 Hello, my name is Tracy Togamaspinosa and this is a video summary of a paper I wrote related to the contributions of neuroscience to early childhood education specifically related to pre-literacy and pre-numeracy skills. What I'd like to do in the short video is discuss the problems that were faced that actually led to the research questions, the methodology that was applied and to conclude with some findings as well as an invitation for your participation in the study in the future. So now for the problem as I perceive it. As someone who's directed early childhood programs, I've been faced with the question of the philosophies that are behind different types of programs. Oftentimes we have extremes, right? There are some people who profess that the philosophy that we really need to have, no structure, you know, open and free play in order to enhance creativity in children and others who push for very structured learning environments to improve self-discipline in children. And there's others that look towards developing the emotional aspect of children's personalities and helping them be more balanced in that sense and working with others in a social context. And there's others that actually push this very strong cognitive or intellectual development. My proposal here is to look for someplace in the middle. Is it possible that we can find this middle space where we can actually meet all of these different types of needs, that we can actually improve student learning outcomes and at the same time create the environment for forming better future citizens who are more balanced and caring for one another? In order to do this, I'd like to approach this from a very multifaceted perspective, which is a transdisciplinary solution that's offered by MindBrain Education Science, which is an area I've been researching for the past 10 years in which I believe holds a lot of the answers to educational problems, especially by looking at contributions from neuroscience to the education field that have as of yet not been integrated into our general teaching formats. So what are the research questions that come out of this particular problem? They're basically two global ones. First, what is out there? What information exists in educational neuroscience that has not been applied in classrooms as of yet? And second, how do our teaching practices stack up against recommended practices if we were to include more information from educational neuroscience? And this begs some several sub-questions, right? In math education, we have to look at really where the standards come from, what type of evidence is sustained, their current structures. And secondly, from neuroscience, what is it that we really know about the brain and math, and especially about little kids, because we know that those types of studies are very few and far between? Then we want to finally look at whether or not the information actually contradicts or complements current practices that we have. And if they do contradict current practices, how do we modify those practices? So the basic running hypothesis here has three different aspects to it. First, this study was inspired by an earlier study I did in 2013 for the Costa Rican government in which they presumed that there was unnecessarily high levels of school failure in grade one in the early years. They estimated as high as 17 percent, basically because there are certain neural pathways that should be primed before kids are ready to do grade one learning, and they aren't being primed in early childhood experiences. Secondly, I make the presumption that there are also just average kids who are not inspired or enthusiastic about our school settings. In part because of the order of introduction of certain types of skill sets that does not allow them to ground the information into real life experiences. So they're missing the big ideas. For example, they can do mathematical procedures, but they don't understand why they work, right? So they don't understand the big concepts behind the execution of certain skill sets. And finally, there's a presumption here that the learning trajectories, if we can actually order the introduction of certain learning skills in the right way, this could actually yield a better approach than we currently have in curriculum design for early childhood education today. So what was the methodology to apply to this study? There were basically five different large pieces to this. First, there was a large scale international view of the early childhood programs around the world and basically what is expected of children zero to six. Second, there was a large scale of a limited study of existing research on the brain and math. The primary came from the United States, France, the UK, Japan, China, and Canada. So it is international, but it doesn't cover very many countries because many countries are just not doing research in this field right now due to the high cost of technology. Third, compare the studies from information from education, information from neuroscience, and then try to see if they aligned on three different levels. One was the order of skills introduction. Second was the estimated age of a student's ability to be able to perform those different tasks. And third, regrouping by competencies, the knowledge skills and attitudes needed in four broad categories. And one was in the physiological field. The second was in cultural, social, emotional aspects of learning. Third was in general cognitive abilities. And the fourth had to do with domain specific, which is limited in this study to math and language. Fourth, I adjusted the existing learning trajectories that are recommended in the educational literature to be modified based on information from neuroscience. And fifth, tried to create a real functioning tool, which ends up being a rubric for observation by early childhood teachers. To understand these steps a little bit better, I'm going to go into a teeny bit of detail trying to compare what is said in education versus what is said in neuroscience. So in early mass education, we have the difficulty that national standards in general begin more or less from about six years of age onward. We don't have the one-year-olds, two-year-olds, three-year-olds. We don't have a lot of specifications of what should happen in those contexts. The information that we have about recommended programs