World Economic Forum—tomorrow’s skills

The top 10 workplace skills of the future.

Sources: Future of Jobs Report, WEF 2017

In a recent blog post—actually in several recent blog posts—I've been emphasizing the importance of building tomorrow's skills. These are the kinds of skills we all need to navigate our increasingly complex and changing world. While I may not agree that all of the top 10 skills listed in the World Economic Forum report (shown above) belong in a list of skills (Creativity is much more than a skill, and service orientation is more of a disposition than a skill.) the flavor of this list is generally in sync with the kinds of skills, dispositions, and behaviors required in a complex and rapidly changing world.

The "skills" in this list cannot be…

  • developed in learning environments focused primarily on correctness or in workplace environments that don't allow for mistakes; or
  • measured with ratings on surveys or on tests of people's ability to provide correct answers.

These "skills" are best developed through cycles of goal setting, information gathering, application, and reflection—what we call virtuous cycles of learning—or VCoLs. And they're best assessed with tests that focus on applications of skill in real-world contexts, like Lectical Assessments, which are based on a rich research tradition focused on the development of understanding and skill.

 

Learning how to learn or learning how to pass tests?

how to learnI've been auditing a very popular 4.5 star Coursera course called "Learning how to learn." It uses all of the latest research to help people improve their learning skills. Yet, even though the lectures in the course are interesting and the research behind the course appears to be sound, I find it difficult to agree that it is a course that helps people learn how to learn.

First, the tests used to determine how well participants have built the learning skills described in this course are actually tests of how well they have learned vocabulary and definitions. As far as I can tell, no skills are involved other than the ability to recall course content. This is problematic. The assumption that learning vocabulary and definitions builds skill is unwarranted. I believe we all know this. Who has not had the experience of learning something well enough to pass a test only to forget most of what they had learned shortly thereafter?

Second, the content in tests at the end of the videos aren't particularly relevant to the stated intention of the course. These tests require remembering (or scrolling back to) facts like "Many new synapses are formed on dendrites." We do not need to learn this to become effective learners. The test item for which this is the correct answer is focused on an aspect of how learning works rather than how to learn. And although understanding how learning works might be a step toward learning how to learn, answering this question correctly doesn't tell us how the participant understands anything at all.

Third, if the course developers had used tests of skill—tests that asked participants to show off how effectively they could apply described techniques, we would be able to ask about the extent to which the course helps participants learn how to learn. Instead, the only way we have to evaluate the effectiveness of the course is through participant ratings and comments—how much people like it. I'm not suggesting that liking a course is unimportant, but it's not a good way to evaluate its effectiveness.

Fourth, the course seems to be primarily concerned with fostering a kind of learning that helps people do better on tests of correctness. The underlying and unstated assumption seems to be that if you can do better on these tests, you have learned better. This assumption flies in the face of several decades of educational research, including our own [for example, 1, 2, 3]. Correctness is not adequate evidence of understanding or real-world skill. If we want to know how well people understand new knowledge, we must observe how they apply this knowledge in real-world contexts. If we want to evaluate their level of skill, we must observe how well they apply the skill in real-world contexts. In other words, a course in learning how to learn—especially a course in learning how to learn—should be building useable skills that have value beyond the act of passing a test of correctness.

Fifth, the research behind this course can help us understand how learning works. At Lectica, we've used the very same information as part of the basis for our learning model, VCoL+7. But instead of using this knowledge to support the status quo—an educational system that privileges correctness over understanding and skill—we're using it to build learning tools designed to ensure that learning in school goes beyond correctness to build deep understanding and robust skill.

For the vast majority of people, schooling is not an end in itself. It is preparation for life—preparation with tomorrow's skills. It's time we held our educational institutions accountable for ensuring that students know how to learn more than correct answers. Wherever their lives take them, they will do better if equipped with understanding and skill. Correctness is not enough.

 


[1] FairTest; Mulholland, Quinn  (2015). The case against standardized testing. Harvard Political Review, May 14.

[2] Schwartz, M. S., Sadler, P. M., Sonnert, G. & Tai, R. H. (2009). Depth versus breadth: How content coverage in high school science courses relates to later success in college science coursework. Science Education, 93, 5, 798-826.

