Industry awards are hugely prominent in the workplace learning field and send a ripple of positive and negative effects on individuals and organizations. Awards affect vendor and consultant revenues and viability, learning department reputations and autonomy, individual promotion, salary, and recruitment opportunities. Because of their outsized influence, we should examine industry award processes to determine their strengths and weaknesses and to ascertain how helpful or harmful they are currently, and suggest improvements if any can be recommended.

The Promise of Learning Industry Awards

Industry awards seem to hold so much promise, with these potential benefits:

Application Effects

• Learning and Development
  Those who apply for awards seem to have the potential to reflect on their own practices and thus learn and improve based on this reflection and any feedback they might get from those who judge their applications.
• Nudging Improvement
  Those who apply (and even those who just review an awards application) maybe be nudged toward better practices based on the questions or requirements outlined.

Publicity of Winners Effect

• Role Modeling
  Selected winners and the description of their work can set aspirational benchmarks for other organizations.
• Rewarding of Good Effort
  Selected winners can be acknowledged and rewarded for their hard work, innovation, and results.
• Promotion and Recruitment Effects
  Individuals selected for awards can be deservedly promoted or recruited to new opportunities.
• Resourcing and Autonomy Effects
  Learning departments can earn reputation credits within their organizations that can be cashed in for resources and permission to act autonomously and avoid micromanagement.
• Vendor Marketing
  Vendors who win can publicize and support their credibility and brand.
• Purchasing Support
  Organizations who need products or services can be directed to vendors who have been vetted as excellent.

Benefits of Judging

• Market Intelligence
  Judges who participate can learn about best practices, innovations, trends that they can use in their work.

NOTE: At the very end of this article, I will come back to each and every one of these promised benefits and assess how well our industry awards are helping or hurting.

The Overarching Requirements of Awards

Awards can be said to be useful if they produce valid, credible, fair, and ethical results. Ideally, we expect our awards to represent all players within the industry or subsegment—and to select from this group the objectively best exemplars based on valid, relevant, critical criteria.

The Awards Funnel

To make this happen, we can imagine a funnel, where people and/or organizations have an equal opportunity to be selected for an award. They enter the funnel at the top and then elements of the awards process winnow the field until only the best remain at the bottom of the funnel.

How Are We Doing?

How well do our awards processes meet the best practices suggested in the Awards Funnel?

Application Process Design

Award Eligibility

At the top of the funnel, everybody in the target group should be considered for an award. Particularly if we are claiming that we are choosing “The Best,” everybody should be able to enter the award application process. Ideally, we would not exclude people because they can’t afford the time or cost of the application process. We would not exclude people just because they didn’t know about the contest. Now obviously, these criterion are too stringent for the real world, but they do illustrate how an unrepresentative applicant pool can make the results less meaningful than we might like.

In a recent “Top” list on learning evaluation, none of the following organizations were included, despite these folks being leaders in learning evaluation. Non-award winners in learning evaluation were the Kirkpatrick’s, the Phillips’, Brinkerhoff, and Thalheimer. They did not end up at the end of the funnel as winners because they did not apply for the award.

Criteria

The criteria baked into the application process are fundamental to the meaningfulness of the results. If the criteria are not the most important, then the results can’t reflect a valid ranking. Unfortunately, too many awards in the workplace learning field give credit for such things as “numbers of trainers,” “hours of training provided,” “company revenues,” “average training hours per person,” “average class size,” “learner-survey ratings,” etc. These data are not related to learning effectiveness, so they should not impact applicant ratings. Unfortunately, these are taken into account in more than a few of our award contests. Indeed, in one such awards program, these types of data were worth over 20% toward the final scoring of applicants.

Application

Application questions should prompt respondents to answer with information and data that is relevant to assessing critical outcomes. Unfortunately, too many applications have generally worded questions that don’t nudge respondents to specificity. “Describe how your learning-technology innovation improved your organization’s business results.” Similarly, many applications don’t specifically ask people to show the actual learning event. Even for elearning programs, sometimes applicants are asked to include videos instead of actual programs.

