When I started to teach engineering classes after many years away from teaching at a college level, I was looking for ways to buttress my teaching and I was looking for a solid reference to give me confidence to overcome my own fear of failure in the classroom.
My first instinct was to research the latest teaching
literature for help. Most of what I was reading was focused on the techniques
and tools that were available to the teacher or coach, not exactly a how-to
approach.
As I broadened my investigation, the literature eventually steered
me towards the cognitive sciences: how learning and retention works
neurologically, a different direction than what I had expected. Instead of
focusing on strategies and tactics for the teacher/coach to be more effective,
this approach focuses on how the instructor should adjust their teaching philosophy
to conform to how students/players can best learn.
Cognitive Load Theory (CLT) shows up continually on my
radar, I first learned about it from the books that I had read upon the
recommendation of Coach Vern Gambetta.
Somewhere along the line, a monograph Cognitive Load Theory
in Action by Oliver Lovell
What follows is a discussion structured along Lovell’s
monograph, after I added some thoughts on the topic that are specific to my
situation: teaching college engineering classes, coaching junior volleyball,
and applying the method to my own learning practice. I do this s a means to
help me understand topics that I felt were important. It aligns with the
following quote from Joan Didion: “I write entirely to find out what
I’m thinking, what I’m looking at, what I see and what it means.”
What is CLT?
Cognitive Load Theory is primarily based on what we know of
the structure of the human memory and how that structure affects how humans learn
and more importantly, recall all the information, knowledge and experience
which makes up our learning arsenal.
There are five main principles which underlie CLT.
1. Memory has Architecture.
2. Knowledge is Categorized as Biologically Primary or Secondary
3. Working Memory can be Categorized as either Intrinsic or Extrinsic
4. The Knowledge Can be Either Domain-General or Domain-Specific
5. Element Interactivity is the Source of Cognitive Load
Memory has Architecture.
The first essential principle of CLT states that humans’ memories
have an architecture which consists of three major resources: environment, working
memory and long-term memory.
The environment is everything that exists outside of our
brain, all the external resources that we can consult for information: anything
and everything that surrounds us which can be used to augment our working and
long-term memory. Both Barbara Tversky’s
The second architecture is the working memory:
· It has limited capacity, able to keep 3-7 elements of information at one time.
· It is where all the thinking takes place.
· It is the domain of conscious thought: that is, we are actively managing the memory consciously.
· It is the limiting factor of human thinking, the bottleneck to our ability to learn effectively.
· It is where the unfamiliar and unknown at that instant takes up more of the working memory capacity than the familiar and known.
· It uses chunking and automating the information to reduce the cognitive load on the working memory.
The last architecture is the long-term memory:
· It is unlimited in capacity although the information that is most often recalled is those that are most easily recalled, i.e., those that are most often recalled or had been recalled most recently.
· It is divided into three key form of knowledge:
o Episodic: refers to life events,
o Semantic: refers to factual information
o Procedural: refers to process memories.
An analogy can be drawn comparing the human memory
definition to computer system configuration. Working memory is analogous to Random
Access Memory (RAM), long term memory is analogous to the hardware memory:
HDD’s or SSD’s. Whereas the CPU is the brain itself, serving as the central
traffic control for the knowledge that is being passed around. Even though this
analogy can only carry us so far, as the human brain and nervous system are not
as clearly defined as a computer system because the computer system is essentially
a Von Neumann machine designed to be a simple imitation of what we think human
cognition functions. Another place where the analogy falls apart is the
transfer of knowledge from the working memory to the long-term memory. Human
memory is such that it takes many iterations of transferring the knowledge from
working to long-term memory before it is made permanent, whereas the computer
has a specific storage function to more the data from the working memory to the
long-term memory.
The analogy holds true for our purposes. The working memory actively
using the brain to consciously process all the new and unknown information,
knowledge, and experiences; while recalling the familiar and known information,
knowledge, and experiences without using the brain because the information has
been chinked and automated so that the recall is done subconsciously, without
adding to the cognitive load.
This architecture is the foundational model for CLT, the
arguments for implementing the strategy and tactics suggested by CLT are built upon
the bedrock assumption that the three-resource model is correct.
Knowledge is Categorized as Biologically Primary or Secondary
The second essential principle states that knowledge can be
categorized as either biologically primary or secondary.
Biologically primary knowledge refers to knowledge that:
· Are unconscious, fast, frugal, and implicit.
· Are acquired by humans through evolution.
· Are Knowledge that cannot be taught.
Biologically secondary knowledge refers to knowledge that:
· Are slow, effortful, deliberate, and conscious.
· Have evolved through the last 1000 years.
· Needs to be taught.
Working Memory Load can be Categorized as either Intrinsic or Extrinsic
The third essential principle states that our working memory
can be categorized as either intrinsic or extrinsic cognitive loads.
Intrinsic cognitive loads are those that are critical to
learning whatever it is that we need to learn. They are:
·
Part of the nature of the information that we
are learning.
·
Core learning.
·
Information that we WANT the learner to have in
their working memory.
Whereas the extrinsic cognitive
loads are:
·
A part of the manner and structure of how the
information is conveyed to the learners.
