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New Research on Learning Evaluation — Conducted with The eLearning Guild

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Released Today: Research Report on Learning Evaluation Conducted with The eLearning Guild.

Report Title: Evaluating Learning: Insights from Learning Professionals.

I am delighted to announce that a research effort that I led in conjunction with Dr. Jane Bozarth and the eLearning Guild has been released today. I’ll be blogging about our findings over the next couple of months.

This is a major report — packed into 39 pages — and should be read by everyone in the workplace learning field interested in learning evaluation!

Just a teaser here:

We asked folks to consider the last three learning programs their units developed and to reflect on the learning-evaluation approaches they used.

While a majority were generally happy with their evaluation methods on these recent learning programs, about 40% where dissatisfied. Later, in a more general question about whether learning professionals are able to do the learning measurement they want to do, fully 52% said they were NOT able to do the kind of evaluation they thought was right to do.

In the full report, available only to Guild members, we dig down and explore the practices and perspectives that drive our learning-evaluation efforts. I encourage you to get the full report, as it touches on the methods we use, how we communicate with senior business leaders, what we’d like to do differently, and what we think we’re good at. Also, the report concludes with 12 powerful action strategies for getting the most out of our learning-evaluation efforts.

You can get the full report by clicking here.

 

 

Research Findings: Current Practices in Gathering Learner Feedback

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Respondents

Over 200 learning professionals responded to Work-Learning Research’s 2017-2018 survey on current practices in gathering learner feedback, and today I will reveal the results. The survey ran from November 29th, 2017 to September 16th, 2018. The sample of respondents was drawn from Work-Learning Research’s mailing list and through extensive calls for participation in a variety of social media. Because of this sampling methodology, the survey results are likely skewed toward professionals who care and/or pay attention to research-based practice recommendations more than the workplace learning field as a whole. They are also likely more interested and experienced in learning evaluation as well.

Feel free to share this link with others.

Goal of the Research

The goal of the research was to determine what people are doing in the way of evaluating their learning interventions through the practice of asking learners for their perspectives.

Questions the Research Hoped to Answer

  1. Are smile sheets (learner-feedback questions) still the most common method of doing learning evaluation?
  2. How does their use compare with other methods? Are other methods growing in prominence/use?
  3. How satisfied are learning professionals with their organizations’ learner-feedback methods?
  4. To what extent are organizations looking for alternatives to their current learner-feedback methods?
  5. What kinds of questions are used on smile sheets? Has Thalheimer’s new approach, performance-focused questioning, gained any traction?
  6. What do learning professionals think their current smile sheets are good at measuring (Satisfaction, Reputation, Effectiveness, Nothing)?
  7. What tools are organizations using to gather learner feedback?
  8. How useful are current learner-feedback questions in helping guide improvements in learning design and delivery?
  9. How widely are the target metrics of LTEM (The Learning-Transfer Evaluation Model) currently being measured?

A summary of the findings indexed to these questions can be found at the end of this post.

Situating the Practice of Gathering Learner Feedback

When we gather feedback from learners, we are using a Tier 3 methodology on the LTEM (Learning-Transfer Evaluation Model) or Level 1 on the Kirkpatrick-Katzell Four-Level Model of Training Evaluation.

Demographic Background of Respondents

Respondents came from a wide range of organizations, including small, midsize, and large organizations.

Respondents play a wide range of roles in the learning field.

Most respondents live in the United States and Canada, but there was some significant representation from many predominantly English-speaking countries.

Learner-Feedback Findings

About 67% of respondents report that learners are asked about their perceptions on more than half of their organization’s learning programs, including elearning. Only about 22% report that they survey learners on less than half of their learning programs. This finding is consistent with past findings—surveying learners is the most common form of learning evaluation and is widely practiced.

The two most common question types in use are Likert-like questions and numeric-scale questions. I have argued against their use* and I am pleased that Performance-Focused Smile Sheet questions have been utilized by so many so quickly. Of course, this sample of respondents is comprised of folks on my mailing list so this result surely doesn’t represent current practice in the field as a whole. Not yet! LOL.