Data Quality

Applicant Responses

To select the best applicants, each of the applicant responses has to be honest and substantial enough to allow judges to make considered judgments. If applicants stretch the truth, then the results will be biased. Similarly, if some applicants employ the use of awards writers—people skilled in helping companies win awards—then fair comparisons are not possible.

Information Verification

Ideally, application information would be verified to ensure accuracy. This never happens (as far as I can tell)—casting further doubt on the validity of the results.

Judge Performance

Judge Quality

Judges must be highly knowledgeable about learning and all the subsidiary areas involved in the workplace learning field, including the science of learning, memory, instruction. Ideally, judges would also be up-to-date on learning technologies, learning innovations, organization dynamics, statistics, leadership, coaching, learning evaluation, data science, and even perhaps on the topic area being taught. It is difficult to see how judges can meet all the desired criteria. One awards organizer allows unvetted conference goers to cast votes for their favorite elearning program. The judges are presumably somewhat interested and experienced in elearning, but as a whole they are clearly not all experts.

Judge Impartiality

Judges should be impartial, unbiased, blind to applicant identities, and have no conflicts of interest. This is made more difficult because screen shots and videos often include branding of the end users and learning vendors. And actually, many award applications ask for the names of the companies involved. In one contest many of the judges listed were from companies that won awards. One person I talked with who was a judge told me how when he got together with his fellow judges and the sponsor contact, he told the team that none of the applicants solutions were any good. He was first told to follow through with the process and give them a fair hearing. He said he had already done that. After some more back and forth he was told to review the applicants by trying to be appreciative. In this case there was a clear bias toward providing positive judgments—and awarding more winners.

Judge Time and Attention

Judges need to give sufficient time or their judgments won’t be accurate. Judges are largely volunteers and they have other involvements. We should assume, I think, that these volunteer judges are working in good faith and want to provide accurate ratings, but where they are squeezed for time—or the applications are confused, off-target, or include large amounts of data, there may be poor decision making. For one awards contest, the organizer claimed there were near 500 winners representing about 20% of all applicants. This would mean that there were 2,500 applicants. They said they had about 100 judges. If this was true, that would be 25 applications for each judge to review—and note that this assumes only one judge per application (which isn’t a good practice anyway, as more are needed). This seems like a recipe for judges to do as little as possible per application they review. In another award event, the judges went from table to table in a very loud room, having to judge 50-plus entries in about 90 minutes. Impossible to judge fully in this kind of atmosphere.

Judging Rubric

Bias can occur when evaluating open-ended responses like the essay questions typical on these award applications. One way to reduce bias is to give each judge a rubric with very specific options to guide judge’s decision making, or ask questions that are in the form of rubrics (see Performance-Focused Smile-Sheet questions as examples). For the award applications I reviewed, such rubrics were not a common occurrence.

Judge Reliability

Given that judging these applications is a subjective exercise—one made more chaotic by the lack of specific questions and rubrics—bias and variability can enter the judging process. It’s helpful to have a set of judges review each application to add some reliability to the judging. This seems to be a common practice, but it may not be a universal one.

Non-Interference

Sponsor Non-Interference

The organizations who sponsor these events could conceivably change or modify the results. This seems a possibility since the award organizations are not uninterested parties. They often earn revenues by getting consulting, advertising, conference, and/or awards-ceremony revenues from the same organizations who are applying for these awards. They could benefit by having low standards or relaxed judging to increase the number of award winners. Indeed, one award winner last year had 26 award categories and gave out 196 gold awards!

Awards organizations might also benefit if well-known companies are among the award winners. Judges may subconsciously give better ratings to a well-respected tech company rather than some unknown manufacturing company if company identities are not hidden. Worse, sponsors may be enticed to put their thumbs on the scale to ensure the star companies rise to the top. When applications ask for number of employees, company revenues, and even seemingly-relevant data points as number of hours trained, it’s easy to see how the books have been cooked to make the biggest, sexiest companies rise to the top of the rankings.

Except for the evidence described above where a sponsor encouraged a judge to be “appreciative,” I can’t document any cases of sponsor direct interference, but the conditions are ripe for those who might want to exploit the process. One award-sponsoring organization recognized the perception problem, and uses a third-party organization to vet the applicants. They also bestow only award one winner in each gold, silver, and bronze category, so the third-party organization has no incentive to be lenient in judging. These are good practices!

