Lectica's tools and services have powerful applications for every process in the human capital value chain. I explain how in the following video.
Lectica's tools and services have powerful applications for every process in the human capital value chain. I explain how in the following video.
An individual's rate of development is affected by a wide range of factors. Twin studies suggest that about 50% of the variation in Lectical growth trajectories is likely to be predicted by genetic factors. The remaining variation is explained by environmental factors, including the environment in the womb, the home environment, parenting quality, educational quality & fit, economic status, diet, personal learning habits, and aspects of personality.
Each Lectical Level takes longer to traverse than the previous level. This is because development through each successive level involves constructing increasingly elaborated and abstract knowledge networks. Don't be fooled by the slow growth, though. A little growth can have an important impact on outcomes. For example, small advances in level 11, can make a big difference in an individual's capacity to work effectively with complexity and change.
The graphs above show possible learning trajectories, first, for the lifespan and second, for ages 10-60. Note that the highest age shown on these graphs is 60. This does not mean that individuals cannot develop after the age of 60.
The yellow circle in each graph represents a Lectical Score and the confidence interval around that score. That's the range in which the "true score" would most likely fall. When interpreting any test score, you should keep the confidence interval in mind.
When we measure development over short time spans, it does not look smooth. The kind of pattern shown in the following graph is more common. However, we have found that growth appears a bit smoother for adults than for children. We think this is because children, for a variety of reasons, are less likely to do their best work on every testing occasion.
When we create a Lectical Assessment, we make a deep (and never ending) study of how the skills and knowledge targeted by that assessment develop over time. The research involves identifying key concepts and skills and studying their evolution on the Lectical Scale (our developmental scale). The collaboration continuum has emerged from this research.
As it applies to decision making, the collaboration continuum is a scale that runs from fully autocratic to consensus-based. Although it is a continuum, we find it useful to think of the scale as having 7 relatively distinct levels, as shown in the table below:
|Level||Basis for decision||Applications||Limitations|
|Fully autocratic||personal knowledge or rules, no consideration of other perspectives||everyday operational decisions where there are clear rules and no apparent conflicts||quick and efficient|
|Autocratic||personal knowledge, with some consideration of others' perspectives (no perspective seeking)||operational decisions in which conflicts are already well-understood and trust is high||quick and efficient, but spends trust, so should be used with care|
|Consulting||personal knowledge, with perspective-seeking to help people feel heard||operational decisions in which the perspectives of well-known stakeholders are in conflict and trust needs reinforcement||time consuming, but can build trust if not abused|
|Inclusive||personal knowledge, with perspective seeking to inform a decision||operational or policy decisions in which the perspectives of stakeholders are required to formulate a decision||time consuming, but improves decisions and builds engagement|
|Compromise-focused||leverages stakeholder perspectives to develop a decision that gives everyone something they want||making "deals" to which all stakeholders must agree||time consuming, but necessary in deal-making situations|
|Consent-focused||leverages stakeholder perspectives to develop a decision that everyone can consent to (even though there may be reservations)||policy decisions in which the perspectives of stakeholders are required to formulate a decision||can be efficient, but requires excellent facilitation skills and training for all parties|
|Consensus-focused||leverages stakeholder perspectives to develop a decision that everyone can agree with.||decisions in which complete agreement is required to formulate a decision||requires strong relationships, useful primarily when decision-makers are equal partners|
As the table shows, all 7 forms of decision making on the collaboration continuum have legitimate applications. And all can be learned in any adult developmental level. However, the most effective application of each successive form of decision making requires more developed skills. Inclusive, consent, and consensus decision making are particularly demanding, and consent decison-making requires formal training for all participating parties.
The most developmentally advanced and accomplished leaders who have taken our assessments deftly employ all 7 forms of decision making, basing the form chosen for a particular situation on factors like timeline, decision purpose, and stakeholder characteristics.
(The feedback in our LDMA [leadership decision making] assessment report provides learning suggestions for building collaboration continuum skills. And our Certified Consultants can offer specific practices, tailored for your learning needs, that support the development of these skills.)
Last week, I received an inquiry about the relation between flow states (Csikszentmihalyi & colleagues) and the natural dopamine/opioid learning cycle that undergirds Lectica's learning model, VCoL+7. The short answer is that flow and the natural learning cycle have a great deal in common. The primary difference appears to be that flow can occur during almost any activity, while the natural learning cycle is specifically associated with learning. Also, flow has been associated with neurochemicals we haven't (yet?) incorporated in our conception of the natural learning cycle. We'll be tracking the literature to see if research on these neurochemicals suggests modifications.
