distraction factor and make sure that the learning message is supreme.

I’d actually like to see a study that tests my little theory.

  • Hypothesis 1: Celebrity helps garner attention, AND increases long-term retrieval of the main learning points.
  • Hypothesis 2: Celebrity helps garner attention, AND decreases long-term retrieval of the main learning points (but increases long-term retrieval of information about the celebrity or the learning event itself).
  • Hypothesis 3: Celebrity does nothing. We only think it does.

The research would have to be very careful to avoid experimenting with captured learners. Why? Because the hypothesized benefit is due to more people attending to the learning event in the first place. If everyone, regardless of whether we use a celebrity or not, is captured, there are less likely to be learning benefits.

In a natural, authentic training environment, I’m betting on Hypothesis #1 above.

If anybody has learners and funds to test this theory, give me a call. Let’s get to work.

Training Implementations

Some of us use celebrity already. I’ve seen e-learning programs from Type A Learning Agency that used in-company celebrities to reinforce messages within e-learning programs.

Celebrity doesn’t have to reside only in individuals either. Executives love being sent off the Harvard Business School events, for example.

Elliott Masie–the training industy’s P. T. Barnum–uses his celebrity to draw people to his conferences, which are pretty good learning events after all.

Local ASTD, ISPI, SHRM (etc.) chapters use celebrity speakers to draw a crowd at their sessions.

More and more, executives (in-company celebrities) are teaching leadership courses to company managers.

The Bottom Line

It’s worth considering how we might use the concept of celebrity to draw learners to our learning initiatives and increase their attention on our learning messages.

I’ve been suspicious of the wiki bandwagon for quite some time. An earlier piece I wrote summed up my concerns. Fortunately, someone else has come along and written more deeply and elegantly on the topic. Check out Jaron Lanier’s piece, DIGITAL MAOISM: The Hazards of the New Online Collectivism, published 5-30-2006 in The Edge an online publication.

Jaron Lanier’s piece points out several things:

  1. Sometimes the collective is wise. Sometimes it is stupid. Sometimes it is very stupid.
  2. We, as individuals, have difficulty knowing what’s good and what’s not good.
  3. There is very often a need for wise and knowledgable editors to filter messages.
  4. Google search results have a similar problem.
  5. Websites and blogs that aggregate info have a similar problem.
  6. Our democracies may be threatened by this collective stupification.

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Relating This to the Learning-and-Performance Field

My main concern with wiki’s is that information from real experts can be stupified to the mediocre averaging of above-average minds. I have a wiki design that overcomes this problem, but I need a generous funder to make it a reality. BIG HINT to you rich philanthropic types. SMILE.

Wiki’s aren’t the only newtech fetish that exhibits the filtering problem. Blogs that simply compile messages from other blogs and web pages offer a prime exhibit. Jay Cross recently noted how the blogosphere was like an echo chamber, where the most prolific bloggers cite the most prolific bloggers into recursion ad absurdium. I see this echoing effect as a potential problem, dulling and twisting the voices of truly brilliant people and fully-vetted ideas.

I don’t want these compilers to stop. I like the material to sift through. But I do hope most people realize that there is very little nutrition left in sifted poop.

And we definitely need others to vet our work. For example, many great books that I read have acknowledgements aplenty for people who have provided feedback to the author(s).

My simple little point is that we need expertise too. We need filtering. We need wisdom. Some of that wisdom can come from the collective, but some must also come from people who have done the hard work of learning a knowledge discipline.

Anyway

Go read Jaron Lanier’s piece, and the vetting it receives from the collective spectrum of people in The Edge’s readership.

Why Level 2 Assessments Given Immediately After Learning Are Generally Dangerous and Misleading

Note from Will Thalheimer: This is an updated version of a newsletter blurb written a couple of years ago, where I made too strong a point about the dangers of Level 2 Assessments. Specifically, I claimed that Level 2 Assessments should never be used immediately after learning, which may have been pushing the point too far. Upon rethinking the issue a bit, I’ve concluded that Level 2 Assessments immediately after learning are still dangerous, but there may be some benefits to using them if the overall assessment process is designed correctly. You can be the judge by reading below.

Introduction to Levels 1 and 2 Assessments

Before we get to Level 2 evaluations, let’s talk about Level 1 of Kirkpatrick’s 4-level model of assessment. Level 1 is represented by the "smile sheets" that we hand out after training or include at the end of an e-learning course. They typically ask learners to rate the course and to judge how likely they are to use the information they learned. These evaluations are valuable to get learner reactions and opinions, but they provide a very poor gauge of learning and performance. The fact that we rely on these almost exclusively to assess the value of our instruction is unconscionable.

Level 1 Assessments are not always good predictors of learning. Learners may give a course a high score but not remember what they learned. Learners are also famously optimistic about what they will remember. Just because they tell us they’ll remember information and use it in their work doesn’t mean they will. Learners also fill in smile sheets based on whether they like the course or the instructor. Courses that challenge learners may be rated poorly, even though a challenge might be exactly what is needed to push a significant behavior change.

Level 2 Assessments are intended to measure learning and retention. We want to know whether the information learned is retrievable from memory. Ideally, we want to know whether the information is retrieved and used on the job. If we measure actual on-the-job performance, we’re really utilizing a Level 3 Assessment. In comparison, Level 2 Assessments measure the retrievability of information, not it’s actual use. This is where the problems start.

What is Meant Here by the Word Assessment?

First let me clarify that I am using the word "assessment" to mean a test given for the purposes of evaluating a learning intervention. Assessments can also be used to bolster learning, as when they are used to promote retrieval practice or provide feedback to the learners. It is the first use of assessments that I am concerned with in this article. Specifically I will argue that Level 2 Assessments given for the purpose of evaluating the success or failure of a learning intervention are dangerous if given immediately after the learning. However, this does not mean that assessments used for the purpose of aiding retrieval or providing corrective feedback are not valuable at the end of learning. In fact, they are excellent for that purpose.