[3] Kontra, C., Goldin-Meadow, S., & Beilock, S. L. (2012). Embodied learning across the lifespan. Topics in Cognitive Science, 4, 4, 731–739.

 

Lectica’s story: long, rewarding, & still unfolding


Lectica's story started in Toronto in 1976…

Identifying the problem

During the 70s and 80s I practiced midwifery. It was a great honor to be present at the births of over 500 babies, and in many cases, follow them into childhood. Every single one of those babies was a joyful, driven, and effective "every moment" learner. Regardless of difficulty and pain they all learned to walk, talk, interact with others, and manipulate many aspects of their environment. They needed few external rewards to build these skills—the excitement and suspense of striving seemed to be reward enough. I felt like I was observing the "life force" in action.

Unfortunately as many of these children approached the third grade (age 8), I noticed something else—something deeply troubling. Many of the same children seemed to have lost much of this intrinsic drive to learn. For them, learning had become a chore motivated primarily by extrinsic rewards and punishments. Because this was happening primarily to children attending conventional schools (Children receiving alternative instruction seemed to be exempt.) it appeared that something about schooling was depriving many children of the fundamental human drive required to support a lifetime of learning and development—a drive that looked to me like a key source of happiness and fulfillment.

Understanding the problem

Following upon my midwifery career, I flirted briefly with a career in advertising, but by the early 90's I was back in school—in a Ph.D. program in U. C. Berkeley's Graduate School of Education—where I found myself observing the same pattern I'd observed as a midwife. Both the research and my own lab experience exposed the early loss of students' natural love of learning. My concern was only increased by the newly emerging trend toward high stakes multiple choice testing, which my colleagues and I saw as a further threat to children's natural drive to learn.

Most of the people I've spoken to about this problem have agreed that it's a shame, but few have seen it as a problem that can be solved, and many have seen it as an inevitable consequence of either mass schooling or simple maturation. But I knew it was not inevitable. Children and those educated in a range of alternative environments did not appear to lose their drive to learn. Additionally, above average students in conventional schools appeared to be more likely to retain their love of learning.

I set out to find out why—and ended up on a long journey toward a solution.

How learning works

First, I needed to understand how learning works. At Berkeley, I studied a wide variety of learning theories in several disciplines, including developmental theories, behavioral theories, and brain-based theories. I collected a large database of longitudinal interviews and submitted them to in-depth analysis, looked closely at the relation between testing and learning, and studied psychological measurement, all in the interest of finding a way to support childrens' growth while reinforcing their love of learning.

My dissertation—which won awards from both U.C. Berkeley and the American Psychological Association—focused on the development of people's conceptions of learning from age 5 through 85, and how this kind of knowledge could be used to measure and support learning. In 1998, I received $500,000 from the Spencer Foundation to further develop the methods designed for this research. Some of my areas of expertise are human learning and development, psychometrics, metacognition, moral education, and research methods.

In the simplest possible terms, what I learned in 5 years of graduate school is that the human brain is designed to drive learning, and that preserving that natural drive requires 5 ingredients:

  1. a safe environment that is rich in learning opportunities and healthy human interaction,
  2. a teacher who understands each child's interests and level of tolerance for failure,
  3. a mechanism for determining "what comes next"—what is just challenging enough to allow for success most of the time (but not all of the time),
  4. instant actionable feedback, and 
  5. the opportunity to integrate new knowledge or skills into each learner's existing knowledge network well enough to make it useable before pushing instruction to the next level. (We call this building a "robust knowledge network"—the essential foundation for future learning.)*

Identifying the solution

Once we understood what learning should look like, we needed to decide where to intervene. The answer, when it came, was a complete surprise. Understanding what comes next—something that can only be learned by measuring what a student understands now—was an integral part of the recipe for learning. This meant that testing—which we originally saw as an obstacle to robust learning—was actually the solution—but only if we could build tests that would free students to learn the way their brains are designed to learn. These tests would have to help teachers determine "what comes next" (ingredient 3) and provide instant actionable feedback (ingredient 4), while rewarding them for helping students build robust knowledge networks (ingredient 5).

