Transcript Learning Styles and Generational Differences

Learning Styles and
Generational Differences:
Do They Really Matter?
Jolly Holden, Ed.D. (USAF, Ret.)
Associate Professor
American InterContinental University
Philip Westfall, Ph.D. (USAF, Ret)
Director, Air Technology Network
Air University
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So What…Why Are You Here?
At a recent national training conference, some of the
preconference workshops and sessions stated you will…
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Identify learning styles
Accommodate different learning styles
Discover your learning style
However, there is no discussion concerning what they are, their
effects, how many there are, and do they really affect learning?
Consequently, in this session, you will get the unbiased answers
to the above questions because…
You are Entering the
“No Spin Zone”
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Goals of This Webinar
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Inform--What are learning styles, cognitive styles, learning
modalities, and generational differences
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Educate--What does the research indicate?
Enlighten—So what? Why so much confusion? What can
instructors/trainers do to facilitate the transfer of learning?
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Presentation Menu
(Click any of the hyperlinks to go directly to that topic)
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What are Learning/Cognitive Styles?
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What are Learning Modalities?
What’s the Difference?
Review of the Research
What Does it All Mean?
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Generational Differences
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Learning /Cognitive Style Resources
• What About Generational Differences?
• Focusing on Generational Differences
• Myths and the Digital Generation
• Beyond the Hype—What Does it All Mean?
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So What?
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A recent article (Dec, 2009) in the Chronicle of Higher
Education entitled Matching Teaching Style to Learning Style
May Not Help Students, challenged the prevailing concept of
learning styles and their affect on student performance.
The investigators found “no evidence…for validating the
educational applications of learning styles into general
educational practice.” and concluded “the instructional
method that proves most effective for students with one
learning style is not the most effective method for students
with a different learning style.”
This is not surprising…these results and conclusions have
been supported repeatedly in the research for the past 30
years.
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So What?
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This is not a new debate but a continuing investigation into
the efficacy of learning styles that has spanned 60 years.
To that end, there is a strong intuitive appeal to the notion
there are individual preferences and styles of learning.
That said, we’re not going to solve the problem today, but…
at the end of this presentation, you will better understand:
The concept of learning styles and assess their variability
and impact on learning
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Why the Confusion?
Ask Google
Let’s explore some Google results. First, it is important to distinguish
between learning styles and cognitive styles…they are not the same
(we’ll discuss the “what” later).
“learning
styles”
AND
“cognitive
styles”
Search terms
“learning styles”
“cognitive styles”
“learning styles ” + “cognitive styles”
“learning
styles”
Results
1,900,000
201,000
28,700
“learning styles ” -“cognitive styles”
1,670,000
“cognitive styles” -“learning styles”
177,000
NOT
“cognitive
styles”
Question: How can one possibly
sift thru 1.9M hits and find the
useful information?
Answer: You can, but you don’t
have the time for that…so we
did it for you.
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What are Learning Styles?
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Basically, learning styles refers to the concept that individuals
differ in regard as to what specific mode in acquiring
information is most effective for them.
However, many educational psychologists believe learning
styles are a myth…that while individual differences in
learning exists, they are a result of acquired/innate
preferences but do not affect learning anymore than the truck
delivering groceries at your local store affects your dietary
habits.
Blaming learning styles as to why some students do not learn is a convenient
scapegoat for poor instruction
Dr. David Merrill, esteemed educational psychologist
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What are Learning Styles?
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Most often learning styles are characterized and categorized
by how information is preferentially perceived (sensory or
intuitive), organized (inductive or deductive), processed
(active or reflective), and modality preference (visual, aural,
or kinesthetic).
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The most commonly used model of learning styles are the visual,
aural, and kinesthetic (VAK) models.
The VAK model is perhaps the most misunderstood and over
emphasized reason as to why some students do not learn
The VAK test reliability and validity scores are so low that any
results and/or conclusions derived from them are suspect (more
on this latter)
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What are Cognitive Styles?
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Cognitive styles are viewed as a bipolar dimension
representing a person's typical or habitual mode of problem
solving, thinking, perceiving and remembering; are
considered stable over time, and related to theoretical or
academic research.
Cognitive styles primarily focus on cognition and how
information is processed in the brain.
So…what’s the difference? A learning style or modality describes
how information enters the brain: visually, aurally, or tactilely, whereas
cognitive style refers to how the information is processed once the
information gets to the brain.
