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The LEARNNOVATORS team (specifically Santhosh Kumar) asked if I would join them in their Crystal Balling with Learnnovators interview series, and I accepted! They have some really great people on the series, I recommend that you check it out!

The most impressive thing was that they must have studied my whole career history and read my publication list and watched my videos because they came up with a whole set of very pertinent and important questions. I was BLOWN AWAY—completely IMPRESSED! And, given their dedication, I spent a ton of time preparing and answering their questions.

It’s a two part series and here are the links:

Here are some of the quotes they pulled out and/or I’d like to highlight:

Learning is one of the most wondrous, complex, and important areas of human functioning.

The explosion of different learning technologies beyond authoring tools and LMSs is likely to create a wave of innovations in learning.

Data can be good, but also very very bad.

Learning Analytics is poised to cause problems as well. People are measuring all the wrong things. They are measuring what is easy to measure in learning, but not what is important.

We will be bamboozled by vendors who say they are using AI, but are not, or who are using just 1% AI and claiming that their product is AI-based.

Our senior managers don’t understand learning, they think it is easy, so they don’t support L&D like they should.

Because our L&D leaders live in a world where they are not understood, they do stupid stuff like pretending to align learning with business terminology and business-school vibes—forgetting to align first with learning.

We lie to our senior leaders when we show them our learning data—our smile sheets and our attendance data. We then manage toward these superstitious targets, causing a gross loss of effectiveness.

Learning is hard and learning that is focused on work is even harder because our learners have other priorities—so we shouldn’t beat ourselves up too much.

We know from the science of human cognition that when people encounter visual stimuli, their eyes move rapidly from one object to another and back again trying to comprehend what they see. I call this the “eye-path phenomenon.” So, because of this inherent human tendency, we as presenters—as learning designers too!—have to design our presentation slides to align with these eye-path movements.

Organizations now—and even more so in the near future—will use many tools in a Learning-Technology Stack. These will include (1) platforms that offer asynchronous cloud-based learning environments that enable and encourage better learning designs, (2) tools that enable realistic practice in decision-making, (3) tools that reinforce and remind learners, (4) spaced-learning tools, (5) habit-support tools, (6) insight-learning tools (those that enable creative ideation and innovation), et cetera

Learnnovators asked me what I hoped for the learning and development field. Here’s what I said:

Nobody is good at predicting the future, so I will share the vision I hope for. I hope we in learning and development continue to be passionate about helping other people learn and perform at their best. I hope we recognize that we have a responsibility not just to our organizations, but beyond business results to our learners, their coworkers/families/friends, to the community, society, and the environs. I hope we become brilliantly professionalized, having rigorous standards, a well-researched body of knowledge, higher salaries, and career paths beyond L&D. I hope we measure better, using our results to improve what we do. I hope we, more-and-more, take a small-S scientific approach to our practices, doing more A-B testing, compiling a database of meaningful results, building virtuous cycles of continuous improvement. I hope we develop better tools to make building better learning—and better performance—easier and more effective. And I hope we continue to feel good about our contributions to learning. Learning is at the heart of our humanity!

For over two years I’ve been compiling and analyzing the research on learning transfer as it relates to workplace learning and development. Today I am releasing my findings to the public.

Here is the Overview from the Research-to-Practice Report:

Learning transfer—or “training transfer” as it is sometimes called—occurs when people learn concepts and/or skills and later utilize those concepts/skills in work situations.1 Because we invest time, effort, and resources to create learning interventions, we hope to get a return on those investments in the form of some tangible benefit—usually some form of improved work outcome. Transfer, then, is our paramount goal. When we transfer, we are successful. When we don’t transfer, we fail.

To be practical about this, it is not enough to help our learners comprehend concepts or understand skills. It is not enough to get them to remember concepts/skills. It is not enough to inspire our learners to be motivated to use what they’ve learned. These results may be necessary, but they are not sufficient. We learning professionals hold transfer sacrosanct because it is the ultimate standard for success and failure.

