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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.

People keep asking me for references to the claim that learner surveys are not correlated—or are virtually uncorrelated—with learning results. In this post, I include them, with commentary.

 

 

Major Meta-Analyses

Here are the major meta-analyses (studies that compile the results of many other scientific studies using statistical means to ensure fair and valid comparisons):

For Workplace Training

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

Hughes, A. M., Gregory, M. E., Joseph, D. L., Sonesh, S. C., Marlow, S. L., Lacerenza, C. N., Benishek, L. E., King, H. B., Salas, E. (2016). Saving lives: A meta-analysis of team training in healthcare. Journal of Applied Psychology, 101(9), 1266-1304.

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.

For University Teaching

Uttl, B., White, C. A., Gonzalez (2017). Meta-analysis of faculty’s teaching effectiveness: Student evaluation of teaching ratings and student learning are not related. Studies in Educational Evaluation, 54, 22-42.

What these Results Say

These four meta-analyses, covering over 200 scientific studies, find correlations between smile-sheet ratings and learning to average about 10%, which is virtually no correlation at all. Statisticians consider correlations below 30% to be weak correlations, and 10% then is very weak.

What these Results Mean

These results suggest that typical learner surveys are not correlated with learning results.

From a practical standpoint:

 

If you get HIGH MARKS on your smile sheets:

You are almost equally likely to have

(1) An Effective Course

(2) An Ineffective Course

 

If you get LOW MARKS on your smile sheets:

You are almost equally likely to have

(1) A Poorly-Designed Course

(2) A Well-Designed Course

 

Caveats

It is very likely that the traditional smile sheets that have been used in these scientific studies, while capturing data on learner satisfaction, have been inadequately designed to capture data on learning effectiveness.

I have developed a new approach to learner surveys to capture data on learning effectiveness. This approach is the Performance-Focused Smile Sheet approach as originally conveyed in my 2016 award-winning book. As of yet, no scientific studies have been conducted to correlate the new smile sheets with measures of learning. However, many many organizations are reporting substantial benefits. Researchers or learning professionals who want my updated list of recommended questions can access them here.

Reflections

  1. Although I have written a book on learner surveys, in the new learning evaluation model, LTEM (Learning-Transfer Evaluation Model), I place these smile sheets at Tier 3, out of eight tiers, less valuable than measures of knowledge, decision-making, task performance, transfer, and transfer effects. Yes, learner surveys are worth doing, if done right, but they should not be the only tool we use when we evaluate learning.
  2. The earlier belief—and one notably advocated by Donald, Jim, and Wendy Kirkpatrick—that there was a causal chain from learner reactions to learning, behavior, and results has been shown to be false.
  3. There are three types of questions we can utilize on our smile sheets: (1) Questions that focus on learner satisfaction and the reputation of the learning, (2) Questions that support learning, and (3) Questions that capture information about learning effectiveness.
  4. It is my belief that we focus too much on learner satisfaction, which has been shown to be uncorrelated with learning results—and we also focus too little on questions that gauge learning effectiveness (the main impetus for the creation of Performance-Focused Smile Sheets).
  5. I do believe that learner satisfaction is important, but it is not most important.

Learning Opportunities regarding Learner Surveys

This is a guest post by Annette Wisniewski, Learning Strategist at Judge Learning Solutions. In this post she shares an experience building a better smile sheet for a client.

She also does a nice job showing how to improve questions by getting rid of Likert-like scales and replacing them with more concrete answer choices.

______________________________

Using a “Performance-focused Smile Sheets” Approach for Evaluating a Training Program

Recently, one of our clients had experienced an alarming drop in customer confidence, so they hired us, Judge Learning Solutions, to evaluate the effectiveness of their customer support training program. I was the learning strategist assigned to the project. Since training never works in isolation, I convinced the client to let me evaluate both the training program and the work environment.

I wanted to create the best survey possible to gauge the effectiveness of the training program as well as evaluate the learners’ work environment, including relevant tools, processes, feedback, support, and incentives. I also wanted to create a report that included actionable recommendations on how to improve both the training program and workforce performance.

I had recently finished reading Will’s book, Performance-focused Smile Sheets, so I knew that traditional Likert-based questions are problematic. They are very subjective, don’t give clear distinction between answer choices, and limit respondents to one, sometimes insufficient, option.

For example, most smile sheets ask learners to evaluate their instructor. A traditional smile sheet question might ask learners to rank the instructor using a Likert-scale.

   How would you rate your course instructor?

  1. Very ineffective
  2. Somewhat ineffective
  3. Somewhat effective
  4. Very effective

But the question leaves too much open to interpretation. What does “ineffective” mean? What does “effective” mean? One learner might have completely different criteria for an “effective” instructor than another. What is the difference between “somewhat ineffective” and “somewhat effective”? Could it be the snacks the instructor brought in mid-afternoon? It’s hard to tell. Also, how can the instructor use this feedback to improve next time? There’s just not enough information in this question to make it very useful.