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What are Learning Modalities?
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Learning, or perceptual modalities, are sensory based and
refer to the primary way our bodies take in information
though our senses: visual (seeing), auditory (hearing),
kinesthetic (moving), and tactile (touching).
Humans are multi-sensory in that the brain can perform
several activities at once when processing information (e.g.,
tasting and smelling, hearing and seeing) .
While the brain processes wholes and part simultaneously,
learning engages the whole body
Note: Multi-sensory processing is not the same as multi-tasking
(partial tasking) in that the brain is not very good at multi-tasking,
per se, attempting to perform multiple tasks simultaneously.
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What’s the Difference?
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Not surprisingly, there is substantial confusion between
learning styles and learning modalities where the terms are
often used interchangeably.
One of the reasons is the complexity of how the human brain
functions as it relates to one’s modalities in receiving
information (visual, aural, kinesthetic) and how the brain
processes that information (cognition).
Continued research into neuroscience is discovering how
the brain processes information acquired through our
primary learning modalities: visual, aural, and tactile.
Note: Neuroscience has estimated 85% of the human brain is wired to
process visual information, and that 90% of what the brain processes
is visual information, so one’s primary learning modality is visual.
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What’s the Difference?
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An important finding from that research is that memory is
usually stored independent of any modality.
You typically store memories in terms of meaning—not in
terms of whether you saw (visual), heard (aural), or physically
(tactile/kinesthetic) interacted with the information.
To that end, our brain is constantly searching its memory for
context based on prior knowledge/experience.
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Note: In the absence of visual cues, our brains create “mental
pictures” based upon our schema to add context to what is
printed/spoken. Click here for an example.
The so what: retention is improved through words and
pictures (visual media) rather than through words alone.
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What Does the Research Reveal
About Learning Styles?
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Simply stated, the research has not revealed a compelling
argument as to the impact of learning styles and their effect
on predicting learning outcomes.
Postulates learning/cognitive styles have <5% effect on the
variability in learning.
The majority of research does not support a significant
statistical relationship between learning/cognitive styles and
learning outcomes.
Based on several decades of empirical evidence, matching learning activities/
strategies with specific learning styles does not often result in improved learning.
Dr. Allan Jeong, Associate Professor, Dept. of Educational Psychology and Learning
Systems, Florida State University
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What Does the Research Reveal
About Learning Styles?
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Small sample sizes, flawed sampling methodology, and nonexperimental research designs casts doubt on the results of
VAK learning style research.
Low validity and reliability scores of the VAK instruments
used to identify specific learning styles raise serious doubts
about their psychometric properties.
In other words, if the tests used to identify learning styles are
not reliable or valid, then any conclusions or results based
upon them are suspect.
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What Does the Research Reveal
About Learning Styles?
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These tests are not controlled in their distribution and/or
reproduction, or administered under controlled conditions.
Scores on the VAK learning style tests vary greatly among same
individual.
VAK test questions focus on out-of-context preferences which
allows for wide range of interpretations.
Intervening variables confound the results.
For examples of the many different VAK learning styles test,
click these external links:
-- VAK test 1
-- VAK test 2
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What Does the Research Reveal
About Cognitive Styles?
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The research pertaining to cognitive styles is quite different
than learning styles in that the reliability and the validity of
the instruments used to identify them are much more robust.
The most researched cognitive style is Herman Witkin’s Field
Dependence/Field Independence.
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The test used to identify Field Dependence (FD) and Field
Independence (FI) is the Embedded Figures Test
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Highly reliable and valid (Cronbach Alpha >80% )
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Characteristics of Field Dependent
and Field Independent Learns
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Field dependent learner…
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Field independent learner…
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Processes information globally
Holistic approach to problem solving
Views the perceptual field as a whole
Socially oriented
Uses spectator approach for concept attainment
Relies on external cues to guide their behavior
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Highly analytical
Relies on internal cues (gravity and vestibular) to guide their
behavior
Breaks the field down into its component parts
Not influenced by the existing structure
Impersonal orientation
Uses hypothesis-testing approach to attain concepts
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What Does the Research Reveal
About Cognitive Styles?