or what are best practices or what is real quality in early childhood education has a lot more to do with affective standards or self-regulation, general cognitive abilities and executive functions, as opposed to specific domain area achievement, for example, in math or in learning how to read or write. What does exist, however, are learning trajectories. And this is something that's built up over the past decade to have quite a lot of literature behind it to explain the hierarchies of learning competencies in distinct areas. For example, in math, some of the most prolific writers are Clemens and Sarama who have come up with a general learning trajectories model. And this includes 13 different aspects of early childhood learning related to math. For example, how a child learns to count over the span of one to eight years old or how they learn to compare and order information or to recognize numbers and subsidizing how do we compose numbers or add or subtract or multiply, divide. How does a child learn how to measure or form geometric shapes or to understand or develop their spatial sense and understanding of motion and then to be able to compare physical shapes. And then more broadly, an understanding of patterning and early algebra and then classifying as well as analyzing data. All of these different things were considered in a learning trajectories approach and this was back in 2004. In 2009, there was a study conducted to actually see the results of such an application and it was found that there was a very significant improvement in learning for children who did follow a learning trajectories models. So what we can find is that conclusions from an educational review of the literature and what is recommended in this example of math, for example, is that basically Piuget was right. It's great that there's still quite a lot of strong evidence in favor of a constructivist design or learning trajectories, which actually create a hierarchy of skill sets that children need to learn. The second big conclusion that can be drawn from this literature is that starting earlier is better and we know that in all areas, not just in this math example that we saw, but there's also substantial and robust literature in language, second language is music and sports that says that the earlier a child begins to rehearse certain skill sets, the better off they are. But this is also true in the other three domains that we talked about. Physiologically speaking, the earlier we can detect problems with hearing, vision or other disabilities, the sooner or the quicker we begin intervention, the less long-term problems that the individual will have. We also know that sensitizing children earlier to cultural, social, emotional aspects of their own learning and of their environments also leads to better learning outcomes from that perspective. And also general cognitive abilities, improving memory, attention and executive functions starting in the earlier years yields better results than beginning to work on these skills after a child has entered schools. We know that these two big ideas, constructivism and early starts, you can say with a lot of confidence this is something that we should be rescuing from existing current structures and then trying to complement them with information from neuroscience. So what do we know from neuroscience? We would hope that they would tell us how math occurs in the brain, but really what they're only showing us is where math is in the brain. So early studies, studying more or less around 2003, we have some great information from Stanis Le Sehani, looked at something called a triple code where he found that the concept of, for example, the number three is actually stored in different pathways depending on whether or not the three is written as an Arabic symbol, as a word, or as an object. So we know that there's different neural pathways related to categorizing numbers in the brain. We also know that mental math processing, for example, adding or subtracting in your mind is located in pretty much a specific area if and when these skill sets are refined and it's done well. You'll see, we'll see a little bit later what happens when it's not done correctly or when there's atypical development of the brain. We also know that there's studies related to magnitude, size estimation, is this set bigger than that set? There's a lot of great studies that have been produced by Kant-Langen colleagues in Piassa. Jacobs have done quite a lot of studies that thankfully these multiple studies are showing similar pathways. So we're not far off and we can have great confidence in the information that's being produced because the studies have been replicated enough that we can actually see that these particular areas of the brain create pathways that are vital to, for example, a magnitude estimation. There's also studies that've been done on numerical cognition, on multiplication, spatial manipulation, and all of these things, the conclusions that can be drawn from these types of studies is, once again, Piaget is still right. We're looking at a different kind of constructivism and this is called neuroconstructivism. One of the studies that actually summarizes this very, very nice and neatly and elegantly is Sir Royce's work in 2008 that basically tells us that, yes, in the brain there are certain types of basic pathways that need to be structured before we can do higher order thinking. So we know that there's an orderly way that the brain is actually craving to learn information and that can actually lead to more efficient learning because we've actually created the fundamental structure upon which we can build more and more complex types of learning. This is very interesting and we can also see this in brain scans and when we look at novice to expert changes in the brain when somebody is learning how to read, their brain is super active all over the place but once they've learned to read it becomes much more refined. Similarly, when they learn a new aspect of mathematical thinking, their brain is very, very disorganized in the way that it's looking for answers but once it's refined itself and has a higher level of understanding of the information where you're able to see more refined areas actually incorporated and not such a haphazard, let's try this angle, let's try this other angle. This is really, really important to consider