For my evaluation project, I wrote the survey question using Will’s guidelines to provide distinct, meaningful options, and then allowed learners to select as many responses as they wanted.

   What statements are true about your course instructor? Select all that apply.

  1. Was OFTEN UNCLEAR or DISORGANIZED.
  2. Was OFTEN SOCIALLY AWKWARD OR INAPPROPRIATE.
  3. Exhibited UNACCEPTABLE LACK OF KNOWLEDGE.
  4. Exhibited LACK OF REAL-WORLD EXPERIENCE.
  5. Generally PERFORMED COMPETENTLY AS A TRAINER.
  6. Showed DEEP SUBJECT-MATTER KNOWLEDGE.
  7. Demonstrated HIGH LEVELS OF REAL-WORLD EXPERIENCE.
  8. MOTIVATED ME to ENGAGE DEEPLY IN LEARNING the concepts.
  9. Is a PERSON I CAME TO TRUST.

It’s still just one question, but in this case, the learner was able to provide more useful feedback to both the instructor and to the course sponsors. As Will recommended, I added proposed standards, and then tracked percentages of each response to include in my report:

I used this same approach when asking learners about the course learning objectives.

Instead of asking a question using a typical Likert scale:

   After taking the course, I am now able to navigate the system.

  1. Strongly agree
  2. Agree
  3. Neither agree nor disagree
  4. Disagree
  5. Strongly disagree

I created a more robust question that provided better information about how well the learner was able to navigate the system and what the learner felt he/she needed to become more proficient. I formatted the question as a matrix, so  I could ask about all of the learning objectives at once. The learner perceived this to be one question, but I gleaned nine questions-worth of data out of it. Here’s a redacted excerpt of that question as it appeared in my report, shortened to the first four learning objectives.

The questions took a little more time to write, but the same amount of time for respondents to answer. At first, the client was hesitant to use this new approach to survey questions, but it didn’t take them long to see how I would be able to gather much more valuable data.

The descriptive answer choices of the survey, combined with interviews and extant data reviews, allowed me to provide my client with a very thorough evaluation report. The report not only included a clear picture of the current training program, but also provided detailed and prioritized recommendations on how to improve both the training program and the work environment.

The client was thrilled. I had given them not only actionable recommendations but also the evidence they needed to procure funding to make the improvements. When my colleague checked back with them several months later, they had already implemented several of my recommendations and were in the process of implementing more.

I was amazed at how easy it was to improve the quality of the data I gathered, and it certainly impressed my client. I will never write evaluation questions again any other way.

If you plan on conducting a survey, try using Will’s approach to writing performance-focused questions. Whether you are evaluating a training program or looking for insights on improving workforce performance, you will be happy you did!

I’m thrilled to announce that my Gold-Certification Workshop on Performance-Focused Smile Sheets is now open for registration, with access available in about a week on Tuesday May 14 (2019).

This certification workshop is the culmination of years of work and practice. First there was my work with clients on evaluation. Then there was the book. Then I gained extensive experience building and piloting smile sheets with a variety of organizations. I taught classroom and webinar workshops. I spoke at conferences and gave keynotes. And of course, I developed and launched LTEM (The Learning-Transfer Evaluation Model), which is revolutionizing the practice of workplace learning—and providing the first serious alternative to the Kirkpatrick-Katzell Four-Level Model.

Over the last year, I’ve been building an online, asynchronous workshop that was rigorous, comprehensive, and challenging enough to offer a certification. It’s now ready to go!

I’d love if you would enroll and join me and others in learning!

You can learn more about this Gold-Certification Workshop by clicking here.

 

Links of Interest:

 

 

As I preach in my workshops on how to create better learner-survey questions (for example my Gold-Certification workshop on Performance-Focused Smile Sheets), open-ended comment questions are very powerful questions. Indeed, they are critical in our attempts to truly understand our learners’ perspectives.

Unfortunately, to get the most benefit from comment questions, we have to take time to read every response and reflect on the meaning of all the comments taken together. Someday AI may be able to help us parse comment-question data, but currently the technology is not ready to give us a full understanding. Nor are word clouds or other basic text-processing algorithms useful enough to provide valid insights into our data.

It’s good to take the time in analyzing our comment-question data, but if there was a way to quickly get a sense of comment data, wouldn’t we consider using it? Of course!

As most of you know, I’ve been focusing a lot of my attention on learning evaluation over the last few years. While I’ve learned a lot, have been lauded by others as an evaluation thought leader, and have even created some useful innovations like LTEM, I’m still learning. Today, by filling out a survey after going to a CVS MinuteClinic to get a vaccine shot, I learned something pretty cool. Take a look.

