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In neuroscience, individual differences refer to variations in learners’ cognitive, emotional, and social development due to genetic and sociocultural/environmental factors.¹ Genetic factors are inherited from parents and are largely non-modifiable, while sociocultural/environmental factors are modifiable and are often influenced by geographical location, parental support, economic status, substance use/abuse, abuse, and more.¹ It is well established that genetic predisposition strongly influences basic brain structure and functioning, but an increasing number of research articles are now highlighting the importance of environmental and social factors on brain structure, function, and development.1 The brain's ability to adapt to environmental stimuli is central to individual learning differences. "Experience-dependent plasticity" allows individuals to develop skills and knowledge based on their unique lived experiences, such as academic learning, physical activities, cultural influences, etc. There is strong evidence that children who are raised in areas that lack proper sensory, cognitive, or social-emotional stimulation develop differently from those of their peers who grow up I more “enriched” environment.¹ This directly combats the notion that low-income city children (many of which who are minorities) are “dumb/stupid”.² In fact, researchers have concluded brain maturation is negatively affected by poverty. The complex relationship between a student’s sociocultural/environment and predefined genetic composition, together are responsible for brain structure, function, development, plasticity, language skills and executive function.¹ How students are nurtured (particularly when young) from parents, teachers, and other influences later dictate how students receive, process, retain, and apply information which lead to distinct learning needs, strengths, and challenges.¹ When parents and educators understand these factors, they can create more inclusive learning experiences that promote learning for a larger segment of the student body.

References:

1. Jolles J, Jolles DD. On Neuroeducation: Why and How to Improve Neuroscientific Literacy in Educational Professionals. Frontiers in Psychology. 2021;12. doi:https://doi.org/10.3389/fpsyg.2021.752151

2. Moran, S. (2024, November 10). Are poor kids really stupid? LinkedIn. https://www.linkedin.com/pulse/poor-kids-really-stupid-sean-moran/

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The Cognitive Load Theory (CLT) is based on knowledge of human cognitive architecture “including the limitations of the working memory, how information is organized in long-term memory (LTM), and how the two memory structures are correlated.”₁ Working memory is the limiting factor for learning because it is limited in both duration and capacity. The CLT surmises that the working memory load cannot surpass its limited capacity during a learning task.₁ There are three types of cognitive load defined by the CLT including (1) intrinsic cognitive load – based on the difficulty of the learning material or task, (2) extraneous cognitive load – information introduced via instructional design that is not needed to master the material, and (3) germane cognitive load – created by learning activities that are beneficial to the development of knowledge structures within long-term memory.₂

The CLT has a significant impact on learning, particularly in relation to instructional design to facilitate effective learning by reducing the required cognitive load. This theory is the basis for formative and summative assessments, micro-learning, and varying instruction to address different learning styles which optimize instructional design.₁ This is achieved through reducing the extraneous load, tailoring the intrinsic load to the learner’s developmental stage, and promoting germane load.₁ Intrinsic load can be managed by progressing from less difficult to more difficult material in a stepwise fashion, progressing from low to high physical fidelity such as paper-based scenarios before simulations, and isolating elements by presenting only some of the information/elements of a task to a novice.₁ Extraneous load can be decreased by performing completion tasks, goal-free tasks, and worked examples; integrating various data sources; utilizing various modalities; decreasing redundancy; and minimizing interruptions and distractions.₁ The germane load can be optimized through “augmenting variability over tasks, implementing contextual interference, encouraging strategies facilitating the production of a precise patient mental model, including prior knowledge activation, performance monitoring, and understanding and compare/contrast, and evoking self-explanation.”₁ While these instructional design strategies can be used to minimize cognitive load and optimize learning, it is important to remember that cognitive loads can change with time and the students experiencing equal intrinsic and extraneous load can experience vastly different stages of germane load due to their effort, motivation, and metacognitive skills.₁ The CLT can be used to inform instructional design for health professions education in order to optimize learning and reduce overall cognitive load and stress.