*Likert-like questions and numeric-scale questions are problematic for several reasons. First, because they offer fuzzy response choices, learners have a difficult time deciding between them and this likely makes their responses less precise. Second, such fuzziness may inflate bias as there are not concrete anchors to minimize biasing effects of the question stems. Third, Likert-like options and numeric scales likely deflate learner responding because learners are habituated to such scales and because they may be skeptical that data from such scales will actually be useful. Finally, Likert-like options and numeric scales produce indistinct results—averages all in the same range. Such results are difficult to assess, failing to support decision-making—the whole purpose for evaluation in the first place. To learn more, check out Performance-Focused Smile Sheets: A Radical Rethinking of a Dangerous Art Form (book website here).

The most common tools used to gather feedback from learners were paper surveys and SurveyMonkey. Questions delivered from within an LMS were the next highest. High-end evaluation systems like Metrics that Matter were not highly represented in our respondents.

Our respondents did not rate their learner-feedback efforts as very effective. Their learner surveys were seen as most effective in gauging learner satisfaction. Only about 33% of respondents thought their learner surveys gave them insights on the effectiveness of the learning.

Only about 15% of respondents found their data very useful in providing them feedback about how to improve their learning interventions.

Respondents report that their organizations are somewhat open to alternatives to their current learner-feedback approaches, but overall they are not actively looking for alternatives.

Most respondents report that their organizations are at least “modestly happy” with their learner-feedback assessments. Yet only 22% reported being “generally happy” with them. Combining this finding with the one above showing that lots of organizations are open to alternatives, it seems that organizational satisfaction with current learner-feedback approaches is soft.

We asked respondents about their organizations’ attempts to measure the following:

  • Learner Attendance
  • Whether Learner is Paying Attention
  • Learner Perceptions of the Learning (eg, Smile Sheets, Learner Feedback)
  • Amount or Quality of Learner Participation
  • Learner Knowledge of the Content
  • Learner Ability to Make Realistic Decisions
  • Learner Ability to Complete Realistic Tasks
  • Learner Performance on the Job (or in another future performance situation)
  • Impact of Learning on the Learner
  • Impact of Learning on the Organization
  • Impact of Learning on Coworkers, Family, Friends of the Learner
  • Impact of Learning on the Community or Society
  • Impact of Learning on the Environment

These evaluation targets are encouraged in LTEM (The Learning-Transfer Evaluation Model).

Results are difficult to show—because our question was very complicated (admittedly too complicated)—but I will summarize the findings below.

As you can see, learner attendance and learner perceptions (smile sheets) were the most commonly measured factors, with learner knowledge a distant third. The least common measures involved the impact of the learning on the environment, community/society, and the learner’s coworkers/family/friends.

The flip side—methods rarely utilized in respondents’ organizations—shows pretty much the same thing.

Note that the question above, because it was too complicated, probably produced some spurious results, even if the trends at the extremes are probably indicative of the whole range. In other words, it’s likely that attendance and smile sheets are the most utilized and measures of impact on the environment, community/society, and learners’ coworkers/family/friends are the least utilized.

Questions Answered Based on Our Sample

  1. Are smile sheets (learner-feedback questions) still the most common method of doing learning evaluation?

    Yes! Smile sheets are clearly the most popular evaluation method, along with measuring attendance (if we include that as a metric).

  2. How does their use compare with other methods? Are other methods growing in prominence/use?

    Except for Attendance, nothing else comes close. The next most common method is measuring knowledge. Remarkably, given the known importance of decision-making (Tier 5 in LTEM) and task competence (Tier 6 in LTEM), these are used in evaluation at a relatively low level. Similar low levels are found in measuring work performance (Tier 7 in LTEM) and organizational results (part of Tier 8 in LTEM). We’ve known about these relatively low levels from many previous research surveys.

    Hardly any measurement is being done on the impact of learning on learner or his/her coworkers/family/friends, the impact of the learning on the community/society/environment, or on learner participation/attention.

  3. How satisfied are learning professionals with their organizations’ learner-feedback methods?

    Learning professionals are moderately satisfied.

  4. To what extent are organizations looking for alternatives to their current learner-feedback methods?

    Organizations are open to alternatives, with some actively seeking alternatives and some not looking.

  5. What kinds of questions are used on smile sheets? Has Thalheimer’s new approach, performance-focused questioning, gained any traction?

    Likert-like options and numeric scales are the most commonly used. Thalheimer’s performance-focused smile-sheet method has gained traction in this sample of respondents—people likely more in the know about Thalheimer’s approach than the industry at large.

  6. What do learning professionals think their current smile sheets are good at measuring (Satisfaction, Reputation, Effectiveness, Nothing)?

