It is by avoiding the rapid decay into the inert state of ‘equilibrium’
that an organism appears so enigmatic; so much so, that from earliest time of
human thought some specific non-physic or supernatural force (vis viva entelechy)
was claimed to be operative in the organism, and in some quarters is still
claimed. How does the living organism avoid decay? The obvious answer is: by
eating, drinking, breathing and assimilating… What then is that precious
something […] which keeps us from death? That is easily answered. Every
process, event, happening – everything going on in nature means an increase of
the entropy of that part of the world where it is going on. Thus a living
organism continually increases its entropy – or as you may say, produces
positive entropy – and thus tends to approach the dangerous state of maximum
entropy, which is death. […] the essential thing in metabolism is that the
organism succeeds in freeing itself from all the entropy it cannot help
producing while alive- Edward Schrodinger, What is Life? 1958
How fortunate I have been over the
past two days to attend a Professional Development session run by three
outstanding academics in The Science of Learning. This series were run in
conjunction with the outstanding work being done by educational researchers at
The Science of Learning Centre in Melbourne, Australia and other affiliates. It
was a brilliant series, and I could give you the specifics. But it all boils
down to one point: they utilise both the science of learning and the expertise
of teachers congruently to better refine and design educational frameworks –
they collapse the distinction between lab and classroom in recognition of the
complexities that arise. These complexities are not ideological, but rather a
necessary consequence of the nature of
reality. I will explore this a bit later on, but for now it can be presented
as thus: the transfer between the science
of learning and its practical application in the classroom is unequivocally
complex.
My prior blog entries really form
the foundation for the argument I am about to present; if you have not read
them I suggest doing so as they will give an indication the presuppositions I
utilise in order to explicate my theory. This blog presides on the presumption
that: firstly, there is a current paradigmatic shift in the methodology of
educational research that can be understood as evidence over intuition or
neuroscience over folk psychology; secondly, that neuroscience will necessarily
explain the science of learning
through Reductionism/Eliminativism; lastly, that transfer of the science of learning into teaching strategies is not
a logical inference but a deceptive one
that exploits the science of learning in much the same manner that behavioural
economics has done.
All this sounds very complex but
let me add a simple point. Everyone, teachers more importantly, already have an
inbuilt strong pre-theoretical
understanding of how learners learn and, thus, how to teach learners. The
strength of our intuitions preside (and can be reduced to) in our inheritance
of selected traits that are conducive to the survival of the species. Our
ability to thrive and evolve as a species can be explained by the success of
our ability to teach. Explicating these pre-theoretical beliefs almost violate
the know-how. How often we hear teachers say “I already do that”, or more
generatively, “I now know I do that but it never occurred to me why I was or
why it was working”.
As pointed out at the Science of
Learning conference, chefs don’t need to understand the science behind taste in
order to cook delicious meals. Chefs already have an inbuilt understanding of
what constitutes a good meal. Any science of taste that was presented to the
chef would only validate his knowledge. We could argue the same for teachers:
we already know how to teach without knowing the science behind the learning. This is a valid analogy – but it
misses a key point. The science of learning is so important for education
because there are so many misnomers in education that disseminate cyclical progress. Our desire to better understand
the conditions for the possibility of
learning will better equip us with being able to discern between theories
that have negative to no effect on rates of development, and those which are
most conducive to producing educational outcomes (Hattie’s Statistical work
here is crucial). What we are really concerned with is efficiency: our desire
to produce outcomes (learning) through inputs (teaching) is guided by the
necessity for efficiency and efficacy. Even educational research is not exempt
from this fact: physics fixes all the facts.
In a brilliant article A Bridge Too Far – Revisited: Reframing
Bruer’s Neuroeducation Argument for Modern Science of Learning Practitioners, Horvath
and Donoghue (2016) argue that translation of neuroscience into
strategies is impossible due to a limitation inherent to all scientific
research: you cannot bridge the gap between non-adjacent compositional levels
of organization. More simply, you cannot define the biological processes
conducive to survival byway of explicating the laws of physics – you must first
translate the laws of physics into
the laws of chemistry, and from there you
can translate the laws of chemistry
into the laws of biology. In order to build a cohesive explication of phenomena
at any level of reality (or level-of-organization) and make predictions (or
build probabilities of phenomena) about what we would expect to observe, we can
only do this by understanding the preceding level-of-complexity. In the context
of education, the level-of-complexity is constituted in the psychological and
mediated through our folk psychological and phenomenological levels of reality.