Unfortunately, conventional standardized tests were focused on "correctness" rather than robust learning, and none of them were based on the study of how targeted concepts and skills develop over time. Moreover, they were designed not to support learning, but rather to make decisions about advancement or placement, based on how many correct answers students were able to provide relative to other students. Because this form of testing did not meet the requirements of our learning recipe, we'd have to start from scratch.

Developing the solution

We knew that our solution—reinventing educational testing to serve robust learning—would require many years of research. In fact, we would be committing to possible decades of effort without a guaranteed result. It was the vision of a future educational system in which all children retained their inborn drive for learning that ultimately compelled us to move forward. 

To reinvent educational testing, we needed to:

  1. make a deep study of precisely how children build particular knowledge and skills over time in a wide range of subject areas (so these tests could accurately identify "what comes next");
  2. make tests that determine how deeply students understand what they have learned—how well they can use it to address real-world issues or problems (requires that students show how they are thinking, not just what they know—which means written responses with explanations); and
  3. produce formative feedback and resources designed to foster "robust learning" (build robust knowledge networks).

Here's what we had to invent:

  1. A learning ruler (building on Commons [1998] and Fischer [2006]);
  2. A method for studying how students learn tested concepts and skills (refining the methods developed for my dissertation);
  3. A human scoring system for determining the level of understanding exhibited in students' written explanations (building upon Commons' and Fischer's methods, refining them until measurements were precise enough for use in educational contexts); and 
  4. An electronic scoring system, so feedback and resources could be delivered in real time.

It took over 20 years (1996–2016), but we did it! And while we were doing it, we conducted research. In fact, our assessments have been used in dozens of research projects, including a 25 million dollar study of literacy conducted at Harvard, and numerous Ph.D. dissertations—with more on the way.

What we've learned

We've learned many things from this research. Here are some that took us by surprise:

  1. Students in schools that focus on building deep understanding graduate seniors that are up to 5 years ahead (on our learning ruler) of students in schools that focus on correctness (2.5 to 3 years after taking socioeconomic status into account).
  2. Students in schools that foster robust learning develop faster and continue to develop longer (into adulthood) than students in schools that focus on correctness.
  3. On average, students in schools that foster robust learning produce more coherent and persuasive arguments than students in schools that focus on correctness.
  4. On average, students in our inner-city schools, which are the schools most focused on correctness, stop developing (on our learning ruler) in grade 10. 
  5. The average student who graduates from a school that strongly focuses on correctness is likely, in adulthood, to (1) be unable to grasp the complexity and ambiguity of many common situations and problems, (2) lack the mental agility to adapt to changes in society and the workplace, and (3) dislike learning. 

From our perspective, these results point to an educational crisis that can best be addressed by allowing students to learn as their brains were designed to learn. Practically speaking, this means providing learners, parents, teachers, and schools with metrics that reward and support teaching that fosters robust learning. 

Where we are today

Lectica has created the only metrics that meet all of these requirements. Our mission is to foster greater individual happiness and fulfillment while preparing students to meet 21st century challenges. We do this by creating and delivering learning tools that encourage students to learn the way their brains were designed to learn. And we ensure that students who need our learning tools the most get them first by providing free subscriptions to individual teachers everywhere.

To realize our mission, we organized as a nonprofit. We knew this choice would slow our progress (relative to organizing as a for-profit and welcoming investors), but it was the only way to guarantee that our true mission would not be derailed by other interests.

Thus far, we've funded ourselves with work in the for-profit sector and income from grants. Our background research is rich, our methods are well-established, and our technology works even better than we thought it would. Last fall, we completed a demonstration of our electronic scoring system, CLAS, a novel technology that learns from every single assessment taken in our system. 

The groundwork has been laid, and we're ready to scale. All we need is the platform that will deliver the assessments (called DiscoTests), several of which are already in production.

After 20 years of high stakes testing, students and teachers need our solution more than ever. We feel compelled to scale a quickly as possible, so we can begin the process of reinvigorating today's students' natural love of learning, and ensure that the next generation of students never loses theirs. Lectica's story isn't finished. Instead, we find ourselves on the cusp of a new beginning! 

Please consider making a donation today.

