This is your brain on education – how an understanding of the human brain can give us insights into best approaches to teaching and learning
Bibliographic record
Abstract
The human brain defines who we are, how we perceive and process the world around us, how we interact with each other and our environment. The human brain has the unique ability for interpretation, imagination, and creativity. The implications of these functions are profound for how we learn and an understanding of how the brain processes and responds to information can lead to new approaches in education. The cortex plays a huge role in all of our cognitive processes – extensive work has been done to map the cortex and areas that are important for learning. From a more generalist perspective, while the temporal cortex is important for the recognition of people, objects, and patterns, the prefrontal cortex is the site of our reasoning and our imagination. The question for educators is how do we motivate and engage students to learn? How do we capture their attention? Subcortical structures such as the limbic system are important for affective‐motivational processing and memory formation, the basal ganglia integrate the sum of our cortical activity into one cortical – cognitive and behavioral – output. Finally, the cerebellum helps automate processes in our brains. Together, these are some of the neuroanatomical substrates and networks that need to be engaged for an effective approach to education. Learning requires a multi‐faceted approach and each aspect of learning will engage neural networks that will process, interpret, remember and forget the information or situation we are presented with. Mindful pedagogical planning can engage the brain on both a cognitive‐procedural level as well as an affective‐motivational level, which leads to better learning outcomes: we remember what we care about. The human brain is not a static organ and the neuroplasticity that occurs during learning can give us insights into what constitutes a truly transformative learning experience. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".