This research review was conducted to determine factors that can be leveraged by workplace learning professionals to increase transfer success. This effort was not intended to be an exhaustive scientific review, but rather a quick analysis of recent research reviews, meta-analyses, and selected articles from scientific refereed journals. The goal of this review was to distill validated transfer factors—learning design and learning support elements that increase the likelihood that learning will transfer—and make these insights practical for trainers, learning architects, instructional designers, elearning developers, and learning professionals in general. In targeting this goal, this review aligns with transfer researchers’ recent admonition to ensure the scientific research on learning transfer gets packaged in a format that is usable by those who design and develop learning (Baldwin, Ford, Blume, 2017).

Unfortunately, after reviewing the scientific articles referenced in this report as well as others not cited here, my conclusion is that many of the most common transfer approaches have not yet been researched with sufficient rigor or intensity to enable us to have full certainty about how to engineer transfer success. At the end of this report, I make recommendations on how we can have a stronger research base.

Despite the limitations of the research, this quick review did uncover many testable hypotheses about the factors that may support transfer. Factors are presented here in two categories—those with strong support in the research, and those the research identifies as having possible benefits. I begin by highlighting the overall strength of the research.

Special Thanks for Early Sponsorship

Translating scientific research involves a huge investment in time, and to be honest, I am finding it more and more difficult to carve out time to do translational research. So it is with special gratitude that I want to thank Emma Weber of Lever Transfer of Learning for sponsoring me back in 2017 on some of the early research-translation efforts that got me started in compiling the research for this report. Without Lever’s support, this research would not have been started!

Tidbits from the Report

There are 17 research-supported recommended transfer factors and an additional six possible transfer factors. Here are a subset of the supported transfer factors:

  • Transfer occurs most potently to the extent that our learning designs strengthen knowledge and skills.
  • Far transfer hardly ever happens. Near transfer—transfer to contexts similar to those practiced during training or other learning efforts—can happen.
  • Learners who set goals are more likely to transfer.
  • Learners who also utilize triggered action planning will be even more likely to transfer, compared to those who only set goals alone.
  • Learners with supervisors who encourage, support, and monitor learning transfer are more likely to successfully transfer.
  • The longer the time between training and transfer, the less likely that training-generated knowledge create benefits for transfer.
  • The more success learners have in their first attempts to transfer what they’ve learned, the more likely they are to persevere in more transfer-supporting behaviors.

The remaining recommendations can be viewed in the report (available below).

Recommendations to Researchers

While transfer researchers have done a great deal of work in uncovering how transfer works, the research base is not as solid as it should be. For example, much of the transfer research uses learners’ subjective estimates of transfer—rather than actual transfer—as the dependent measure. Transfer researchers themselves recognize the limitations of the research base, but they could be doing more. In the report, I offer several additional recommendations to the improvements they’ve already suggested.

The Research-to-Practice Report

 

Access the report by clicking here…

 

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Will’s Note: ONE DAY after publishing this first draft, I’ve decided that I mucked this up, mashing up what researchers, research translators, and learning professionals should focus on. Within the next week, I will update this to a second draft. You can still read the original below (for now):

 

Some evidence is better than other evidence. We naturally trust ten well-designed research studies better than one. We trust a well-controlled scientific study better than a poorly-controlled study. We trust scientific research more than opinion research, unless all we care about is people’s opinions.

Scientific journal editors have to decide which research articles to accept for publication and which to reject. Practitioners have to decide which research to trust and which to ignore. Politicians have to know which lies to tell and which to withhold (kidding, sort of).

To help themselves make decisions, journal editors regular rank each article on a continuum from strong research methodology to weak. The medical field regularly uses a level-of-evidence approach to making medical recommendations.

There are many taxonomies for “levels of evidence” or “hierarchy of evidence” as it is commonly called. Wikipedia offers a nice review of the hierarchy-of-evidence concept, including some important criticisms.

Hierarchy of Evidence for Learning Practitioners

The suggested models for level of evidence were created by and for researchers, so they are not directly applicable to learning professionals. Still, it’s helpful for us to have our own hierarchy of evidence, one that we might actually be able to use. For that reason, I’ve created one, adding in the importance of practical evidence that is missing from the research-focused taxonomies. Following the research versions, Level 1 is the best.

