Synopsis/Reflection – What does it mean to learn? (from a neuroscience perspective, that is)
Learning, at its most fundamental, is a process that changes the brain. According to Gary’s warm-up video, neuroscience states that “learning has occurred when information can be retrieved after a period of disuse and applied to new situations.” This neuroscience definition of learning digs deeper than just a brain changing process: learning entails retrieving information/data after not using that information/data for some period of time (i.e. disuse) and applying that information/data in new contexts. This definition of learning requires so much more dedicated practice and work than simply retrieving information/data after one’s immediately learned it. I wonder how many of my student peers would identify their own learning through the neuroscience definition rather than the latter definition.
Learning becomes even more complex when we discuss optimal or effective learning. According to the University of California at Berkeley’s GSI (Graduate Student Instructor) Teaching and Learning Center,
“For optimal learning to occur, the brain needs conditions under which it is able to change in response to stimuli (neuroplasticity) and able to produce new neurons (neurogenesis).
The most effective learning involves recruiting multiple regions of the brain for the learning task. These regions are associated with such functions as memory, the various senses, volitional control, and higher levels of cognitive functioning.” (https://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/neuroscience/)
Learning requires building new synapses in the brain to interconnect bits of information/data. These synapses are gaps (i.e. connections) that form between axons that grow from one neuron (presynaptic) to another neuron (postsynaptic). Neurons communicate when a neuron “fires” (an electrical signal response to stimulation) and neurotransmitter molecules diffuse across the synapses from the presynaptic neuron to the receptors on the postsynaptic neuron. The new axons formed need to undergo myelination in order solidify the connection between the neurons. Figure 1 provides a visual representation of the formation of synapses.
Figure 1. Two representative neurons in the brain communicate via an electrical current (shown with yellow arrows). Inset shows a close-up view of the synapse and the neurotransmitter release and receptor response across the synaptic junction. Illustration taken from (Owens & Tanner, 2017, p. 2).
This process of strengthening and increasing the number of connections between existing neurons is called synaptic plasticity. Building memories requires three processes – encoding, consolidation, and retrieval – that consistently work together to enable storage in long-term memory. Storage of learning in long-term memory is needed to enable its retrieval after long periods of disuse. Figure 2 details these processes, which also cycle after retrieving memories to encode new knowledge, which leads to reconsolidation, which in turn increases synaptic plasticity.
Figure 2. Encoding, Consolidation, Retrieval. The three memory-forming processes that interweave to store learning in long-term memory. (Smith, 2019)
Memory comes in many different forms, and we can modify our teaching pedagogy to maximize the number of different kinds of memory formed. The kinds of memory are shown in Figure 3. Active learning techniques often stimulate neurons by piquing student interest, tying concepts to other topics or real life, and relieving student stress by evaluating student learning through low stakes formative assessments. Our pedagogical goal might be to stimulate as many kinds of memory as we can in order to maximize the placement of student learning in long-term memory.
Figure 3. The different kinds of memory. Declarative memory comes in two forms: semantic and episodic. A pedagogical goal could be to design learning opportunities that maximize the use of both declarative memory forms. (Smith, 2019)
My research questions this week revolve around this interconnection between enabling students to form long-term memories of their learning through using certain teaching techniques. My research questions therefore are:
- What pedagogical techniques best enable the encoding and consolidation into long term memory for students?
- Are there certain criteria we can use to evaluate these pedagogical techniques that show greater long-term memory formation?
Research – Using Neuroscience in the Classroom
Owens and Tanner (2017) evaluate several evidence-based teaching practices, including think-pair-share, concept mapping, frequent homework, problem-based learning, and using culturally diverse examples. Table 1 shows how specific neuroscientific principles may correspond to psychological or educational findings, which, in turn, may be harnessed by a single or a series of teaching technique(s).
Table 1. How teaching techniques correspond to neuroscience principles and psychological or educational findings (Owens & Tanner, 2017, p. 5).
As previously discussed, deliberate practice, in this case through frequent and active homework, helps build expertise in a domain. Now we know that deliberate practice works to build expertise because it helps build synaptic plasticity. Think-pair-share also increases synaptic plasticity by engaging students’ brains in ways that recall semantic information but also may include the formation of skills and habits, depending on the questions posed. Concept maps rationally encode knowledge, which allows memories to build as synaptic networks. Problem-based learning encourages students in terms of motivation and attention, which in turn increase learning by increasing synaptic plasticity. Using culturally diverse examples in one’s pedagogy helps to alleviate or eliminate stereotype threat, which decreases stress.
Clearly, from these examples and many more, pedagogical techniques can be used to increase encoding and consolidation for long-term memory formation. The question that remains is how do we evaluate these techniques in terms of their relative increase or decrease in long-term memory formation?
Basu, Mondoux, Whitt, Isaacs & Narita (2017) argue that integrative thinking may be an evaluation criterion that might at least begin to show how the techniques differ. Integrative thinking is similar to concept mapping in that memories can be encoded as synaptic networks. Basu et al. hypothesized that interdisciplinary awareness builds integrative thinking. Integrative thinking increases student synaptic plasticity by requiring students to form skills and habits that then can be recalled later in spaced intervals (i.e. iterations) throughout an “Introduction to Neurology” class. Faculty from several core STEM disciplines and those from interdisciplinary neurology positively supported the creation of the course and were happy to participate in it, which, in turn, created an environment for students that was exciting and motivating. Basu et al.’s article mainly described the formation of the flipped class, but certain analyses, like those mentioned above, were clearly promising from the minimal student evaluation performed during the administration of the class.
