Thank you so much for uploading this talk. This is such a rare commodity we teachers might have to contemplate on the implication of research result. God bless you and I would appreciate if you continue this talk program. Please continue. Many thanks from Korea.
I took a 'Neuroscience of Education' class from one of the paper authors a few years before this came out (not the PD described in the paper, but a regular graduate education class), and it included neuroscience basics and the review of several neuroscience/education journal articles. I don't know that it changed any of my specific teaching practices afterwards, but one big benefit was that I believe it made me a much better, more informed consumer of "brain-based", "brain-friendly", and "neuroeducation" materials that I encountered afterwards. So if the only thing I got out of was a better background for being skeptical about the marketing use of a "neuro-", that was still useful :)
One of the important ways neuroscience has shaped by teaching is the research of number processing in the brain. I always begin new math concepts by having learning experiences that use geometric representations because I purposely leverage the findings that magnitude is innate in the mammalian brain. Furthermore, the math lessons that I design use the same manipulatives to represent different concepts. This is to reduce cognitive load when students learn new concepts and also create a cognitive network that is spatially and symbolically consistent. Neuroscience also informs me what are the best sensorimotor experiences that I should provide to my students( a spatial). I know from Dynamic Skill Theory that these hands-on experiences provide an important foundation for authentic learning. I also make sure the learning experiences have a clear goal, action, and feedback for the student. I keep in mind prior knowledge and cognitive load to analyze this part. And also that the best type of feedback is one provided by their environment because it creates Flow, therefore engaging the student. I could add here the importance of neuroscience and cognitive work on attention for learning to occur. Another model I keep in mind is Dr. Fuster’s Cognits or cognitive network. The framework connects sensorimotor experiences with working memory, long term memory, and action-feedback. Further informing me of the bridge between pedagogy, the mind, and the brain. That all of these processes physically occur in the brain tells me that my learning design is on the right track. Seeing learning as a dynamic system model helps me understand that this process will look different when the context changes i.e. different concepts, different grade levels, or different subjects. This implies that in addition to understanding how the brain and mind construct meaning, I need to understand my subject to correctly chunk and order. These important details will come from experience and content knowledge. Furthermore, the true test of learning is transferring a skill into a new context. I see this transfer as creating and connecting a deeper cognitive network. I understand that due to the synaptic nature of learning(synaptic connections have to be made and made stronger), it will take diverse types of practice and time for transferring of skill to occur. Therefore measuring student progress with one type of assessment provides an illusion of understanding. In short, the Brain, Mind, and Education paradigm provides a deeper level of understanding than retrieval practice is useful or hands-on activity are helpful.
A more integrative lens and understand that education is a complex system of human interaction(peer, learner and teacher), and that a derivative approach to research in education leaves only vague answers. While there is merit in using neuroscience to dismiss neuromyths, there is more to gain by systematically connecting and integrating different levels of knowledge. I agree with their implication that cognitive science is the middle man between neuroscience and education. But cognitive science like education has subjective elements, therefore neuroscience helps in selecting what elements of cognitive science to use. For example, the retrieval practice they cite as a cognitive model is directly tied to neuroplasticity. In our Brain U class we directly talked about this point. So here you have a direct path from brain to mind to pedagogy. Continuing their example of retrieval practice the models of Mind, Brain, and Education perspective poses interesting research questions. For example, if the brain uses a spatial circuit to understand numbers then retrieval practice based on geometric representation should be more effective than symbolic representation when it comes to understanding numbers. What matters for this hypothesis is not the details of how molecules interact in the brain but that they form neural networks with hubs. Neural networks are the physical part of cognitive networks. But more importantly for the hypotheses posed is that educators can use their experience and knowledge that is directly linked to neurocognitive models to identify hubs of cognition. The implications of understating learning from this perspective are immense.
Thank you so much for uploading this talk. This is such a rare commodity we teachers might have to contemplate on the implication of research result. God bless you and I would appreciate if you continue this talk program. Please continue. Many thanks from Korea.
I took a 'Neuroscience of Education' class from one of the paper authors a few years before this came out (not the PD described in the paper, but a regular graduate education class), and it included neuroscience basics and the review of several neuroscience/education journal articles. I don't know that it changed any of my specific teaching practices afterwards, but one big benefit was that I believe it made me a much better, more informed consumer of "brain-based", "brain-friendly", and "neuroeducation" materials that I encountered afterwards.
So if the only thing I got out of was a better background for being skeptical about the marketing use of a "neuro-", that was still useful :)
One of the important ways neuroscience has shaped by teaching is the research of number processing in the brain. I always begin new math concepts by having learning experiences that use geometric representations because I purposely leverage the findings that magnitude is innate in the mammalian brain. Furthermore, the math lessons that I design use the same manipulatives to represent different concepts. This is to reduce cognitive load when students learn new concepts and also create a cognitive network that is spatially and symbolically consistent.
Neuroscience also informs me what are the best sensorimotor experiences that I should provide to my students( a spatial). I know from Dynamic Skill Theory that these hands-on experiences provide an important foundation for authentic learning. I also make sure the learning experiences have a clear goal, action, and feedback for the student. I keep in mind prior knowledge and cognitive load to analyze this part. And also that the best type of feedback is one provided by their environment because it creates Flow, therefore engaging the student. I could add here the importance of neuroscience and cognitive work on attention for learning to occur.
Another model I keep in mind is Dr. Fuster’s Cognits or cognitive network. The framework connects sensorimotor experiences with working memory, long term memory, and action-feedback. Further informing me of the bridge between pedagogy, the mind, and the brain. That all of these processes physically occur in the brain tells me that my learning design is on the right track. Seeing learning as a dynamic system model helps me understand that this process will look different when the context changes i.e. different concepts, different grade levels, or different subjects. This implies that in addition to understanding how the brain and mind construct meaning, I need to understand my subject to correctly chunk and order. These important details will come from experience and content knowledge. Furthermore, the true test of learning is transferring a skill into a new context. I see this transfer as creating and connecting a deeper cognitive network. I understand that due to the synaptic nature of learning(synaptic connections have to be made and made stronger), it will take diverse types of practice and time for transferring of skill to occur. Therefore measuring student progress with one type of assessment provides an illusion of understanding.
In short, the Brain, Mind, and Education paradigm provides a deeper level of understanding than retrieval practice is useful or hands-on activity are helpful.
A more integrative lens and understand that education is a complex system of human interaction(peer, learner and teacher), and that a derivative approach to research in education leaves only vague answers. While there is merit in using neuroscience to dismiss neuromyths, there is more to gain by systematically connecting and integrating different levels of knowledge. I agree with their implication that cognitive science is the middle man between neuroscience and education. But cognitive science like education has subjective elements, therefore neuroscience helps in selecting what elements of cognitive science to use. For example, the retrieval practice they cite as a cognitive model is directly tied to neuroplasticity. In our Brain U class we directly talked about this point. So here you have a direct path from brain to mind to pedagogy. Continuing their example of retrieval practice the models of Mind, Brain, and Education perspective poses interesting research questions. For example, if the brain uses a spatial circuit to understand numbers then retrieval practice based on geometric representation should be more effective than symbolic representation when it comes to understanding numbers. What matters for this hypothesis is not the details of how molecules interact in the brain but that they form neural networks with hubs. Neural networks are the physical part of cognitive networks. But more importantly for the hypotheses posed is that educators can use their experience and knowledge that is directly linked to neurocognitive models to identify hubs of cognition. The implications of understating learning from this perspective are immense.