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Is learning styles-based instruction effective? A comprehensive analysis of recent research on learning styles

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  • University of North Georgia Dahlonega


In an influential publication in 2009, a group of cognitive psychologists revealed that there was a lack of empirical evidence supporting the concept of learning styles-based instruction and provided guidelines for the type of research design necessary to verify the learning styles hypothesis. This article examined the literature since 2009 to ascertain whether the void has been filled by rigorous studies designed to test the matching hypothesis and identify interaction effects. Correlational and experimental research recently published on learning styles is reviewed, along with an examination of how the subject is portrayed in teacher education texts. Results revealed that the more methodologically sound studies have tended to refute the hypothesis and that a substantial divide continues to exist, with learning styles instruction enjoying broad acceptance in practice, but the majority of research evidence suggesting that it has no benefit to student learning, deepening questions about its validity.
Theory and Research in Education
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DOI: 10.1177/1477878515606621
Is learning styles-based
instruction effective? A
comprehensive analysis
of recent research on
learning styles
Joshua Cuevas
University of North Georgia, USA
In an influential publication in 2009, a group of cognitive psychologists revealed that there
was a lack of empirical evidence supporting the concept of learning styles-based instruction
and provided guidelines for the type of research design necessary to verify the learning styles
hypothesis. This article examined the literature since 2009 to ascertain whether the void has been
filled by rigorous studies designed to test the matching hypothesis and identify interaction effects.
Correlational and experimental research recently published on learning styles is reviewed, along
with an examination of how the subject is portrayed in teacher education texts. Results revealed
that the more methodologically sound studies have tended to refute the hypothesis and that a
substantial divide continues to exist, with learning styles instruction enjoying broad acceptance
in practice, but the majority of research evidence suggesting that it has no benefit to student
learning, deepening questions about its validity.
Achievement, cognition, interaction effect, learning styles, matching hypothesis, research-based
Over the last two decades, learning styles instruction has become ubiquitous in public
education. It has gained influence and has enjoyed wide acceptance among educators at
all levels, parents, and the general public (Pashler et al., 2009). It is prevalent in teacher
Corresponding author:
Joshua Cuevas, 210 A Dunlap Hall, University of North Georgia, Dahlonega, GA 30597, USA.
606621TRE0010.1177/1477878515606621Theory and Research in EducationCuevas
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2 Theory and Research in Education
education programs, adult education programs (Bishka, 2010), promoted in k-12 schools
in many countries (Scott, 2010), and frequently a main attraction at academic confer-
ences. School districts and universities spend millions of dollars each year on assess-
ments, training programs, textbooks, materials, and speakers who advocate for learning
styles instruction.
The phrase learning styles refers to the concept that different people prefer to process
information in different ways and therefore learn more effectively when they receive
instruction in a way that conforms to their preferences (Pashler et al., 2009). The inven-
tories created to measure learning style preferences generally classify learners into dif-
ferent style categories. Since at least the 1960s researchers have hypothesized about
aptitude–treatment interactions (ATIs), the idea that a student’s aptitude, in some cases
characterized by a student’s preference such as learning style, can interact with a corre-
sponding treatment (instructional approach) to produce an enhanced effect, most com-
monly purported to be increased learning (Scott, 2010). By the 1970s, the bulk of the
empirical research had refuted the most common hypotheses associated with ATIs, yet
the idea remerged a decade later to find unprecedented acceptance and widespread use in
the form of learning styles-based instruction. These practices are so widely accepted that
they go largely unquestioned (Bishka, 2010). The vast amount of educational time,
resources, and funds spent on learning styles would suggest that it is warranted to closely
examine the claims behind the practice and the research that supports it.
Pashler et al. (2009) trace the history of learning styles to the Myers–Briggs assess-
ment that became popular in the 1940s and continues to find extensive use today. The
Myers–Briggs is commonly used by businesses to make occupational decisions about
the suitability of potential employees. The idea that people cluster into categories as
conceived by the Myers–Briggs is not strongly supported by research, yet that has not
limited its popularity. In essence, there seems to be an appeal for industries and the
general public to find out what ‘type of person’ someone is by slotting them into prede-
termined categories, and this concept has found its way into a wide variety of educa-
tional settings.
Other researchers trace the learning styles phenomenon to the much more recent
development of Gardner’s concept of multiple intelligences. Gardner initially proposed
that there are eight forms of intelligence that all people possess: visual-spatial, verbal-
linguistic, logical-mathematical, bodily-kinesthetic, interpersonal, intrapersonal, musi-
cal, and naturalistic. Allcock and Hulme (2010) argue that Gardner’s multiple intelligence
theory (Gardner, 1991, 1993) has influenced the learning styles approach by advocating
matching instruction to students’ preferred learning style. They point out that many
teachers are expected to consider all intelligences when lesson planning in order to
appeal to students’ learning styles. Fridley and Fridley (2010) also link the expansion of
learning styles to Gardner’s hypothesis and emphasize inherent weaknesses in Gardner’s
model. While Gardner’s propositions have encountered substantial criticism in the field
of psychology due to a lack of empirical support, this analysis will focus only on learning
styles, as it has become an extensive field in its own right.
Kolb’s (1984, 1985) inventories are the most commonly used learning styles models
in recently published research. The Kolb inventory classifies learners along two dimen-
sions: a preferred mode of perception (concrete or abstract) and a preferred mode of
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Cuevas 3
processing (active experimentation or reflective observation) (Gogus and Gunes, 2011;
Pashler et al., 2009; Zacharis, 2011). Based on these categories, it classifies learners into
one of the four categories: divergers who favor feeling and watching (concrete, reflec-
tive), assimilators who favor thinking and watching (abstract, reflective), convergers
who favor thinking and doing (abstract, active), and accommodators who favor feeling
and doing (concrete, active). As with other learning styles frameworks, there have been
concerns about the validity of the constructs measured in the Kolb inventories as well
(Kappe et al., 2009; Martin, 2010).
But while Kolb’s inventories are commonly used in research, the visual/auditory/
kinesthetic (VAK) or visual/auditory/read–write/kinesthetic (VARK) is the most com-
mon learning styles taxonomy in practice (Bishka, 2010; Fridley and Fridley, 2010;
Riener and Willingham, 2010) and has become commonplace at all levels of education
and through a wide range of commercial products. VAK/VARK instruments can be found
in a wide variety of different forms and can be traced to numerous theorists, but are most
commonly associated with Fleming (2001). Scott (2010) suggests that the VAK/VARK
model may have taken hold to the extent that it did in educational settings because the
categories relate to specific senses and are concrete in comparison to other learning
styles models which can appear abstract to the point of ambiguity. But this grounding in
our natural senses should also make this model more straightforward to study. For
instance, since the premise of the learning styles hypothesis is that matching learning
style to instructional mode produces increased learning, for the VAK/VARK models, this
would mean matching instruction to students’ sensory functions – a visual learner would
be provided visually oriented instruction, an auditory learner would be provided with
verbal instruction, and so on. This would seem to be more readily measurable than the
more fluid constructs of the Kolb inventory.
Some researchers, however, have questioned the validity and reliability of various
learning styles inventories. Fridley and Fridley (2010) argue that VAK instruments have
little or no predictive value. They note that according to the learning styles hypothesis, if
instruction is matched to students’ learning preferences, then we should see an increase
in learning, yet research does not yet support this claim. Scott (2010) points out that fac-
tor analyses have shown Kolb’s learning styles inventories to be unreliable, bringing into
question the validity of the constructs they purport to measure. Another popular model,
Honey and Mumford’s (1986) Learning Style Questionnaire (LSQ) was developed pre-
cisely because of concerns about the validity of the Kolb assessments (Kappe et al.,
2009). The LSQ identified four types of learners: activists, theorists, pragmatists, and
reflectors. But factor analyses have shown the LSQ to have reliability issues as well
(Scott, 2010).
However, despite concerns about the validity and the reliability of the measures, the
commercial component of the field is so vast that there is little incentive for critical
reflection based on objective empirical findings (Bishka, 2010). These commercial enti-
ties have been a powerful force behind the propagation of learning styles instruction, a
curious dynamic at odds with the reality that educational psychologists, those who are
best equipped to study the concept, generally regard it with great skepticism (Scott,
2010). But lay people in the business world, administrators in education, and teachers in
the classroom tend to be unfamiliar with psyc顺心彩票tric evidence and remain unconvinced
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4 Theory and Research in Education
with it when it is presented to them, instead allowing the marketing of the product to
influence their decision-making. Fridley and Fridley (2010) speculate that the expansion
of learning styles is mainly due to flourishing professional development programs where
educational and commercial goals overlap despite the fact that support for learning styles
is sparse in peer-reviewed literature. A number of other researchers have noted the seem-
ingly incongruous dynamic of a highly profitable and thriving learning styles industry on
one hand and a lack of empirical support for the method on the other (Kappe al., 2009;
Pashler et al., 2009; Rohrer and Pashler, 2012).
