Should Instructional Designers Accommodate Learning Styles?
Gurupreet K. Khalsa
Instructional Design & Development, College of Education
University of South Alabama
Mobile, AL, USA
gkk901@jagmail.southalabama.edu
Abstract
In many ways, human learners are very similar. Learners take in sensory information, process
it within more or less standard neurological structures, and retain knowledge for future use.
However, some theorists propose that learners can be divided into different categories according to
how they prefer to access new information. As a result, a number of theories have emerged about
how people can be classified into different groups as learners, and proponents claim that learning
will improve if the instructional style is matched to learners’ styles. Instructional designers are
faced with a challenging dilemma: whether and to what degree to consider learning style categories
in order to design appealing and fruitful learning experiences that fit the particular needs of
individual learners. Because all learners engage in similar cognitive processes, instruction that
activates all processing systems will benefit each individual learner more effectively than will
focusing on specific learning styles serially or separately.
What are Learning Styles?
In the past few decades, educational theorists have expanded the work of early psychologists in the area of
individual learner differences. As a result, a number of theories have emerged about how people can be classified
into different groups as learners. Some have suggested additional propositions that learning styles meshed with
instructional methods will improve learning, although clear conclusions about the efficacy of this matching as a way
to maximize learning have not emerged (Coffield, Mosely, Hall, & Ecclestone 2004; Kinshuk, Liu, & Graf 2009).
The concept of learning styles has become more popular than previous iterations of biological or psychological
individual differences (Natale, Libertella, & Sora 2007; Paschler, McDaniel, Rohrer, & Bjork 2002). Generally
most theorists describe learning styles as different aspects of the ways that people learn, or process information, or
respond to environmental stimuli, in different dimensions (Dzakiria, Razak, & Mohamed 2004; Yilmaz-Soylu &
Akkoyunlu 2009). The way that individuals attend to and integrate complex new information determines which style
categorizes a learner (Dunn et al. 2009). Additionally, personal choice and preference play a role in what style one
chooses for learning (Kolb, Boyatzis, & Mainemelis 2001).
Diverse Models of Learner Categorization
Among the many frameworks or models that classify learners into specific learning styles, several have
achieved a stable and durable status due to continuing investigation and use of the measurement instruments in
experimental and educational situations. These models are the ones popularized and sold commercially through such
organizations as the International Learning Styles Network (www.learningstyles.net).
One of the most popular and enduring approaches to learning styles divides learners into three broad
categories: Visual, Auditory, and Kinesthetic (Friedman & Alley 1984). Many teacher education programs include
discussion in how to measure, recognize, and incorporate these styles into their lesson plans. Other models such as
Dunn et al. (2009), Kolb (1984) and Honey and Mumford (Honey, n.d.) categorize learning preferences under
several broad strands or characterizations of learning styles.
Felder and Silverman (1988, as cited in Graf, Viola, & Leo 2007) created an index of learning styles that
extends beyond the sorting of individuals into a few groups, acknowledging that while learners may have distinct
preferences, they also may exhibit other tendencies in different situations. This model has also been characterized as
having the most flexibility and applicability for online learning delivery systems. The dimensions of FelderSilverman’s Learning Style Model (FSLSM) distinguish between active vs. reflective processing, sensing vs.
intuitive learning, visual vs. verbal attention, and sequential vs. global understanding.
A different approach to styles centers on the concept of thinking styles. Sternberg and Grigorenko (1997),
in their Theory of Mental Self-Government, propose that cognitive styles integrate what seem initially to be discrete
psychological constructs: cognition and personality. Individuals are not distinctly categorized by one preference, but
exhibit characteristics of all levels and styles, differing in strength. The functions of mental self-government are
defined as legislative, executive, and judicial style. Within and congruent to these overall functions are various
levels, forms, scopes, and leanings that define approaches to mental tasks.
Besides what are projected to be innate or personality-based learning styles, other influences may impact an
individual’s learning preferences. Learning approaches may be culturally based (McAnany 2009; Simy & Kolb
2009), influenced by age or affected by experience with technology (Dzakiria, Razak, & Mohamed 2004), and
shaped by the discipline being studied. Whether knowledge is perceived and constructed differently, for example, in
scientific fields as opposed to the humanities is still open to debate.
Arguments for Accommodating Learning Styles in Instructional Design
If learning styles constitute our habits and preferences for learning, then a logical hypothesis would be that
meshing instruction with learners’ individual types will lead to better educational outcomes (Kinshuk, Liu, & Graf
2009). Many researchers have argued for accommodating learning style consideration into instructional products.
There has been considerable discussion around the concept of “matching” – that learners will perform better if their
learning style is matched to the instructional style, whether an actual teacher or a distance-learning delivery system
(Kozub 2010; Van Zwanenberg, Wilkinson, & Anderson 2000).