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Results of Field Independent (FI) and Field Dependent (FD)
cognitive styles reveal:
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FI attracted to engineering, hard sciences, mathematics,
computer sciences/engineering
FI able to extrapolate detailed information from complex figures
FI correlated in predicting success in engineering courses
FI less influenced by visual effects in media
No difference in learning outcomes based on instructional media
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Given the Research,
Why all the Confusion?
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It’ s not surprising the reference to learning styles is one of
the most misunderstood and overused issues confronting
educational and training communities.
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Part of the reason is the wide disparity in the definition of
learning styles and their relationship to cognitive styles.
Furthermore, there is continued debate as to whether learning
styles even exist, with the only current evidence of their
existence being the tests used to identify them.
Confusion is further exacerbated in that the research has
identified over 71 different types of learning styles (Table 1),
summarized into the 13 most influential models (Table 2),
and families (Table 3)
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So What Does it all Mean?
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Since neuroscience has revealed 90% of what the brain
processes is visual information, one’s primary learning
modality is visual.
Therefore, compliment text-based presentations and lectures
with visual components/aids.
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This adds context to the written/spoken word
Provides a structure for learning when a task cannot be
completed through a series of steps
Note: Research has indicated prior knowledge and intrinsic motivation
account for ~70% of the variability in learning.
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So What Does it all Mean?
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Cognitive science has revealed learners differ in their
abilities with different modalities, but teaching to a learner’s
best modality doesn't affect learning outcomes.
What does matter is whether the learner is taught in the
content's best modality…people learn more when content
drives the choice of modality.
Different ways of knowing and understanding demand
different ways of designing and delivering instruction
Consider adapting learning environments and/or designing
instruction to accommodate a diverse learning population
by integrating cognitive learning strategies.
For more on cognitive learning strategies and knowledge construction, click here
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Conclusion
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Do learning styles exist…read the research and decide for
yourself.
Do learning styles affect performance and/or learning
outcomes…overwhelming body of research says no.
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To many variables affect performance.
Attempting to isolate variables requires a robust multivariate
experimental design.
Intervening /confounding variables that cannot be identified
may affect performance.
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Conclusion
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If you want conduct research on learning/cognitive styles…
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Ensure the tests used to identify them are reliable and valid.
Use an experimental research design.
Use valid sampling methodology.
Clearly define your independent (effect) variable.
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Generational Differences
Philip Westfall, Ph.D.
Director, Air Technology Network
Air University
Department of the Air Force
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What About Generational Differences?
Should generational membership be considered
when designing and delivering instruction?
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What About Generational Differences?
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Chronicle of Higher Education’s The Millennial Muddle (Oct. 2009)
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“The brightest bunch of do-gooders in modern history” or “selfinvolved knuckleheads”?
“To accept generational thinking, one must find a way to swallow
two large assumptions. That tens of millions of people, born over
about 20 years, are fundamentally different from people of other
age groups—and that those tens of millions of people are similar
to each other in meaningful ways.”
Palmer H. Muntz, Director of admissions Lincoln Christian University
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What About Generational Differences?
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Cooperative Institutional Research Program at UCLA show
small, gradual changes; differences are not significant
between generations but only over multiple generations.
Annual surveys since 1966. American Freshman: Forty Years of Trends
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Focusing on Generational Differences
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Chronicle of Higher Education’s Generational Myth report of
research on literacy and technology, Sept 2008:
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“Today’s young people—including college students—are just
more complicated than any analysis of imaginary generations
can ever reveal.”
Must consider vast range of skills, knowledge & experience of
many segments of society—avoid focusing more on needs of
socially or financially privileged and overestimate the digital
skills of young people in general
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Focusing on Generational Differences
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Familiarity with, understanding of, and dexterity with
technology varies greatly within the 18-23 age group a few
with amazing skills, a large number who can’t deal with
computers.
Keep in mind that in any large social grouping of people —
by geography, ethnicity, generation, etc.—there is far
greater variability on measures of attitudes & aptitudes within
a group than there is between groups
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Focusing on Generational Differences
Chronicle of Higher Education’s Generational Myth report
continues:
 Thinking in generations too simplistic—keeps us from
examining ethnic, gender, and class distinctions too closely.
 “Once we assume that all young people love certain forms
of interaction and hate others, we forge policies and design
systems and devices that match those predispositions. By
doing so, we either pander to some marketing cliché or
force otherwise diverse group of potential users into a one
size-fits-all system that might not meet their needs.”