because this can actually lead to this general presumption that a careful sequential ordering of a hierarchy of skills can actually contribute to a better understanding of information at least in math and language and by better I mean it can be probably more efficient and probably less painful to children. If these core ideas are introduced and those concepts are understood well before a child moves on to higher order learning. This is a real big problem, especially with things like math because there is a very clear hierarchy of order of introduction of concepts and what tends to happen is some kids miss out on some core concepts and then we try to teach higher order concepts and they don't have a strong foundation upon which they can build this new learning. This point is emphasized even further by other types of studies that have to do with the effects of stress on learning. Math anxiety, for example. Math anxiety takes over your brain and the level of stress that children can experience and that will actually cut off their abilities and this has to do with neurotransmitters in the brain, it doesn't have to do with just a psychological idea, it's a physiological thing. It will impede new learning of concepts. So we know also from other types of studies from John Hattie's work, a student's own self perception of himself as a learner has a great influence on whether or not they actually do learn. So if a child experiences failure, for example, in math learning, reinforces idea that, oh, he's no good at this, I'm not able to do this and this is a self-fulfilling prophecy. So we have to get our heads around the idea that there are a lot of things that we have to look at not only in the cognitive realm but also in the affective realm when it comes down to early childhood learning processes. So all of this information is important but it's pure conjecture because while there are a lot of studies about math in the brain, there are very few studies on math in the brain and education. So none of the studies that I just showed you here actually tell us how to teach better. So this is where we're having to make this big leap where we're connecting the two pieces of information. So when we try to bridge the information from education to neuroscience, how can we take what we know and make a better model for them what we have right now? What we tried to do in this particular study is to look at the distinct neural pathways that exist. We know that there are networks and there are pathways and then there are brain areas that are related to those different parts and to look at those different networks and to see how they can be reinforced over time with different types of practice. And so basically the schematic breakdown of the study that I looked into is that whereas in education you might consider language, this big domain area that we presume that children are going to learn within our formal school structures, right? But language is broken down into several different pieces. It can be reading, writing, speaking, listening. There's a lot of sub-pieces there. But if we take one of those sub-pieces which would be a general network and we have reading, then we have several different pathways that are related to that. So in order to be able to read well, you have to have word recognition, you have to know spelling, you have to understand intonation. And each one of those things breaks down into different observable behavior. In order to be able to have clear word recognition, a child can sound at a word based on basic phonemic understanding, that would be an observable behavior, right? What we have here with the pathways then would be actually how our brain is connecting all of those sub-pieces here in a physiological way. We can actually see this in different types of brain scans when we actually see somebody learning a new word. So for example, during word learning there are documented circuits, for example, this is by Davis and colleagues showing how the word is perceived through an auditory pathway and then how it is actually reviewed and checked for semantic understanding and then comes back and a child can actually say, okay, I can read this word, I know what it means. So when we talk about being able to get from this general global idea, what we have typically done in education is talked about these academic domains, these sub-areas, and then we jumped to the conclusion that we can have observable behavior. What this study has tried to do is add on these neural pathways that are important in actually looking at their hierarchy of what needs to come first, second, and third in order to suggest a correct order for introduction of those skill sets. Many of you are familiar with the terminology of a synapse. These are the connections between groups of neurons and the connections that are made. They are create neural pathways, these pathways. Then when they're grouped together we'll create a network in the brain. And this network in the brain is actually very important because when we talk about things that we want kids to do, we want them to be able to read, or we want them to be able to understand quantity, or we want them to understand patterning, it's not as simple as that. It's not a single thing. There are many, many multiple pathways that have to contribute towards the building up of that particular network. And all of those things to make those new connections need different types of activities in our classrooms. So this means then, if we go back and we've revisited our original hypothesis there's an unnecessary level of children's failure and we also see that kids are not interested in the classroom settings many times because they cannot grasp or because we've skipped steps for them, they're not getting those big ideas. So this means that there's a shift in the order of introduction and or in the age of introduction that needs to occur in order for our curriculum to take hold and to be more effective. So how do we do this? We can link the science of education to neuroscience through a trajectories-based or a learning hierarchies model. Then we would have a new way to approach early childhood education in a form that would probably be more efficient. So what did I do? I laid out information in basically what would be a developmental stage related to school ages, what the equivalency is from zero to six years old, the