The similarity between flow states and the dopamine/opioid learning cycle are numerous. Both involve dopamine (striving & focus) and opioids (reward). And researchers who have studied the role of flow in learning even use the term "Goldilocks Zone" to describe students' learning sweet-spot—the place where interest and challenge are just right to stimulate the release of dopamine, and where success happens just often enough to trigger the release of opioids (which stimulate the desire for more learning, to start the cycle again).
Since psychologist Mihalyi Csikszentmihalyi began his studies of flow, it has been linked to feelings of happiness and euphoria, and to peak performance among workers, scientists, athletes, musicians, and many others. Flow has also been shown to deepen learning and support interest.
Flow is gradually making its way into the classroom. It's featured on UC Berkeley's Greater Good site in several informative articles designed to help teachers bring flow into the classroom.
"Teachers want their kids to find “flow,” that feeling of complete immersion in an activity, where we’re so engaged that our worries, sense of time, and self-consciousness seem to disappear."
Advice for stimulating flow is similar to our advice for teaching and learning in the Goldilocks Zone, and includes suggestions like the following:
If you've been following our work, these suggestions should sound very familiar.
All in all, the flow literature provides additional support for the value of our mission to deliver learning tools that help teachers help students learn in the zone.
Here are a few links to additional information:
Our learning model, the Virtuous Cycle of Learning and its +7 skills (VCoL+7) is more than a way of learning—it's a set of tools that help students build a relationship with knowledge that's uniquely compatible with democratic values.
Equal opportunity: In the company of good teachers and the right metrics, VCoL makes it possible to create a truly level playing field for learning—one in which all children have a real opportunity to achieve their full learning potential.
Freedom: VCoL shifts the emphasis from learning a particular set of facts, vocabulary, rules, procedures, and definitions, to building transferable skills for thinking, communicating, and learning, thus allowing students greater freedom to learn essential skills through study and practice in their own areas of interest.
Pursuit of happiness: VCoL leverages our brain's natural motivational cycle, allowing people retain their inborn love of learning. Thus, they're equipped not only with skills and knowledge, but with a disposition to adapt and thrive in a complex and rapidly changing world.
Citizenship: VCoLs build skills for (1) coping with complexity, (2) gathering, evaluating, & applying information, (3) perspective seeking & coordination, (4) reflective analysis, and (5) communication & argumentation, all of which are essential for the high quality decision making required of citizens in a democracy.
Open mindset: VCoLs treat all learning as partial or provisional, which fosters a sense of humility about one's own knowledge. A touch of humility can make citizens more open to considering the perspectives of others—a useful attribute in democratic societies.
All of the effects listed here refer primarily to VCoL itself—a cycle of goal setting, information gathering, application, and reflection. The +7 skills—reflectivity, awareness, seeking and evaluating information, making connections, applying knowledge, seeking and working with feedback, and recognizing and overcoming built in biases—amplify these effects.
VCoL is not only a learning model for our times, it could well be the learning model that helps save democracy.
I'm not sure I buy the argument that reason developed to support social relationships, but the body of research described in this New Yorker article clearly exposes several built-in biases that get in the way of high quality reasoning. These biases are the reason why learning to think should be a much higher priority in our schools (and in the workplace).
I'm frequently asked about the relation between transformative learning and what we, at Lectica, call robust, embodied learning.
According to Mezirow, there are two kinds of transformative learning, Learning that transforms one's point of view and learning that transforms a habit of mind.
Transforming a point of view: This kind of transformation occurs when we have an experience that causes us to reflect critically on our current conceptions of a situation, individual, or group.
Transforming a habit of mind: This is a more profound and less common kind of transformation that occurs when we become critically reflective of a generalized bias in the way we view situations, people, or groups. This kind of transformation is less common and more difficult than a transformation of point of view and occurs only after several transformations in point of view.
Embodied learning occurs through natural and learned virtuous cycles in which we take in new information, apply it in some way, and reflect on outcomes. The natural cycles occur in a process Piaget referred to as reflective abstraction. The learned process, which we call VCoL (for virtuous cycle of learning) deliberately reproduces and amplifies elements of this unconscious process, incorporating conscious critical reflection as part of every learning cycle. These acts of critical reflection reinforce connections that are affirmed (or create new connections) and prune connections that are negated. Virtuous learning cycles, both conscious and unconscious, incrementally build a mental network that not only connects ideas, but also different parts of the brain, including those involved in motivation and emotion.
Learning through intentional virtuous cycles ensures that our mental network is constantly being challenged with new information, so alterations to point of view are possible any time we receive information that doesn't easily fit into the existing network. But this kind of learning is also part of a larger developmental process in which our mental networks undergo major reorganizations called hierarchical integrations that produce fundamental qualitative changes in the way we think.