·
Disruptive to the learning task because it
distracts the learner from learning by occupying valuable working memory space.
The crux of the problem is that
the working memory capacity is finite; that is, the intrinsic and the extrinsic
loads are vying for the same finite resource: the working memory. Ideally, the
learner needs to optimize the use of the working memory for intrinsic loads and
minimize the use of the working memory for extrinsic loads. Note that it is
desirable to optimize the intrinsic load rather than also minimizing the
intrinsic load as we want to minimize the extrinsic load. The reason for the
difference in goals is that we wish to devote as much of the working memory to
the intrinsic (productive) learning mode, what is variable is whether an
appropriate amount of intrinsic load is placed on the working memory for
optimal learning, or whether too much, or too little intrinsic load is placed
on the working memory.
The Knowledge Can be Either Domain-General or Domain-Specific
A fourth essential principle states that the knowledge that
the learner is being exposed to can be divided into domain-general versus
domain-specific.
Domain-general skills are:
· Biologically primary
· General capabilities
· Generally applicable
· Transferable
· Examples are:
o
Problem solving
o
Creativity
o
Communication
o
Teamwork
o
Critical thinking
Domain-general skills are assumed to exist, can be taught,
learned and transferred.
Domain -specific skills are:
· Biologically secondary
· Applicable to only specific domains.
The difference between novices and experts in each domain is
that experts have more relevant domain-specific knowledge. Which means that the
novice uses more thinking in performing tasks while experts use more knowledge.
The novices use up more of the working memory to think (conscious) rather than
recall and the experts use up more of the working memory to recall knowledge
rather than to think (subconscious).
An expert:
· Has a larger collection of situations and associated actions stored in their long-term memory.
· Can explain why these situations imply taking the specific actions,
· Can derive the solutions from foundational principles,
· Can explain the mechanism behind them,
· Can recognize the situations and execute an appropriate action,
The payoff is that the expert can recognize a larger cache
of problems and situations and the necessary actions to deal with each problem
and situation, whereas the novice has a much smaller cache of memory.
If the biologically secondary domain-specific knowledge is
not transferable, how does the learner go about improving their performance in
a specific domain? The answer, according to Lovell and Sweller, is to increase
domain specific knowledge in the long-term memory to expand the number of
potential solutions available from the long term memory which can be recalled
into the working memory.
Conversely, how does one leverage the biologically primary,
domain-general, and transferrable knowledge to improve the biologically
secondary, domain specific, and non-transferable knowledge? The answer is to
apply transferable knowledge within the context and the constraints of the
specific domain. It is an indirect way of leveraging what is already resident
within us when we are born to acquire new knowledge that is new to us?
Element Interactivity, the Source of Cognitive Load
The final principle is that the number of elements in a
given problem and situation and the amount of interactivity each element has
with the other elements determine the amount of cognitive load being placed on
the working memory. Element interactivity depends on the nature of the activity
as well as the background knowledge stored in the long-term memory inherent in
the learner: novice learners will have minimal knowledge base to draw upon,
while the expert will have an extensive knowledge base. In addition, the expert
also is experienced, so that they can effectively “chunk”, i.e. consolidate and
automate the knowledge to effectively bypass thinking and allow their
knowledgeable self to react automatically.
Perniciously, the amount of interactivity rises
geometrically as the number of elements increases linearly, this kind of growth
in interactivity will overwhelm the working memory in short order. The elements and the interactivity of the
elements can also be divided into intrinsic and extrinsic loads following the
second principle stated.
Indeed, this is the main idea that saves our working memory
from constantly being overwhelmed: we can separate the intrinsic and the
extrinsic; optimizing the learning from the former and minimizing the chatter
from the latter.
According to Lovell, intrinsic load is optimized through
well-crafted curriculum sequencing and the extrinsic load is minimized through
good instructional design. All of which is also covered Lovell’s book, digging
deeper into the granularity of
· Strategies and tools to optimize the intrinsic load.
· Strategies and tools to minimize the intrinsic load.
Which will be covered in a separate article, CLT Part 2,
because this first part has optimized my intrinsic memory, and I need a break.
Part 2 is on how teachers can minimize extrinsic load on the learner through honing their presentation. (https://polymathtobe.blogspot.com/2023/04/learning-and-teaching-cognitive-load.html)
Part 3 is on how teachers can minimize the extrinsic load on
the learner through structuring their practices and lessons. (https://polymathtobe.blogspot.com/2023/05/learning-and-teaching-cognitive-load.html)
Part 4 is on how teachers can optimize intrinsic loads on the student. (https://polymathtobe.blogspot.com/2023/08/learning-and-teaching-cognitive-load.html)
References
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Helping People Perform: https://danabrahams.com/blog/
Brown, P. C. (2014). Make It Stick: The Science
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John Sweller, J. J. (1998). Cognitive Architecture
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John Sweller, J. J. (2019). Cognitive Architecture
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Lemov, D. (2010). Teach Like a Champion: 49
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Lemov, D. (2020). The Coaches Guide to Teaching.
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Lemov, D., Woolway, E., & Yezzi, K. (2012). Practice
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