References:

1.      Ghanbari, S., Haghani, F., Barekatain, M., & Jamali, A. (2020). A systematized review of cognitive load theory in health sciences education and a perspective from cognitive neuroscience. Journal of Education and Health Promotion, 9, 176. 10.4103/jehp.jehp_643_19

2.      Timothy, V., Fischer, F., Watzka, B., Girwidz, R., & Stadler, M. (2023). Applying cognitive load theory in teacher education – An experimental validation of the scale by Leppink et al. Psychological Test Adaptation and Development, 4, 246-256. https://doi.org/10.1027/2698-1866/a000052

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Neuroscience research on attention mechanisms supports learning theories that stress the importance of engaging learners and maintaining their focus to enhance learning outcomes⁸.

⁸: Applications of Cognitive Neuroscience in Educational Research

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The role of feedback in learning is emphasized in both behaviorist theories and neuroscience, showing that feedback can strengthen neural connections⁷.

⁷: Educational neuroscience: bridging theory and practice

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Neuroscience identifies critical periods during which the brain is particularly receptive to learning certain skills, such as language, supporting early childhood education theories⁶.

⁶: The Neuroscience of Learning | SpringerLink

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Neuroscience research highlights the variability in brain function and structure among individuals, supporting differentiated instruction and personalized learning approaches⁵.

⁵: Frontiers | On Neuroeducation: Why and How to Improve Neuroscientific Literacy in Educational Professionals

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Neuroscience has demonstrated that emotions significantly impact learning and memory, aligning with learning theories that incorporate emotional and social aspects⁴.

⁴: Learning from a Neuroscience Perspective | SpringerLink

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The theory that the brain has a limited capacity for processing information is supported by neuroscience findings. Effective instructional design aims to manage cognitive load to optimize learning.³

³: Applications of Cognitive Neuroscience in Educational Research

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Neuroscience provides insights into how memories are formed, stored, and retrieved, which is crucial for understanding learning processes. This is discussed in various educational neuroscience texts².

²: The Neuroscience of Learning | SpringerLink

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Research has shown that the brain’s ability to change and adapt, known as neuroplasticity, is fundamental to learning. This concept is supported by studies in educational neuroscience¹.

¹: Educational neuroscience: bridging theory and practice

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Emotional impact on learning is a concept that is being researched to explore the association between emotion, brain function, and learning. The article details three areas that are being researched in this domain; reinforcement and conditioning, Hebbian learning, and feature extraction

Reinforcement and conditioning: Focuses on strengthening the associations between stimuli and response. Pavlov's theory associates learning with strengthening the association between an unconditional stimulus that is rewarding to a conditioned stimulus that is neutral. Therefore, "learning pertains to an increase in the frequency of the response that accompanies a satisfying result and a decrease in those that have an unpleasant outcome."¹

Hebbian learning: Focuses on the connections between neurons, the rate at which action potentials are produced, that then lead to sensory stimuli. To summarize my understanding of Hebbian theory, an increase in neurons leads to an increase in synaptic weight. Which in turn results in repetitive firing patterns that further strengthen the neuronal connection to the stimuli.

Feature extraction: This research refers to neurons having unique functional characteristics known as a receptive field. Neurons in these fields respond at a higher spiking rate in relation to a particular stimuli. This results in them being "feature extractors" in that they selectively respond to specific patterns.

Resource:

Babadi, B. (2021). Learning from a Neuroscience Perspective. In: Ikeuchi, K. (eds) Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-63416-2_823

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Hi Jamal,

Great summary on how individual differences can significantly impact learning. It's crucial for educators to recognize that learning is shaped not just by genetics but also by the environment in which a student is immersed. The ongoing debate between nature and nurture is complex, but it's clear that while genetics play a role, the 'nurture' aspect, which can encompassing social, emotional, and educational factors, can have an equally profound influence on how a student learns and excels. Understanding the role each plays in a students learning allows educators to better support students and create environments that foster growth.