different factors that are involved and then the network that was being described by the pathways here. Now the pathways were basically broken down by neuroscientific evidence, whereas the educational criteria, what is this observable behavior that would match to this? What would that look like? So we looked for multiple areas of the brain that were stimulated in order to define these new pathways and then we looked at multiple sources of information that confirmed that yes, these are descriptors that would be acceptable to say that a child has actually achieved that milestone of learning. So this created changes to the past models that we have related to trajectories of learning in two different ways. Mainly, there were some slight changes that occurred in the order of the skills and also it created a different type of grouping of skill sets that might be more palatable to teachers. In general, the main changes that we could see is there was actually things that we had considered higher order understanding and things we have to do when a child is much older could actually be introduced far earlier because there was a natural sequence of learning that was being ignored in general early childhood classrooms. So the basic monoline proposing here is based on 16 neural networks. This is specifically the mathematical explanation. There's another video related to the literacy explanation. And these were divided into four broad categories. This has to do with the physiological aspects of learning, the cultural, social, emotional aspects of learning, the general cognitive aspects of learning and the cognitive domains. And I'm afraid that we don't have enough time to go into a whole lot of detail, but just to give you a sense of how intricate and how detailed the information is, we know that there are distinct neural pathways for example, for determining pitch, then there are from tempo and tone and prosody and loudness and background of foreground music. And in fact, the brain actually distinguishes between speech and non-speech sounds. So there's a lot of sub elements to just hearing that we need to take into consideration to ensure that our primed well for children to be able to learn early math skills. In the second category, there are things that are related to cultural, social and emotional aspects of learning that have to do with the greater broader context that a person lives within. And also what's going on inside of an individual as far as the relationships and their contact with other individuals. So there are general cognitive skills that are needed for all learning. If you don't have memory, you can't learn. If you don't have a tangent, you can't learn. So these things are fundamental aspects to learning as are executive functions, which have to do with your ability to inhibit other information and to think more flexibly in a cognitive sense. We know that there are aspects of mathematical learning that have to do with quantity, initial number sense, that have to do with coding of symbols, as well as understanding of symbols in different forms. We also understand that a child needs to understand a concept of order, ordinarily and cardinality before that they can learn higher order math. They need to be able to decipher patterns and understand how patterns are formed. They need to be able to understand categorization and classification of information, as well as the relationships that exist between numerical quantities as well as numerical symbols. All of these different aspects review them in the context of neuroscience, psychology, and education, and confirming them that we had physiological, social, emotional, and cognitive elements taken into consideration. So in reviewing the literature, which was nearly 2,000 different studies, we come up with this idea that hopefully can translate now into usable knowledge. We converted the established neural pathways into observable behavioral indicators. I decided to divide them by age because this is the current situation that we live within, divided by a hierarchy of skills within the age group. So what does this look like? Basically we have a developmental stage, the typical age range. Which of the four areas? Is this physiological? Is it emotional? Is it general cognitive? Or is it domain specific? And then the network that's being stimulated by doing this. And the general description, which would be the pathway that was followed. And then how does this look in a classroom? So the idea would be to decide whether or not a child has, well, there's no real evidence that this is occurring or he has a beginning understanding of this or he seems pretty proficient in this or he really dominates the skillset. He is able to do all of these different things. So I broke down all of the 171 indicators based on these 114 pathways, which were based on the 16 neural networks, and laid them out in a rubric form. This has us at a benefit of being done in a backward design concept. So where we have a clear objective, this is what we're trying to achieve. We want to know if kids can actually classify different characteristics because we know that it's a fundamental base for later learning of different types of mathematical skillsets. So here is my evaluation criteria. And then now it leaves the teacher very free and creative to actually come up with what types of activities then. Can you do in the classroom that actually give you this kind of evidence so that we can measure progress towards that objective? There's an added advantage of using backwards design structure, which is actual differentiation. Once we have those clear objectives designed and the evaluation criteria set, this means that we can differentiate the activities that we do in class in order to measure the way a child reaches that particular objective. So the main benefits of this change in this model have to do with precision. It's based on science. It respects this hierarchy of complexities of the learning trajectories that have been established. It responds apparently to how the brain organizes itself as far as neuro-constructivism is concerned. It appears to be a university applicable. It's based on the union of studies that are found from around the world. So it does not have this cultural bias that it's only gonna work in American classrooms, for example. And it offers improved precision with which one can identify