    Learning professionals think their current smile sheets are fairly good at measuring the satisfaction of learners. A full one-third of respondents feel that their current approaches are not valid enough to provide them with meaningful insights about the learning interventions.

  7. What tools are organizations using to gather learner feedback?

    The two most common methods for collecting learner feedback are paper surveys and SurveyMonkey. Questions from LMSs are the next most widely used. Sophisticated evaluation tools are not much in use in our respondent sample.

  8. How useful are current learner-feedback questions in helping guide improvements in learning design and delivery?

    This may be the most important question we might ask, given that evaluation is supposed to aid us in maintaining our successes and improving on our deficiencies. Only 15% of respondents found learner feedback “very helpful” in helping them improve their learning. Many found the feedback “somewhat helpful” but a full one-third found the feedback “not very useful” in enabling them to improve learning.

  9. How widely are the target metrics of LTEM (The Learning-Transfer Evaluation Model) currently being measured?

    As described in Question 2 above, many of the targets of LTEM are not being adequately measured at this point in time (November 2017 to September 2018, during the time immediately before and after LTEM was introduced). This indicates that LTEM is poised to help organizations uncover evaluation targets that can be helpful in setting goals for learning improvements.

Lessons to be Drawn

The results of this survey reinforce what we’ve known for years. In the workplace learning industry, we default to learner-feedback questions (smile sheets) as our most common learning-evaluation method. This is a big freakin’ problem for two reasons. First, our learner-feedback methods are inadequate. We often use poor survey methodologies and ones particularly unsuited to learner feedback, including the use of fuzzy Likert-like options and numeric scales. Second, even if we used the most advanced learner-feedback methods, we still would not be doing enough to gain insights into the strengths and weaknesses of our learning interventions.

Evaluation is meant to provide us with data we can use to make our most critical decisions. We need to know, for example, whether our learning designs are supporting learner comprehension, learner motivation to apply what they’ve learned, learner ability to remember what they’ve learned, and the supports available to help learners transfer their learning to their work. We typically don’t know these things. As a result, we don’t make design decisions we ought to. We don’t make improvements in the learning methods we use or the way we deploy learning. The research captured here should be seen as a wake up call.

The good news from this research is that learning professionals are often aware and sensitized to the deficiencies of their learning-evaluation methods. This seems like a good omen. When improved methods are introduced, they will seek to encourage their use.

LTEM, the new learning-evaluation model (which I developed with the help of some of the smartest folks in the workplace learning field) is targeting some of the most critical learning metrics—metrics that have too often been ignored. It is too new to be certain of its impact, but it seems like a promising tool.

Why I have turned my Attention to Evaluation (and why you should too!)

For 20 years, I’ve focused on compiling scientific research on learning in the belief that research-based information—when combined with a deep knowledge of practice—can drastically improve learning results. I still believe that wholeheartedly! What I’ve also come to understand is that we as learning professionals must get valid feedback on our everyday efforts. It’s simply our responsibility to do so.

We have to create learning interventions based on the best blend of practical wisdom and research-based guidance. We have to measure key indices that tell us how our learning interventions are doing. We have to find out what their strengths are and what their weaknesses are. Then we have to analyze and assess and make decisions about what to keep and what to improve. Then we have to make improvements and again measure our results and continue the cycle—working always toward continuous improvement.

Here’s a quick-and-dirty outline of the recommended cycle for using learning to improve work performance. “Quick-and-dirty” means I might be missing something!

  1. Learn about and/or work to uncover performance-improvement needs.
  2. If you determine that learning can help, continue. Otherwise, build or suggest alternative methods to get to improved work performance.
  3. Deeply understand the work-performance context.
  4. Sketch out a very rough draft for your learning intervention.
  5. Specify your evaluation goals—the metrics you will use to measure your intervention’s strengths and weaknesses.
  6. Sketch out a rough draft for your learning intervention.
  7. Specify your learning objectives (notice that evaluation goals come first!).
  8. Review the learning research and consider your practical constraints (two separate efforts subsequently brought together).
  9. Sketch out a reasonably good draft for your learning intervention.
  10. Build your learning intervention and your learning evaluation instruments (Iteratively testing and improving).
  11. Deploy your “ready-to-go” learning intervention.
  12. Measure your results using the previously determined evaluation instruments, which were based on your previously determined evaluation objectives.
  13. Analyze your results.
  14. Determine what to keep and what to improve.
  15. Make improvements.
  16. Repeat (maybe not every step, but at least from Step 6 onward)

And here is a shorter version:

  1. Know the learning research
  2. Understand your project needs.
  3. Outline your evaluation objectives—the metrics you will use.
  4. Design your learning.
  5. Deploy your learning and your measurement.
  6. Analyze your results.
  7. Make Improvements
  8. Repeat.

