How I was seduced into trying to fix education

Driven to learn…Image by: Theo Dawson

Updated 2022–09–07

It all 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.

Image by: Theo Dawson

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. Interestingly, this was happening primarily to children attending conventional schools. Children receiving alternative instruction seemed to be more or less exempt. It appeared that something about conventional 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 loss of the love of learning I’d observed as a midwife. Both the academic research and my own lab experience exposed the early loss of students’ natural love of learning. My concern was amplified by the newly emerging trend toward high stakes multiple choice testing, which my colleagues and I saw as a further threat.

Most of the people I’ve spoken to about the early loss of students’ natural love of learning 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. I don’t think the loss of children’s natural desire to learn is a shame. I think it’s a tragedy.

So, I set out to find out why children lose their natural love of learning — 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 has been published and 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 hands-on 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 I understood what learning should look like, I 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 I originally saw as an obstacle to robust learning — was actually the solution , but only if my colleagues and I 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 primarily upon “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

Over the next few years I put together a research team to work on reinventing testing. We eventually founded Lectica, Inc., a nonprofit dedicated to this mission. We knew that reinventing educational testing to serve robust learning would require many years of R&D. In fact, we would be committing to possible decades of effort without a guaranteed result. It’s the vision of a future educational system in which all children retain the inborn drive for learning that compels us to keep moving 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, then 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’s taken over 25 years, but we’ve achieved all four goals. And while we were pursuing these goals, 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. As shown in the figure above, students in schools that focus primarily on building deep understanding (high VCoL) graduate seniors that are up to 5 years ahead (on our learning ruler, the Lectical Scale) of students in schools that focus primarily on correctness (low VCoL). After taking socioeconomic status (SES)into account), the difference drops to around 2.5 years. See paper.
2. Individual students who learn robustly today, develop faster tomorrow. See paper.
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, appear to 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 learning tools that reward and support teaching that fosters robust learning while keeping students’ love of learning alive.

Where we are today

Lectica’s mission is to foster greater individual satisfaction and fulfillment while preparing learners to meet 21st century challenges. We do this by creating and delivering learning tools and educational programs (ViP) that help people to learn the way their brains were designed to learn. And we aim to ensure that children and adolescents who need our learning tools the most get them first by providing free subscriptions to individual teachers everywhere.

To realize our mission, we have 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 adult learning sector and income from grants and donations. Our background research is rich, our methods are well-established, and our technology works even better than we thought it would. Early in 2022, we switched from human scoring to electronic scoring when CLAS, our electronic scoring system, showed itself to be more accurate than human scorers.

Today, we are more dedicated than ever to ensuring that our children are educated in a way that optimizes mental development while preserving our inborn love of learning.

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

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