Implications

There is so much here—and I’m afraid I am only touching the surface. Despite the dirt and treasure left to be dug and discovered, I am convinced of one thing. I cannot trust the results of most of the learning industry awards. More importantly, these awards don’t give us the benefits we might hope to get from them. Let’s revisit those promised benefits from the very beginning of this article and see how things stack up.

Application Effects

• Learning and Development
  We had hoped that applicants could learn from their involvement. However, if the wrong criteria are highlighted, they may actually learn to focus on the wrong target outcomes!
• Nudging Improvement
  We had hoped the awards criteria would nudge applicants and other members of the community to focus on valuable design criteria and outcome measures. Unfortunately, we’ve seen that the criteria are often substandard, possibly even tangential or counter to effective learning-to-performance design.

Publicity of Winners Effect

• Role Modeling
  We had hoped that winners would be deserving and worthy of being models, but we’ve seen that the many flaws of the various awards processes may result in winners not really being exemplars of excellence.
• Rewarding of Good Effort
  We had hoped that those doing good work would be acknowledged and rewarded, but now we can see that we might be acknowledging mediocre efforts instead.
• Promotion and Recruitment Effects
  We had hoped that our best and brightest might get promotions, be recruited, and be rewarded, but now it seems that people might be advantaged willy-nilly.
• Resourcing and Autonomy Effects
  We had hoped that learning departments that do the best work would gain resources, respect, and reputational advantages; but now we see that learning departments could win an award without really deserving it. Moreover, the best resourced organizations may be able to hire award writers, allocate graphic design help, etc., to push their mediocre effort to award-winning status.
• Vendor Marketing
  We had hoped that the best vendors would be rewarded, but we can now see that vendors with better marketing skills or resources—rather than the best learning solutions—might be rewarded instead.
• Purchasing Support
  We had hoped that these industry awards might create market signals to help organizations procure the most effective learning solutions. We can see now that the award signals are extremely unreliable as indicators of effectiveness. If ONE awards organization can manufacture 196 gold medalists and 512 overall in a single year, how esteemed is such an award?

Benefits of Judging

• Market Intelligence
  We had hoped that judges who participated would learn best practices and innovations, but it seems that the poor criteria involved might nudge judges to focus on information and particulars not as relevant to effective learning design.

What Should We Do Now?

You should draw your own conclusions, but here are my recommendations:

1. Don’t assume that award winners are deserving or that non-award winners are undeserving.
2. When evaluating vendors or consultants, ignore the awards they claim to have won—or investigate their solutions yourself.
3. If you are a senior manager (whether on the learning team or in the broader organization), do not allow your learning teams to apply for these awards, unless you first fully vet the award process. Better to hire research-to-practice experts and evaluation experts to support your learning team’s personal development.
4. Don’t participate as a judge in these contests unless you first vet their applications, criteria, and the way they handle judging.
5. If your organization runs an awards contest, reevaluate your process and improve it, where needed. You can use the contents of this article as a guide for improvement.

Mea Culpa

I give an award every year, and I certainly don’t live up to all the standards in this article.

My award, the Neon Elephant Award, is designed to highlight the work of a person or group who utilizes or advocates for practical research-based wisdom. Winners include people like Ruth Clark, Paul Kirschner, K. Anders Ericsson, Julie Dirksen (among a bunch of great people, check out the link).

Interestingly, I created the award starting in 2006 because of my dissatisfaction with the awards typical in our industry at that time—awards that measured butts in seats, etc.

It Ain’t Easy — And It Will Never Be Easy!

Organizing an awards process or vetting content is not easy. A few of you may remember the excellent work of Bill Ellet, starting over two decades ago, and his company Training Media Review. It was a monumental effort to evaluate training programs. So monumental in fact that it was unsustainable. When Bill or one of his associates reviewed a training program, they spent hours and hours doing so. They spent more time than our awards judges, and they didn’t review applications; they reviewed the actual learning program.

Is a good awards process even possible?

Honestly, I don’t know. There are so many things to get right.

Can they be better?

Yes!

Are they good enough now?

Not most of them!