More Later Maybe

The results shared here are the result from all respondents. If I get the time, I’d like to look at subsets of respondents. For example, I’d like to look at how learning executives and managers might differ from learning practitioners. Let me know how interested you would be in these results.

Also, I will be conducting other surveys on learning-evaluation practices, so stay tuned. We have been too long frustrated with our evaluation practices and more work needs to be done in understanding the forces that keep us from doing what we want to do. We could also use more and better learning-evaluation tools because the truth is that learning evaluation is still a nascent field.

Finally, because I learn a ton by working with clients who challenge themselves to do more effective interventions, please get in touch with me if you’d like a partner in thinking things through and trying new methods to build more effective evaluation practices. Also, please let me know how you’ve used LTEM (The Learning-Transfer Evaluation Model).

Some links to make this happen:

Appreciations

As always, I am grateful to all the people I learn from, including clients, researchers, thought leaders, conference attendees, and more… Thanks also to all who acknowledge and share my work! It means a lot!

The Learning-Transfer Evaluation Model (LTEM)

NOTICE OF UPDATE (17 May 2018):

The LTEM Model and accompanying Report were updated today and can be found below.

Two major changes were included:

  • The model has been inverted to put the better evaluation methods at the top instead of at the bottom.
  • The model now uses the word “Tier” to refer to the different levels within the model—to distinguish these from the levels of the Kirkpatrick-Katzell model.

This will be the last update to LTEM for the foreseeable future.

 

This blog post introduces a new learning-evaluation model, the Learning-Transfer Evaluation Model (LTEM).

 

Why We Need a New Evaluation Model

It is well past time for a new learning-evaluation model for the workplace learning field. The Kirkpatrick-Katzell Model is over 60 years old. It was born in a time before computers, before cognitive psychology revolutionized the learning field, before the training field was transformed from one that focused on the classroom learning experience to one focused on work performance.

The Kirkpatrick-Katzell model—created by Raymond Katzell and popularized by Donald Kirkpatrick—is the dominant standard in our field. It has also done a tremendous amount of harm, pushing us to rely on inadequate evaluation practices and poor learning designs.

I am not the only critic of the Kirkpatrick-Katzell model. There are legions of us. If you do a Google search starting with these letters, “Criticisms of the Ki,” Google anticipates the following: “Criticisms of the Kirkpatrick Model” as one of the most popular searches.

Here’s what a seminal research review said about the Kirkpatrick-Katzell model (before the model’s name change):

The Kirkpatrick framework has a number of theoretical and practical shortcomings. [It] is antithetical to nearly 40 years of research on human learning, leads to a checklist approach to evaluation (e.g., ‘we are measuring Levels 1 and 2, so we need to measure Level 3’), and, by ignoring the actual purpose for evaluation, risks providing no information of value to stakeholders…

The New Model

For the past year or so I’ve been working to develop a new learning-evaluation model. The current version is the eleventh iteration, improved after reflection, after asking some of the smartest people in our industry to provide feedback, after sharing earlier versions with conference attendees at the 2017 ISPI innovation and design-thinking conference and the 2018 Learning Technologies conference in London.

Special thanks to the following people who provided significant feedback that improved the model and/or the accompanying article:

Julie Dirksen, Clark Quinn, Roy Pollock, Adam Neaman, Yvon Dalat, Emma Weber, Scott Weersing, Mark Jenkins, Ingrid Guerra-Lopez, Rob Brinkerhoff, Trudy Mandeville, Mike Rustici

The model, which I’ve named the Learning-Transfer Evaluation Model (LTEM, pronounced L-tem) is a one page, eight-level model, augmented with color coding and descriptive explanations. In addition to the model itself, I’ve prepared a 34-page report to describe the need for the model, the rationale for its design, and recommendations on how to use it.

You can access the model and the report by clicking on the following links:

 

 

Release Notes

The LTEM model and report were researched, conceived, and written by Dr. Will Thalheimer of Work-Learning Research, Inc., with significant and indispensable input from others. No one sponsored or funded this work. It was a labor of love and is provided as a valentine for the workplace learning field on February 14th, 2018 (Version 11). Version 12 was released on May 17th, 2018 based on feedback from its use. The model and report are copyrighted by Will Thalheimer, but you are free to share them as is, as long as you don’t sell them.