gaps in a child's knowledge. We're able to remediate those skills with a better precision as well. Final benefit that I would say is that rubrics are familiar to teachers. It's not a new tool. This is something any teacher could actually apply and use. And it offers a more precise diagnostic of where a child actually is, permitting a teacher then to create activities that remediate any types of skills that might be missing. The implications of this model at a teacher's level, it actually has us rethink some of the activities that we're doing within our classroom setting. Okay, not everything is beautiful and rosy. There are criticisms of this structure. And there's at least six that I can identify. One is that as the focus was on specific math and pre-literacy skills, the general cognitive abilities were not pulled apart as far as they needed to be. And so they were clumped into general age groupings. We know that those things can actually be refined further and they need to be refined further. Second, we know that there are different types of tools. So we're mixing and matching the tools that are used, for example, in neuroscientific studies. We don't know if other types of imaging would yield different types of results. So that's something I'll also explore. Third, the vocabulary that's being used in neuroscience can be quite confusing on two different levels. Depending on if the study's coming out of Europe or out of Asia or out of North America, there's different coding schemes that are preferred. So some people are still using broadman's areas. Other people are using the desk in atlas and other people are using physical placement or areas of the brain. This leads to the second problem is that oftentimes when an individual, when we list these areas of the brain that are being stimulated, it looks like quite a long list, but it actually could be reduced probably about 30% in each case when we eliminate the repetition. The fourth, we're mixing and matching observation, observational studies with neuroimaging studies. And so there's a big doubt here about the ecological validity of somebody lying in an fMRI machine, responding to math questions lying on their back versus sitting in a class or pencil and paper answering of those same questions. So I would have a dream that someday we would do something similar to what John Haddy did in education and be able to compare these rather distinct looking studies by methodological form in a united vision. But up to the current date, there is no such thing. And fifth and finally, while priority was always given to studies that looked at small children zero to six years old, they're very far and few between. And that, and we also know that a lot of the information has to do with damaged brains, things that, you know, this piece is missing. Therefore, we think this piece is important as opposed to actually confirming that in small children, this area of the brain is actually used. That is still something that we don't have. These types of criticisms were echoed. For example, by answers work, we don't have very many studies of kids under six years old. So that makes us question the validity of much of the literature that we're looking at. And the second point has to do with over generalization of the findings. There are a lot of promises related to the cognitive neuroscientific contributions towards mathematics learning. So we have to actually be able to take all of this with a grain of salt and not try to extend the findings beyond their true limitations. And there are several right now we have to look at. I have to remain totally optimistic that on the balance of things, despite all of these potential criticisms and the problems that might occur within this rubric, the combination of this information is yielding a better tool. And it seems evident just from the preliminary study that was done in Costa Rica and Ecuador that the typical teaching practices at least in our context in Latin America are not stimulating as needed those neural networks. And finally, I would have to say given our original research questions, to what extent really are teaching practices, actually taking the best of this information and being able to apply it, at the least it is highly probable that some parts of early math failure are due to an incorrect stimulation. These are activity class or an incorrect introduction at age level, appropriate of information related to math and early literacy skills. So this creates a lot of new opportunities. The tool hasn't been tested yet. So we need you to help out using the rubric in your own context. We might find that some of the neural pathways are missing. If we disaggregate, for example, memory, attention, executive functions even further, could it be that it's gonna be a distinct neural pathways found there? So there might be things added here. While most of these pathways are identified in multiple studies, they still could be wrong, right? So we need to confirm and continue to nurture our information with new studies that continue to come out. While the pathways might be correct, maybe they have been placed in an incorrect age group. And finally, we don't even know whether or not teachers are going to use this rubric. Is a rubric that has all of these elements too complex? Is this too much to ask a really childhood teacher? I don't think so, but it's something that might come up. And the final thing, which I think is the most creative element of all of this, is that there's no complete list of activities that we might suggest and test in the classroom to actually see if we are able to stimulate these different neural pathways in the correct way to actually create a better foundation for children to have math success in the future. So what are our next steps? We need to apply the rubrics. We need to reflect on changes in teaching practices that might be new, different adding or subtracting different types of activities that we already do in class. And then we need to, after we've done those first two steps, come up with a recommended suggestion of good practice. So I invite you to join me in doing this. I think that the real legitimacy of any of these tools has to do with its cross-cultural applications. So if we can find friends in different parts of the world who are willing to apply this, I think that would be a great next step. Thank you very much.