While evaluating integrative thinking may be one criterion to assess student long-term memory formation, Basu et al.’s article really didn’t provide enough information to assess the second the research question. I fear we may need more information about the brain and how it functions as well as more defined research in this area to adequately assess this question.
Relate – Neuromyths and our surprising lack of awareness of them
In learning about neuroscience and the actual cognitive functions that signify learning from a biological standpoint, we also learned about neuromyths. Howard-Jones (2014) states that neuromyths were defined by the Brain and Learning project of the UK’s Organization of Economic Co-operation and Development (OECD) as “a misconception generated by a misunderstanding, a misreading or a misquoting of facts scientifically established (by brain research) to make a case for use of brain research in education and other contexts” (OECD as found in Howard-Jones, 2014, p. 817). Educational interventions then arise from these neuromyths, which then propagate new neuromyths. Several neuromyths exist, and four of the most predominant are labeled below in Table 2.
|Neuromyth||What the neuromyth says||Why the neuromyth is incorrect|
|Learning styles||A student learns most effectively when taught in their preferred learning style.||“The implicit assumption seems to be that, because different regions of the cortex have crucial roles in visual, auditory, and sensory processing, learners should receive information in visual, auditory or kinaesthetic forms according to which part of their brain works better” (Howard-Jones, 2014, pp. 817-818). The brain’s interconnectivity undermines this assumption, and research done on learning styles have failed to support this approach to teaching.|
|Left-brain right-brain theory||Learners have a dominant side of their brain, which then decrees what kind of learner they will be.||Again, the brain is incredibly interconnected. Everyday tasks result in a highly distributed neural activity, as shown by neuroimaging studies.|
|Multiple Intelligences||Learners are characterized in terms of a small number of independent intelligences (i.e. interpersonal, music, logical-mathematical).||“The general processing complexity of the brain makes it unlikely that anything resembling Multiple Intelligences theory can ever be used to describe it, and it seems neither accurate nor useful to reduce the vast range of complex individual differences at neural and cognitive levels to any limited number of capabilities” (Howard-Jones, 2014, p. 818).|
|The ‘Myth of Three’||The time from zero to three years of age is a critical period when the majority of brain development occurs. After that time, human development is mainly fixed.||Neither neuroscience nor educational research support this claim. Findings instead suggest that “success of educational interventions aiming to improve the learning and well-being of children requires attention be paid to the specific needs and characteristics of [those] children…human development and learning arise from a range of interrelated neural circuits subserving a range of cognitive and other skills, which develop at different rates until early adulthood, sometimes in a discontinuous manner” (brackets mine, Howard-Jones, 2014, p. 820).|
Table 2. Prevalent neuromyths that may undermine the use of evidence-based teaching pedagogies.
Several of these neuromyths are detailed in Merriam & Bierema (2014) and treated as if they are fact. Perhaps this interesting observation simply shows how prevalent and how undermining to an adequate understanding of neuroscience these neuromyths can be.
Dekker, Lee, Howard-Jones, & Jolles (2012) found that the most significant predictor of a teacher’s belief in neuromyths was their previous general knowledge of the brain. Therefore, teachers who are highly interested in brain research are more susceptible to neuromyths (a result I find depressing). Differences in language between that used in neuroscience and education plays a significant role in the misunderstandings that arise that eventually result in neuromyths. The advocacy for a discipline/field that specifically bridges education and neuroscience is therefore probably well-founded and will hopefully “inform educational approaches [and] encourage scientific insight regarding the relationship of neural processes to the complex behaviors that are observed in the classroom” (Howard-Jones, 2014, p. 822).
Basu, A. C., Mondoux, M. A., Whitt, J. L., Isaacs, A. K., & Narita, T. (2017). An integrative approach to STEM concepts in an introductory neuroscience course: Gains in interdisciplinary awareness. The Journal of Undergraduate Neuroscience Education, 16(1), A102-A111. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5777831/.
Dekker, S., Lee, N. C., Howard-Jones, P., & Jolles, J. (2012). Neuromyths in Education: Prevalence and Predictors of Misconceptions among Teachers. Frontiers in Psychology, 3. https://doi.org/10.3389/fpsyg.2012.00429
Graduate Student Instructor: Teaching & Resource Center. (2019). Neuroscience and How Students Learn. Retrieved from https://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/neuroscience/.
Howard-Jones, P. A. (2014). Neuroscience and education: myths and messages. Nature Reviews Neuroscience, 15(12), 817–824. https://doi.org/10.1038/nrn3817
Merriam, S. B., & Bierema, L. L. (2014). Adult learning: Linking theory and practice. San Francisco, CA: Jossey-Bass.
Owens, M. T., & Tanner, K. D. (2017). Teaching as brain changing: Exploring connections between neuroscience and innovative teaching. CBE—Life Sciences Education, 16(2), 1-9. https://doi.org/10.1187/cbe.17-01-0005
Smith, G. A. (2019). Blackboard Learn Video Warm-Up and Tutorial for Module 5.
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