If you would like to contact me (Will Thalheimer), you can do that at this link: https://www.worklearning.com/contact/

If you would like to sign up for my list, you can do that here: https://www.worklearning.com/sign-up/

 

 

What Do Senior Business Leaders Want in Terms of Learning Evaluation?

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Let’s find out by asking them!

And, let’s ask ourselves (workplace learning professionals) what we think senior leaders will tell us.

NOTE: This may take some effort on our part. Please complete the survey yourself and ask senior leaders at your organization (if your organization is 1000 people or more) to complete the survey.

 

The Survey Below is for both Senior Organizational Leaders AND for Workplace Learning Professionals.

We will branch you to a separate set of questions!

Answer the survey questions below, or you need it, here is a link to the survey.

 



Send me an email if you want to talk more about learning evaluation...

Jobs in Learning Measurement — Let’s Try Something

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Dear Readers,

Many of you are now following me and my social-media presence because you’re interested in LEARNING MEASUREMENT. Probably because of my recent book on Performance-Focused Smile Sheets (which you can learn about at the book’s website, SmileSheets.com).

More and more, I’m meeting people who have jobs that focus on learning measurement. For some, that’s their primary focus. For most, it’s just a part of their job.

Today, I got an email from a guy looking for a job in learning measurement and analytics. He’s a good guy, smart and passionate, and so he ought to be able to find a good job where he can really help. So here’s what I’m thinking. You, my readers are some of the best and brightest in the industry — you care about our work and you look to the scientific research as a source of guidance. You are also, many of you, enlightened employers, looking to recruit and hire the best and brightest. So it seems obvious that I should try to connect you…

So here’s what we’ll try. If you’ve got a job in learning measurement, let me know about it. I’ll post it here on my blog. This will be an experiment to see what happens. Maybe nothing… but it’s worth a try.

Now, I know many of you are also loyal readers because of things BESIDES learning measurement, for example, learning research briefs, research-based insights, elearning, subscription learning, learning audits, and great jokes… but let’s keep this experiment to LEARNING MEASUREMENT JOBS at first.

BOTTOM LINE: If you know of a learning-measurement job, let me know. Email me here…

Providing Feedback on Quiz Questions — Yes or No?

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I was asked today the following question from a learning professional in a large company:

It will come as no surprise that we create a great deal of mandatory/regulatory required eLearning here. All of these eLearning interventions have a final assessment that the learner must pass at 80% to be marked as completed; in addition to viewing all the course content as well. The question is around feedback for those assessment questions.

  • One faction says no feedback at all, just a score at the end and the opportunity to revisit any section of the course before retaking the assessment.
  • Another faction says to tell them correct or incorrect after they submit their answer for each question.
  • And a third faction argues that we should give them detailed feedback beyond just correct/incorrect for each question. 

Which approach do you recommend? 

 

 

Here is what I wrote in response:

It all depends on what you’re trying to accomplish…

If this is a high-stakes assessment you may want to protect the integrity of your questions. In such a case, you’d have a large pool of questions and you’d protect the answer choices by not divulging them. You may even have proctored assessments, for example, having the respondent turn on their web camera and submit their video image along with the test results. Also, you wouldn’t give feedback because you’d be concerned that students would share the questions and answers.

If this is largely a test to give feedback to the learners—and to support them in remembering and performance—you’d not only give them detailed feedback, but you’d retest them after a few days or more to reinforce their learning. You might even follow-up to see how well they’ve been able to apply what they’ve learned on the job.

We can imagine a continuum between these two points where you might seek a balance between a focus on learning and a focus on assessment.

This may be a question for the lawyers, not just for us as learning professionals. If these courses are being provided to meet certain legal requirements, it may be most important to consider what might happen in the legal domain. Personally, I think the law may be behind learning science. Based on talking with clients over many years, it seems that lawyers and regulators often recommend learning designs and assessments that do NOT make sense from a learning standpoint. For example, lawyers tell companies that teaching a compliance topic once a year will be sufficient — when we know that people forget and may need to be reminded.