 


A final note: There are many benefits associated with our approach to assessment that were not mentioned here. For example, because the assessment scores are all calibrated to the same learning ruler, students, teachers, and parents can easily track student growth. Even better, our assessments are designed to be taken frequently and to be embedded in low-stakes contexts. For grading purposes, teachers are encouraged to focus on growth over time rather than specific test scores. This way of using assessments pretty much eliminates concerns about cheating. And finally, the electronic scoring system we developed is backed by the world's first "taxonomy of learning," which also serves many other educational and research functions. It's already spawned a developmentally sensitive spell-checker! One day, this taxonomy of learning will be robust enough to empower teachers to create their own formative assessments on the fly. 

 


*This is the ingredient that's missing from current adaptive learning technologies.

 

Adaptive learning. Are we there yet?

Adaptive learning technologies are touted as an advance in education and a harbinger of what's to come. But although we at Lectica agree that adaptive learning has a great deal to offer, we have some concerns about its current limitations. In an earlier article, I raised the question of how well one of these platforms, Knewton, serves "robust learning"—the kind of learning that leads to deep understanding and usable knowledge. Here are some more general observations.

The great strength of adaptive learning technologies is that they allow students to learn at their own pace. That's big. It's quite enough to be excited about, even if it changes nothing else about how people learn. But in our excitement about this advance, the educational community is in danger of ignoring important shortcomings of these technologies.

First, adaptive learning technologies are built on adaptive testing technologies. Today, these testing technologies are focused on "correctness." Students are moved to the next level of difficulty based on their ability to get correct answers. This is what today's testing technologies measure best. However, although being able to produce or select correct answers is important, it is not an adequate indication of understanding. And without real understanding, knowledge is not usable and can't be built upon effectively over the long term.

Second, today's adaptive learning technologies are focused on a narrow range of content—the kind of content psychometricians know how to build tests for—mostly math and science (with an awkward nod to literacy). In public education during the last 20 years, we've experienced a gradual narrowing of the curriculum, largely because of high stakes testing and its narrow focus. Today's adaptive learning technologies suffer from the same limitations and are likely to reinforce this trend.

Third, the success of adaptive learning technologies is measured with standardized tests of correctness. Higher scores will help more students get into college—after all, colleges use these tests to decide who will be admitted. But we have no idea how well higher scores on these tests translate into life success. Efforts to demonstrate the relevance of educational practices are few and far between. And notably, there are many examples of highly successful individuals who were poor players in the education game—including several of the worlds' most, productive and influential people.

Fourth, some proponents of online adaptive learning believe that it can and should replace (or marginalize) teachers and classrooms. This is concerning. Education is more than the accumulation of facts, it has an enormous impact on socialization. Ideal learning environments offer abundant opportunities to build skills for engaging and working with diverse others. 

Lectica has a strong interest in adaptive learning and the technologies that deliver it. We anticipate that over the next few years, our assessment technology will be integrated into adaptive learning platforms to help expand their subject matter and ensure that students are building robust, usable knowledge. We will also be working hard to ensure that these platforms are part of a well-thought out, evidence-based approach to education—one that fosters the development of tomorrow's skills—the full range of skills and knowledge required for success in a complex and rapidly changing world.

Four keys to optimizing learning & development

There are four keys to optimizing learning and development and ensuring that it continues over a lifetime. 

  1. Don't cram content. Learning doesn't work optimally when it is rushed or when learners are over-stressed. In Finland, students only go to school three 6-hour days a week, rarely have homework, and do better on PISA than students anywhere else in the world. (Unfortunately, PISA primarily measures correctness, but it's the best international metric we have at present.) Their educational system is focused on building students' knowledge networks. Students don't move on to the next level until they master the current level. The Fins have figured out what our research shows—stuffing content has the long-term effect of slowing or halting development, while a focus on building knowledge networks leads to a steeper learning trajectory and a lifetime of learning and development.

