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For those of you who don’t know Matt Richter, President of the Thiagi Group, he’s one of the most innovative thinkers when it comes to creating training that both sizzles and supports work performance. Recently, Matt and I began partnering in a new podcast, Truth In Learning, which I’ll have more to say about later once I figure out where the escape hatch is.

NOW, I want to share with you a brilliant new article, that Matt surprised me with, on his efforts to brainstorm innovative ways to use LTEM (The Learning-Transfer Evaluation Model).

You should read his article, but just to give you the list of seven uses for LTEM:

  1. Learning Evaluation—The primary intent of the LTEM framework.
  2. Instructional Design—To negotiate with stakeholders the outcomes desired.
  3. Training Game Design—To ensure games/activities have an instructional purpose.
  4. Coaching—Helping to build a development plan for those who are coached.
  5. Performance Consulting—To focus on performances that matter along the journey.
  6. Keynoting/Presenting—To ensure a focus on meaningful outcomes, not just infotainment.
  7. Sales/Business Development—To keep sales conversations focused on meaningful outcomes.

We are All in this Together

One of the great benefits of publishing LTEM is that since its publication last year I’m regularly being contacted by people whose organizations are finding new and innovative ways to utilize LTEM—and not just for learning evaluation but as a central element of their learning strategy and practice.

I’m especially pleased with those who have taken LTEM really deep, and I’d like to give a shout out to Elham Arabi who is doing her doctoral dissertation using LTEM as a spur to supporting a hospital’s effort to maximize the benefits or their learning interventions. Congrats to her for being accepted as a speaker at the upcoming eLearning Guild Learning Solutions Conference, March 31 to April 2 (2020) in Orlando. The title of her talk is: Using Evaluation Data to Enhance Your Training Programs.

Share Your Examples and Innovations

Please share your innovations and ideas about using LTEM in your workplace, on social media, or by contacting me at https://www.worklearning.com/contact/. I would really love to hear how it’s going, including any obstacles you’ve faced, your success stories, etc.

And, of course, if you’d like me to help your organization utilize LTEM, or just be the face of LTEM to your organization, please contact me so we can set up a time to talk, and consider my LTEM workshop to introduce LTEM to your team.

 

 

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

I’ve had the distinct honor of being invited to speak at the Learning Technologies conference in London for three years in a row. This year, I talked about two learning innovations:

  • Performance-Focused Learner Surveys
  • LTEM (The Learning-Transfer Evaluation Model)

It was a hand-raising experience!

Most importantly, they have done a great job capturing my talk on YouTube.

Indeed, although I’ve made some recent improvements in the way I talk about these two learning innovations, the video does an excellent job of capturing some of the main points I’ve been making about the state of learning evaluation and two innovations that are tearing down some of the obstacles that have held us back from doing good evaluation.

Thanks to Stella Collins at Stellar Learning for organizing and facilitating my session!

Special thanks to the brilliant conference organizer and learning-industry influencer Robert Taylor for inviting and supporting me and my work.

Again, click here to see the video of my presentation at Learning Technologies London 2019.

While I was in London a few months ago, where I talked about learning evaluation, I was interviewed by the LearningNews about learning evaluation.

Some of what I said:

  • “Most of us have been doing the same damn thing we’ve always done [in learning evaluation]. On the other hand, there is a breaking of the logjam.”
  • “A lot of us are defaulting to happy sheets, and happy sheets that aren’t effective.”
  • “Do we in L&D have the skills to be able to do evaluation in the first place?…. My short answer is NO WAY!”
  • “We can’t upskill ourselves fast enough [in terms of learning evaluation].

It was a fun interview and LearningNews did a nice job in editing it. Special thanks to Rob Clarke for the interview, organizing, and video work (along with his great team)!!

Click here to see the interview.

At a recent industry conference, a speaker, offering their expertise on learning evaluation, said this:

“As a discipline, we must look at the metrics that really matter… not to us but to the business we serve.”

Unfortunately, this is one of the most counterproductive memes in learning evaluation. It is counterproductive because it throws our profession under the bus. In this telling, we have no professional principles, no standards, no foundational ethics. We are servants, cleaning the floors the way we are instructed to clean them, even if we know a better way.

