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Record W4394575368 · doi:10.3389/feduc.2024.1342473

The brain on playdo: neuroscience in education

2024· article· en· W4394575368 on OpenAlexaffabout
Kim Calder Stegemann

Bibliographic record

VenueFrontiers in Education · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsNeuroscienceComputer scienceCognitive sciencePsychology

Abstract

fetched live from OpenAlex

If a parent of one of your students asked you to explain what is going on in the head of their child, what would you say? You may panic and sputter something about "left brain -right brain" learning, or that a brain that is fed good nutrition is a brain that can learn, or maybe that most people only use 10% of their brain (a false neuro-myth, by the way). If you are like I was, you wouldn't be able to say much about the brain functioning of their child.ButThough teachers are master observers who strive to create strong student-teacher relationships. However, behavioural observations only tell us so much. You might say, well, I can also do some standardized testing to figure out a bit more about how the child is processing information in their brain. That, too, only gets us so far. We need to go deeper and yet, education hasn't steppededucators are reluctant to step up to the neuroscience plate. As Dr. Daniel Amen (2005) would say, we are among the only professions that never look at the organ that we deal with. Why is it that teachers don't consider the brain? There are several reasons for this, but I would argue that educators absolutely must begin to understand the brain and how it is impacted by teaching and learning.Teacher training programs don't typically address the brain. Even educational psychology courses fail to adequately discuss the brain and how it relates to affect, body states, and self-regulation. And yetYet, many provincial curricula specifically talk about self-regulation to adjust brain and body states. For example, in one province in Canada, British Columbia, the curriculum aims for students to develop "healthy personal practices" and "understand that physical, emotional, and mental health are interconnection" (Province of British Columbia, 2019, p.interconnected" ("Physical and HealthEducation K-10 -Big Ideas Grade K-1" 2019, p. 1). What do physical, emotional, and mental health all have in common? The brain. Healthy brains support all-over health and wellness. As Rueda (2020) noted, it is a closed loop where optimal learning leads to optimal brain functioning, which is essential, and leading back to optimal learning. Therefore, the brain and how it functions, or doesn't function, is relevant to every teacher, regardless of their curriculum specialty. Some, such as Dr. Stephen Campbell, founder of the ENGRAMMETRON educational neuroscience lab at the Simon Fraser University in Canada, would contend that it is essential for teachers to learn about neuroscience and the brain to maintain agency within education (Campbell, 2011). His fear is that educational neuroscience will be dominated by scientists and neurologists, with little input from educators. Then, all research and treatment would be driven by the scientists and not educators, and the knowledge generated remainswould remain clinical and potentially not practical, translatable, or useable. This outcome has been one of the barriers to educators stepping up to the neuroscience plate. Interdisciplinary collaboration (in this case neuroscience and education) is challenging, and well documented by others (see Brown and Daly 2016;Palghat, Horvath, and Lodge 2017). As Bruer (1997) posited so, it is simply a "bridge too far" (p.4).There is a solution to this and that is developing a discipline of educational neuroscience where teaching and learning is informed by neuroscience. At the very least, teacher training programs must include educational neuroscience in their curriculum. If teachers are better informed about the brain/body/behaviour connection, they are less likely to believe neuromyths (Dekker et al 2012; Torrijos-Muelas González-Villora, and Bodoque-Osma 2021), such as "right-brain / left-brain learning". Additional neuroeducation also leads to more positive attitudes for teachers dealing with students with complex needs (Chang et al. 2021;Gola et al. 2022). Inservice teachers can bridge the knowledge gap by reading peer-reviewed publications or taking graduate courses in educational neuroscience (Torrijos-Muelas González-Villora, and Bodoque-Osma 2021). Tan and colleagues (Amiel and Tan 2019; Tan & Amiel 2022) have demonstrated how collaborative action research enhanced teacher knowledge and application of neuroscience concepts.Another solution to the "bridge" common in interdisciplinary collaborations is to embed scientists in schools, jointly researching how neuroscience informs the learning and teaching process. One example of collaboration between educational neuroscientists and teachers, is the Synapse School in California which is connected to Stanford University's Educational Neuroscience Initiative. They created the Brainwave Learning Center within the school, and their educational neuroscientists play an integral part of the day-to-day functioning (White, 2023). Director Lyn Toomarian notes that there "has always been this … separation between neuroscientists studying the way kids learn and the places where kids are actually learning….[but] we've been able to integrate the two" (White, 2023, p. 4). It is a superan excellent example of bringing neuroscience into the school. and successful interdisciplinary collaboration.However, even if you don't have educational neuroscientists in your school, there are many other reasons to stand up and pay attention to the brain.Science and education hashave come a long way from "right-brain, left-brain". Everyday, teachers are changing the brains of their students, and makingnow we have the technology to see how pedagogical choices that impactimpacts the brain in different ways (Brault Foisy et al.,. 