  • Level 1 — Evidence from systematic research reviews and/or meta-analyses of all relevant randomized controlled trials (RCTs) that have ALSO been utilized by practitioners and found both beneficial and practical from a cost-time-effort perspective.
  • Level 2 — Same evidence as Level 1, but NOT systematically or sufficiently utilized by practitioners to confirm benefits and practicality.
  • Level 3 — Consistent evidence from a number of RCTs using different contexts and situations and learners; and conducted by different researchers.
  • Level 4 — Evidence from one or more RCTs that utilize the same research context.
  • Level 5 — Evidence from one or more well-designed controlled trial without randomization of learners to different learning factors.
  • Level 6 — Evidence from well-designed cohort or case-control studies.
  • Level 7 — Evidence from descriptive and/or qualitative studies.
  • Level 8 — Evidence from research-to-practice experts.
  • Level 9 — Evidence from the opinion of other authorities, expert committees, etc.
  • Level 10 — Evidence from the opinion of practitioners surveyed, interviewed, focus-grouped, etc.
  • Level 11 — Evidence from the opinion of learners surveyed, interviewed, focus-grouped, etc.
  • Level 12 — Evidence curated from the internet.

Let me consider this Version 1 until I get feedback from you and others!

Critical Considerations

  1. Some evidence is better than other evidence
  2. If you’re not an expert in evaluating evidence, get insights from those who are–particularly valuable are research-to-practice experts (those who have considerable experience in translating research into practical recommendations).
  3. Opinion research in the learning field is especially problematic, because the learning field is comprised of both strong and poor conceptions of what works.
  4. Learner opinions are problematic as well because learners often have poor intuitions about what works for them in supporting their learning.
  5. Curating information from the internet is especially problematic because it’s difficult to distinguish between good and poor sources.

Trusted Research to Practice Experts

(in no particular order, they’re all great!)

  • (Me) Will Thalheimer
  • Patti Shank
  • Julie Dirksen
  • Clark Quinn
  • Mirjam Neelen
  • Ruth Clark
  • Donald Clark
  • Karl Kapp
  • Jane Bozarth
  • Ulrich Boser

CEO’s are calling for their companies to be more innovative in the ever-accelerating competitive landscape! Creativity is the key leverage point for innovation. Research I’ve compiled (from the science on creativity) shows that unique and valuable ideas are generated when people and teams look beyond their inner circle to those in their peripheral networks. GIVEN THIS, a smart company will seed themselves with outside influencers who are working with new ideas.

But what are a vast majority of big companies doing that kills their own creativity? They are making it difficult or virtually impossible for their front-line departments to hire small businesses and consultants. It’s allowed, but massive walls are being built! And these walls have exploded over the last five to ten years:

  1. Only fully vetted companies can be hired, requiring small lean companies to waste time in compliance—or turn away in frustration. Also causing large-company managers to favor the vetted companies, even if a small business or consultant would provide better value or more-pertinent products or services.
  2. Master Service Agreements are required (pushing small companies away due to time and legal fees).
  3. Astronomical amounts of insurance are required. Why the hell do consultants need $2 million in insurance, even when they are consulting on non-safety-related issues? Why do they need any insurance at all if they are not impacting critical safety factors?
  4. Companies can’t be hired unless they’ve been in business for 5 or 10 or 15 years, completely eliminating the most unique and innovative small businesses or consultants—those who recently set up shop.
  5. Minimum company revenues are required, often in the millions of dollars.

These barriers, of course, aren’t the only ones pushing large organizations away from small businesses or consultants. Small companies often can’t afford sales forces or marketing budgets so they are less likely to gain large companies’ share of attention. Small companies aren’t seen as safe bets because they don’t have a name, or their website is not as beautiful, or they haven’t yet worked with other big-name companies, or the don’t speak the corporate language. Given these surface characteristics, only the bravest, most visionary frontline managers will take the risk to make the creative hire. And even then, their companies are making it increasingly hard for them to follow through.