Year after year we hear from so-called industry thought leaders that our primary responsibility is to the organizations that pay us. This is a dangerous half truth. Of course we owe our organizations some fealty and of course we want to keep our jobs, but we also have professional obligations that go beyond this simple “tell-me-what-to-do” calculus.

This monomaniacal focus on measuring learning in terms of business outcomes reminds me of the management meme from the 1980s and 90s, that suggested that the goal of a business organization is to increase stakeholder value. This single-bottom-line focus has come under blistering attack for its tendency to skew business operations toward short-term results while ignoring long-term business results and for producing outcomes that harm employees, hurt customers, and destroy the environment.

If we give our business stakeholders the metrics they say that matter to them, but fail to capture the metrics that matter to our success as learning professionals in creating effective learning, then we not only fail ourselves and our learners but we fail our organization as well.

Evaluation What For?

To truly understand learning evaluation, we have to ask ourselves why we’re evaluating learning in the first place! We have to work backwards from the answer to this question.

Why does anyone evaluate? We evaluate to help us make better decisions and take better actions. It’s really that simple! So as learning professionals, we need information to help us make our most important decisions. We should evaluate to support these decisions!

What are our most important decisions? Here’s a few:

  • Which part of the content taught, if any, is relevant and helpful to supporting employees in doing their work? Which parts should be modified or discarded?
  • Which aspects of our learning designs are helpful in supporting comprehension, remembering, and motivation to learn? Which aspects should be modified or discarded?
  • Which after-training supports are helpful in enabling learning to be transferred and utilized by employees in their work? Which supports should be kept? Which need to be modified or discarded?

What are our organizational stakeholders’ most important decisions about learning? Here are a few:

  • Are our learning and development efforts creating optimal learning results? What additional support and resources should the organization supply that might improve learning results? What savings can be found in terms of support and resources—and are these savings worth the lost benefits?
  • Is the leadership of the learning and development function producing a cycle of continuous improvement, generating improved learning outcomes or generating learning outcomes optimized given their resource constraints? If not, can they be influenced to be better or should they be replaced?
  • Is the leadership of the learning and development function creating and utilizing evaluation metrics that enable the learning and development team to get valid feedback about the design factors that are most important in creating our learning results? If not, can they be influenced to use better metrics or should they be replaced?

Two Goals for Learning Evaluation

When we think of learning evaluation, we should have two goals. First, we should create learning-evaluation metrics that enable us to make our most important decisions regarding content, design components (i.e., focused at least on comprehension, remembering, motivation to apply learning), and after-training support. Second, we should do enough in our learning evaluations to gain sufficient credibility with our business stakeholders to continue our good work. Focusing only on the second of these is a recipe for disaster. 

Vanity Metrics

In the business start-up world there is a notion called “vanity metrics,” for example see warnings by Eric Ries, the originator of the lean startup movement. Vanity metrics are metrics that seem to be important, but that are not important. They are metrics that often make us look good even if the underlying data is not really meaningful.

Most calls to provide our business stakeholders with the metrics that matter to them result in beautiful visualizations and data dashboards that focus on vanity metrics. Ubiquitous vanity metrics in learning include the number of trainees trained, the cost per training, the estimates of learners for the value of the learning, complicated benefit/cost analyses of that utilize phantom measures of benefits, etc. By focusing only or primarily on these metrics we don’t have data to improve our learning designs, we don’t have data that enables us create cycles of improvement, we don’t have data that enables us to hold ourselves accountable.

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.

 

 

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!

 

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...

Here's a great article, from NPR, entitled Higher Ed's Moneyball, about Higher Ed's attempts to use data to provide their instructors with real-time data they can use to support their students.

The examples cited seem to mostly target usage data, not more rigorous learning data, but I can't be sure. Certainly, over time, someone will figure out how to capture data that is more meaningful.

Still, even the usage data seems helpful. For example, students who are inactive for a while can get extra attention, etc.

Something to keep an eye on for the future…