If Horvath and Donoghue are correct, and I believe they are, we cannot form a bridge between neuroscience and
phenomenology as it negates the mediating level-of-complexity of biology, irrespective of the fact that
it is constituted in the phenomena itself. Neuroscience simply cannot make any
predictions about what we might expect to observe in the classroom at any
moment in time even though the phenomena it studies is a determinant of that
phenomena.[1] This
leaves us with two options: disregard Neuroscientific research as it cannot
allow us to successfully build theories of teaching, or reduce our everyday familiar folk psychological concepts of
learning to Neuroscientific concepts and make predictions within this
level-of-complexity.
Insofar as translation is necessarily incommensurable (as seen above), Horvath
and Donoghue make an excellent distinction that is important in addition when
considering translation. Firstly,
what generally concerns educators is a prescriptive
translation that attempts to ‘instruct an educator and learner on what to do
and how to do it’ – a process many claim to have achieved. The second translation would be a conceptual one whereby neuroscience is
conceptually instrumental in explaining the
phenomena of learning – something I hope more educators will take seriously
through becoming better informed about the science of learning research, but
researchers will invert insofar as
folk psychological concepts need be eliminated in order to accurately explicate theories of learning – folk psychology is instrumentally useful insofar as it
provides us with a familiar and intuitive conceptualisation but not epistemically useful when it comes to
making claims about the science of
learning. Thirdly, a functional bridge
allows educators to better understand the implications of neuroscience on
learning – whether individually (for example, for students with developmental
issues or brain damage) or, more broadly, as a species in general. Lastly, a diagnostic bridge allows educators to
deduce from observation why phenomena
may be the case when reduced to Neuroscientific levels-of-complexity (Horvath & Donoghue, 2016, p. 2) – something we are ill equipped to do and the
reason why we work with professionals to better understand our students .
So far we have a nice linear
picture of reality. Unfortunately, levels-of-complexity also produce unpredictable
outcomes. As Horvath and Donoghue (2016) note, prescriptive translation is undermined
by emergence, ‘a process whereby
novel and coherent structures, patterns, and/or properties arrive at ascending
levels that are not exhibited within or predictable by preceding levels’ (p. 3) .
If we associate a bridge with the
means by which translation occurs,
translation itself becomes only a means by which probabilistic outcomes can be inferred
and also it must adhere to unforseen probabilities byway of emergence.
For a procedural bridge, good theories of learning based on
Neuroscientific principles: may or may not not work in the classroom; may or
may not work for some students; may work successfully and unsuccessfully for
some; might work with varying degrees of success depending on different causal
factors etc. For conceptual translation, it
remains highly speculative and unable to epistemically justify its assertions
without further reductionist investigation – investigations that may undermine
or contradict initial speculations but still be consistent with the phenomena. Conceptual translation is permissible
insofar as it provides approximations
through explanatory power and predictive success, but it must necessarily carry
a reductionist perspective and be open to falsification. In this manner it is instrumentally
valuable but devoid of epistemic certainty. Thirdly, a functional bridge may show the causal associations between the
levels-of-complexity, but these complexities are subjected to determinants of
probabilistic outcomes – once again, instrumentally valuable but devoid of
epistemic certainty. Lastly, a diagnostic
bridge would still be subject to uncertainty
– think of how often psychiatrists misdiagnose and mistreat patients using
the same bridge.
