No Brainer Education Research

As political pressure builds for improving completion rates of community college students, we teachers find that our practices, organizational and pedagogical, require justification.  The assumption “out there” seems to be that research will uncover the appropriate institutional structure and teaching methods.  Looking for a silver bullet is paying a lot of peoples’ mortgages, but the findings, on examination, are so tentative (or should be) as to offer little guidance.

The news from the Department of Education’s Institute for Education Sciences in 2013 that only “12% of the [90] interventions produced positive effects” should be a heads-up that all is not well for our intentions to practice “research-based” instruction.  Results are weakly correlated to practices because of poor experimental design or execution in part, but also because live social science experimentation is inherently messy.  Controlling for experimenter bias is also a concern, as Diane Ravitch documents in The Death and Life of the Great American School System (128-145).  And then there’s the issue of publication bias, the tendency of journals to publish positive results rather than failures to confirm hypotheses.

Some school districts have been hyping neuroscience as the truly reliable source of scientific information on how we learn and remember.  Beginning with a look at brain geography and function, the in-service training typically demonstrates, through visuals of various brain images, the centers of various functions such as short and long-term memory, language, logical processing, and so on.  Changes in neural structures are described as the physical manifestation of “learning.”    

Even a cheerleader like Eric Jensen, in his Teaching with the Brain in Mind, concedes early that “…educators can apply only a small percentage of brain research” (5).  Why that might be true is perhaps suggested by excerpts from The Learning Brain:  Lessons for Education by neuroscientists Sarah-Jayne Blakemore, and Uta Frith:

            So what is happening in the brain when we learn something at this unconscious, implicit, level?  Using positron emission tomography (PET), Jonathan Cohen and colleagues at the University of Pittsburgh mapped the brain regions that are responsive to implicit learning of sequences.  Volunteers performed the task (of tacit rule building when shown sequences of letters).  They had no idea of learning anything.

            Once the participants were trained, they were scanned.  When there was a subtle change in the nature of the sequence, this resulted in blood flow increases in a network of brain regions including the left premotor area and anterior cingulate, and part of the basal ganglia on the right.  Blood flow decreases at the rule break were observed in the right prefrontal cortex.  These changes suggest that these regions are responsive to the rule break, which can occur without awareness.  The brain notices things that you do not (142).

Now I think it’s interesting that our brains indeed “think” without our awareness of it, but I don’t know how else our normal speech would otherwise be possible, as we don’t generally formulate our thoughts in words consciously unless we are being very careful, and even then our access to our lexicon is not an act we can monitor.  The words just come—or not.  I don't need a neuroscientist to tell me that it happens.

And what increasing blood flow in an area of the brain can tell me about how I should organize Freshman Comp or teach comma use is an open question.

Another comment from our British brain scientists: 

It is unlikely that there is one single all-purpose type of learning for everything.  In terms of brain structures involved, learning mathematics differs from learning to read, which differs from learning to play the piano.  Each memory system relies on a different brain system and develops at a slightly different time.  Remembering who you are differs from remembering where you are (139).

I have argued this point in earlier posts without the aid of neuroscience, so this can’t be considered particularly insightful.  If we are to accept Chomsky’s contention of the innateness of language structures, that doesn’t preclude our ability to generalize from instances (empiricism) or develop knowledge that language can not adequately describe (Polanyi’s “personal” knowledge).  This should surprise no one.  It makes sense that our ancient ancestors would process any and all available experience and sensory inputs to survive and thrive to the best of their ability.  They didn’t debate epistemology, they just learned whatever they could in whatever way they could or had to.  The invention of writing simply gave them new sources of packaging knowledge and learning it anew.

Even Blakemore and Frith seem to acknowledge that the era of neuroscience-inspired pedagogy is a ways off:  “Based on findings from neuroscience, we can imagine a day when we will be able to use all sorts of radical new ways to improve learning and memory” (17).

There may be a problem, however, with this prognosis.  William Uttal, Professor Emeritus in Psychology at the University of Michigan, challenges any notion of mapping what the brain does to what we know ourselves to be doing and learning:

The empirical facts we encounter…should raise our awareness of the actual complexity of the mind-brain system.  As such, they emphasize the increasing difficulty and ambiguity of the scientific situation rather than clarify the nature of the mind-brain relation.  The brain probably should not now be considered to be an aggregate of isolated or isolatable components located in a particular place but, rather, as a dynamically changing four-dimensional (x, y, z, t) network of interconnecting and interacting components of vaguely defined, redundant, and overlapping functions and, thus, ever-changing spatial limits.  No longer is the crutch for simplifying research designs offered by the neophrenological idea of the localized representation of cognitive faculties (either singly or as separate components of a “system”) available to us as a means of simplifying and organizing our findings.  It is now increasingly apparent that brain images at the very least reflect the fact that vast regions of the brain, if not all of it, are involved in the simplest cognitive processes (45).

He concludes pessimistically:

            What all this means is that the MRI and the EEG are blunt instruments—epistemological sledge hammers—when it comes to understanding or even representing the detailed neuronal network mechanisms that actually underlie cognitive processes.  They are techniques that operate at the wrong level of analysis; where we need information about the patterns of microscopic neuronal activity to understand something like learning, we only have available measures that pool all of the truly salient microscopic information into an unanalyzable compound.  It may not be too severe a criticism to point out that whatever signs or biomarkers of cognitive activity may ultimately emerge from brain imaging studies, the whole enterprise is theoretically sterile because of this disconnect between the level at which we observe (molar chunks of the brain) and the level at which mind is actually instantiated (the details of the network).  A logical conclusion of this argument is that any hope for practical applications that assume that we will be able to “read the mind” or use these tools as “biomarkers” for either normal or dysfunctional mental processes is unlikely to be realized (46).

How the brain actually works and what we can see are incommensurable.  Furthermore, the knowledge of what the brain does and the knowledge of what we do and how we learn are fundamentally and inherently non-translatable one to the other:  “there is no direct access to the conscious experiences that permit us to directly compare mental and neural events” (8).

Time to re-boot.

Resources

Blakemore, Sarah-Jayne and Uta Frith.  The Learning Brain:  Lessons for Education.  Malden, MA:  Blackwell, 2005. Print.

Jensen, Eric.  Teaching with the Brain in Mind.  Alexandria, VA: ASCD, 1998. Print.

Ravitch, Diane.  The Death and Life of the Great American School System:  How Testing and Choice are undermining Education.  New York:  Basic Books, 2010.

Uttal, William R.  Mind and Brain:  A Critical Appraisal of Cognitive Neuroscience.  Cambridge, MA:  MIT, 2011. Print.