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In 2016 I published a book on how to radically transform learner surveys into something useful. The book won an award from ISPI and helped thousands of companies update their smile sheets. Now, I’m updating the book with the knowledge I’ve gained in consulting with companies in the learning-evaluation efforts. The second edition will be titled: Performance-Focused Learner Surveys: A Radical Rethinking of a Dangerous Art Form (Second Edition).

In the first edition, I listed nine benefits of learner surveys, but I had only touched the surface. In the coming book, I will offer 20 benefits. Here’s the current list:

Supporting Learning Design Effectiveness

  1. Red-flagging training programs that are not sufficiently effective.
  2. Gathering ideas for ongoing updates and revisions of learning programs.
  3. Judging the strengths, weaknesses, and viability of program updates and pilots.
  4. Providing learning architects and trainers with feedback to aid their development.
  5. Judging the competence of learning architects and trainers.
  6. Judging the contributions to learning made by people outside of the learning team.
  7. Assessing the contributions of learning supports and organizational practices.

Supporting Learners in Learning and Application

  1. Helping learners reflect on and reinforce what they learned.
  2. Helping learners determine what (if anything) they plan to do with their learning.
  3. Nudging learners to greater learning and application efforts.

Nudging Action Through Stealth Messaging

  1. Guiding learning architects to create more effective learning by sharing survey questions before learning designs are finalized and sharing survey results after data is gathered.
  2. Guiding trainers to utilize more effective learning methods by sharing survey questions before learning designs are finalized and sharing survey results after data is gathered.
  3. Guiding organizational stakeholders to support learning efforts more effectively by sharing survey questions and survey results.
  4. Guiding organizational decision makers to better appreciate the complexity and depth of learning and development—helping the learning team to gain credibility and autonomy.

Supporting Relationships with Learners and Other Key Stakeholders

  1. Capturing learner satisfaction data to understand—and make decisions that relate to—the reputation of the learning intervention and/or the instructors.
  2. Upholding the spirit of common courtesy by giving learners a chance for feedback.
  3. Enabling learner frustrations to be vented—to limit damage from negative back-channel communications.

Maintaining Organizational Credibility

  1. Engaging in visibly credible efforts to assess learning effectiveness.
  2. Engaging in visibly credible efforts to utilize data to improve effectiveness.
  3. Reporting out data to demonstrate learning effectiveness.

If you want to learn when the new edition is available, sign up for my list. https://www.worklearning.com/sign-up/.

The second edition will include new and improved question wording, additional questions, additional chapters, etc.

Matt Richter and I, in our Truth-in-Learning Podcast, will be discussing learner surveys in our next episode. Matt doesn’t believe in smile sheets and I’m going to convince him of the amazing power of well-crafted learner surveys. This blog post is my first shot across the bow. To join us, subscribe to our podcast in your podcast app.

Are Your Smile Sheets Giving You Good Data Larger

In honor of April as “Smile-Sheet Awareness Month,” I am releasing a brand new smile-sheet diagnostic.

Available by clicking here:
http://smilesheets.com/smile-sheet-diagnostic-survey/

This diagnostic is based on wisdom from my award-winning book, Performance-Focused Smile Sheets: A Radical Rethinking of a Dangerous Art Form, plus the experience I’ve gained helping top companies implement new measurement practices.

The diagnostic is free and asks you 20 questions about your organization’s current practices. It then provides instant feedback.

Original post appeared in 2011. I update it here.

Updated Article

When companies think of evaluation, they often first think of benchmarking their performance against other companies. There are important reasons to be skeptical of this type of approach, especially as a sole source of direction.

I often add this warning to my workshops on how to create more effective smile sheets: Watch out! There are vendors in the learning field who will attempt to convince you that you need to benchmark your smile sheets against your industry. You will spend (waste) a lot of money with these extra benchmarking efforts!

Two forms of benchmarking are common, (1) idea-generation, and (2) comparison. Idea-generation involves looking at other company’s methodologies and then assessing whether particular methods would work well at our company. This is a reasonable procedure only to the extent that we can tell whether the other companies have similar situations to ours and whether the methodologies have really been successful at those other companies.

Comparison benchmarking for training and development looks further at a multitude of learning methods and results and specifically attempts to find a wide range of other companies to benchmark against. This approach requires stringent attempts to create valid comparisons. This type of benchmarking is valuable only to the extent that we can determine whether we are comparing our results to good companies or bad and whether the comparison metrics are important in the first place.