When more is known about how individuals learn, and what their preferences are for processing new
content, then the learning environments can be more personalized and adaptive (Graf, Viola, & Leo 2007). When
all these variables are acknowledged in a design task, then the instruction is flexible, balanced, and varied, and
serves all learners. Presenting information in text, visual, and experiential form covers the whole continuum of
Kolb’s Learning Cycles: concrete evidence, reflective observation, abstract conceptualization, and life experience.
Distance learning that includes journaling, metacognitive activities, problem solving, field trips with reports, and
direct observation of events, as well as explication of facts and concepts, incorporates attention to diverse learning
styles and suits individual preferences.
Dunn et al. (2009) report that universities where pre-service teachers study learning styles have brought
about an improved awareness of diverse approaches to delivering instruction. Two major meta-analyses revealed
moderate effects from matching teaching styles to learning styles, using the Dunn and Dunn inventory and
strategies. More importantly, however, Dunn et al. report that student attitudes toward learning improved, citing
increased engagement, willingness to explore alternative approaches, and examining achievement in ways beyond
standardized test scores.
Learners seem to be more satisfied with their learning when they are able to choose the type of interaction
with content that suited their style (Moallem 2007), exploring the content in different ways and spending more time
with the materials. An additional aspect of Moallem’s study required learners to perform tasks outside of their
preferred style, and this resulted in an increased willingness to go beyond their comfort zone to attempt different
approaches.
Silver and Perini (2010) argue that critics of the concept of learning styles have focused too narrowly on
experiments that try to match instruction to a specific learner style. Although minimal evidence has been found to
corroborate the theory that meshing instruction with learning style results in better learning, Silver & Perini dispute
that higher test scores are what really matter in learning. At the least, conversations about learning style have greatly
increased both teachers’ and students’ metacognitive capacities about how they learn, rather than taking for granted
the traditional delivery methods. At best, students who struggle to learn can examine their own preferences and
habits to discover ways content can be made more accessible due to their focused approach on “what works.”
Learning in different types of problem spaces (Jonassen and Hung 2008) may require different approaches
to the task depending on how well-structured or ill-structured the problem is. Zheng, Flygare, and Dahl (2009), cited
Witkin’s (1971) construct of learning styles, and determined that Field-Dependent (FD) learners performed better in
well-structured tasks than ill-structured ones, choosing a global approach and social cues. Field-Independent learners
(those who preferred an autonomous approach) did not do as well. However, the style-matching worked only part of
the time. The conclusion was that style matching works best when learners’ cognitive abilities are also scaffolded.
Arguments against Accommodating Learning Styles: Phantom Constructs
It is evident that even among the various models discussed above there is a great deal of disparity in how
learners can be classified and how this information can be used to develop instruction. There is no doubt that
individuals develop preferences for learning that can be categorized using any one of the systems that theorists have
proposed. However, the question arises as to whether any one model, or all models taken together, are valid
indicators of how instruction should be designed. Do learning styles exist other than as theoretical constructs? Given
the multitude of intersecting and overlapping style inventories, as well as the nature of the varied constructs
(psychological, thinking styles, cognitive processing, personality), and the numerous style models (71 and growing,
according to Paschler, McDaniel, Rohrer, & Bjork 2008), an argument can be made that style models and systems
are artificial layers imposed on basic learning processes common to all individuals.
Learners stereotyped as having one particular learning style may find themselves locked into one kind of
learning experience, discouraging experimentation with other learning approaches. (Zheng, Flygare, & Dahl 2009).
Rather, all learners will likely benefit, over time, by being exposed to and using a varied toolbox of cognitive
strategies to access content knowledge.
In the early days of Instructional Design, modernist models such as the Dick, Carey, and Carey (2009)
model did not include specific mention of learning styles. While an important part of the design process is Learner
Analysis, this is usually taken to mean general learner knowledge base, experience, age and attitudes rather than
specific learning styles. In later editions of the model, there is a post-modernist nod to constructivist, learnercentered instructional strategies, but without a specific focus on individual learner styles or meshing the teacher style
to student preferences. Gagne’s (1969) Nine Events of Instruction suggest that instructors determine and activate
background knowledge of the learners for the most effective learning, but there is no decree to incorporate the
preferences of individual learners.
Learning style advocates propose that learners who know their own style are more effective at learning;
thus, it follows theoretically that if a teacher is cognizant of the style sets in his or her own instructional realm,
teaching can be tailored to the needs of the individual students. However, as Franklin (2006) points out, most people
learn in a highly visual manner anyway; educational materials consist primarily of words in books or on screens,
charts, diagrams, and images. Visual materials have come a long way from black-and-white printed books, and it
could be argued that interesting visual material will benefit all learners, not just ones who prefer to look at things.