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Myths and the Digital Generation
Myth: “The kids will take to e-learning like ducks to water”
Reality
 “Not quite . . . students do want to be connected, but
principally to one another; they want to be entertained,
principally by games, music, and movies. . . . E-learning at
its best is seen as a convenience and at its worst as a
distraction.”
Thwarted Innovation What Happened to e-learning and Why by Robert Zemsky
and William F. Massy 2004 by The Learning Alliance at the University of
Pennsylvania
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Myths and the Digital Generation
Myth: “The Digital Generation is far more adept at learning
through technology”
Reality:
 Greatest disappointment of our time: Huge investments made
in technology (beginning with Telecommunications Act of
1996) in public schools with negative results.
 Leisure-time tech skills don’t translate to educational &
training use of technology.
 Fast scanning doesn’t translate into academic reading.
 Reading proficiency dropped from 40% to 35% from 1992 to
2005.
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Myths and the Digital Generation
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Intellectual habits such as deep reflection decrease with
increase time spent on browsing, blogging, IMing,
Twittering, and Facebook
Online Literacy Is a Lesser Kind, Mark Bauerlein, Chronicle of Higher
Education, Sep., 2008
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Myths and the Digital Generation
Myth: “The Digital Natives lead the way in exploiting the
new social media”
Reality:
Recent study asked information workers of all ages in US, UK, France,
Germany, & Australia about business communication habits:
 Gen X—and not Gen Y—make up the majority of those who use
social networking for business, followed closely by Boomers age 55
and older.
 Gen Y is least likely to share information via text message (26%
compared to 47% of those age 55-plus), and least likely to use video
conferencing, video chat, and Web conferencing tools.
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Myths and the Digital Generation
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Gen Y uses social networking the least frequently (40% of
Gen Y workers who use social media for business do so
daily, compared to 50% of those age 55-plus).
Older Boomers (age 55-plus) have increased their business
use of social media 79% in the last year.
Gen Y Social Networking Technology Surprise, Citrix Systems, Inc, Tech Talk,
Feb 11, 2011
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Myths and the Digital Generation
Myth: “Research conclusively shows that learning abilities
and styles of the Digital Natives are significantly different
than those of previous generations”
Reality:
A comprehensive literature review in 2006 conducted by
Professor Thomas Reeves , Univ. of Georgia: Do Generational
Differences Matter in Instructional Design?
• Research on generational differences suffers from many
same weaknesses found in learning styles research—throws
grave doubt on validity of using learning styles as basis for
accommodating students of any generation.
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Myths and the Digital Generation
“A large group of university researchers has launched a
campaign to refute claims that generational differences in the
workplace are huge and pervasive. For example, researchers
from the University of Kentucky and Kutztown University argue:
Much prior generational research is based on samples limited to
college-bound adolescents, college students, or white-collar
workers… As such, claims of generational differences have been
limited by sample selection and other factors, leaving broad
judgments about entire generations open to skepticism and
criticism (Real, Mitnick, and Maloney, 2010).
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Myths and the Digital Generation
“Researchers at the University of Western Ontario, Michigan
State University, and the University of California, Davis, argue
against major generational differences in the workplace, citing
sample limitations, failure to replicate findings in a national
sample, and issues with the measures and instruments in studies
that found broad generational differences (Trzesniewski,
Donnellan, and Robins, 2008a).
Put simply, the generational stereotypes perpetuated in popculture media, misguided organizational training, and
lunchroom conversations have very little basis in fact.”
Age-Based Stereotypes: Silent Killer of Collaboration and Productivity by Chris
Blauth, Jack McDaniel, Craig Perrin, Paul B. Perrin, Ph.D. Published by Achieve
Global
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Beyond the Hype—
What Does It All Mean?
Student performance is not based in a significant way on:
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Learning style
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Generational membership
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Technology
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Beyond the Hype—
What Does the Research Reveal?
So what are the most significant learner variables?
The two most significant factors that have significant predictive
validity:
 Prior knowledge (ability/self-regulation)
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SAT scores r=0.64
USAFA PlaVal predictive validity r=0.67
Motivation (intrinsic value/self-efficacy)
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Journal of Ed Psy (APA), r=0.32-0.36
The rest is noise-level
Note: r = Pearson’s Product Moment Correlation
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Beyond the Hype—
Conclusion
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Instead of worrying about whether Boomers, GenXers or
Millennials will learn more from direct instruction or virtual
reality games…[one] should begin by identifying the needs
of any given set of learners, design the best possible
prototype learning environments in situ.