This is a question on their survey, delivered to me right after I’d answered a comment question. This gives the survey analyzers a way to quickly categorize the comments. It DOES NOT REPLACE, or should not replace, a deeper look at the comments (for example, my comment was very specific and useful i hope), but it does enable us to ascribe some overall meaning to the results.

Note that this is similar to what I’ve been calling a hybrid question, where we first give people a forced-choice question and then give them a comment question. The forced choice question drives clarity whereas the follow-up comment question enables more specificity and richness.

One warning! Adding a forced choice question after a comment question should be seen as a tool in our toolbox. Let’s not overuse it. More pointedly, let’s use it when it is particularly appropriate.

If we’ve asked two open-ended comment questions—one asking for positive feedback and one asking for constructive criticism—we might not need a follow-up forced choice question, because we’ve already prompted respondents to give us the good and the bad.

The bottom line is that we now have two types of hybrid questions to add to our toolbox:

  1. Forced-choice question followed by clarifying comment question.
  2. Comment question followed by categorizing forced-choice question.

Freakin’ Awesome!

 

This week, Brett Christensen published an article on how he’s used a Performance-Focused Smile Sheet to support him in teaching one of ISPI’s flagship workshops.

What I found particularly striking is how Brett used the smile-sheet results to make sense of learning effectiveness. His goal was to help his learners be able to take what they’ve learned and use it back on the job.

One smile-sheet question he used pointed to results that suggested that learners felt they had gained awareness of concepts, but they might not be fully able to put what they learned into practice. This raised a red flag, so Brett examined results from another question on the amount of practice received in the workshop. The learners told him that practice was only a little more than 50% of the workshop, and Brett used this information to consider changes for adding more practice.

He also used a question to get a sense of whether the spacing effect was utilized to support long-term remembering–a key research-based learning approach. He got good news there–so that even in a one-day workshop–many learners felt repetitions were delivered after a delay of an hour or more. Good instructional design!

For a century or more, our learner-feedback questions have focused on satisfaction, course reputation, and other factors that are NOT directly related to learning effectiveness. Now we have a new methodology, first described in the award-winning book, Performance-Focused Smile Sheets: A Radical Rethinking of a Dangerous Art Form. We ought to use this to get feedback about what we can do better.

Brett offers a wonderful case study from his work teaching a course offered through ISPI (Developed by Dr. Roger Chevalier). We are no longer hogtied with evaluations that provide us with bogus information. We can look for ways to get better feedback, improve our learning interventions, and get better results.

To read Brett’s full article, click here…

One of the most common questions I get when I speak about the Performance-Focused Smile-Sheet approach (see the book’s website at SmileSheets.com) is “What can be done to get higher response rates from my smile sheets?”

Of course, people also refer to smile sheets as evals, level 1’s, happy sheets, hot or warm evaluation, response forms, reaction forms, etc. They also refer to both paper-and-pencil forms and online surveys. Indeed, as smile sheets go online, more and more people are finding that online surveys get a much lower response rate than in-classroom paper surveys.

Before I give you my list for how to get a higher response rate, let me blow this up a bit. The thing is, while we want high response rates, there’s something much more important than response rates. We also want response relevance and precision. We want the questions to relate to learning effectiveness, not just learning reputation and learner satisfaction. We also want the learners to be able to answer the questions knowledgeably and give our questions their full attention.

If we have bad questions — one’s that use Likert-like or numeric scales for example — it won’t matter that we have high response rates. In this post, I’m NOT going to focus on how to write better questions. Instead, I’m just tackling how we can motivate our learners to give our questions more of their full attention, thus increasing the precision of their responding while also increasing our response rates as well.

How to get Better Responses and Higher Response Rates

  1. Ask with enthusiasm, while also explaining the benefits.
  2. Have a trusted person make the request (often an instructor who our learners have bonded with).
  3. Mention the coming smile sheet early in the learning (and more than once) so that learners know it is an integral part of the learning, not just an add-on.
  4. While mentioning the smile sheet, let folks know what you’ve learned from previous smile sheets and what you’ve changed based on the feedback.
  5. Tell learners what you’ll do with the data, and how you’ll let them know the results of their feedback.
  6. Highlight the benefits to the instructor, to the instructional designers, and to the organization. Those who ask can mention how they’ve benefited in the past from smile sheet results.
  7. Acknowledge the effort that they — your learners — will be making, maybe even commiserating with them that you know how hard it can be to give their full attention when it’s the end of the day or when they are back to work.
  8. Put the time devoted to the survey in perspective, for example, “We spent 7 hours today in learning, that’s 420 minutes, and now we’re asking you for 10 more minutes.”
  9. Ensure your learners that the data will be confidential, that the data is aggregated so that an individual’s responses are never shared.
  10. Let your learners know the percentage of people like them who typically complete the survey (caveat: if it’s relatively high).
  11. Use more distinctive answer choices. Avoid Likert-like answer choices and numerical scales — because learners instinctively know they aren’t that useful.
  12. Ask more meaningful questions. Use questions that learners can answer with confidence. Ask questions that focus on meaningful information. Avoid obviously biased questions — as these may alienate your learners.