Pashler etal. (2009), the matching hypothesis, and an
interaction effect
Recently, a group of distinguished cognitive psychologists were commissioned to assess
the type of evidence that would be required to confirm the learning styles hypothesis and
to search for empirical research that met those criteria (Pashler et al., 2009). These
researchers concluded that in order for the learning styles hypothesis to be confirmed,
numerous well-designed studies would have to test the matching hypothesis and show
significant interaction effects. The matching, or meshing, hypothesis implies that stu-
dents’ learning is enhanced when a mode of instruction is used that matches their learn-
ing preference. It is not enough for research to simply show that students may have
preferences for certain modes of learning because studies on metacognition have consist-
ently shown that students’ preferences and evaluation of their own learning tend to be
highly inaccurate when compared to actual learning. Consistent, replicable evidence of
achievement is necessary to justify the cost and effort required to implement learning
styles-based instruction.
The criteria Pashler et al. (2009) identified for a study to provide adequate evidence
for the learning styles hypothesis are as follows: multiple groups or conditions, random
assignment of participants, all subjects must be given the same achievement test, and
findings must show a crossover interaction effect where students show higher achieve-
ment when they are in a condition in which their learning style matches the instructional
mode (i.e. visual learners excel more with visual instruction and auditory learners excel
more under auditory conditions such as lecture or discussion) and lower achievement
when there is a mismatch. Pashler et al. differentiate between learning preference and
ability although they acknowledge that in practice educators make little or no distinc-
tion between the two. If learning style is conceived as a proxy for ability, as some
researchers have noted is often the case (Scott, 2010), then the hypothesized effect
would be classified as an ATI. Aptitude would be represented by the learning style,
treatment would be represented by a mode of instruction that matched at least some
students’ preferred learning styles, and a significant interaction effect would reveal
greater learning in the matched groups.
Having identified the type of research design that would be necessary to validate the
learning styles hypothesis, Pashler et al. (2009) set out to find published peer-reviewed
studies that met those criteria. Remarkably, even though there is a vast amount of litera-
ture on learning styles in the form of books, training materials, practitioner guides, theo-
retical articles, and so on, only one study could be found that met the criteria and reported
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positive results. And that study’s findings were questionable because of a number of
methodological issues. In addition, the instructional modes it tested did not correspond
to any of the widely promoted learning styles inventories. Pashler’s team could not find
one study of sufficient quality that supported any of the learning styles models com-
monly promoted and marketed to teachers. They did, however, find a number of studies
with strong designs that showed negative findings contradicting the learning styles
hypothesis. In short, there was almost no research whatsoever that supported the learning
styles hypothesis, but there was some high quality research that seemed to refute it. So as
of 2009, there was little evidence that learning style-based instruction improves student
learning. Pashler et al. came to the conclusion that the application of learning styles
instruction is unwarranted given the lack of support for the method.
Since then other researchers have highlighted the need for learning styles proponents
to produce rigorous studies showing a significant interaction effect to confirm the match-
ing hypothesis and have emphasized that prior research has failed to do that (Allcock and
Hulme, 2010; Bishka, 2010; Martin, 2010). Norman (2009) acknowledges that most
research surveys students’ learning styles and then simply makes the assumption that it
would be good to tailor instruction to those styles without actually ever testing that
assumption. However, he notes, studies that have tested that assumption empirically
have almost universally found no effect. He concludes that while learning styles instruc-
tion has very broad appeal, it has very little to do with learning. Mayer (2011) similarly
argues that learning styles research has persistently lacked rigor and that there has been
no evidence that clearly supports the application or practice of learning styles-based
instruction. While much has been written about learning styles, very little of it contrib-
utes to evidence-based support for the concept.
Two other cognitive psychologists, Riener and Willingham (2010), classify learning
styles as a myth. They contend that there is no credible evidence that learning styles exist
and that real harm may be done by the education establishment’s continued insistence on
implementing instructional methods that we know do not work. While learners have
preferences, Riener and Willingham note that when put to the test empirically under
controlled conditions, these preferences have no effect in terms of the amount of material
learned or the pace of learning. They simply make no difference. And from an instruc-
tional point of view, there may be another problem with learning styles. It is not only
difficult, but largely ineffective to try to find ways of delivering instruction that are based
purely on preference yet independent of content. For instance, it would seem inefficient
and unproductive to attempt to teach math through auditory means and music through
visual means when other formats match the content better.
Others have expressed concerns that, in addition to a lack of credible empirical evi-
dence, learning styles research has not been grounded in credible psychological concepts
(Allcock and Hulme, 2010; Pham, 2012). Instead, studies on the subject have generally
been published in periodicals other than psychology journals and have used research
designs that do not conform to basic and widely accepted psychological principles. This
is a major weakness of the field considering that the learning styles concept is based on
assumptions about cognition and is, at its heart, a psychological hypothesis. Kappe et al.
(2009) and Rohrer and Pashler (2012) have also questioned the methodology of the bulk
learning styles research and contend that little of that research is linked to cognition or
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6 Theory and Research in Education
achievement. Fridley and Fridley (2010) illustrate a curious case in which an inordinate
number of dissertations focused on learning styles have emerged from the college of
education at St. John’s University, yet relatively few were published, presumably because
they were unable to pass peer review. So despite the continued prevalence and expansion
of the learning styles concept in practice, a number of scholars, researchers, and psy-
chologists have expressed substantial doubts about its validity.
The purpose of the investigation detailed in this article was to examine the empirical
research on learning styles since 2009 when Pashler et al. published their influential find-
ings. After 2009, when it was revealed that despite its vast popularity there was virtually
no empirical research to support the learning styles approach, the expectation was that
researchers would rush in to fill that void in the research literature. Because Pashler et al.
so clearly laid out a template for how a study should be designed and revealed the criteria
necessary to confirm the learning styles hypothesis, researchers would have a blueprint,
definitive guidelines for how to design their studies. The current investigation searched
for this type of evidence in an attempt to evaluate whether a gap remains between
research findings and the methods advocated in pre-service teacher education programs
and those practiced in the classroom. Has new evidence, based on rigorous methodology,
surfaced to support widespread learning style practices, or does the latest research con-
tinue to suggest that learning styles instruction is a misguided and wasteful endeavor? It
also went further and examined how learning styles are represented in teacher education
textbooks, as this could potentially be one of the causes of the gap between research on
the subject and its acceptance in practice.
The learning styles hypothesis is arguably more important to teacher education than any
other field because what tens of thousands of pre-service teachers learn in certification
programs and subsequently take with them into the classroom can potentially impact the
instruction of millions of k-12 students over the decades they teach. But, like students in
other undergraduate fields, undergraduate students in the field of education do the bulk
of their assigned readings from textbooks and do not tend to read a great deal of peer-
reviewed primary source research studies, which are usually not picked up en masse until
the graduate level. For this reason, it is worth briefly examining how learning styles are
portrayed in a number of textbooks common to teacher education programs. Both gen-
eral education texts containing advice for pre-service undergraduate teacher candidates
and undergraduate educational psychology texts were included, and their portrayals of
the subject of learning styles are contrasted below.
General teacher education texts
All of the general teacher education texts reviewed for this article included sections on
learning styles, most commonly in conjunction with a discussion of multiple intelligences,
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and presented the topic as a way to differentiate instruction for learners. Some presented
information on multiple intelligences and learning styles as if the two were synonymous,
flowing seamlessly from one to the other (Hipsky, 2011; Silver et al., 2000). The model
that was most frequently described was the sensory-based visual, auditory, kinesthetic,
tactile (VAKT) framework (Carjuzaa and Kellough, 2013; Powell, 2012; Smith and
Throne, 2009). One text used the 4MAT system in discussing learning styles, a model that
encompasses experiences, viewing, doing, and exploring what-if questions (Wormeli,
2007). Another advocated for the Myers–Briggs model for math instruction, which
includes mastery, understanding, self-expressive, and interpersonal domains (Smith and
Throne, 2009).