2020) (electroencephalogram). This isn't science fiction! Imagine that you would be able to determine the best teaching methods for a student based on their brain activity! ). Using EEG technology (electroencephalogram), Another example of adapting pedagogy/curriculum based on neuroscientific data relates to printing and handwriting. Though many primary schools have removed formal printing/handwriting instruction from the curriculum, James (2017) found the importance of handwriting for brain development which specifically supports learning to read. Further, research has also revealed distinct phenotypes or biomarkers of brain activity that are directly related to learning and emotional behaviours. That is, by looking at brain activity, we can identify or anticipate learning or emotional challenges that a student may experience (Xiao et al., in press). Further, there are numerous technologies available, and even more emerging, that can alter brain activity, such as the Muse ( 2023) which teaches the user to calm the brain and body. Why would we leave this type of intervention to non-educators? . 2023). There are specific applications for special education by identifying where in the brain cognitive processes are breaking down or may be bottlenecked (Kropotov 2016). If educators understood more about the brain and why it does or doesn't learn, they would be optimally situated to guide interventions and seek appropriate pedagogy. To do this, we absolutely must learn about basic brain functioning.Let me provide a few examples of how neuroscience in education could improve our practice and learning for our students. As stated above, one example is identifying reading programs that are best suited to an individual's brain functioning (Yoncheva, Wise, and McCandliss, 2015). This isn't science fiction! Imagine that you would be able to determine the best teaching methods for a student based on their brain activity! Another example of how neuroscience could benefit education would be to identify where in the brain cognitive processes are breaking down. It is possible to identify areas in the brain where processes may be bottlenecked (Kropotov, 2016). This would help to remove the trial-and-error process for intervening. Yet another application of neuroscience in education could be to measure brain health throughout a child's education, and in particular, if students are involved in physically demanding contact sports. Using EEGs, we could measure brain health in our student athletes at the beginning and end of each sport season, which is vitally important should they sustain any head injuries (ThatcherThanjavur et al., 2001(ThatcherThanjavur et al., . 2021)). Finally, another application of neuroscience in education is simpler and more direct. Students, themselves, can learn about their own brain and body functioning, and acquire appropriate strategies to self-regulate. Afterall (Goldberg 2022;Moreno & Schulkin, 2020). After all, isn't this one of our main goals as educators, and which reflects the demands of the curriculum, as stated at the outset of this paper?There are many realities that our students encounter, including digital technology. Numerous devices are available that can alter brain activity, such as the Muse ("Science | Muse TM EEG-Powered Meditations & Sleep Headband," n.d.) which teaches the user to calm the brain and body, or more radically, a brain chip to implant memories ("Neuroscience News Science Magazine-Research Articles-Psychology Neurology BrainAI" n.d.). In addition, the use or misuse of gaming (Swingle, 2019), social media, virtual reality, and online learning (Firth et al. 2019; see also Tokuhama-Espinosa 2021) will impact the developing brain. Educators need to know the impact in order to appropriately adjust pedagogy and policies. Why would we leave these types of applications to non-educators? If educators understood more about the brain and why it does or doesn't learn, they would be optimally situated to guide interventions and seek appropriate pedagogy. Again, to do this, we absolutely must learn about basic brain functioning.As we plunge into this new reality, we are right to be cautious. Indeed, there are numerous ethical issues to consider. One of these issues relates to the use and security of the biometric brain data collected ("Guidelines for Practice | ISNR | Neurofeedback Training and Resources," n.d.). Another is using technology as interventions, and the need to research the long-term impacts of devices such as the Muse, neurofeedback (Thibault, Lifshitz, and Raz 2016) or other brain stimulation technologies, on the developing brain. Fortunately, the IEEE (Frankston et al. n.d.) is working to develop a neuro-ethics framework for use in education and other disciplines, as a beginning point to guide our plunge.My challenge to you would be to learn as much as you can about the brain now, despite a potentially steep learning curve. You can do this by enrolling in a course in educational neuroscience, reading peer-reviewed journals, or by finding out more about the work of neurotherapists and how they can complement the teaching and learning process. We can no longer ignore what is going on inside the heads of our students, and more importantly, we have the technology to do it!

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.536

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.299
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2024
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