In the learning-assessment domain, lawyers and regulators may say that it is acceptable to provide a quiz with no feedback. They are focused on having a defensible assessment. This may be the advice you should follow given current laws and regulations. However, this seems ultimately indefensible from a learning standpoint. Couldn’t a litigant argue that the organization did NOT do everything they could to support the employee in learning — if the organization didn’t provide feedback on quiz questions? This seems a pretty straightforward argument — and one that I would testify to in a court of law (if I was asked).

By the way, how do you know 80% is the right cutoff point? Most people use an arbitrary cutoff point, but then you don’t really know what it means.

Also, are your questions good questions? Do they ask people to make decisions set in realistic scenarios? Do they provide plausible answer choices (even for incorrect choices)? Are they focused on high-priority information?

Do the questions and the cutoff point truly differentiate between competence and lack of competence?

Are the questions asked after a substantial delay — so that you know you are measuring the learners’ ability to remember?

Bottom line: Decision-making around learning assessments is more complicated than it looks.

Note: I am available to help organizations sort this out… yet, as one may ascertain from my answer here, there are no clear recipes. It comes down to judgment and goals.

If your goal is learning, you probably should provide feedback and provide a delayed follow-up test. You should also use realistic scenario-based questions, not low-level knowledge questions.

If your goal is assessment, you probably should create a large pool of questions, proctor the testing, and withhold feedback.

 

Benchmarking Your Smile Sheets Against Other Companies may be a Fool’s Game!

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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.

Smile-Sheet Workshop in Suffolk, VA — June 10th, 2016

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OMG! The best deal ever for a full-day workshop on how to radically improve your smile-sheet designs! Sponsored by the Hampton Roads Chapter of ISPI. Free book and subscription-learning thread too!

 

Friday, June 10, 2016

Reed Integration

7007 Harbour View Blvd #117

Suffolk, VA

 

Click here to register now…

 

Performance Objectives:

By completing this workshop and the after-course subscription-learning thread, you will know how to:

  1. Avoid the three most troublesome biases in measuring learning.

  2. Persuade your stakeholders to improve your organization’s smile sheets.

  3. Create more effective smile sheet questions.

  4. Create evaluation standards for each question to avoid bias.

  5. Envision learning measurement as a bulwark for improved learning design.

 

Recommended Audience:

The content of this workshop will be suitable to those who have at least some background and experience in the training field. It will be especially valuable to those who are responsible for learning evaluation or who manage the learning function.

 

Format:

This is a full-day workshop. Participants are encouraged to bring laptops if they prefer to use a computer to write their questions.  

 

Bonus Take-Away:

Each Participant will receive a copy of Dr. Thalheimer’s Book, Performance-Focused Smile Sheets: A Radical Rethinking of a Dangerous Art Form.

Can Instructor Attractiveness lead to Higher Smile-Sheet Ratings? More Learning? A Research Brief.

In a recent research article, Tobias Wolbring and Patrick Riordan report the results of a study looking into the effects of instructor “beauty” on college course evaluations. What they found might surprise you — or worry you — depending on your views on vagaries of fairness in life.

Before I reveal the results, let me say that this is one study (two experiments), and that the findings were very weak in the sense that the effects were small.

Their first study used a large data set involving university students. Given that the data was previously collected through routine evaluation procedures, the researchers could not be sure of the quality of the actual teaching, nor the true “beauty” of the instructors (they had to rely on online images).

The second study was a laboratory study where they could precisely vary the level of beauty of the instructor and their gender, while keeping the actual instructional materials consistent. Unfortunately, “the instruction” consisted of an 11-minute audio lecture taught by relatively young instructors (young adults), so it’s not clear whether their results would generalize to more realistic instructional situations.

In both studies they relied on beauty as represented by facial beauty. While previous research shows that facial beauty is the primary way we rate each other on attractiveness, body beauty has also been found to have effects.

Their most compelling results:

1.

They found that ratings of attractiveness are very consistent across raters. People seem to know who is attractive and who is not. This confirms findings of many studies.

2.

Instructors who are more attractive, get better smile sheet ratings. Note that the effect was very small in both experiments. They confirmed what many other research studies have found, although their results were generally weaker than previous studies — probably due to the better controls utilized.

3.

They found that instructors who are better looking engender less absenteeism. That is, students were more likely to show up for class when their instructor was attractive.

4.

They found that it did not make a difference on the genders of the raters or instructors. It was hypothesized that female raters might respond differently to male and female instructors, and males would do the same. But this was not found. In previous studies there have been mixed results.

5.