Because all of the texts were targeted at training future practitioners, they were heav-
ily focused on practical application in the classroom. Smith and Throne (2009) provide
the reader with a reference to an assessment designed to measure multiple intelligences,
which is simultaneously listed as a learning styles assessment. The implication is that
teachers should test students for learning style preferences so that they can modify
instruction to conform to those preferences. Likewise, Powell (2012) suggests that teach-
ers incorporate multiple intelligences and learning styles information into their instruc-
tional plans. Carjuzaa and Kellough (2013) also recommend that teachers should take
learning styles into account and plan instruction based on them. Hipsky (2011) provides
suggestions for different ways for teachers to modify their instruction to conform to stu-
dents’ learning styles. Learning styles seems to be a foundational aspect of this text, as it
is mentioned early and much of the advice that follows throughout the book branches
from the idea that instruction should be differentiated according to students’ learning
styles. Silver et al. (2000) argue that when designing performance assessments for the
classroom, the four learning styles should be integrated with the seven multiple intelli-
gences. So, for instance, for verbal-linguistic intelligence, teachers would offer a sepa-
rate assignment relating to each of the four VAKT learning styles. Presumably, the same
would be done for logical-mathematical, spatial, kinesthetic, musical, interpersonal,
intrapersonal, and naturalist forms of learning.
In order to justify their advocacy for incorporating learning styles-based instruction
into teaching practice, a number of the authors make a variety of strong claims in their
texts. Carjuzaa and Kellough (2013) make the case that learning modalities influence
students’ academic learning and that teaching to these modalities has been shown to
increase academic achievement, a very tenuous assertion based on the empirical research
we now have. In reference to learning styles instruction, Wormeli (2007) states that the
‘single greatest tool you have as a teacher is your knowledge about how the mind works’
(p. 75). Smith and Throne (2009) describe multiple intelligences and learning styles as
‘brain-based predispositions’ (p. 90). In describing learning styles as ‘how the mind
works’, it suggests that the hypothesis has been confirmed as part of the human cognitive
process. In using the term ‘brain-based predispositions’, it suggests that the learning
styles hypothesis is based on a biological reality, that some identified neurological struc-
ture guides learning in a way these authors describe. Again, these assumptions are not
supported by current research in the field.
Considering that these texts make a number of substantial claims and advise teachers
to deliver instruction that conforms to the learning styles hypothesis, the evidence they
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8 Theory and Research in Education
present for the concept becomes highly relevant. Yet, almost none of these texts referred
to peer-reviewed research on the subject. Powell (2012) did not mention anything about
the empirical support or lack thereof for the models advocated. Wormeli (2007) did not
discuss the status of empirical research behind the theories or provide critical analysis.
Likewise, Smith and Throne (2009) made no mention of empirical research or doubts
about the efficacy of the methods. Carjuzaa and Kellough (2013) discussed a handful of
theorists on the topic but did not mention doubts about the validity of the method or any
information about empirical research on the matter. Instead, they presented the informa-
tion as if it was widely accepted and verified. Silver et al. (2000) cited theoretical sources,
most often books. Very few empirical studies were mentioned, and those that were dis-
cussed were comprised of survey research that gauged student preferences, not learning.
The text was entirely devoid of any mention of empirical studies that showed that the
learning styles approach could have a positive impact on student learning. The authors
discussed the topic as if the hypothesis had been confirmed. Hipsky (2011) also dis-
cussed learning styles as if the matter is settled and accepted by consensus with no men-
tion of doubts among researchers or controversy in the literature. It is worth noting that
none of the authors of these texts appear to have a background in cognitive psychology,
educational psychology, or neuroscience, the fields most well-equipped to delve into the
legitimacy of the learning styles hypothesis.
The evolution of textbooks’ treatment of learning styles can be illustrated through two
interesting cases: one in which a current version actually expanded its discussion of the
topic and one in which the treatment of the topic changed dramatically in a more recent
work. In a 2010 version of an undergraduate, pre-service teacher education textbook, the
authors explain learning styles in terms of individual differences (Parkay and Stanford,
2010). They acknowledge that critics have pointed out that there is little evidence to sup-
port the idea of learning styles or the validity of learning styles assessments. Yet the
authors still argue that teachers should identify students’ learning styles and tailor instruc-
tion to conform to those styles. The following edition of the same text has virtually the
same wording but expands on the section dealing with multiple intelligences (Parkay,
2013). In contrast, in a 2001 textbook, a prominent education author, Tomlinson, men-
tions both multiple intelligences and learning styles in the ‘How To’s of Planning
Lessons’ section. Both are discussed matter-of-factly, with the text suggesting that they
are accurate models of cognition that should be applied to student learning. It then goes
on to describe ways that teachers can tailor instruction to address students’ learning
styles. No mention is made of doubts concerning the method or of research that may
reject the concept. In a subsequent text written by the same author after the publication
of Pashler et al. (2009), the new text did not appear to mention multiple intelligences or
learning styles at all (Tomlinson and Imbeau, 2010). Instead, it tended to focus on issues
such as planning, organization, structure, environment, and so on rather than cognitive or
‘brain-based’ methods of instruction. This may reflect a realization that current research
does not support claims that had been made previously.
On the whole, the general education texts were likely to include discussions on the
subject of learning styles and also to advocate for applying the method in the classroom.
Yet they did not support their claims by referencing research, nor were they likely to
make any mention of the studies that have called the concept into question. They
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essentially asserted that learning styles instruction was a valid concept and encouraged
pre-service teachers to incorporate it into instruction without providing evidence to sub-
stantiate those assertions.
Educational psychology texts
In contrast to the way learning styles are depicted in general education textbooks, under-
graduate educational psychology texts in general appear to be more measured in their
discussion of the topic, with most recent texts making some mention of the research on
the subject. For example, Ormond (2012) covers learning styles such as analytic versus
holistic and visual versus verbal, yet notes that matching instruction to students’ pre-
ferred learning styles does not necessarily have any impact on academic achievement.
The text acknowledges that the evidence is sparse and does not advocate for the popular
models of learning style instruction such as VAK though neither does it refute their
claims. Henson and Eller (2012) give only a cursory mention of learning styles. They do,
however, point out that research on the subject has been inconclusive and that teachers
should be cautious in implementing such an approach.
Slavin (2012) briefly explains learning styles theories but does note that research has
not provided support for an ATI effect. However, later the text makes the case that learn-
ing styles affect student achievement, which seems incongruous with the preceding
information. Santrock (2011) also only briefly mentions learning styles. This text focuses
on two models: impulsive versus reflective and deep versus surface, with no mention of
the far more common types such as VAK or Kolb’s. It discusses two common criticisms
of learning styles: the low reliability of the styles and poor validity. It discusses multiple
intelligences in greater detail, lending more credibility to that hypothesis. However, the
author acknowledges that critics have pointed out that the empirical research supporting
multiple intelligences is not strong and that research appears to be stronger for the alter-
nate concept of general intelligence.
Bohlin et al. (2012) do not mention learning styles at all. They give detailed explana-
tions of Gardner’s multiple intelligences and Sternberg’s theory of successful intelli-
gence. The authors admit that while Sternberg’s theory is supported by a body of research
evidence, there are no published research studies that provide evidence for the validity of
Gardner’s hypothesis. Since current thinking on learning styles often stems from
Gardner’s hypothesis, it would follow that learning styles instruction may also lack
validity. The authors caution educators about implementing any theory that is not sup-
ported by research evidence, a responsible position to take and one that did not appear
among any of the general education texts reviewed.
While most of the educational psychology texts included only a cursory discussion of
learning styles, Woolfolk (2013) provides a relatively in-depth analysis about the con-
cerns associated with learning styles. Woolfolk notes that research has not been able to
verify the matching hypothesis and that because a learning styles approach has not been
shown to increase achievement its use in education is questionable. The text also dis-
cusses how the commercial aspects of the movement have made claims that go far
beyond what evidence can support. In a rather blunt assertion, Woolfolk argues that the
research behind learning styles is suspect, the measures unreliable, and the claims
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10 Theory and Research in Education
inflated but does suggest that there is some value to learning styles, mostly in treating
students as individuals and in helping them develop metacognition about their own
Kauchak and Eggen (2011) cover learning styles, focusing on the Dunn model. They
do make it clear that research on the issue has been controversial and that researchers
have questioned both the validity and efficacy of the learning styles approach. They also
admit that the vast majority of studies support the critics. However, they also claim,
somewhat paradoxically, that it is important for teachers to adapt their instruction to the
learning styles of students and to present information visually, verbally, and tactilely. A
more recent text by the same authors covers learning styles such as analytic versus holis-
tic and visual versus verbal (Eggen and Kauchak, 2013). Again, it does mention the
controversies stirred by a lack of positive findings in the research and the questions
concerning the validity and efficacy of learning styles instruction. And again, it also sug-
gests that attention to learning styles can be useful in relating to students and providing
them with individualized instruction. In this case, the authors’ treatment of the subject
did not appear to change in the more recent edition.