Both types of benchmarking require exhaustive efforts and suffer from validity problems. It is just too easy to latch on to other company’s phantom results (i.e., results that seem impressive but evaporate upon close examination). Picking the right metrics are difficult (i.e., a business can be judged on its stock price, its revenues, profits, market share, etc.). Comparing companies between industries presents the proverbial apple-to-orange problem. It’s not always clear why one business is better than another (e.g., It is hard to know what really drives Apple Computer’s current success: its brand image, its products, its positioning versus its competitors, its leaders, its financial savvy, its customer service, its manufacturing, its project management, its sourcing, its hiring, or something else). Finally, and most pertinent here, it is extremely difficult to determine which companies are really using best practices (e.g., see Phil Rosenweig’s highly regarded book on The Halo Effect) because companies’ overall results usually cloud and obscure the on-the-job realities of what’s happening.

The difficulty of assessing best practices in general pales in comparison to the difficulties of assessing its training-and-development practices. The problem is that there just aren’t universally accepted and comparable metrics to utilize for training and development. Where baseball teams have wins and losses, runs scored, and such; and businesses have revenues and profits and the like; training and development efforts produce more fuzzy numbers—certainly ones that aren’t comparable from company to company. Reviews of the research literature on training evaluation have found very low levels of correlation (usually below .20) between different types of learning assessments (e.g., Alliger, Tannenbaum, Bennett, Traver, & Shotland, 1997; Sitzmann, Brown, Casper, Ely, & Zimmerman, 2008).

Of course, we shouldn’t dismiss all benchmarking efforts. Rigorous benchmarking efforts that are understood with a clear perspective can have value. Idea-generation brainstorming is probably more viable than a focus on comparison. By looking to other companies’ practices, we can gain insights and consider new ideas. Of course, we will want to be careful not to move toward the mediocre average instead of looking to excel.

The bottom line on benchmarking from other companies is: be careful, be willing to spend lots of time and money, and don’t rely on cross-company comparisons as your only indicator.

Finally, any results generated by brainstorming with other companies should be carefully considered and pilot-tested before too much investment is made.

 

Smile Sheet Issues

Both of the meta-analyses cited above found that smile sheets were correlated with an r = 0.09, which is virtually no correlation at all. I have detailed smile-sheet design problems in detail in my book, Performance-Focused Smile Sheets: A Radical Rethinking of a Dangerous Art Form. In short, most smile sheets focus on learner satisfaction, and fail to focus on factors related to actual learning effectiveness. Most smile sheets utilize Likert-like scales or numeric scales that offer learners very little granularity between answer choices, opening up responding to bias, fatigue, and disinterest. Finally, most learners have fundamental misunderstandings about their own learning (Brown, Roediger & McDaniel, 2014; Kirschner & van Merriënboer, 2013), so asking for their perceptions with general questions about their perceptions is too often a dubious undertaking.

The bottom line is that traditional smile sheets are providing almost everyone with meaningless data in terms of learning effectiveness. When we benchmark our smile sheets against other companies’ smile sheets we compound our problems.

 

Wisdom from Earlier Comments

Ryan Watkins, researcher and industry guru, wrote:

I would add to this argument that other companies are no more static than our own — thus if we implement in September 2011 what they are doing in March 2011 from our benchmarking study, then we are still behind the competition. They are continually changing and benchmarking will rarely help you get ahead. Just think of all the companies that tried to benchmark the iPod, only to later learn that Apple had moved on to the iPhone while the others were trying to “benchmark” what they were doing with the iPod. The competition may have made some money, but Apple continues to win the major market share.

Mike Kunkle, sales training and performance expert, wrote:

Having used benchmarking (carefully and prudently) with good success, I can’t agree with avoiding it, as your title suggests, but do agree with the majority of your cautions and your perspectives later in the post.

Nuance and context matter greatly, as do picking the right metrics to compare, and culture, which is harder to assess. 70/20/10 performance management somehow worked at GE under Welch’s leadership. I’ve seen it fail miserably at other companies and wouldn’t recommend it as a general approach to good people or performance management.

In the sales performance arena, at least, benchmarking against similar companies or competitors does provide real benefit, especially in decision-making about which solutions might yield the best improvement. Comparing your metrics to world-class competitors and calculating what it would mean to you to move in that direction, allows for focus and prioritization, in a sea of choices.

It becomes even more interesting when you can benchmark internally, though. I’ve always loved this series of examples by Sales Benchmark Index:
http://www.salesbenchmarkindex.com/Portals/23541/docs/why-should-a-sales-professional-care-about-sales-benchmarking.pdf

 

Citations

Alliger, Tannenbaum, Bennett, Traver, & Shotland (1997). A meta-analysis of the relations among training criteria. Personnel Psychology, 50, 341-357.

Brown, P. C., Roediger, H. L., III, & McDaniel, M. A. (2014). Make It Stick: The Science of Successful Learning. Cambridge, MA: Belknap Press of Harvard University Press.

Kirschner, P. A., & van Merriënboer, J. J. G. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183.

Sitzmann, T., Brown, K. G., Casper, W. J., Ely, K., & Zimmerman, R. D. (2008). A review and meta-analysis of the nomological network of trainee reactions. Journal of Applied Psychology, 93, 280-295.