Tag Archive for: feedback

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.

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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!

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.