     

     

  2. Focus on the network. To learn very large quantities of information, we must effectively recruit System 1 (the fast unconscious brain). System 1 makes associations. (Think of a neural network.) When we learn content through VCoL, we network System 1, connecting new content to already networked content in a way that creates a foundation for what comes next. This does not happen robustly without VCoL, which builds and solidifies the network through application/practice and reflection. System 1 can handle vast amounts of information and processes it rapidly. It serves us well when we learn well.
  3. Make reflection a part of every learning moment. People cannot reason well about things they don't understand well. When we foster deep understanding through VCoL (and the +7 skills), we recruit System 2 (the slow reasoning brain) to consciously shape the creation and modification of connections in System 1—ensuring that our network of knowledge is growing in a way that mirrors "reality." The constant practice of analytical and reflective skills not only builds a robust network, but also increases our capacity for making reasonable connections and inferences and enhances our mental agility and capacity for making useful intuitive "leaps." We learn to think by thinking—and we think better when we have a robust knowledge network to rely on.
  4. Educate the whole person. We believe that education should focus on the development of the entire human being. This means supporting the development of competent, compassionate, aware, and attentive human beings who work well with others. A good way to develop these qualities is through embedded practices that foster interpersonal awareness and skill, such as collaborative or shared learning. These practices provide another benefit as well. They tend to excite emotions that are known to enhance learning.

 

The rate of development

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.  

growth trajectories lifespan Growth trajectories over the lifespan

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.  

Test results are not tidy

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.

Report card showing jagged growth 

Factors that increase the rate of development

  • The test-taker's current developmental trajectory. (A person whose history places her on the green curve in the first two graphs is unlikely to jump to the blue curve.)
  • The amount of reflective activity (especially VCoLing) the individual typically engages in (no reflective activity, no growth)
  • Participation in deliberate learning activities that include lots of reflective activity (especially VCoLing)
  • Participating in supported learning (coaching, mentoring) after a long period of time away from formal education (can create a spurt)

 

VCoL & flow: Can Lectical Assessments increase happiness?

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:

  • Challenge kids—but not too much. 
  • Make assignments relevant to students’ lives.
  • Encourage choice, feed interest.
  • Set clear goals (and give feedback along the way).
  • Offer hands on activities.

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:

Transformative & embodied learning

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:

  1. Both are based on developmental mechanisms (reflecting abstraction, assimilation, accommodation, hierarchical integration, chunking, qualitative change, and emergence) that were the hallmarks of Piagetian and Neo-Piagetian theory. The jargon and applications may be different, but the fundamental ideas are very similar.
  2. Both are strongly influenced by the work of Habermas (communicative action) and Freire (critical pedagogy).
  3. Both lead to a pedagogy that emphasizes the role of critical reflection and perspectival awareness in high quality learning. 
  4. Both emphasize the involvement of the whole person in learning.
  5. Both transcend conventional approaches to learning.

Here are some differences I've identified so far:

  1. Terminology: Overcoming this problem requires pretty active perspective seeking!
  2. Role of critical reflection: For us, critical reflection is both a habit of mind to cultivate (In VCoL+7, it's one of the +7 skills) and a step in every (conscious) learning cycle (the "reflect" step). I'm not sure how this is viewed in Transformative learning circles.
  3. Target: We have two learning/development targets, one is meta, the other is incremental. Our meta target is long-term development, including the major transformations that take place between levels in our developmental model. Our incremental target is the micro-learning or micro-development that prepares our neural networks for major transformations. 
  4. Measurement: As far as I can tell, the metrics used to study transformative learning are primarily focused on the subjective experience of transformation. We take a different approach by measuring the way in which learning experiences change our conceptions or the way in which we approach real-world problems. We don't ask what people think or what they learned, we ask how they think with what they learned.

How to teach critical thinking: make it a regular practice

We've argued for years that you can't really learn critical thinking by taking a critical thinking course. Critical thinking is a skill that develops through reflective practice (VCoL). Recently, a group of Stanford scientists reported that a reflective practice approach not only works in the short term, but it produces "sticky" results. Students who are routinely prompted to evaluate data get better at evaluating data—and keep evaluating it even after the prompts are removed. 

Lectica is the only test developer that creates assessments that measure and support this kind of learning.

Support from neuroscience for robust, embodied learning

Human connector, by jgmarcelino from Newcastle upon Tyne, UK, via Wikimedia Commons

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 learninga 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.