Don’t be fooled by the high-visibility anecdotes that show a CEO hiring a book author or someone featured in Wired, HBR, or on some podcast. Yes, CEO’s and senior managers can easily find ways to hire innovators, and the resulting top-down creativity infusion can be helpful. But it can be harmful as well!!!! Too many times senior managers are too far away from knowing what works and what’s needed on the front lines. They push things innocently not knowing that they are distracting the troops from what’s most important, or worse, pushing the frontline teams to do stupid stuff against their best judgment.

Even more troublesome with these anecdotes of top-down innovation is that they are too few and far between. There may be ten senior managers who can hire innovation seeds, but there are dozens or hundreds or thousands of folks who might be doing so but can’t.

A little digression: It’s the frontline managers who know what’s needed—or perhaps more importantly the “leveraging managers” if I can coin a term. These are the managers who are deeply experienced and wise in the work that is getting done, but high enough in the organization to see the business-case big picture. I will specifically exclude “bottle-cap managers” who have little or no experience in a work area, but were placed there because they have business experience. Research shows these kind of hires are particularly counterproductive in innovation.

Let me summarize.

I’m not selling anything here. I’m in the training, talent development, learning evaluation business as a consultant—I’m not an innovation consultant! I’m just sharing this out of my own frustration with these stupid counter-productive barriers that I and my friends in small businesses and consultancies have experienced. I also am venting here to provide a call to action for large organizations to wake the hell up to the harm you are inflicting on yourselves and on the economy in general. By not supporting the most innovative small companies and consultants, you are dumbing-down the workforce for years to come!

Alright! I suppose I should offer to help instead of just gripe! I have done extensive research on creativity. But I don’t have a workshop developed, the research is not yet in publishable form, and it’s not really what I’m focused on right now. I’m focused on innovating in learning evaluation (see my new learning-evaluation model and my new method for capturing valid and meaningful data from learners). These are two of the most important innovations in learning evaluation in the past few years!

However, a good friend of mine did, just last month, suggest that the world should see the research on creativity that I’ve compiled (thanks Mirjam!). Given the right organization, situation, and requirements—and the right amount of money—I might be willing to take a break from my learning-evaluation work and bring this research to your organization. Contact me to try and twist my arm!

I’m serious, I really don’t want to do this right now, but if I can capture funds to reinvest in my learning-evaluation innovations, I just might be persuaded. On the contact-me link, you can set up an appointment with me. I’d love to talk with you if you want to talk innovation or learning evaluation.

The 70-20-10 Framework has been all the rage for the last five or ten years in the workplace learning field. Indeed, I organized a great debate about 70-20-10 through The Debunker Club (you can see the tweet stream here). I have gone on record saying that the numbers don’t have a sound research backing, but that the concept is a good one—particularly the idea that we as learning professionals ought to leverage on-the-job learning where we can.

What is 70-20-10?

The 70-20-10 framework is built on the belief that 10% of workplace learning is, or should be, propelled by formal training; that 20% is, or should be, enabled by learning directly from others; and that 70% of workplace learning is, or should, come from employee’s learning through workplace experiences.

Supported by Research?

Given all the energy around 70-20-10, you might think that lots of rigorous scientific research has been done on the framework. Well, you would be wrong!

In fact, up until today (April 19, 2019), only one study has been published in a scientific journal (my search of PsycINFO only reveals one study). In this post, I will review that one study, published last year:

Johnson, S. J., Blackman, D. A., & Buick, F. (2018). The 70:20:10 framework and the transfer of learning. Human Resource Development Quarterly. Advance online publication.

Caveats

All research has strengths, weaknesses, and limitations—and it’s helpful to acknowledge these so we can think clearly. First, one study cannot be definitive, and this is just one study. Also, this study is qualitative and relies on subjective inputs to draw its conclusions. Ideally, we’d like to have more objective measures utilized. It is also gathering data from a small sample of public sector workers, where ideally we want a wider range of diverse participants.

Methodology

The researchers found a group of organizations who had been bombarded with messages and training to encourage the use of the 70-20-10 model. Specifically, the APSC (The Australian Public Sector Commission), starting in 2011, encouraged the Australian public sector to embrace 70-20-10.