The Millennial Muddle” by E. Hoover, Chronicle of Higher Education, Oct 2009
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The End
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Describing and “Seeing” the
Constellation Orion
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The constellations are totally imaginary things that have been made
up over the past 6,000 years . So how would you describe something
imaginary to your students?
You may begin by describing the three bright stars in a row that
form Orion’s belt and the other stars that form his sword.
But your students have trouble “visualizing” how the stars shape the
figure of Orion. To assist them in creating a mental picture, you show
them a star chart of Orion to help them “visualize” this imaginary
figure.
But they still can’t quite get it, so to further enhance their mental
image, you show them another detailed chart depicting Orion.
The aha moment…they got it because they now can “see” Orion, so
they conclude they must be visual learners.
But…are they really visual learners or did you create the
visual image for them by adding context to the description?
Orion Star Chart
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Orion Figure Outlined
in a Star Chart
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The Constellation Orion
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Intentionally Left Blank
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Table 1: Types of
Learning/Cognitive Styles*
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 convergers vs. divergers
 verbalisers vs. imagers
 holists vs. serialists
 deep vs. surface learning
 activists vs. reflectors
 pragmatists vs. theorists
 adaptors vs. innovators
 assimilators vs. explorers
 field dependent vs. field
independent
 globalists vs. analysts
 assimilators vs.
accommodators
 imaginative vs. analytic
learners
 intuitionists vs. analysts
 extroverts vs. introverts
 seeing vs. hearing
 sensing vs. intuition
 thinking vs. feeling
 non-committers vs.
plungers
 common-sense vs.
dynamic learners
 concrete vs. abstract
learners
 random vs. sequential
learners
 initiators vs. reasoners
 judging vs. perceiving
 left brainers vs. right
brainers
 meaning-directed vs.
undirected
 theorists vs. humanitarians
 activists vs. theorists
 pragmatists vs. reflectors
 organizers vs. innovators
 analytics/inductives/succe
ssive processors vs.
globals/deductivess/simul
taneous processors
 executive, hierarchic,
conservative vs.
legislative, anarchic,
liberal
* Adapted from: Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Should we be using learning styles: What
research has to say to practice. Learning Skills and Research Centre, London. Retrieved from
http://www.ttrb.ac.uk/attachments/c455e462-95c4-4b0d-8308-bbc5ed1053a7.pdf
Table 2: Most Influential Models of
Learning/Cognitive Styles*
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Allinson and Hayes’ Cognitive Styles Index (CSI)
Apter’s Motivational Style Profile (MSP)
Dunn and Dunn model and instruments of learning styles
Entwistle’s Approaches and Study Skills Inventory for
Students (ASSIST)
Gregorc’s Mind Styles Model and Style Delineator (GSD)
Herrmann’s Brain Dominance Instrument (HBDI)
Honey and Mumford’s Learning Styles Questionnaire (LSQ)
Jackson’s Learning Styles Profiler (LSP)
Kolb’s Learning Style Inventory (LSI)
Myers-Briggs Type Indicator (MBTI)
Riding’s Cognitive Styles Analysis (CSA)
Sternberg’s Thinking Styles Inventory (TSI)
Vermunt’s Inventory of Learning Styles (ILS)
* Adapted from: Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Should we be using learning styles: What
research has to say to practice. Learning Skills and Research Centre, London. Retrieved from
http://www.ttrb.ac.uk/attachments/c455e462-95c4-4b0d-8308-bbc5ed1053a7.pdf
Table 3: Families of
Learning/Cognitive Styles*
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Learning styles are
largely sensory based
Learning styles reflect
deep-seated cognitive
structure
Learning styles reflect
relatively stable
personality type
Learning styles are
flexibly stable learning
preferences
Betts (1909) Betts
Inventory
Bartlett (1932)
Gordon (1949) Scale of
Imagery Control
Scheehan (1967)
Shortened Betts Inventory
Paivio (1971) Individual
Difference Questionnaire
(IDQ)
Marks (1973) Marks
Vividness of Visual
Imagery Questionnaire
Dunn and Dunn (1975,
1979, 1992, 2003) VAK
Learning Style Theory;
Learning Style
Inventory(LSI); Building
Excellence Survey (BES)
Torrance (1990) Style of
Learning and Thinking
Riding (1991) Cognitive
Style Analysis (CSA)
Guilford (1950)
Convergent/divergent
thinking