So, there appears to be a substantial difference between how learning styles are por-
trayed in general teacher education texts and how they are presented in educational psy-
chology texts. The general teacher education texts almost universally portrayed learning
styles in a positive light and advocated for learning styles-based instruction in the class-
room. And, almost universally, they failed to cite any empirical research to support their
claims, mention doubts about the validity of the learning styles hypothesis, or provide a
critical analysis based on research findings. In contrast, the educational psychology texts
tended to treat the subject with greater skepticism and presented the topic as a sort of
curious phenomenon. Each one also discussed the lack of research findings to support
learning styles instruction. While a number of them cautioned against the use of such
unsupported practices, some also advocated for the inclusion of learning styles instruc-
tion nonetheless.
Search criteria
The search for empirical studies on the learning styles hypothesis encompassed a variety
of academic, education, and psychology research databases. Out of approximately 1400
articles post-2009 with ‘learning styles’ in the abstract, 31 empirical studies were identi-
fied that examined the learning styles concept as it is currently conceptualized. Those
studies are discussed below.
In sifting through the empirical articles, the first trend that stood out was that there seems
to be a great deal of research activity on learning styles being conducted in the Middle
East and Asia, with much of the published research on the topic coming from Turkey and
Iran, specifically. Another interesting trend was that the topic appeared to be popular in
medical education, engineering education, and educational technology journals. One
unexpected finding was the preponderance of learning styles research that has been
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published in predatory journals, sources that charge authors fees to publish their research
and have questionable publication standards. One of these journals charged the authors
as much as $1100 to publish their work. The decision was made not to cite articles from
such journals to avoid lending credibility to them, and if it was determined that a study
was published in a pay-to-publish journal it was not included in the 31 studies covered in
this review. There were 16 studies omitted for this reason.
It should be noted that the majority of the studies on learning styles published in these
predatory journals shared a few characteristics. Most of them tended to be correlational
in nature and did not test the matching hypothesis. Instead, they surveyed students on
their learning styles and correlated those results with data from some other survey or
demographic variable. These studies generally did not include an instructional interven-
tion of any sort. Finally, almost all of the studies published in these predatory journals
reported positive findings confirming the notion that learning styles-based instruction is
important to student learning despite the fact that the research was not designed to answer
such a question.
Correlational and descriptive research
The vast majority of the research published recently on learning styles has been correla-
tional in nature. Most of the studies used college students as the sample population,
administered a learning styles inventory of some sort, and then ran a correlation analysis
comparing the learning styles data to the results of another survey or demographic vari-
ables such as gender. Indeed, all of the following correlational research described below
was done with college students. This is somewhat concerning because the broadest appli-
cation of learning styles-based instruction is not at the college level but at the k-12 level,
yet few recent studies have focused on k-12 students.
The learning styles inventory most commonly used was some variation of the Kolb
inventory. Yenice (2012) administered Kolb’s learning style inventory (LSI) to college
students and searched for correlations between those results and gender, age groups, and
data based on a second survey, the California Scale of Critical Thinking Disposition.
Tumkaya (2012) similarly examined the relationship between data gathered on univer-
sity students from the Kolb inventory and the Epistemological Beliefs Questionnaire.
Muscat and Mollicone (2012) sought to determine learning style preferences in college
students using Kolb’s LSI. A revised version of Kolb’s inventory, the LSI-2, was used in
an attempt to determine college students’ learning styles and to identify relationships
between learning style and gender (Al BuAli et al., 2013). A third version, the LSI-3, was
used in one of a number of studies published by researchers in Iran to search for correla-
tions between learning style preference and gender, proficiency level, and achievement
scores (Aliakbari and Qasemi, 2012).
Two correlational studies specifically examined the relationship between students’
learning styles based on the Kolb inventory and academic outcomes. Gogus and Gunes
(2011) surveyed 418 Turkish undergraduate students using the Kolb LSI and compared
the results to achievement data and study skills information collected using a separate
survey. They found that none of the four different learning styles (accommodator, diver-
ger, assimilator, or converger) made any contribution to students’ use of effective study
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12 Theory and Research in Education
habits or achievement as measured by grade point average (GPA). Likewise, in a sepa-
rate study, Nguyen and Zhang (2011) failed to find any significant relationships between
college students’ responses to the Kolb inventory and course outcomes.
The learning styles assessment that is far more common in practice in public educa-
tion, the VAK/VARK/VAKT model, was less likely to appear in published research
although several studies did utilize this framework. For example, Breckler et al. (2011)
asked second language college students in a biology course to predict their learning
styles and then compared their responses to the results of a VARK assessment.
Katsioloudis and Fantz (2012) also used the VARK questionnaire and sought to deter-
mine learning styles in college students and faculty members. They attempted to detect
differences between learning styles of students and their professors. In another study
published out of Iran, Gholami and Bagheri (2013) used the VAK questionnaire in an
attempt to identify the learning styles of college students and investigated differences
between learning styles and gender, and also examined relationships between learning
styles and problem solving, as measured by a second questionnaire. In research that was
strictly descriptive and did not involve the use of inferential statistics, Anu and Anuradha
(2012) sought to determine the various learning styles of college students using the VAK
questionnaire. They simply surveyed students to determine what their preferred learning
styles were and reported the findings in terms of percentages.
The common thread that runs through all of this research is that none of the studies
implemented an instructional intervention, nor did they test the validity of learning styles
as a construct through factor analysis. They correlated the results of a LSI with some
other variable, often a demographic variable. None of them were designed in such a way
that they could test the matching hypothesis and, in turn, provide evidence that tailoring
instruction to students’ purported learning style improves learning or retention of aca-
demic material. These types of studies contribute little to the field because up until now
the validity of the central construct that they examine has been an open question.
Experimental research
Experimental research supporting the learning styles hypothesis
There have been a handful of empirical studies published since 2009 using experimental-
type methods that have found some measure of support for the learning styles hypothe-
sis. These studies have varied in the quality of their research designs and the credibility
of their findings. Each will be discussed in some detail below, as these are the only recent
studies that could be identified that went beyond simple correlational research and found
support for the widely held, yet still very tenuous, proposition that tailoring instruction
to students’ learning styles improves learning.
In a study published in a medical education journal, Alghasham (2012) used Felder’s
LSI to determine whether learning style had an effect on students’ learning behavior. The
participants were 70 first-year undergraduate medical students in Saudi Arabia. Alghasham
focused on the active and reflective styles and classified each student into one category or
the other. Four competencies, or instructional modes, were incorporated into the teaching
methods for the course over 5 weeks: independent study, group interaction, reasoning/
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problem-solving skills, and active participation. Results revealed that there were differ-
ences in the learning behaviors in each of the categories. Active learners used multiple
activities to further their learning, while reflective learners relied on multiple types of
reading materials that they studied on their own. Active learners communicated more dur-
ing group work, while reflective learners listened more intently to others. Active learners
formulated a greater variety of novel solutions in problem-solving activities, while reflec-
tive learners tended to draw more on previously acquired information.
While clear differences in the behavior of the medical students during the learning
activities seemed to emerge, the findings revealed no differences in overall learning
(Alghasham, 2012). On assessments of course content, active learners scored better on
two of the five assessments and reflective learners scored better on two. There was no
difference on the fifth assessment. The matching hypothesis was not actually tested in
this study because all students were exposed to the same instruction, and no true inven-
tion was implemented.
Learning styles research has been popular in the field of educational technology, most
likely because technology may expand the possibilities for delivering content in a variety
of modes. Popescu (2010) recently reported positive findings for the learning styles
hypothesis as a result of applying a web-based learning system. Instead of using one of
the many learning styles inventories in existence, Popescu created a learning styles
assessment that combined the constructs from many different learning styles question-
naires: visual versus verbal, abstract versus concrete, field dependence versus field inde-
pendence, deductive versus inductive reasoning, synthesis versus analysis, motivation,
persistence, pacing, social aspects, and affectivity versus thinking. A number of these,
such as motivation, persistence, and social aspects, do not closely align with the more
prevalent learning styles models and instead are more commonly studied in their own
right in broader areas of psychology. No data on the reliability of the new instrument or
factor analysis were provided.
The 64 undergraduates were divided into two groups: one that learned via instruction
intended to match the students’ learning styles and one that learned via instruction that
was mismatched to the students’ learning styles (Popescu, 2010). The matched condition
produced increased learning efficiency in terms of time and necessary resources but did
not produce increased gains in achievement, that is, more learning. Details of the analy-
sis or results of the academic assessments were not included in the article, and instead the
results section focused almost entirely on a questionnaire students completed at the end
of the study that gauged students preferences and perceptions of the instruction. While
potentially interesting theoretically, this type of survey instrument that measures affec-
tive traits cannot provide evidence for the matching hypothesis, and the specifics of the
academic findings that could speak to the quality of learning were not reported in this
study. Therefore, this particular study could not contribute to verifying the validity of the
learning styles hypothesis.