Here are some of the similarities I see between transformative learning and our learning model:
Here are some differences I've identified so far:
Fluid intelligence Connectome
For many years, we've been arguing that learning is best viewed as a process of creating networks of connections. We've defined robust learning as a process of building knowledge networks that are so well connected they allow us to put knowledge to work in a wide range of contexts. And we've described embodied learning—a way of learning that involves the whole person and is much more than the memorization of facts, terms, definitions, rules, or procedures.
New evidence from the neurosciences provides support for this way of thinking about learning. According to research recently published in Nature, people with more connected brains—specifically those with more connections across different parts of the brain—demonstrate greater intelligence than those with less connected brains—including better problem-solving skills. And this is only one of several research projects that report similar findings.
Lectica exists because we believe that if we really want to support robust, embodied learning, we need to measure it. Our assessments are the only standardized assessments that have been deliberately developed to measure and support this kind of learning.
During the last 20 years, children in our public schools have been required to learn important concepts earlier and earlier. This is supposed to speed up learning. But we, at Lectica, are finding that when students try to learn complex ideas too early, they don’t seem to find those ideas useful.
For example, let's look at the terms reliable, credible, and valid, which refer to different aspects of information quality. These terms used to be taught in high school, but are now taught as early as grade 3. We looked at how these terms were used by over 15,000 students in grades 4-12. These students were asked to write about what they would need to know in order to trust information from someone making a claim like, "Violent television is bad for children."
As you can see in the following graph, until grade 10, fewer than 10% of these students used the terms at all—even though they were taught them by grade 5. What is more, our research shows that when these terms are used before Lectical Level 10 (see video about Lectical Levels, below), they mean little more than “correct” or “true”, and it's not until well into Lectical Level 10 that people use these terms in a way that clearly shows they have distinct meanings.
Children aren't likely to find the words reliable, valid, or credible useful until they understand why some information is better than other information. This means they need to understand concepts like motivation, bias, scientific method, and expertise. We can get 5th graders to remember that they should apply the word "valid" instead of "true" when presented with a specific stimulus, but this is not the same as understanding.
Reliable, valid, and credible aren't the only words taught in the early grades that students don't find useful. We have hundreds of examples in our database.
The pattern above is what we see when students are taught ideas they aren't yet prepared to understand. When children learn ideas they're ready for—ideas that are in "the zone"—the pattern looks very different. Under these conditions, the use of a new word quickly goes from zero to frequent (or even constant, as parents of 4-year-olds know only too well). If you're a parent you probably remember when your child first learned the words "why," "secret," or "favorite." Suddenly, questioning why, telling and keeping secrets, or having favorites became the focus of many conversations. Children "play hard" with ideas they're prepared to understand. This rapidly integrates these new ideas into their existing knowledge networks. But they can't do this with an idea they aren't ready for, because they don't yet have a knowledge network that's ready to receive it.
The curve shown in the figure above shows what it would look like if these terms were taught when students were more prepared with knowledge networks that were ready to receive them. Acquisition would be relatively rapid, and students would find the terms more useful because they would be more likely to grasp aspects of their distinct meanings. For example, they might choose to use the term "reliable" rather than "factual" because they understand that these two terms mean different things.
If you're a parent, think about how many times your child is asked to learn something that isn’t yet useful. Consider the time invested, and ask yourself if that time was well spent.
In a fully developed Lectical Assessment, we include separate measures of aspects of arguments such as mechanics (spelling, punctuation, and capitalization), coherence (logic and relevance), and persuasiveness (use of evidence, argument, & psychology to persuade). (We do not evaluate correctness, primarily because most existing assessments already concern themselves primarily with correctness.) When educators use Lectical Assessments, they use information about Lectical Level, mechanics, coherence, persuasiveness, and sometimes correctness to diagnose students' learning needs. Here are some examples:
This student has relatively high Lectical, mechanics, and correctness scores, but their performance is low in coherence and the persuasiveness of their answers is average. Because lower coherence and persuasiveness scores suggest that a student has not yet fully integrated their new knowledge, this student is likely to benefit most from participating in activities that require them to apply their existing knowledge in relevant contexts (using VCoL).
This student's scores, with the exception of their correctness score, are high relative to expectations. This students' knowledge appears to be well integrated, but the combination of average persuasiveness and low correctness suggests that there are gaps in their content knowledge relative to targeted content. Here, we would suggest filling in the missing content knowledge in a way that integrates it into this students' well-developed knowledge network.
The scores received by this student are high for correctness, while they are average for mechanics, and low for Lectical Level, coherence, and persuasiveness. This pattern suggests that the student is memorizing content without integrating it effectively into his or her knowledge network and has been doing this for some time. This student is most likely to benefit from applying their existing content knowledge in personally relevant contexts (using VCoL) until their coherence, persuasiveness, and Lectical scores catch up with their correctness scores.