If we are troubled by these
conclusions then we appreciate the complexities inherent. But you may be
thinking that this all begs the question – how come teachers so often get it
right? How can teachers successfully teach and learners successfully learn if
we are so constrained by probabilistic outcomes and emergent properties? Do teachers
possess some biological aptitude for understanding how learning occurs - do
they in some way have more accurate expert
intuitions about learning? I think so, but with major restrictions – they
are confined to folk psychological conceptions of learning and are not gifted with the intuitive capability to reduce phenomenological phenomena to
lower levels-of-complexity. More simply, teachers can determine with high
degrees of certainty what works and
does not work, but cannot claim why it
works without resorting to folk psychological concepts to explain. We are
confined to folk psychology in order to explain learning as it exists on our
level-of-complexity. But, as I have stated in the past, Folk Psychology cannot
tell us why things work. Simply, our
ability to understand learning is concerned with our desire to help students learn and not explicating the science behind the learning process. Expert
educators have expert intuitions, which have instrumental value, but are devoid
of epistemic certainty.
Are we left with any certainty? It
appears we are only left with probabilistic outcomes. There is a reason why translating neuroscience into
educational frameworks cannot determine learning and it comes down to the probabilistic nature of reality – after all things have been reduced, all we
are left with is uncertainty guided by entropy. All the science of learning
can do is improve the probability that learning will occur insofar as it
designs or implements teaching and learning experiences that are most probably conducive to learning. Any
translation that occurs is
necessarily subject to probabilistic outcomes – wether linear or jumping
between levels-of-complexity. Why? Because it is built into the laws of
physics. Horvath and Donoghue are right insofar as they claim the bridges are
incommensurable between levels-of-complexity, but they don’t go far enough.
Even inherent bridges between the same levels-of-complexity do not produce
necessary outcomes. Any open system provides only probabilistic outcomes from one moment to the next. The one thing
that unifies all levels of complexities is not a thing, but rather a process: that process is entropy. One thing that scientific research has taught
us is that the working of any organism, or the condition by which makes any
organism possible, is exact physical laws. To draw an analogy, if translation
is the bridge between levels-of-complexity,
entropy built that bridge.[2]
All things on all
levels-of-complexity are guided by entropy. The causal link between
levels-of-complexities is entropic. The levels-of-complexity for, let’s say,
the human body, form a cohesive structure guided by entropy on each level. Each
level-of-complexity can be thought of as an open
system that tends towards disorganised states due to statistical
probabilities. At the atomic and molecular levels-of-complexity, entropy is a measurement of order/disorder of its
particles; at the cellular level in humans, the process of borrowing energy from
highly ordered structures happen through mitochondria.
Vital organs are made up of cells that feed off energy through metabolism;
this process is necessary to keep organs alive, and the decline of organs can
be reduced to the increases in systemic molecular disorder. A neuron in the
brain is a great example of an open system insofar as it exchanges and
transmits energy with its environment – a neuron is an entropy processing
mechanism.
The theory of emergence and its
link with entropy is important. Open systems (whether a cell, an atom, a human
or a society) at all levels-of-complexity are not made possible by discrete
subsystems or components – they are made possible (and their functions made
probable) by a composition of a multiplicity of parts unified by a set of
well-defined relationships that determine the scope of probabilistic outcomes. Everything
is subject to this: our classrooms are not made possible with the multiplicity
of parts – students, teacher, classroom, equipment, and lighting, heat, everything
– and cannot process output without well-defined relationships. These
subsystems (parts) are engineered in such a way that is most conducive to learning, but they necessarily can also lead to
unexpected or emergent results when unified under a higher level-of-complexity.
We cannot look at the parts and determine an outcome. Well defined
relationships between parts constrain what is made possible, but is still no
determinant because when the parts work together to create a new
level-of-complexity. Though the parts and relationships are constituted in the
new phenomena, they were only the conditions
for the possibility of emergent properties. Having said that, emergent
properties must also adhere to entropy, even
if they appear to contradict it in the
formation of more complex and ordered states.
This takes us beyond our simplistic
reductionist picture of reality by which reductions can account for and give
rise to causal relationships between levels-of-complexity. We now know that,
whilst each level-of-complexity is the condition for the possibility of the
next, its casual chain only defines probabilistic outcomes and the outcomes are
also able to develop emergent properties that could not have been made probable
at the preceding level-of-complexity.