In another study published in the field of educational technology, Hung (2012) sought
to test the matching hypothesis with 98 Taiwanese program design students using web-
based education systems over an 18-week semester. The study used the Felder and
Silverman (1988) model that includes five dimensions: processing (active/reflective),
perception (sensing/intuitive), input (visual/verbal), understanding (sequential/global),
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14 Theory and Research in Education
and organization (inductive/deductive). Hung (2012) focused on only two styles, input-
oriented and perception-oriented, because a previous survey had suggested that students
with these styles performed more poorly in program design courses. For students who
were identified as input-oriented (visual/verbal), a diagram-based instructional method
was adopted. This seemed an appropriate match theoretically given the description of the
dimension. For students who were identified as perception-oriented (sensing/intuitive),
an analogy-based instructional method was adopted. Descriptors for the perception-
oriented category were numerous and broad so that many, if not most people, would
identify with some of the characteristics. For this reason, it was difficult to see a clear,
justifiable link between the perception-oriented dimension and the analogy instructional
method that was chosen as a match for that learning style. A third group, the control, was
described as having ‘unidentified’ learning styles. It was not clear whether this group
contained an equal mixture of students with the five dimensions or if it was skewed and
included a higher ratio of one or some of the types. Very little was reported about the
sample or teaching methods beyond what is stated here.
Students in the three groups were given a pre-test, three midterm tests, and a post-test
(Hung, 2012). A two-way analysis of covariance (ANCOVA) was used to examine ATI
effects between learning style and instructional method. Interestingly, significant inter-
action effects were found on the post-test scores for both experimental groups. Students
with the perception-oriented style who were exposed to analogy-based instruction per-
formed better than those who did not receive that type of instruction, while students with
the input-oriented style who were exposed to diagrammatic-based instruction performed
better than those who were not. While the results do seem to support the Felder and
Silverman model to some extent, it must be noted that it only tested two of its five dimen-
sions. Dual-coding theory may explain the input-oriented students’ success with the dia-
gram instruction as well or better than learning style. The strongest performance was
shown by the perception-oriented group when they were matched with analogy-based
instruction, but as mentioned, because of the broad nature of the perception-oriented
criteria the link to analogy-based instruction seemed somewhat precarious.
The study also did not report important information about the procedures (Hung,
2012). In addition to other important details, it was not clear whether the experimental
groups were homogenous and only contained students of a single learning style or if they
were heterogeneous and contained students with a variety of styles. The authors did state
that ‘each student underwent instruction through a teaching method matched to their
learning style’ (p. 417), suggesting a homogenous structure, but tables included in the
study seemed to indicate that students in each of the three conditions also received
instruction that was not matched to their learning style. This, and the fact that the stu-
dents in the control group were described as having unidentified learning styles, left
some obscurity in regard to the validity of the results although the findings did appear to
be more interesting than much of the other available research on the topic.
Another study from the field of educational technology also tested the matching
hypothesis, but with 39 Taiwanese fifth-grade students (Hsieh et al., 2011). The research-
ers used Felder and Soloman’s (1997) Index of Learning Styles which categorizes stu-
dents into two learning styles: active or reflective. Two classes were involved, both
taught by the same two teachers. Pre- and post-tests were administered, with each test
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comprised of a single open-ended essay-type question. A rating scale was used to assess
the students’ work, and the authors reported strong inter-rater reliability of .97 for the
pre-test and .99 for the post-test. The intervention consisted of a single lesson, most of
which was delivered via personal digital assistants (PDA). Then the active class brain-
stormed as a group for 15 minutes, and the reflective class received instruction and
prompts for 10 minutes on their PDAs before being asked to summarize for 5 minutes.
Hsieh et al. (2011) controlled for prior knowledge using ANCOVA. A significant
interaction effect did emerge, with reflective learners’ gains being significantly higher
when they were taught via reflective means, such as instruction and recall, and the active
learners’ gains being significantly higher when they were taught via active means such
as brainstorming. Matched groups learned significantly more than mismatched groups.
The researchers concluded that teachers should take learning style into account and
match instruction accordingly. This study did seem to test the matching hypothesis and
appeared to produce positive results supporting the validity of the active/reflective learn-
ing styles and their impact on learning. There were some concerns about the methodol-
ogy, however. The study took place during a single 1-hour lesson, and the instruction for
both groups was identical except for the last 15 minutes of the lesson. The sample size
was also small. It is difficult to make the claim that an intervention that lasted 15 minutes
for a total of 39 students constitutes robust evidence or is indicative of enduring learning.
This study and the results would have to be replicated, preferably with a larger sample
size, multiple lessons, and a longer intervention of at least 2 weeks in order to build a
stronger case for the validity of the findings. In addition, for the purpose of practical
application, the effect sizes would need to be large in order to justify the time and expense
of administering learning styles inventories and tailoring instruction and materials to a
variety of learning styles (Pashler et al., 2009).
Perhaps the strongest and most interesting research on learning styles was published
recently by Mahdjoubi and Akplotsyi (2012). This was one of the few studies that used
the VAK model, the most widely used assessment in schools. The researchers used a
39-item assessment that was made up of 13 items for each of the three learning modali-
ties. The participants were 151 elementary school students from four schools in the
United Kingdom. The purpose of the study was to test students’ sensitivity to sensory
cues, not academic learning. All students were assessed on their learning style and then
all of them were given the same three tasks to complete, which were designed to
address the three different learning styles. For the visual condition, students completed
a photo-safari. For the auditory condition, students took part in small discussion groups
of 10–15 students lasting about 45 minutes. For the kinesthetic condition, students
wore global positioning system (GPS) loggers for 2 days and were allowed to freely
explore the outdoor environment around the school. All the students were exposed to
all three conditions.
Mahdjoubi and Akplotsyi (2012) found a significant interaction effect that appeared
for all three conditions. Visual learners chose to take more photographs and tended to
gravitate toward more picturesque locations. Auditory learners spoke the most fre-
quently in discussion groups. Kinesthetic learners were the most active during the free
outdoor exploration time. The research used an adequate sample size, was conducted
over the course of multiple school days, and appeared to show clear results suggesting
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16 Theory and Research in Education
that VAK learning styles may have some influence on learning behaviors. This study
does not provide support for the matching hypothesis because academic learning was
not measured, but it does offer some interesting findings that suggest that there may be
some validity to the hypothesis that the VAK learning styles are related to learning
choices and may have some real-world implications. The implications do not lend
themselves to the traditional learning styles approach in which a classroom teacher
would attempt to present the same subject matter in a variety of modes based on stu-
dents’ learning styles in an attempt to have them all learn the same material at an opti-
mum level. Rather these results might have some value in assisting students with
choosing academic courses or occupational tracks they would find interest in and for
which they might have increased chances of success.
Experimental research refuting the learning styles hypothesis
There are also a handful of recent empirical studies that were designed to reveal an inter-
action effect, if one exists, between students’ learning styles and their instruction, yet
were unable to provide evidence for that effect, thereby reinforcing the questions about
the validity of the learning styles hypothesis. The quality of these studies varied, much
as those supporting the hypothesis did. However, in general the following studies used
somewhat stronger research designs than those above and their findings appeared to be
more valid, although admittedly, in any study it is typically more likely for the researcher
to be forced to accept the null hypothesis than to be able to control enough variables to
show causation. And, like those above, the studies that refuted the hypothesis tended to
emanate from the field of educational technology and used some type of inventory other
than the ubiquitous VAK/VARK models. Interestingly, a number of the researchers
whose findings refuted the learning styles hypothesis began their experiments as sup-
porters of the method and continued to argue for a learning styles instructional approach
even though their data appeared to contradict that conclusion.
Choi et al. (2009) tested the learning styles hypothesis in an e-learning environment
with 70 third-year anesthesiology students from a dental school in South Korea. The
purpose of the study was to examine how students’ learning styles influence learning
while students solve complex problems. The study used Felder and Soloman’s
(1991/1994) Index of Learning Styles Questionnaire, which includes four dimensions:
sensing or intuitive, visual or verbal, active or reflective, and sequential or global. All
four learning styles were represented evenly. Students completed five complex case
problems while working through five learning modules during the 16-week course. The
researchers tested for interaction effects between the students’ learning styles and their
achievement as measured by their written responses to the case problems. Choi et al.
found no significant interaction effects, suggesting that learning styles did not have an
influence on students’ problem-solving ability. The researchers concluded that learning
styles’ impact on learning outcomes is negligible and that it is more effective to have
students adapt to different learning environments than to design instruction to conform
to students’ learning styles.