The analysis in this article also assumes that Level 2 Assessments are well designed. Specifically, it assumes that the assessments prompt learners to retrieve from memory the same information that they will have to retrieve in their on-the-job situations. It also assumes that the cues that trigger retrieval will be similar to those that will trigger their thinking and performance on the job. It is true that most current Level 2 Assessments don’t meet these criteria, but they should.

The Problems With Immediate Assessments

When we learn a concept, we think about it. When we think about something, it becomes highly retrievable from memory, at least for a short time. Thus, during learning and immediately afterward, our newly learned information is highly retrievable. If we test learners then, they are likely to remember all kinds of stuff they’ll forget in a day.

This problem is compounded because learning is contextualized. If we learn in Room A, we’ll be better able to retrieve the information we learned if we have to retrieve it in Room A as opposed to Room B (by up to 55% or so). Thus, if we test learners in the training room or while they’re still at their desks using an e-learning program, we’re priming them for a level of success they won’t attain when they’re out of that learning situation and back on the job.

Giving someone a test immediately after they learn something is cheating. It provides an inflated measure of their learning. More importantly, it tells us very little—if anything—about how well learners will be able to retrieve information when they get back to their jobs.

On-the-job retrieval depends on both the amount of learning and the amount of forgetting.

Retrieval = Learning – Forgetting

Our instructional designs need to maximize learning and minimize forgetting. If we measure learners immediately after they learn, we’ve accounted for the learning part of the retrieval equation, but we’ve ignored forgetting all together. Not only are immediately-given Level 2 Assessments poor tools to use in measuring an individual’s performance, but they also give us poor feedback about whether our instructional designs are any good. In short, they’re double trouble. First they don’t measure what we want them to measure, and then they don’t hold us accountable for our work.

But What Happens On The Job?

All this is true in most cases, but there are complications when we consider what happens after the learning event. The analysis above is accurate in those situations when learners forget much of what they learn as they move from learning events back to their jobs. Look at the following graph and imagine doing a Level 2 Assessment at the end of the learning—before the forgetting begins. It would show strong results even though later performance would be poor.

Curves_forgetting_jpeg_3

But what happens in those all-too-rare situations when learners take the learning and begin to apply what they’ve learned as soon as they get back to the workplace? When they do this, they’re much less likely to forget—and they may even take their competence and learning to a higher level than they achieved in the actual learning event. Check out the graph below as an example.

Curves_workplace_learning_jpeg_2

In this case, if we did a Level 2 Assessment at the end of the initial learning—before the workplace learning begins, again the assessment wouldn’t be accurate. This time it might not adequately assess the ability of the learning intervention to facilitate the workplace learning.

Real learning interventions often generate both types of results. Learners utilize some of what they’ve learned back on the job—facilitating their memory; but the rest of what they learned is not used and so is forgotten. The following graph depicts this dichotomous effect.

Curves_both_jpeg_2

So Why Use Level 2 Assessments At All?

It should be clear that Level 2 Assessments delivered immediately after the learning are virtually impossible to interpret. However, it may be useful to use them in conjunction with a later Level 2 Assessment to determine what is happening to the learning after the learners get back to the job.
If the learners’ level of retrieval improves, then we can be fairly certain that the learners have made positive use of the learning event. Of course, a better way to draw this conclusion is to use a comparison-group design, but such an investment is normally not feasible.

If the learners’ level of retrieval remains steady, then we can be fairly certain that the course did some good in preventing forgetting. Again, a comparison-group design will be more definitive.
If the learners’ level of retrieval deteriorates, then we can be fairly certain that the learning event did not prevent forgetting and/or was probably not targeted at real work skills. Deteriorating retrieval is the one result we want to make sure we don’t produce with our learning designs—and because forgetting is central to human learning—if we’re preventing forgetting, we’re doing something important. Finally, because forgetting is normal, a comparison-group design is not as critical in ruling out alternative explanations. In other words, if we find forgetting after the learner returns to the job, we can conclude that the learning event wasn’t good enough.

To summarize this section, it appears that though Level 2 Assessments given immediately after learning have dubious merit because they’re impossible to interpret, there may be value in using immediate Level 2 Assessments in combination with delayed Level 2 Assessments.

How to Do Level 2 Assessments

Level 2 Assessments should be utilized in the following manner:

1. Be administered at least a week or two after the original learning ends, or be administered twice—immediately after learning and a week or two later.

2. Be composed of authentic questions that require learners to retrieve information from memory in a way that is similar to how they’ll have to retrieve it on the job. Simulation-like questions that provide realistic decisions set in real-world contexts are ideal.

3. Cover a significant portion of the most important performance objectives.

March Madness, three weeks of college basketball tournaments in the month of March, has been estimated to cost U.S. companies 3.8 billion dollars of lost productivity.

Various estimates and commentary:

There can be upsides to such an energizing event as well, of course. What I wonder is whether any learning or organizational development initiatives might be wrapped around such events. Imagine the following:

  1. Company intranet portal offers March Madness links, and also includes corporate advertising of key business initiatives, strategic messages, or even training opportunities.
  2. Maybe company even makes links unavailable except through this central march-madness portal.
  3. Managers initiate business-critical conversations in staff meetings after highlighting the latest results for the office pool.
  4. Managers utilize March Madness frenzy for reward and recognition.

Anyway, those are just some ideas off the top of my head. I’d love to get your more thoughtful ideas in the comments. Better yet, have you seen any real-world implementations? Have they been successful?