5DL Tools: High Performance Learning with LADDER²^TM

We’ve said it elsewhere on this site and we’ll say it again here: today is one of the most exciting moments in human history—perhaps the most exciting moment—to be a learner of any kind. In the past few years, new technologies such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) scanning have enabled scientists to study human brains while they work. The evidence that these new tools produce has allowed learning scientists to understand how humans learn better than ever before.

Many of the new findings reinforce understandings that humans have evolved over centuries, but many others are counter-intuitive. Some of those counter-intuitive findings are revolutionary; for example, we now understand the relationship between reason and emotion in learning very differently than we did just a few years ago. Others are comparatively mundane; for example, we now know that the practice of “highlighting” text as one reads is not as effective at promoting learning as pausing and interrogating oneself about what one has just learned. Taken all together, these new understandings, large and small, have revolutionized the theory and practice of learning.

At fiveDlearning, one of the most important things we do is to work to apply all these new research insights in a systematic way to maximize the ROI of our clients’ investments in human development. Our goal is to maximize learning productivity, the amount of learning achieved per unit of resource (including learner time) spent.

This is more challenging work than it may sound. These new findings in learning science are scattered across many disciplines and research organizations. They are pursued by independent researchers who are not usually terribly concerned with applying their research. Each researcher is a specialist, so the resulting findings often lack clear connections to other, potentially related findings. Many findings are validated only in a laboratory, not in real-world environments. Of those that do reach the real world, many are deployed only at small scales, so it is difficult to know how to scale them. Of those that do scale, many do so only in G7 environments, which are meaningfully different contexts than the emerging and developing economies where we primarily work.

Perhaps most challenging, new findings are released as they are found, not in any logical or coherent order. In our work, we often identify questions for which no one has yet developed good answers, while at the same time, researchers are flooding scholarly publications with refinements to answers that are already good-enough for our purposes. Overall, the field is making tremendous progress at a pace that is still accelerating. However, that broad progress is quite uneven, in terms of addressing everything one would like to know in order to maximize learning productivity.

Anyone trying to apply the latest research must find a way to impose order on this fertile scientific chaos—and to maintain that order in the face of continuing and even accelerating floods of new information. Our solution to this challenge is a conceptual learning framework we call LADDER²^TM.

The name LADDER²^TM is trademarked, but its contents are not proprietary; in fact, nothing about it is unique to fiveDlearning. LADDER²^TM is the way we convert the ongoing flood of new learning-science findings into something that is useful to our learning designers, learning materials developers, and clients.

As with many of our other tools, LADDER²^TM is an algorithm, and its name is an acronym for its steps:

LADDER²^TM stands for Label, Assess, Designate, Develop, Extend, Relate, and Rethink. Let’s look at each of those steps briefly, then discuss why we’ve put together this specific arrangement of steps from the many pieces and different arrangements that are readily available in the learning science literature:

• Step 1 is Labeling. In this important step, the learner decides what it is that they are going to learn and name, or “label” it. The step sounds simple, but there are hidden complexities: it’s possible to decide to learn many different lessons from the same learning experience.
• Step 2 is Assessing or attaching a value to what the learner is going to learn. By attaching a value, the learner’s brain realizes it’s important and focuses more effort on learning it. That doesn’t necessarily happen consciously; instead, the learner becomes more and better motivated to learn it. Improved motivation is a vital part of higher performance learning.
• Step 3 is Designating or setting an explicit learning goal. The better (clearer, more measurable, more achievable) is the learning goal, the better and faster the learner will learn. Showing learners how to set more powerful, more effective learning goals is an important step in maximizing learning productivity.
• Step 4 is Developing or practicing what one is learning. This is what most people think of as “learning:” it’s the process of acquiring the skill or knowledge or concept. There are many, many tips and techniques available to improve the speed, power, and accuracy of Developing, and new tips emerge in the research frequently. Those familiar with the field will recognize techniques such as “distributed practice,” “directed practice,” and “habit stacking,” all of which are subsumed in the Developing step or stage and all of which are used extensively in our own work. The LADDER²^TM framework helps us to guide learners to the right tips at the right moments in their learning experiences, so they can maximize their productivity.
• Step 5 is Extending, the process of applying new learning to a new challenge. This step is vital to maximizing learning productivity because it both reinforces the learning and applies it. Our job as learning designers is to make sure that the learner applies it in ways that drive real-world results, maximizing the client’s ROI as well as the learner’s productivity.
• Step 6 is Relating. Step 6 puts the learning in a broader context, relating it to other things the learner knows. As with Extending, the goal of Relating is twofold: to improve learning productivity and to drive improved client ROI.
• Step 7 is Rethinking. LADDER²^TM begins and ends with mindfulness about learning. In Step 1, learners “label” what they will learn. In Step 7, learners revisit that label and Rethink by reflecting on whether they achieved their goals for learning *and* for the learning process, and on how they can further improve their mastery of what they have learned and their learning process. This reflection is vital if learning performance is going to continue to improve over time, and is therefore essential to our Continuing Quality Improvement and Continuing Impact Improvement efforts.

Those are the seven steps of LADDER²^TM. One can find all of them in one or another cognitive-science textbook or research publication (sometimes under slightly different names). But they are not always found together; in fact, we developed and named LADDER²^TM because we couldn’t find another algorithm that combined these seven steps the way we needed.

There are at least two different things going on here. One is that learning science research, like educational innovation, is concentrated in G7 nations. As a result, it often makes unexamined assumptions that do not hold up in contexts outside the G7. In particular, many learning frameworks list only 3-4 steps instead of 7. On close inspection, it’s apparent that they assume the rest of the seven LADDER²^TM steps, but don’t feel the need to make them explicit—quite likely because the learners they study have mastered those additional steps already. Unfortunately, most of the learners with whom we work in emerging and developing economies haven’t mastered all seven steps; for example, for graduates of education systems emphasizing rote memorization, most or all of the steps except Developing (practicing) are almost entirely unfamiliar. For those learners to achieve globally competitive learning performance, they must internalize all seven steps. Making every step explicit is an important aid to that effort.

Another factor is that our learning experiences are intended to drive client ROI as well as learner productivity. Often, learning research is charmingly idealistic: researchers study learning for its own sake. Personally, we share that passion, but professionally, our job is to make sure that clients see real-world results from their learning investments. If one is learning for its own sake, the Extending and Relating steps look very different than they do if one is learning for the sake of applying what is learned. Making that difference explicit helps to remind our designers, and our learners and clients, that real-world results are at the heart of what we do.

In summary, we developed LADDER²^TM to accomplish several goals important to our work. We needed a framework that:

• Allows us to accommodate a steady stream of new research findings without needing to reinvent our whole approach to learning design each time a new finding arrived.
• Captures every element of the learning process that is needed by learners in emerging and developing economies.
• Focuses learners on applying learning in ways that drive both personal learning productivity and client ROI.
• Is easy for learners to grasp and understand, so that they can remember and use it quickly and effectively.

LADDER²^TM  is a work in progress: we update it as often as new research opens new learning possibilities, several times per year at a minimum, and we adjust even the overall framework as new research makes that desirable. We’re not committed to the concept of LADDER²^TM; in fact, we give that away for free. Instead, we’re committed to achieving the real-world human development results that drove its creation, and that continues to drive its refinement.

We welcome your thoughts and comments below. Interested in learning more? Please drop us a note at info@fivedlearning.com.