One of the few studies that overtly linked the learning styles hypothesis to Gardner’s
multiple intelligence hypothesis was conducted by Allcock and Hulme (2010). The study
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used Honey and Mumford’s Learning Styles Questionnaire and one created by the
researchers based on Gardner’s work. The purpose was to test the performance of 33
college-level psychology students based on lessons differentiated either by learning style
or by ability while controlling for initial achievement levels. The teacher, topics, amount
of time for instruction, and tests were all the same for each condition, and all learning
styles/intelligences were equally represented. For nine sessions, students were taught via
lessons differentiated by either learning style or ability. Students were given tasks
matched to their learning style or grouped by learning style, so the researchers did test
the matching hypothesis. Only the learning tasks and method of delivery varied between
When the results were analyzed, there were no significant differences based on learn-
ing style (Allcock and Hulme, 2010). The test scores were consistently higher for the
students who received differentiation by ability than they were for those differentiated by
learning style, and those in the ability group improved more from pre-to post-test
although not significantly. So both groups improved, but the learning styles instruction
did not produce more learning, and actually produced slightly less than the ability group.
Therefore, the researchers had to conclude that the improvement was due not to the
learning styles approach but simply to being involved in academic work. In fact, students
reported the most satisfaction with the one task that was assigned that contrasted with
their preferred learning style. The researchers concluded that learning styles-based
instruction was not effective and that instructors should carefully consider whether
adopting such a method is prudent.
Another recent study examined how learning styles impact course selection and
achievement by comparing college students’ outcomes in web-based and face-to-face
computer science courses (Zacharis, 2011). The participants were 161 freshmen, 77 of
which took the class online while 84 took the class in a traditional format over a 12-week
period. Students took part in two 90-minute lectures plus one 2-hour laboratory per
week. Students in both conditions had access to all the same materials and took the same
assessments. Since all materials were available to all students, Zacharis reasoned that
students would gravitate toward the material that fit their particular learning style. The
Kolb LSI was administered, and achievement was measured by grades determined by a
midterm exam, eight 顺心彩票work assignments, two group projects, and a final exam.
Results indicated that students’ learning style did not influence their course selection, so
their rationale for taking the course in a specific format had to do with reasons other than
learning style. More importantly for this discussion, there were no significant differences
in the achievement between the two groups and there was no statistically significant
interaction between students’ learning style and method of instruction based on course
grades. Zacharis argued that diversifying instruction is useful but acknowledged there is
not sufficient evidence to conclude that matching instruction to learning style has an
effect on learning or students’ ability to successfully complete a course in either of the
two instructional environments.
Kozub (2010) tested the matching hypothesis with 159 undergraduate business
school students using two different web-based instruction modules and Kolb’s LSI-2a.
One instruction module was in a text-only format and the other provided the same text
but also was enhanced with multimedia and interactive components such as games,
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18 Theory and Research in Education
pictures, pop-up elaborations, and links to additional information. In-class lectures were
also used. Assessments were administered online for the two module conditions and in
class for the lecture material. It was posited that if the matching hypothesis was correct,
students with different learning styles should perform differently based on the type of
instruction they received. Kozub suggested that divergers and accommodators in par-
ticular should perform better on the enhanced module. However, there were no signifi-
cant differences in students’ performance based on the type of program, and the mean
test scores for students of different learning styles showed no significant differences. In
addition, there were no significant interaction effects between learning style and the
type of online instruction the students received. There were also no significant differ-
ences on the in-class exam scores due to students’ learning styles, which suggest that
students from all four learning styles categories performed equally well when exposed
to the lecture-only instruction.
So in this study (Kozub, 2010), learning style appeared to have no impact on how well
the students learned the material, regardless of the mode of instruction. Thus, learning
style did not have any predictive value in determining how well students learned in any
of the three instructional conditions. Furthermore, students’ American College Testing
(ACT) composite scores, which were collected in the research as a measure of initial
cognitive ability, did predict students’ scores in all three conditions, yet there was no
relationship between ACT scores and learning styles. This suggests that learning styles,
while possibly an indicator of preference, are not related to measurable cognitive ability.
This research provided a relatively well-designed format for testing the matching hypoth-
esis, and the results of a variety of analyses consistently refuted the matching hypothesis
and indicated that students with different learning styles learned at similar rates regard-
less of the mode of instruction.
A three-year longitudinal study focusing on 99 undergraduate students was conducted
to test the predictive validity of a LSI using multiple learning criteria (Kappe et al.,
2009). The authors noted that Kolb (1984) had proposed that matching the learning envi-
ronment to students’ learning style would enhance learning, but because Kolb’s LSI had
low face validity and other problems, Kappe et al. used Honey and Mumford’s LSQ. The
LSQ identified four types of learners: activists, theorists, pragmatists, and reflectors.
Students completed the LSQ twice, at the beginning of their first year and end of their
third year. Five criterion measures were used: lectures, skills training, group projects,
on-the-job training, and written theses. Each of these measures coincided with at least
one of the learning styles so Kappe et al. (2009) were able to test the matching hypoth-
esis. Achievement was assessed in each of the five categories via different means: multi-
ple choice and essay exams for lectures, mentors and independent judges for training and
projects, and so on.
Kappe et al. (2009) found that the LSQ’s test–retest reliabilities were strong, particu-
larly for a 2-year time period. However, contrary to their expectations, none of the cor-
relations between learning styles and criterion measures were significant. Even though
students reliably self-identified their preferred learning style over time, learning style
had no validity in predicting achievement outcomes for instruction and assignments
matched to that learning style. In other words, no interaction effect between learning
style and instruction emerged. The groups that should have performed better on certain
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types of tasks based on their learning style did not perform any better on those tasks than
those students whose learning style preference contrasted with the type of task. Kappe
et al. (2009) admitted that the “most important finding of this study is, in essence, a non
finding” (p. 466). Although the researchers designed the tasks to match students’ learn-
ing styles, their learning style offered no predictive validity and none of the groups
scored significantly better on tasks matched to their learning style. They concluded that
measures that assess generalized individual differences are not adequate for explaining
performance on tasks that are specific to content or context.
Because the Kolb inventories and, to a lesser degree, Honey and Mumford’s are used
so commonly, it is worthwhile to examine the validity of the measures. Martin (2010) did
so in a study in the United Kingdom, where learning styles instruction is popular. It was
one of the few studies available dealing with students and teachers at the k-12 level. The
study examined the results of Kolb’s LSI-2 and Honey and Mumford’s LSQ, which mir-
ror each other closely. It involved four UK secondary schools identified as highly suc-
cessful schools by governing boards, all heavily invested in learning styles instruction at
the institutional level and the classroom level. The schools had much investment in
learning styles in terms of time, training, money, and resources for both administrators
and teachers. Both LSIs were administered to 16 classes, or 394 total students. No inter-
vention was implemented; instead, factor analyses were conducted to determine the
strength of the instruments’ validity and reliability, and qualitative interviews were done
with faculty and staff.
Factor analysis showed that the assessments had very poor validity and reliability,
so poor in fact that the internal constructs that should have theoretically been related
were only found to be related by chance (Martin, 2010). This means that teachers who
tried to make judgments about a student’s learning style would likely come to differ-
ent conclusions about that student depending on which assessment was used. Factor
analysis showed that teachers would have had the same quality of information in
terms of identifying students’ learning styles if they randomly assigned learning styles
to students as they would have with the LSI-2 and only slightly better than that for the
LSQ. Essentially, if teachers had asked students to pick learning styles labels out of a
hat and then based instruction for each student on that randomly chosen label, they
would have had about the same level of accuracy as was provided by the two inven-
tories. Clearly, these concerns about both validity and reliability raise serious ques-
tions about whether the Kolb LSI-2 or Honey and Mumford LSQ measure legitimate
The study suggests that teachers could not use these learning styles inventories to
improve student learning because the inventories could not actually determine students’
learning styles even if those constructs do exist (Martin, 2010). This would make basing
instruction on learning styles fruitless because there is nothing to match the instruction
to or no way to tell what should be matched. When this information and critical results
from other studies were revealed to the faculty, they were unperturbed by it and said they
continued to have confidence in the learning styles approach. Most of the faculty were
exposed to the learning styles hypothesis during initial teacher training, but were never
exposed to the research that questioned its efficacy or educational value. After they
began teaching, they did not explore research on the subject or look into research
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20 Theory and Research in Education
in general. Instead, they were guided by anecdotal experiences about what types of
instruction were effective. While the teachers insisted they used a learning styles
approach in the classroom based on matching instruction to students’ preferences gleaned
from these inventories, when queried about their practices, it was discovered that they
used relatively standard teaching methods. Martin suggests that as university instruction
for pre-service teachers has moved away from theory and research in recent years to a
model based more on placements in schools, subsequent practice has actually declined
due to a lack of understanding of theory and research.