Auditory information is also common in educational systems. Of course, most people will “tune out” if the
sound is a droning lecture. However, oral interchange and discussion are not necessarily valuable for only auditory
learners. Further, active learning that involves manipulatives or hands-on activities can be instructive for all learners,
not just those who have trouble sitting in a chair for long periods of time or those who are unfortunately classified as
“non-academic” because they are athletically proficient.
Many of the learning style instruments in popular use have not been validated (Coffield, Mosely, Hall, &
Ecclestone 2004). In a series of experiments that were designed to test whether matching instruction to the modality
of the learner would result in better learning and memory, Kratzig and Arbuthnot (2006), using the Visual, Auditory,
and Kinesthetic categories, discovered that there was no significant relationship between learning style and memory
performance. It appears from their work that people are able to learn very well through all three modalities or
through a combination of them, and that “learning is not static but, instead, is situationally motivated and goal
motivated” (p. 241).
Franklin (2006) proposes that the entire idea of Visual, Auditory, and Kinesthetic learning styles is
overrated and scientifically unproven. Although a “vast commercial enterprise based on the notion of learning
styles” (p. 81) has grown in recent years, Franklin argues that it is much more important to understand how universal
cognitive processes can contribute to better learning than it is to try to artificially assign children to a “style” that
might be misleading or incorrect. Whether such attributes as athletic/kinesthetic ability or “musical intelligence” are
innate or simply a result of dedicated practice is a subject of hot debate in psychological circles. Howard Gardner
developed his theory of Multiple Intelligences in the 1980’s. Since that time, he has repeatedly distanced himself
from educational policy implications of the theory, having conceived intelligence as a construct, not a learning style
or domain (Gardner 1995). He does not assert that implementing instruction based on a child’s particular
intelligence is a better system than a teaching style that uses a variety of instructional strategies (Moran, Kornhaber,
& Gardner 2006). What he does encourage is for teachers to move from a traditional lecture-delivery model toward
a teaching style that includes different media, active experiences, constructivist approaches, and many different
learning tasks, whether or not those tasks are specifically targeted for one “intelligence” or another.
Another argument against the validity of learning styles is that in answering a questionnaire, a respondent
might be mentally situated in a particular context; one’s reactions might be very different at a different time or place,
or with different frameworks in mind. Learning style theorists do not claim that a person fits exclusively into one
category or another; some note that a person might display different preferences for one style of learning on one
occasion, a different style on another. A learner might be an Assimilator one day and a Converger in another
context. No research has firmly established an exclusivity of categories, but rather stronger or weaker inclinations
depending on the circumstances in which learning occurs. Sternberg and Grigorenko (1997) argue that “thinking
styles seem to be largely a function of people’s interactions with tasks and situations” (p. 708) which supports the
hypothesis that people adapt their style to the immediate learning task. In online learning environments, learners
seem to adapt their preferences according to the demands of the instruction and activities (Moallem 2007). It may be
that technology alters learning for everyone, despite what one’s preferences might be.
Cengizhan (2008), using the Grasha-Reichmann Student Learning Style Scale (1994), did not find any
significant impact in long-term retention when learning styles were considered in instruction. What did make an
impact, positively affecting student outcomes, was instruction using a “modular” system that included a variety of
materials and active participation in a flexible learning environment, which inherently would make content much
more accessible than a lecture/test approach. Even Kolb, Boyatzis, and Mainemalis (2001) acknowledge that the
evidence for the predictive ability of Kolb’s Learning Style Inventory for learner outcomes is mixed and not
significantly supported by empirical evidence.
Different individuals possess varying degrees of specific abilities. Yet in a meta-analysis of studies
investigating whether or how learning styles increased learner outcomes, Paschler, McDaniel, Rohrer, and Bjork
(2008) found only one study, by Sternberg, Grigorenko, Ferrari, and Clinkenbeard (1999), that even moderately met
the criteria of convincing evidence for significant impacts. The authors note that a “search of the learning-styles
literature has revealed only a few fragmentary and unconvincing pieces of evidence that met [the] standard, and
therefore we conclude that the literature fails to provide adequate support for applying learning-style assessments in
school settings” (p. 116). Moreover, in an exhaustive 16-month study of 13 (of 71) popular learning style models,
Coffield, Mosely, Hall, and Ecclestone (2004) found almost no evidence that learning styles have significant
implications for pedagogy.