The specific study “draws from the experiences of two groups of Australian public sector managers: senior managers responsible for implementing the 70:20:10 framework within their organization; and middle managers who have undergone management capability development aligned to the 70:20:10 framework. All managers were drawn from the Commonwealth, Victorian, Queensland, and Northern Territory governments.”

A qualitative approach was chosen according to the researchers “given the atheoretical nature of the 70:20:10 framework and the lack of theory or evidence to provide a research framework.”

The qualitative approaches used by the researchers were individual structured interviews and group structured interviews.

The researchers chose people to interview based on their experience using the 70-20-10 framework to develop middle managers. “A purposive sampling technique was adopted, selecting participants who had specific knowledge of, and experience with, middle management capability development in line with the 70:20:10 framework.”

The researchers used a text-processing program (NVivo) to help them organize and make sense of the qualitative data (the words collected in the interviews). According to Wikipedia, “NVivo is intended to help users organize and analyze non-numerical or unstructured data. The software allows users to classify, sort and arrange information; examine relationships in the data; and combine analysis with linking, shaping, searching and modeling.”

Overall Results

The authors conclude the following:

“In terms of implications for practice, the 70:20:10 framework has the potential to better guide the achievement of capability development through improved learning transfer in the public sector. However, this will only occur if future implementation guidelines focus on both the types of learning required and how to integrate them in a meaningful way. Actively addressing the impact that senior managers and peers have in how learning is integrated into the workplace through both social modeling and organizational support… will also need to become a core part of any effective implementation.”

“Using a large qualitative data set that enabled the exploration of participant perspectives and experiences of using the 70:20:10 framework in situ, we found that, despite many Australian public sector organizations implementing the framework, to date it is failing to deliver desired learning transfer results. This failure can be attributed to four misconceptions in the framework’s implementation: (a) an overconfident assumption that unstructured experiential learning will automatically result in capability development; (b) a narrow interpretation of social learning and a failure to recognize the role social learning has in integrating experiential, social and formal learning; (c) the expectation that managerial behavior would automatically change following formal training and development activities without the need to actively support the process; and (d) a lack of recognition of the requirement of a planned and integrated relationship between the elements of the 70:20:10 framework.”

Specific Difficulties

With Experiential Learning

“Senior managers indicated that one reason for adopting the 70:20:10 framework was that the dominant element of 70% development achieved through experiential learning reflected their expectation that employees should learn on the job. However, when talking to the middle managers themselves, it was not clear how such learning was being supported. Participants suggested that one problem was a leadership perception across senior managers that middle managers could automatically transition into middle management roles without a great deal of support or development.”

“The most common concern, however, was that experiential learning efficacy was challenged because managers were acquiring inappropriate behaviors on the job based on what they saw around them every day.”

“We found that experiential learning, as it is currently being implemented, is predominantly unstructured and unmanaged, that is, systems are not put in place in the work environment to support learning. It was anticipated that managers would learn on the job, without adequate preparation, additional support, or resourcing to facilitate effective learning.”

With Social Learning

“Overall, participants welcomed the potential of social learning, which could help them make sense of their con-text, enabling both sense making of new knowledge acquired and reinforcing what was appropriate both in, and for, their organization. However, they made it clear that, despite apparent organizational awareness of the value of social learning, it was predominantly dependent upon the preferences and working styles of individual managers, rather than being supported systematically through organizationally designed learning programs. Consequently, it was apparent that social learning was not being utilized in the way intended in the 70:20:10 framework in that it was not usually integrated with formal or experiential learning.”

Mentoring

“Mentoring was consistently highlighted by middle and senior managers as being important for both supporting a middle manager’s current job and for building future capacity.”

“Despite mentoring being consistently raised as the most favored form of development, it was not always formally supported by the organization, meaning that, in many instances, mentoring was lacking for middle managers.”

“A lack of systemic approaches to mentoring meant it was fragile and often temporary.”

Peer Support

“Peer support and networking encouraged middle managers to adopt a broader perspective and engage in a community of practice to develop ideas regarding implementing new skills.”

“However, despite managers agreeing that networks and peer support would assist them to build capability and transfer learning to the workplace, there appeared to be few organizationally supported peer learning opportunities. It was largely up to individuals to actively seek out and join their own networks.”