One study was identified that used the VARK assessment. Sankey et al. (2011)
recruited 60 undergraduate and post-graduate students from Australia to take part in
the study. The students’ ages ranged from 17 to 60 years, but they were all high achiev-
ers. A multimedia approach was used to deliver six different experimental conditions,
each of which received a slightly different instructional intervention. In all, 10 stu-
dents were included in each condition. Each learning condition was comprised of two
students from each of the five learning styles. So, for instance, the 10 students in
condition one consisted of two visual, two aural, two read/write, two kinesthetic, and
two multimodal learners. The same was true for the other five learning conditions. A
pre-test and a post-test were administered to control for prior knowledge. The type of
statistical analysis used by the researchers was not revealed, but they reported that the
experimental data did not show any significant differences in learning across the six
groups based on learning style, that is, there was no interaction effect. The authors
acknowledged that their inability to find support for the matching hypothesis was
consistent with previous research findings. The researchers also sought to determine
whether there was an optimal blend of instructional practices based on learning style
preference, but since there were no significant differences in learning performance
across the conditions in relation to learning style, this could not be determined. The
bulk of the results section in this study was devoted to qualitative data based on stu-
dents’ perceptions of the instructions, possibly because the experimental data did not
yield significant findings.
Despite the lack of evidence to substantiate the matching hypothesis, the authors were
supportive of the method nonetheless (Sankey et al., 2011). They suggested that the feed-
back from students was an indication that learning styles-based instruction would have a
positive impact on motivation and therefore would be beneficial to include in instruction.
The authors seemed to miss several salient points in this assertion. First, research on
metacognition tells us that students are not always the best judges of their academic
needs or progress, and that preferences and learning are two different things, constructs
that often have little correlation with each other. It is also well established that using a
variety of instructional approaches can enhance engagement and motivation. This
dynamic has nothing to do with learning styles, as variety simply works to decrease
boredom and stimulate interest when students experience different methods in the class-
room. The qualitative data were also not uniform and do not show clear delineations
according to learning style. And finally, the researchers own experimental data failed to
show a positive impact on learning based on the approach. For these reasons, their insist-
ence on recommending the continued use of learning styles instruction did not seem to
be consistent with the evidence they collected.
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Despite a great deal of literature having been published on the concept of learning styles
since 2009, the empirical evidence for the validity of the learning styles hypothesis
seems to have gotten weaker in recent years. While a number of studies suggest that
learning styles may have some impact on behavior, only two (Hsieh et al., 2011; Hung,
2012) reported an interaction effect supporting the matching hypothesis, indicating that
learning styles had a positive impact on learning. Neither of these incorporated the ubiq-
uitous VAK/VARK model, and there were substantial limitations in both of the studies.
Six studies tested the matching hypothesis in search of an interaction effect yet were
unable to find one. These studies tended to be stronger in their designs than those that
produced positive results. There may, however, have been other rigorous studies con-
ducted during that time that were never published because they did not produce statisti-
cally significant findings, and this could be a weakness of a peer-review system that
tends to strongly favor studies that report positive, significant findings. Non-findings
tend not to be considered ‘news’. Regardless, had methodologically sound studies been
conducted that did show a significant interaction effect, they should have appeared in the
research databases, yet none did.
A notable finding in the present investigation was the lack of published research in
reputable psychology journals or high-level education journals, where experimental
research on learning styles was practically nonexistent. Instead, the studies that reported
positive results in regard to learning styles tended to be over-represented in relatively
obscure journals and in predatory journals in particular. There was also a great deal of
correlational research that could not contribute to verifying learning styles’ effect on
student learning or achievement. Another significant finding was an almost complete
lack of VAK/VARK studies in the research database. This is very concerning considering
that the VAK/VARK framework is the most common model, widely applied in k-12 edu-
cation and heavily advocated in teacher education programs. Yet, there is a total absence
of evidence that the implementation of this framework has any benefit whatsoever to
students’ academic learning.
Going beyond instructional interventions, Bishka (2010) makes the case that neuro-
imaging shows that the various sensing modalities (visual–auditory–kinesthetic) are
actually interlinked in the brain so that they are triggered in unison, suggesting no single
mode can operate in isolation. If so, this would further erode the foundations of the
learning styles hypothesis and strike a blow to other so-called brain-based hypotheses
such as multiple intelligences. Barring a disability, we are all visual learners, just as we
are all auditory learners, and we are all kinesthetic learners. And we normally use a
combination of senses when learning. Yet, educators have often confused learning
styles with cognitive abilities. Learning styles, if they are a valid construct, represent a
preference while cognitive abilities represent a capacity or proficiency. Abilities can be
measured objectively and with relative accuracy, but self-reported preferences, as we
have seen here, are highly subjective and have not been measured reliably or with suf-
ficient validity.
So the question remains as to why learning styles proponents continue to advocate for the
method. As Pashler et al. (2009) note, “There is growing evidence that people hold beliefs
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22 Theory and Research in Education
about how they learn that are faulty in various ways, which frequently lead people to man-
age their own learning and teach others in non-optimal ways” (p. 117). Even though decades
of research have failed to confirm the learning styles hypothesis, it has not stopped its pro-
liferation among practitioners from preschool to the university level who attempt to apply
the theory in classrooms and administer unreliable tests (Martin, 2010; Scott, 2010). This
constitutes a tremendous waste of valuable and finite learning time, distracting teachers and
students from instructional methods that have been shown to be successful in increasing
learning. One possible answer for the continued propagation of the learning styles approach
is that proponents might simply be unaware of the research evidence. If so, it may under-
mine the credibility of the scholars, educators, researchers, or professors who strongly advo-
cate for a method without having knowledge of the research base underlying it. Scott argues
that university instructors in teacher education programs should be well aware of the research
that questions, if not directly refutes, the learning styles hypothesis, yet all too often they are
not, as evidenced by its continued expansion in educational circles.
Riener and Willingham (2010) posit that the wide popularity and acceptance of the
learning styles hypothesis is due to confirmation bias, the tendency for people to only
consider support for the perspective they favor, and at this time the hypothesis has
become ‘common knowledge’ in education so instructors may have cause to view it posi-
tively even in the face of stronger contradictory evidence. If this is the case, then advo-
cates of the model might base their views on the rather superficial theoretical literature
and disregard the more substantial scientific findings of empirical studies. Another pos-
sibility for its continued popularity is that acceptance of the learning styles hypothesis
transfers responsibility for learning from the student to the teacher because if adequate
learning does not transpire a claim can be made that the teacher’s instruction did not
properly conform to the student’s learning style (Pashler et al., 2009). This explanation
corresponds with conventional wisdom in the current era of teacher accountability and
differentiation for students.
So, part of the attraction of learning styles may have to do with parents’ desire to have
their child treated as a unique individual and, as a consequence, require teachers to
deliver individually tailored instruction to meet the child’s needs, which seems intui-
tively appealing (Fridley and Fridley, 2010). However, the irony is that by classifying all
students into several basic categories it may serve to do exactly the opposite by labeling
students as a certain ‘type’ (Pashler et al., 2009) despite the fact that learning styles
instruction is often adopted in the name of tolerance and diversity for different types of
learners. Indeed, at its core, learning styles is about classifying individuals into a handful
of limited categories and treating them differently based on those simple labels (Fridley
and Fridley, 2010). Scott (2010) argues that the concept of learning styles also promotes
an entity view of intelligence, as opposed to an incremental or process view of intelli-
gence. This results in an individual being seen as a certain type of learner by nature
instead of as a complex, changing human being, and essentially creates another way to
label or stereotype students, ultimately limiting them.
Some psychologists contend that real harm may be done by continued insistence on
implementing instructional methods that we know do not work (Fridley and Fridley,
2010; Riener and Willingham, 2010). For instance, it is common for students who have
difficulty in the classroom to be categorized as tactile or kinesthetic learners, which
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Cuevas 23
based on the research evidence is clearly a dubious assumption in terms of both the clas-
sification and its instructional ramifications (Scott, 2010). Then interventions are imple-
mented as a result of that questionable categorization instead of teachers conducting a
more thorough diagnosis and remediation program founded on more well-substantiated
methods that would actually have some chance of helping those struggling students. In
essence, despite the good intentions of their teachers, those students are left to flounder
due to a hypothesis that may amount to little more than pseudoscience.