Accommodation of Learning Styles is Not Critical to Good Instructional Design
Along with other educational researchers, I acknowledge the uniqueness of each individual as a learner,
and I believe it is possible in a post-modern world to provide instruction that will serve a wide range of learners
without getting too tangled in the maze of learning style theories. The evidence for the efficacy of incorporating
learning styles as a way to achieve maximum learner performance is slim (Coffield, Mosely, Hall, & Ecclestone
2004; Guven & Ozbeck 2007; Franklin 2006; Paschler, McDaniel, Rohrer, & Bjork 2008). Yet it is very possible to
design varied, engaging instruction that is attentive to a wide range of learner experience. Good instruction based on
careful analysis of the intended learners’ knowledge base, age, needs, and characteristics, even if each learner’s
personal style preferences are not addressed, will accomplish its purpose.
The complexity of trying to distinguish and develop distinctive learning experiences for each individual
learner can be overwhelming. If one person is, for example, an Asian visual, sequential converger, and another is a
Western kinesthetic, intuitive assimilator, then the range of necessary activities for a designer to develop becomes
enormous. Finding places on the endless continuum of possible learning style models is a monumental undertaking.
Further, catering to one particular preference throughout one’s life may preclude opportunities to experiment with,
practice, and master other modes of learning.
There are a number of principles that can guide an instructional designer outside of the debate of whether to
focus on one or another learning style model. Exposure to many different instructional approaches is important. No
student would like to endure month after month, year after year, of lectures and exams, although this has been a
traditional method of instruction for a very long time. Recent learning theories, such as Social Learning Theory ,
Constructivism, Cognitive Load Theory, and Cognitive Theory of Multimedia Learning (Mayer 2005) have
provided instructional designers with multiple avenues through which diverse learning experiences can be
structured, giving learners a wide variety of ingress into the desired content. Incorporating a variety of teaching and
assessment methods, being aware of cultural and gender differences, and providing students opportunities to reflect
on thinking and metacognition will improve learning outcomes.
The type of instruction needed depends significantly on the discipline, the context and the content of what
is being taught (Paschler, McDaniel, Rohrer, & Bjork 2008). A thorough analysis of these elements ensures that
instruction will be aligned with expected outcomes. A geometry course, for example, will likely have a much
different presentation than will a writing course. Plenty of opportunities to engage in critical thinking provide
students with a way to deconstruct and reconstruct the desired content in any discipline.
Learners do not fit into type categories of just one of many proposed models. No matter what the discipline
or the context of instruction may be, if a designer includes a spectrum of sensory components that genuinely
advance learning, not as frivolous add-ons or to reach specifically targeted learner styles, then every learner will
benefit (Sorden 2005). When a learner is exposed to different media in an instructional module, he or she will
“develop multiple schemas” (p. 268) working together for mastery of content. Mayer’s (2005) Cognitive Theory of
Multimedia Learning proposes that the best instruction includes information presented both visually and aurally,
without redundancy or overload.
Particularly in this new age of computer-based, multi-media, essentially “instructor-less” learning delivery
systems, the notion that teacher and learner styles should match may be passé. Sternberg and Grigorenko (1997)
suggest that it is a stretch to try to mesh teaching styles with learning styles. For face-to-face instruction, it is critical
for teachers to have a wide repertoire of teaching strategies and styles, in order to adapt to the learners and the
content in any given situation. For digital environments, designers need to have a sundry toolbox to make the
instruction clear and adaptable.
Conclusion: Implications for the Future
An instructional designer would find it challenging to determine which system of learning-style
classifications would be most appropriate for a particular design project. There are so many style inventories that, in
their own right, seem logical and suitable for dividing people into groups for instructional planning purposes.
However, no one model is going to fit or match the potential range of learners. Age, cultural background, prior
knowledge, and habit may play important and unknown parts in the profile of an individual learner. And learning
styles may change due to the influence and pervasiveness of digital media delivery systems. It is simply not possible
to discern every overlapping component to which instruction should be tailored.
According to Mayer (2008), who proposes a multiple representation principle for multimedia instruction
that includes both animation and narration, learners will retain and transfer more knowledge than if just one
modality is used in presenting information. This holds true for all learners, not just those with a professed or
preferred style of learning. It is most critical to scaffold and support learners to develop increased cognitive abilities,
and to increase their knowledge base. Because all learners engage in similar cognitive processes involving selecting,
organizing, and integrating information, instruction that activates all processing systems without overloading any
one of them will benefit each individual learner more effectively than will focusing on specific learning styles
serially or separately.
Instruction that is engaging, flexible, and relevant to the intended audience, and that is presented in ways
that encompass varied avenues of access is likely to meet the needs of diverse learners, no matter what their current
or preferred style may be (Paschler, McDaniel, Rohrer, & Bjork 2008). A person’s thinking about and attention to
his or her individual learning preferences may be useful in developing a metacognitive awareness of how best to
approach a particular learning goal, which is highly dependent on the discipline, the content, and the context of the
task, but it is not useful to lock oneself into a learning style that limits participation in engaging learning experiences
offered through a variety of well-designed instructional strategies.
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