We could end the story here and
resign to the fate of the incommensurability: due to the emergence of
unpredictable phenomena that are irreducible to lower levels-of-organisation. We
could argue the futility of designing theories of teaching based on theories of
learning as learning occurs at Neuroscientific level of complexity whereas
theories of teaching occur at a psychological. Nonetheless, we know there is a
correlation between the science of learning and theories of teaching, and we
can predict with a great degree of success if
students will learn. The question is whether we can prescriptively design theories of teaching that are translated from
neuroscience? According to Horvath and Donoghue, the answer is no. But what if we
change the language by which the prescriptions
are given. What I am saying is that in order to design a theory of teaching
that is translated from a theory of learning, we necessarily have to reduce the theory of teaching (which is
at our level-of-complexity) to the level-of-complexity of the science of learning. In effect, we are to do with education
what Churchland has done with Eliminativism of Folk Psychology; destroy the way
in which we talk about it and understand it i.e. reduce folk psychological
concepts of learning to Neuroscientific concepts of science.
It would look something like this: if
by ‘Tim has learned well’ we really mean ‘Tim’s neurons have built dendrites
and strengthened neural pathways byway of releasing neurochemicals into the
synapse between the neurons’, then we are correct in stating Tim has learned.
Learning, in this way, has undergone a reduction
to be more accurately explicated. Only when we understand this reductionism
better can we more accurately build theories of teaching. The question becomes
not if translation is possible
(because we know it is not as it is incommensurable), but which theories of
teaching better enhance or provide the conditions
for the possibility of the science of
learning to take place? Because theories of teaching are grounded in folk
psychology and communicated or embodied through our social interactions with
the world, it is the only vehicle by which learning can, and does, occur. Teachers
and researchers would utilise folk psychology as instrumentally useful, and
neuroscience that provides epistemic certainty, because we would be taking the science of learning behind phenomenology
seriously. Because levels-of-complexity build a cohesive scale of reality, with
indeterminate and emergent properties considered, there is a fundamental correlation
between our folk psychological concepts and the neuroscience behind it – the
question is, how do we design theories of teaching that exploit it to maximise educational outcomes? We know this is
possible. After all, we have been successfully
teaching and learning as a species for eons irrespective of an inbuilt
incommensurability. To enhance this, we don’t need to translate up
levels-of-complexity because we can reduce folk psychological concepts to
neuroscience in order to produce a prescriptive
translation that then is mediated through folk psychology. This, in turn,
is conducive to our survival (folk psychological concept), as its function is
to assist in momentarily deferring decay by borrowing negative entropy from our
open system (reduction to entropy). We have a cohesive picture irrespective of
the incommensurability. Why translate up when
we can reduce down; turn a weakness into a strength.
Learners
and learning is no simple process. It is vastly more complex system than our
folk psychology can intuit – but intuitions were not designed to make sense of
reality, only to assist in inheriting, utilising and reproducing traits that
are conducive to biological principles that are fundamentally aimed at slowing
down the corrosive yet inescapable decay of order into disorder. Science of
learning translated into theories of
teaching ought to be to a new area of research that studies the correlation between the phenomena of
each level-of-complexity and reduces folk psychological conceptions on learning
(which we intuitively understand) to well defined scientific theories of
learning (that only the scientific methodology can disclose). The Science of
Learning Centre ought to be commended for building partnerships between
teachers (whom have expert intuitions about what works and does not work in the
classroom) and researchers (who can explain, or are starting to understand the science behind the phenomena of learning).
This is new and exciting research and will have repercussions for the way we view
learning and teaching.
Works Cited
Horvath, J. &
Donoghue, G., 2016. A Bridge Too Far - Revisited: Reframing Bruer's
Neuroeducation Argument for Modern Science of Learning Practitioners. Frontiers
in Psychology, 16 March, 7(377), pp. 1-12.
Schrodinger, E., 2015. What is Life?. 17th ed. Cambridge:
Cambridge University Press.
[1] But
wait… what about all those claims (even those ‘evidence-based claims’) about
Neuroeducation? It’s for these reasons I have claimed that the education
system is rife with exploitative and profit driven agendas. Quite literally,
if we take the science seriously then it necessarily undermines the claims of
Neuroeducators. But we have already established this point – it’s time we go
deeper.
[2]
Rather fittingly it will also be the process that erodes it – but thus the
literal has violated the metaphoric