With Formal Learning

“Formal learning programs were recognized by middle and senior managers as important forms of capability development. Attendance was often encouraged for new middle managers.”

“However, not all experiences with formal training programs were positive, with both senior and middle managers reflecting on their ineffectiveness.”

“For the most part, participants reported finishing formal development programs with little to no follow up.”

“There was a lack of both social and experiential support for embedding this learning. The lack of social learning support partly revolved around the high workloads of managers and the lack of time devoted to development activities.”

“The lack of experiential support and senior management feedback meant that many middle managers did not have the opportunity to practice and further develop their new skills, despite their initial enthusiasm.”

“A key issue with this was the lack of direct and clear guidance provided by their line managers.”

“A further issue with formal learning was that it was often designed generically for groups of participants…  The need for specificity also related to the lack of explicit, individualized feedback provided by their line manager to reinforce and embed learning.”

What Should We Make of This Preliminary Research?

Again, with only one study—and a qualitative one conducted on a narrow type of participant—we should be very careful in drawing conclusions.

Still, the study can be helpful in helping us develop hypotheses for further testing—both by researchers and by us as learning professionals.

We also ought to be careful in casting doubt on the 70-20-10 framework itself. Indeed, the research seems to suggest that the framework was not always implemented as intended. On the other hand, when it is demonstrated that a model tends to be used poorly in its routine use, then we should become skeptical that it will produce reliable benefits.

Here are a list of reflections generated in me by the research:

  1. Why so much excitement for 70-20-10 with so little research backing?
  2. Formal training was found to have all the problems normally associated with it, especially the lack of follow-through and after-training support—so we still need to work to improve it!
  3. Who will provide continuous support for experiential and social learning? In the research case, the responsibility for implementing on-the-job learning experiences was not clear, and so the implementation was not done or was poorly done.
  4. What does it take in terms of resources, responsibility, and tasking to make experiential and social learning useful? Or, is this just a bridge too far?
  5. The most likely leverage point for on-the-job learning still seems, to me, to be managers. If this is a correct assumption—and really it should be tested—how can we in Learning & Development encourage, support, and resource managers for this role?

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Dani Johnson at RedThread Research has just released a wonderful synopsis of Learning Evaluation Models. Comprehensive, Thoughtful, Well-Researched! It also has suggestions of articles to read!!!

This work is part of an ongoing effort to research the learning-evaluation space. With research sponsored by the folks at the adroit learning-evaluation company forMetris, RedThread is looking to uncover new insights about the way we do workplace learning evaluation.

Here’s what Dani says in her summary:

“What we hoped to see in the literature were new ideas – different ways of defining impact for the different conditions we find ourselves in. And while we did see some, the majority of what we read can be described as same. Same trends and themes based on the same models with little variation.”

 

“While we do not disparage any of the great work that has been done in the area of learning measurement and evaluation, many of the models and constructs are over 50 years old, and many of the ideas are equally as old.

On the whole, the literature on learning measurement and evaluation failed to take into account that the world has shifted – from the attitudes of our employees to the tools available to develop them to the opportunities we have to measure. Many articles focused on shoe-horning many of the new challenges L&D functions face into old constructs and models.”

 

“Of the literature we reviewed, several pieces stood out to us. Each of the following authors [detailed in the summary] and their work contained information that we found useful and mind-changing. We learned from their perspectives and encourage you to do the same.”

 

I also encourage you to look at this great review! You can see the summary here.

 

 

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!

My research-and-consulting practice, Work-Learning Research, was 20 years old last Saturday. This has given me pause to reflect on where I’ve been and how learning research has involved in the past two decades.

Today, as I’m preparing a conference proposal for next year’s ISPI conference, I found an early proposal I put together for the Great Valley chapter of ISPI to speak at one of their monthly meetings back in 2002. I don’t remember whether they actually accepted my proposal, but here is an excerpt:

 

 

Interesting that even way back then, I had found and compiled research on retrieval practice, spacing, feedback, etc. from the scientific journals and the exhaustive labor of hundreds of academic researchers. I am still talking about these foundational learning principles even today—because they are fundamental and because research and practice continue to demonstrate their power. You can look at recent books and websites that are now celebrating these foundational learning factors (Make it Stick, Design for How People Learn, The Ingredients for Great Teaching, Learning Scientists website, etc.).

