The latest developments in neuro-science have contributed significantly
to our understanding of learning in
relation to information retrieval. 4 Forgetting is now considered to be a good
thing because it forces the learner to
use effort to cognitively engage and
recall or reconstruct newly acquired
concepts through different neural
pathways or links that exists and are retrievable. So, the more links to associated concepts, the higher the chances
of recalling the newly acquired concept
when needed later. Furthermore, cognitive retrieval practices attempted at
different times, various settings, and
contexts are good because every time
the recall is attempted it establishes
more links that will help the remembering and learning. Exposure to new
concepts, then, through links to multiple views from different fields of study
is an effective retrieval strategy recommended by cognitive psychologists.
Basically, retrieval sounds like an
act of creative reimagination and what
is retrieved is not the original pattern
but one with some holes or extra bits.
Consequently, neuroscientists see little
or no distinction now between the acts
of information storage/retrieval and
the act of thinking. Such a consolidated
view of storage, retrieval, and thinking
is very much in tune with our model
(Figure 1) of how information behaves
naturally. Applying it to translate what
neuroscientists say about storage and
retrieval, 4 we posit that a memory or a
newly learned concept can be a combination or outcome of previously
formed memories and concepts, each
of which might also involve another
level of vast network of concepts and
details mapped onto the brain’s neural
network in a hierarchical way. When
new information arrives, it lights up
all related cues, neurons and pathways in a distributive process that is
similar to the top-down action, where
new concept is broken up into related
pieces. By the same token, retrieving a
memory is a reassembly of its original
pattern of neurons and pathways in
an associative process that is similar to
the bottom-up action.
Accordingly, the brain attempts to
analyze deductively every new concept
and information that it encounters in
terms of previously registered models—objects, faces, scenarios, and so on.
models of the mind to study how computation may be generating thinking.
Electronic computers have evolved
to showcase many structural and functional similarities with the brain. So,
we may have a chance to better understand how the brain works through
easier access, use, and control of electronic devices. I suggest the similarities arise from quantifiable aspects of
information constructs, as suggested
by Alan Turing, 27 and the appearance
of a universal mechanism (see Figure
1) by which quantifiable things form
and evolve. 30 That is, like the granular
matter, information constructs either
unite associatively, as shown by the
bottom-up arrows in Figure 1, to make
bigger constructs or break down distributively, as shown by the top-down
arrows, to smaller ones. Computing
devices, be it electronic or biological,
are likely to use similar ways to track
and tally this invariant behavior of information. Another reason for similarities is the design, use, and control of
electronic computing devices by biological computing agents.
Continuing the legacy of Turing
to focus on device-independent processes (see Figure 2), we want to create more links between CT and cognition by identifying common patterns
of information processing that are
known to facilitate thinking. This
may give us a framework to suggest a
universal definition for CT—thinking
generated and facilitated by computation, regardless of the device that does
the computation—along with an electronic computing methodology to facilitate relevant cognitive processes.
While the CS community is willing to
modify its original definition along
these lines, 1, 28 current curricular CT
practices still deal only with teaching
of electronic CT skills.
A clear distinction should be made
between electronic and biological CT
to more effectively integrate desired CT
skills to the relevant grade-level curri-cula. Having dealt with many issues of
CT education for three decades at both
college and K– 12 levels, 30–34 I want to
present an interdisciplinary perspective to address both cognitive and curricular aspects of CT by merging CS
education research with concepts from
epistemology, cognitive and neurosci-ences as briefly described here.
Neuroscience’s View of Information
Storage, Retrieval, and Thinking
Contrary to the early compartmentalized and centralized design of electronic computers, the brain employs a distributed network of neurons to store,
retrieve, and process information. Information gets stored into the memory in the form of a specific pattern of
neurons placed on a pathway and fired
together, 14 as shown in Figure 3. Therefore, the number and strength of neural
pathways are key to improving storage
and retrieval of information.
Humans are born with 100 billion
neurons that get connected to each
other in various ways as we grow older.
Other key factors that affect our mental
growth include the functionality that
each neuron or groups of neurons assume, the size they grow into, and the
placement in different parts of the brain
that they migrate towards. More important is the number of neural connections, which could go up to 100 trillion.
As we learn things, new connections are
being made while the existing ones are
strengthened, weakened, or even eliminated if not revisited often enough.
little or no distinction
now between the
acts of information
the act of thinking.
Figure 2. Information processing by
electronic and biological computing
devices include both device-independent
and device-dependent processes.
Device-independent computational processes