Prettigrew (1958)
Scale of Cognitive Style
Gardner et al. (1959)
Tolerant/ intolerant
Broverman (1960)
Kagen (1967) Matching
Familiar Figures Test
Messick (1976)
Analytic / non-analytic
conceptualizing
Hunt (1978) Paragraph
Completion Method
Cooper (1997)
Learning Styles ID
Weinstein,
Zimmerman, Palmer
(1988) Learning and
Study Strategies
Inventory
Witkin (1962) Group
Embedded Figure Test
(GEFT)
Myers – Briggs (1962)
Myers-Briggs Type
Indicator (MBTI)
Apter (1998) Motivation
Style Profile (MSP)
Epstein-Meier (1989)
Constructive Thinking
Inventory (CTI)
Miller (1991)
Personality typology:
cognitive, affective,
conative
Harrison- Branson
(1998) revised Inquiry
Mode Questionnaire
Jackson (2002)
Learning Style Profiles
(LSP)
Kolb (1976, 1985, 1999)
Learning Style
Inventory (LSI);
Revised Learning Style
Inventory (R-LSI); LSI
Version 3
Schmeck (1977)
Inventory of Learning
Processes
Honey and Mumford
(1982) Learning Style
Questionnaire (LSQ)
Felder and Silverman
(1989) Index of
Learning Styles (ILS)
Kaufmann (1989) The
A-E Inventory
Allinson and Hayes
(1996) Cognitive Style
Index (CSI)
Herrmann (1995) Brain
Dominance Instrument
(BDI)
* Adapted from: Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Should we be using
learning styles: What research has to say to practice. Learning Skills and Research Centre, London
Resources
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Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Should we be using
learning styles: What research has to say to practice. Learning Skills and
Research Centre, London. Retrieved from
http://www.ttrb.ac.uk/attachments/c455e462-95c4-4b0d-8308bbc5ed1053a7.pdf
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What are Cognitive
Learning Strategies?
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Cognitive learning strategies are methods used to help
learners link new information to prior knowledge in
facilitating the transfer of learning through the systematic
design of instruction
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Focuses on how the learner processes the knowledge
Provides a structure for learning when a task cannot be
completed through a series of steps (scaffolding)
Supports the learner as s/he develops internal procedures that
enable him/her to perform tasks that are complex, and can
increase the efficiency with which the learner approaches a
learning task.
Tailoring instruction for different levels of prior knowledge
What are Cognitive
Learning Strategies?
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Learning these strategies are aided by their incorporation into
instruction.
The utility of cognitive learning strategies can be employed by
faculty to facilitate the activation and retention of prior
knowledge by focusing on knowledge construction.
Knowledge construction is a methodological approach that
assumes knowledge needs to be constructed
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Involves the opportunity to critically analyze information, dialogue
with others about its meaning, reflect how the information fits within
one’s belief and value systems (schema), and arrive at a meaningful
understanding of that information
In this process, information becomes transformed into knowledge
What is Schema?
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The contents of long term memory are sophisticated
structures that permit us to perceive, think, and solve
problems, rather than a group of rote learned facts.
These structures are known as schemas (a mental
framework for understanding and remembering
information) and permit us to treat multiple elements as a
single element.
Schemas are the cognitive structures that make up our
knowledge base and assist us in knowledge construction.
Schemas can be “activated” through the use of cognitive
learning strategies
What is Schema Activation?
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Schema activation refers to an array of activities designed to
activate relevant knowledge in students’ memory prior to
encountering new, to be learned information.
Schema activation is the process of engaging prior knowledge,
which is organized in the brain in schemata .
Schema activation is an important scaffolding tool where learning
depends upon the activation of old knowledge to provide an
appropriate schema into which new knowledge can be
incorporated .
Schema
Activation
Prior knowledge: Schema activation
engages prior knowledge
New knowledge: Schema
activation links prior knowledge
to new knowledge
Comprehension: Schema
activation creates connections
which increase comprehension
Group Embedded Figures
Test Example (GEFT)