How to get Better Responses and Higher Response Rates on DELAYED SMILE SHEETS

Sometimes, we’ll want to survey our learners well after a learning event, for example three to five weeks later. Delayed smile sheets are perfectly positioned to find out more about how the learning is relevant to the actual work or to our learners’ post-learning application efforts. Unfortunately, prompting action — that is getting learners to engage our delayed smile sheets — can be particularly difficult when asking for this favor well after learning. Still, there are some things we can do — in addition to the list above — that can make a difference.

  1. Tell learners what you learned from the end-of-learning smile sheet they previously completed.
  2. Ask the instructor who bonded with them to send the request (instead of an unknown person from the learning unit).
  3. Send multiple requests, preferably using a mechanism that only sends these requests to those who still need to complete the survey.
  4. Have the course officially end sometime AFTER the delayed smile sheet is completed, even if that is largely just a perception. Note that multiple-event learning experiences lend themselves to this approach, whereas single-event learning experiences do not.
  5. Share with your learners a small portion of the preliminary data from the delayed smile sheet. “Already, 46% of your fellow learners have completed the survey, with some intriguing tentative results. Indeed, it looks like the most relevant topic as rated by your fellow learners is… and the least relevant is…”
  6. Share with them the names or job titles of some of the people who have completed the survey already.
  7. Share with them the percentage of folks from his/her unit who have responded already or share a comparison across units.

What about INCENTIVES?

When I ask audiences for their ideas for improving responses and increasing response rates, they often mention some sort of incentive, usually based on some sort of lottery or raffle. “If you complete the survey, your name will be submitted to have chance to win the latest tech gadget, a book, time off, lunch with an executive, etc.”

I’m a skeptic. I’m open to being wrong, but I’m still skeptical about the cost/benefit calculation. Certainly for some audiences an incentive will increase rates of completion. Also, for some audiences, the harms that come with incentives may be worth it.

What harms you might ask? When we provide an external incentive, we might be sending a message to some learners that we know the task has no redeeming value or is tedious or difficult. People who see their own motivation as caused by the external incentive are potentially less likely to seriously engage our questions, producing bad data. We’re also not just having an effect on the current smile sheet. When we incentivize people today, they may be less willing next time to engage in answering our questions. They may also be pushed into believing that smile sheets are difficult, worthless, or worse.

Ideally, we’d like our learners to want to provide us with data, to see answering our questions as a worthy and helpful exercise, one that is valuable to them, to us, and to our organization. Incentives push against this vision.

 

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.

Is my book, Performance-Focused Smile Sheets: A Radical Rethinking of a Dangerous Art Form, award worthy?

I think so, buy I'm hugely biased! SMILE.

Boxshot-rendering redrawn-no-shadow2

Here's what I wrote today on an award-submission application:

Performance-Focused Smile Sheets: A Radical Rethinking of Dangerous Art Form is a book, published in February 2016, written by Will Thalheimer, PhD, President of Work-Learning Research, Inc.

The book reviews research on smile sheets (learner feedback forms), demonstrates the limitations of traditional smile sheets, and provides a completely new formulation on how to design and deploy smile sheets.

The ideas in the book — and the example questions provided — help learning professionals focus on "learning effectiveness" in supporting post-learning performance. Where traditional smile sheets focus on learner satisfaction and the credibility of training, Performance-Focused Smile Sheets can also focus on science-of-learning factors that matter. Smile sheets can be transformed by focusing on learner comprehension, factors that influence long-term remembering, learner motivation to apply what they've learned, and after-learning supports for learning transfer and application of learning to real-world job tasks.

Smile sheets can also be transformed by looking beyond Likert-like responses and numerical averages that dumb-down our metrics and lead to bias and paralysis. We can go beyond meaningless averages ("My course is a 4.1!") and provide substantive information to ourselves and our stakeholders.

The book reviews research that shows that so-called "learner-centric" formulations are filled with dangers — as research shows that learners don't always know how they learn best. Smile-sheet questions must support learners in making smile-sheet decisions, not introduce biases that warp the data.

For decades our industry has been mired in the dishonest and disempowering practice of traditional smile sheets. Thankfully, a new approach is available to us.

Sure! I'd love to see my work honored. More importantly, I'd love to see the ideas from my book applied wisely, improved, and adopted for training evaluation, student evaluations, conference evaluations, etc. 

You can help by sharing, by piloting, by persuading, by critiquing and improving! That will be my greatest award!