In the second experiment, where they actually gave learners a test of what they’d learned, attractive instructors engendered higher scores on a difficult test, but not an easy test. The researchers hypothesize that learners engage more fully when their instructors are attractive.

6.

In the second experiment, they asked learners to either: (a) take a test first and then evaluate the course, or (b) do the evaluation first and then take the test. Did it matter? Yes! The researchers hypothesized that highly-attractive instructors would be penalized for giving a hard test more than their unattractive colleagues. This prediction was confirmed. When the difficult test came before the evaluation, better looking instructors were rated more poorly than less attractive instructors. Not much difference was found for the easy test.

Ramifications for Learning Professionals

First, let me caveat these thoughts with the reminder that this is just one study! Second, the study’s effects were relatively weak. Third, their results — even if valid — might not be relevant to your learners, your instructors, your organization, your situation, et cetera!

  1. If you’re a trainer, instructor, teacher, professor — get beautiful! Obviously, you can’t change your bone structure or symmetry, but you can do some things to make yourself more attractive. I drink raw spinach smoothies and climb telephone poles with my bare hands to strengthen my shoulders and give me that upside-down triangle attractiveness, while wearing the most expensive suits I can afford — $199 at Men’s Warehouse; all with the purpose of pushing myself above the threshold of … I can’t even say the word. You’ll have to find what works for you.
  2. If you refuse to sell your soul or put in time at the gym, you can always become a behind-the-scenes instructional designer or a research translator. As Clint said, “A man’s got to know his limitations.”
  3. Okay, I’ll be serious. We shouldn’t discount attractiveness entirely. It may make a small difference. On the other hand, we have more important, more leverageable actions we can take. I like the research-based findings that we all get judged primarily on two dimensions warmth/trust and competence. Be personable, authentically trustworthy, and work hard to do good work.
  4. The finding from the second experiment that better looking instructors might prompt more engagement and more learning — that I find intriguing. It may suggest, more generally, that the likability/attractiveness of our instructors or elearning narrators may be important in keeping our learners engaged. The research isn’t a slam dunk, but it may be suggestive.
  5. In terms of learning measurement, the results may suggest that evaluations come before difficult performance tests. I don’t know though how this relates to adults in workplace learning. They might be more thankful for instructional rigor if it helps them perform better in their jobs.
  6. More research is needed!

Research Reviewed

Wolbring, T., & Riordan, P. (2016). How beauty works. Theoretical mechanisms and two
empirical applications on students’ evaluation of teaching. Social Science Research, 57, 253-272.

A Better Youth Soccer Evaluation Form

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As a youth soccer coach for many years I have struggled to evaluate my own players and have seen how my soccer league evaluates players to place them on teams. As a professional learning-and-performance consultant who has focused extensively on measurement and evaluation, I think we can all do better, me included. To this end, I have spent the last two years creating a series of evaluation tools for use by coaches and youth soccer leagues. I’m sure these forms are not perfect, but I’m absolutely positive that they will be a huge improvement over the typical forms utilized by most youth soccer organizations. I have developed the forms so that they can be modified and they are made available for free to anyone who coaches youth soccer.

At the bottom of this post, I’ll include a list of the most common mistakes that are made in youth-soccer evaluation. For my regular blog readers–those who come to me for research-based recommendations on workplace learning-and-performance–you’ll see relevance to your own work in this list of evaluation mistakes.

I have developed four separate forms for evaluation. That may seem like a lot until you see how they will help you as a coach (and as a soccer league) meet varied goals you have. I will provide each form as a PDF (so you can see what the form is supposed to look like regardless of your computer configuration) and as a Word Document (so you can make changes if you like).

I’ve also provided a short set of instructions.

 

Note from Will in November 2017:

Although my work is in the workplace learning field, this blog post–from 2012–is one of the most popular posts on my blog, often receiving over 2000 unique visitors per year.

 

The Forms

1.   Player Ranking Form:  This form evaluates players on 26 soccer competencies and 4 player-comparison items, giving each player a numerical score based on these items AND an overall rating. This form is intended to provide leagues with ranking information so that they can better place players on teams for the upcoming season.


2. Player Development Form:
  This form evaluates players on the 26 soccer competencies. This form is intended for use by coaches to help support their players in development. Indeed, this form can be shared with players and parents to help players focus on their development needs.


3. Team Evaluation Form:
  This form helps coaches use practices and games to evaluate their players on the 26 key competencies. Specifically, it enables them to use one two-page form to evaluate every player on their team.

