Neuroscience-Based Learning Design Guidelines
I. Communicate to learners that while each enters an educational context with different knowledge, skills, and abilities resulting from diverse life experiences, every person has a chance to improve the quality of their brain’s functioning through learning.
The brain’s process of synaptic overproduction and pruning generally concludes after 10 years or so (Bransford, 2000, p. 4). As learners engage in formal education contexts, then, they will be generally working with an initial synaptic organization shaped by their early experiences. These experiences –comprising culturally-situated artifacts, practices, people, languages – lay the initial functional “wiring” of the brain
While this initial density and organization of neurons do have important implications for the brain’s efficiency in processing new information (as discussed in guideline #3), the brain continues to change as a result of learning. Throughout one’s lifespan, the brain will modify existing synapses and add new synapses in response to new kinds of experiences and inputs (Bransford, 2000, p. 118).
As Carol Dweck argues in her work on “mindsets”, those who believe that intelligence and skill are fixed traits are often unwilling to attempt new experiences – fearing that failure in a new domain is indicative of a lack of innate brains and talent. According to Dweck’s research, individuals believing that intelligence can be developed through effort are more comfortable engaging with new experiences – which in turn foster learning and neural additions (Dweck, 2006).
Giving learners evidence of their own neural plasticity may help them to adopt a so-called “growth mindset”, a belief which has been linked with the qualities of motivation and resilience.
II. Allow ample opportunities for practice of targeted cognitive skills.
Bransford notes that the amount of experience in a given environment corresponds with the amount of corresponding structural change in the brain, concluding that practice increases learning (Bransford, 2000, p. 125). As the brain gains repeated exposure to a type of input, it demonstrates decreased activity in the corresponding region as it requires fewer neural resources to process (Park & Huang, 2010, p. 393). The more the brain performs a given task, the more efficient and effective it becomes at performing that task. It does so by creating categories for processing inputs, creating relational connections between existing and new information. To support the brain in developing this “cognitive economy” (Gibson, 1969), allow learners multiple opportunities and ways of practicing targeted cognitive skills (Bransford, 2000, p. 124).
III. Consider the cognitive load required by a particular learning experience or activity.
Stick with broadly familiar objects, concepts, or skills unless they are the particular focus of the learning task.
The “wiring” of the brain develops a functional organizational structure that optimizes processing of the kinds of inputs to which it is exposed (Bransford, 2000 p. 117). The effect of this wiring diagram is revealed in the fact that the brain requires the use of fewer neural resources to process repeated stimuli – that is, it is more efficient in processing familiar or “culturally preferred” information (Park & Huang, 2010, p. 393-5). Unfamiliar, or non preferred, stimuli require greater cognitive effort to process. Requiring learners to engage with unfamiliar and unrelated stimuli will lead to greater brain activity in response to those inputs – neural resources which would be more productively dedicated to the targeted learning task. While it’s clear that no concept, task, or object is culturally neutral, a learning designer should consider making supporting or contextual variables in a learning activity as familiar as possible to all learners so as not to disproportionally disadvantage some groups of learners.
(Note: I’m not fully convinced of this design recommendation. A few concerns:
1. I’m uncertain if the brain has a finite quantity of neural resources. Does activation in one area of the brain necessarily imply a decreased ability to activate in another region?
2. Unfamiliar or startling supporting variables can be used to catch learner interest, encouraging them to devote their attention to the task at hand. For example, a lesson on calculating volume that examines a pint of polar bear blood might be more engaging than one relying on a more familiar substance like water.
3. This kind of abstracting, de-contextualizing strategy is uncomfortably reminiscent of the approach that Brown, Collins, & Duguid decry in their 1989 treatise on situated learning.)
Accounting for Culture as Learning Designers
Learning designers should begin with an awareness that no learning task, object, skill, or tool is culturally inert. Every learning experience, activity, or context may favor a certain individual or group of learners based on their prior personal and cultural experiences.
This concept has been documented in much of the socioculturally-oriented literature on learning. For example, Tate (2005) as cited in Ukpokodu (2011) described a situation in which a teacher, attempting a degree of cultural situativity in her lesson design, presented a math problem using the example of Thanksgiving pumpkin pie. While Euro-American students demonstrated a degree of intrinsic engagement with the problem, an African American student appeared to be disengaged. Tate attributed this disconnect to the fact that for many African Americans, sweet potato pie is the standard Thanksgiving dessert. So here, the “object-to-think-with” – the pumpkin pie – added an additional degree of cognitive challenge for those learners for whom it was a “culturally non preferred” artifact (Park & Huang, 2010, p. 393-5).
From a somewhat different angle, in his 1980 book Mindstorms, Papert discussed the relative disadvantage of learners who developed in an environment that was poor in “math-speaking adults” – those who enjoy and engage in logic-based problems like “puzzles, puns, and paradoxes” (Papert, 2008, p. 9). He found that those children arrive at formal math instruction lacking the personal experiences necessary for easy learning of the domain. Here it is early exposure to vocabulary and thinking strategies that primes some learners and disadvantages others.
Tate (2005) shares one effort to integrate culturally relevant content into instruction, describing a teacher who used hip-hop CD sales as the object of algebraic equations in a predominantly African American and Hispanic class (p. 51). While Tate reports increased engagement, we still have to wonder about those students who do not belong to the cultural majority, or even those who don’t prefer hip-hop music.
So with an awareness that no learner approaches a learning activity with the same cultural and personal background (and resulting neural wiring), learning designers should present multiple representations and perspectives to frame a task or concept. Further, an instructor might allow learners to use different tools and strategies to express (and so assess) their learning. These design recommendations are consistent with the principles of “Representation” and “Action & Expression” proposed by the Universal Design for Learning Guidelines (“UDL Guidelines”, 2016).
Accounting for Culture as Learning Researchers
Researchers should allow the possibility of culture to be an independent variable in a study’s design. Failure to account for the influence of cultural experience in the brain’s development can lead researchers to draw inaccurate conclusions about their findings. Learning science research might go beyond a simple question of participants’ ethnicity, and drill deeper to identify the cultures to which they have been exposed in their early and ongoing lives. The granularity of this analysis might be at the level of hemispheres (as with the studies of Westerners and East Asians cited in Park & Huang’s 2010 article), but might also progress to a participant’s country, region, city, neighborhood, and home. Each of these cultural contexts can bring with them a unique set of people, environments, tools, practices, languages, that can come to bear on a participant’s performance in a research task. With this information, a researcher can decide on a case-by-case basis whether those cultural variables should be controlled for in the study.
References
Bransford, J. (2000). Mind and Brain. In How people learn: Brain, mind, experience, and school (pp. 114-127). Washington, D.C.: National Academy Press.
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.
Park, D. C., & Huang, C. (2010). Culture Wires the Brain: A Cognitive Neuroscience Perspective. Perspectives on Psychological Science, 5(4), 391-400. doi:10.1177/1745691610374591
UDL Guidelines: Theory & Practice Version | National Center On Universal Design for Learning. (n.d.). Retrieved October 14, 2016, from http://www.udlcenter.org/aboutudl/udlguidelines_theorypractice
Ukpokodu, O. N. (2011, Spring). How Do I Teach Mathematics in a Culturally Responsive Way? Multicultural Education, 19(3), 47-56. Retrieved from ERIC.