Feeling blessed today, as we here in the United States move into a weekend where we honor our workers, that I have been able to use my labor to advance these proven principles, uncovered first by brilliant academic researchers such as Bjork, Bahrick, Mayer, Ebbinghaus, Crowder, Sweller, van Merriënboer, Rothkopf, Runquist, Izawa, Smith, Roediger, Melton, Hintzman, Glenberg, Dempster, Estes, Eich, Ericsson, Davies, Garner, Chi, Godden, Baddeley, Hall, Hintzman, Herz, Karpicke, Butler, Kirschner, Clark, Kulhavy, Moreno, Pashler, Cepeda, and many others.

From these early beginnings, I created a listing of twelve foundational learning factors—factors that I have argued should be our first priority in creating great learning—reviewed here in this document.

Happy Labor Day everyone and special thanks to the researchers who continue to make my work possible—and enable learning professionals of all stripes to build increasingly effective learning!

If you’d like to leave a remembrance in regard to Work-Learning Research’s 20th anniversary, or just read my personal reflections about it, you can do that here.

 

Back in 2008, I began discussing the scientific research on “implementation intentions.” I did this first at an eLearning Guild conference in March of 2008. I also spoke about it in 2008 at a talk to Salem State University, in a Chicago Workshop entitled Creating and Measuring Learning Transfer, and in one of my Brown Bag Lunch sessions delivered online.

In 2014, I wrote about implementation intentions specifically as a way to increase after-training follow-through. Thinking the term “Implementation Intentions” was too opaque and too general, I coined the term “Triggered Action Planning,” and argued that goal-setting at the end of training—what was often called action planning—would not be effective as triggered action planning. Indeed, in recounting the scientific research on implementation intentions, I often talked about how researchers were finding that setting situation-action triggers could create results that were twice as good as goal-setting alone. Doubling the benefits of goal setting! These kinds of results are huge!

I just came across a scientific study that supports the benefits of triggered action planning.

 

Shlomit Friedman and Simcha Ronen conducted two experiments and found similar results in each. I’m going to focus on their second one because it focused on a real training class with real employees. They used a class that taught retail sales managers how to improve interactions with customers. All the participants got the same exact training and were then randomly assigned to two different experimental groups:

  • Triggered Action Planning—Participants were asked to visualize situations with customers and how they would respond to seven typical customer objections.
  • Goal-Reminding Action Planning—Participants were asked to write down the goals of the training program and the aspects of the training program that they felt were most important.

Four weeks after the training, secret shoppers were used. They interacted with the supervisors using the key phrases and rated each supervisor on dichotomously-anchored rating scales from 1 to 10, with ten being best. The secret shoppers were blind to condition—that is they did not know which supervisors had gotten triggered action planning and which received the goal instructions. The findings showed that the triggered action planning produced improvements over the goal-setting condition by 76%, almost doubling the results.

It should be pointed out that this experiment could have been better designed to have the control group select their own goals. There may be some benefit to actual goal-setting compared with being reminded about the goals of the course. The experiment had its strengths too, most notably (1) the use of observers to record real-world performance four weeks after the training, and (2) the fact that all the supervisors had gone through the exact same training and were randomly assigned to either triggered action planning or the goal-reminding condition.

Triggered Action Planning

Triggered Action Planning has great potential to radically improve the likelihood that your learners will actually use what you’ve taught them. The reason it works so well is that it is based on a fundamental characteristic of human cognition. We are triggered to think and act based on cues in our environment. As learning professionals we should do whatever we can to:

  • Figure out what cues our learners will face in their work situations.
  • Teach them what to do when they encounter these cues.
  • Give them a rich array of spaced, repeated practice in handling these situations.

To learn more about how to implement triggered action planning, see my original blog post.

Research Cited

Friedman, S., & Ronen, S. (2015). The effect of implementation intentions on transfer of training. European Journal of Social Psychology, 45(4), 409-416.

This blog post took three hours to write.