4. Field Evaluation Form:  This form enables skilled evaluators to judge the performance of players during small-group scrimmages. Like the Player Ranking Form, it provides player-comparison information to leagues (or to soccer clubs).

The Most Common Mistakes in Youth-Soccer Evaluation

  1. When skills evaluated are not clear to evaluators. So for example, having players rated on their “agility” will not provide good data because “agility” will likely mean different things to different people.
  2. When skills are evaluated along too many dimensions. So for example, evaluating a player on their “ball-handling skills, speed, and stamina” covers too many dimensions at once—a player could have excellent ball-handling skills but have terrible stamina.
  3. When the rating scales that evaluators are asked to use make it hard to select between different levels of competence. So for example, while “ball-handling” might reasonably be evaluated, it may be hard for an evaluator to determine whether a player is excellent, very good, average, fair, or poor in ball-handling. Generally, it is better to have clear criteria and ask whether or not a player meets those criteria. Four or Five-Point scales are not recommended.
  4. When evaluators can’t assess skills because of the speed of action, the large number of players involved, or the difficulty of noticing the skills targeted. For example, evaluations of scrimmages that involve more than four players on a side make it extremely difficult for the evaluators to notice the contributions of each player.
  5. When bias affects evaluators’ judgments. Because the human mind is always working subconsciously, biases can be easily introduced. So for example, it is bad practice to give evaluators the coaches’ ratings of players before those players take part in a scrimmage-based evaluation.
  6. When bias leads to a generalized positive or negative evaluation. Because evaluation is difficult and is largely a subconscious process, a first impression can skew an evaluation away from what is valid. For example, when a player is seen as getting outplayed in the first few minutes of a scrimmage, his/her later excellent play may be ignored or downplayed. Similarly, when a player is intimidated early in the season, a coach may not fully notice his/her gritty determination later in the year.
  7. When bias comes from too few observations. Because evaluation is an inexact process, evaluation results are likely to be more valid if the evaluation utilizes (a) more observations (b) by more evaluators (c) focusing on more varied soccer situations. Coaches who see their players over time and in many soccer situations are less likely to suffer from bias, although they too have to watch out that their first impressions don’t cloud their judgments. And of course, it is helpful to get assessments beyond one or two coaches.
  8. When players are either paired with, or are playing against, players who are unrepresentative of realistic competition. For example, players who are paired against really weak players may look strong in comparison. Players who are paired as teammates with really good players may look strong because of their teammates’ strong play. Finally, players who only have experience playing weaker players may not play well when being evaluated against stronger players even though they might be expected to improve by moving up and gaining experience with those same players.
  9. When the wrong things are evaluated. Obviously, it’s critical to evaluate the right soccer skills. So for example, evaluating a player on how well he/she can pass to a stationary player is not as valid as seeing whether good passes are made in realistic game-like situations when players are moving around. The more game-like the situations, the better the evaluation.
  10. When evaluations are done by remembering, not observing. Many coaches fill out their evaluation forms back home late at night instead of evaluating their players while observing them. The problem with this memory-based approach is that introduces huge biases into the process. First, memory is not perfect, so evaluators may not remember correctly. Second, memory is selective. We remember some things and forget others. Players must be evaluated primarily through observation, not memory.
  11. Encouraging players to compare themselves to others. As coaches, one of our main goals is to help our players learn to develop their skills as players, as teammates, as people, and as thinkers. Unfortunately, when players focus on how well they are doing in comparison to others, they are less likely to focus on their own skill development. It is generally a mistake to use evaluations to encourage players to compare themselves to others. While players may be inclined to compare themselves to others, coaches can limit the negative effects of this by having each player focus on their own key competencies to improve.
  12. Encouraging players to focus on how good they are overall, instead of having them focus on what they are good at and what they still have to work on. For our players to get better, they have to put effort into getting better. If they believe their skills are fixed and not easily changed, they will have no motivation to put any effort into their own improvement. Evaluations should be designed NOT to put kids in categories (except when absolutely necessary for team assignments and the like), but rather to show them what they need to work on to get better. As coaches, we should teach the importance of giving effort to deliberate practice, encouraging our players to refine and speed their best skills and improve on their weakest skills.
  13. Encouraging players to focus on too many improvements at once. To help our players (a) avoid frustration, (b) avoid thinking of themselves as poor players, and (c) avoid overwhelming their ability to focus, we ought to have them only focus on a few major self-improvement goals at one time.