Education policy has often been driven by fads and the ‘newest’ methods of categorizing
students, even in the absence of empirical evidence supporting the efficacy of those
methods (Fridley and Fridley, 2010). It should be part of an educator’s professional
responsibility to seek to implement instruction that is scientifically supported, or at least
to have an understanding of what is and what is not. Just because someone self-reports
that they prefer to learn a certain way does not mean that they will learn all concepts best
if they are presented in that fashion, regardless of the specific content. Instead, the nature
of the subject matter should determine how it is best taught and how it is best learned.
Good teachers develop a variety of ways to present their content over the years and treat
each student as a unique individual without pigeonholing them into unfounded
One question a reader might have is whether the learning styles hypothesis has by
now been debunked. The answer at this point is ‘not completely’. Additional research is
always warranted, but correlational and theoretical research on the issue currently has
little if any value. Only experimental research that tests the matching hypothesis for
interaction effects can meaningfully contribute to the knowledge base at this point. But
the learning styles hypothesis has been refuted by empirical research to the extent that it
may be considered irresponsible for teacher education programs and public educators to
apply the method in practice. When educators insist on advocating for discredited
hypotheses that have not been shown to work, it takes the focus off of interventions and
instructional strategies that have a stronger scientific basis and are more likely to help
learners in their development. There are instructional strategies such as those involving
dual-encoding, interleaving, and temporal spacing, for example, that have the potential
to positively impact student learning in real terms, yet too often educators waste valuable
instructional time on misguided and unsupported models. It appears to be time to put the
learning styles approach to rest in practice unless researchers can produce convincing
evidence that the hypothesis is a valid one, and that has not begun to happen.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.
The author(s) received no financial support for the research, authorship, and/or publication of this
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Author biography
Joshua Cuevas is an Assistant Professor in the College of Education at the University of North
Georgia where he teaches courses in research methods, assessment, educational psychology, and
literacy, as well as overseeing graduate-level research studies. He received a PhD in educational
psychology from Georgia State University. Prior to that, he worked in assessment at the state level at
the University of Georgia Educational Research Laboratory and the national level through the
American Council on Education. His research interests include applied cognition, educational meas-
urement, evidence-based reasoning, instructional methods, memory, and quantitative methodology.
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... Independently of the many controversies (be these scientific, psychological or even ethical) about the soundness and real outcomes that such ideas can bring to improve learning (Lilienfeld et al. 2010), (Coffield et al. 2004), (Kirschner 2017), (Knoll et al. 2017), (Rogowsky et al. 2020), (Cuevas 2020) it's a worthy intriguing research area for many researchers in Artificial Intelligence. The main premise is that the pursued research goals could enlighten the way towards confirming some learning theories. ...
... Roughly defined, a learning style intends to be a model of the way and media an apprentice acquires knowledge and hence the way a teacher should present that knowledge to the apprentice matching his/her learning style. Since its diffusion in the seventies until today, as many theories, this concept has gained many supporters as well as critics about its scientific basis and real outcomes which varies in intensity from true believers, passing through agnostics, skeptical up to strong detractors (Kolb 2014), (Felder and Spurlin 2005), (Scott et al. 2014), (Pashler et al. 2008), (Cuevas 2020). The last critic opinions are based on well sound arguments (from psychological to neuroscience foundations), but in general suffer in certain way the same lack of enough empirical data that lead undoubtedly to an absolute scientific refutation. ...
Moodle represents a great contribution to the educational world since it provides an evolving platform for Virtual Learning Management Systems (VLMS) that became a standard de facto for most of the educational institutions around the world. Through the pedagogical functions provided, it collects in the many globally spread out databases a huge amount of information regarding the activities that teachers and students perform during the learning process. This reality makes Moodle a natural choice for conducting experimental research by Artificial Intelligence researchers interested in theories for improving learning and teaching; particularly those related with the controversial learning styles concept. Roughly defined, a learning style intends to be a model of the way and media an apprentice acquires knowledge and hence the way a teacher should present that knowledge to the apprentice matching his/her learning style. Independently of the many controversies (be these scientific, psychological or even ethical) about the soundness and real outcomes that such ideas can bring to improve learning, it’s a worthy intriguing research area for many researchers pursuing the ideal automated teacher: the teachbot dream. Behind this goal we have developed Middle, a Moodle plug-in able to infer the learning style of each student taking a course using an advanced version of a Bayesian network model that we previously tested. Middle intends support personalized teaching based on the Felder-Silverman’s ILS model and has been evaluated through controlled experiments and pilot test in high schools and university courses. Such experiments showed promising results that shed some light on learning styles modeling and its potential outcomes. During the experience we found strong limitations in the Moodle design regarding its supposed flexibility to incorporate new functionalities. From a strict software architecture point of view, we found that such flexibility is far from being enough to easier the implementation of the dynamic computational behavior required to support a teachbot. This made our effort much harder than expected, perhaps because of the illusion induced by the widespread use of Moodle. In this article we present our results and experiences extending Moddle with intelligent behavior from a software architecture point of view, focusing on the lessons learnt in such extension. Our experience indicates that this simplicity is far from being so and hence it is worth to share the limitations and how we overcome them.
... Tiering is one way to differentiate assignments in the classroom. Cuevas (2015), Tomlinson (2014), and Wormeli (2005) described tiering as an adjustment in the learning experiences of students to create meaningful learning for the varying levels of students. For instance, some students may compose a single paragraph summary based on assigned reading materials, while other students may work on a project to show what they have learned. ...
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Access to technologies and understanding the potential uses of technology to differentiate
... According [2] learning style refers to students' preferred learning approaches for all learning situations while teaching styles refer to the lecturers' behavior, beliefs and selected instructional methods used to present lessons to students [2,3,24]. Currently, they are many research that are conducted on the concepts of students' learning style and educators' teaching style [4,5]. ...
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span>Individuals learn in different ways using several learning styles, but lecturers may not always share material and learning experiences that match students’ learning preferences. Mismatches between learning and teaching styles can lead to disappointment with students are taking, and lead to underperformance among them. The aim of this study is to identify the learning styles of the students enrolled in Universiti Malaysia Pahang who were registered in Programming Technique course and to investigate the relationship between students’ learning styles and teachers’ teaching styles. Five lecturers and 251 students were involved in the study as participants and. Data from students were collected using Leonard, Enid’s VAK Learning Style Survey. Meanwhile, the teaching styles of the lecturers were identified using Grasha and Reichmann’s Teaching Style Survey. The findings revealed that majority of the student’s preferred visual learning style. The result also shows that the lecturers’ teaching styles give an impact towards the students’ academic performance. From this study, we can conclude that teaching styles have significant impacts on students’ learning styles and academic performances.</span
... Tiering is one way to differentiate assignments in the classroom. Cuevas (2015), Tomlinson (2014), and Wormeli (2005) described tiering as an adjustment in the learning experiences of students to create meaningful learning for the varying levels of students. For instance, some students may compose a single paragraph summary based on assigned reading materials, while other students may work on a project to show what they have learned. ...
Full-text available
Access to technologies and understanding the potential uses of technology to differentiate instruction have been a concern for the teachers and students in a local school district located in the southeastern United States. Despite the emergence of digital voice assistants (DVAs) as tools for instructions, teachers lack knowledge and strategies for using DVAs to differentiate instruction in their classrooms. The purpose of this qualitative study was to identify teacher knowledge and strategies employed among special education (SPED) teachers using DVAs to differentiate instruction in their classrooms. The concepts of Carol Tomlinson’s differentiation theory and Mishra and Koehler’s TPACK framework served as the foundation of this study. The research questions examined middle school SPED teachers’ perceptions of challenges using DVAs to differentiate instruction, resources, and strategies available to these teachers as well as their perceived knowledge of using DVAs to differentiate instruction. In this basic qualitative study, data were collected from 6 SPED teachers using semistructured interviews. Interviews were recorded, transcribed, and analyzed thematically. The findings suggest that teachers had little to no perceived challenges when using DVAs to differentiate instructions. However, the overutilization of DVAs might rob students of their ability to think independently. This study offers several prospects for future research related to the topic and findings. Further research is needed at the elementary and high school levels that may include core content teachers. The findings in this study serve as grounds for social change for schools and school districts to develop training solutions, policies, and guidelines for teachers to follow when implementing technology.
... [20] Among the cognitive characteristics, variables such as intelligence and creativity have traditionally been the subject of attention, and the study of learning styles stands out as the main element of interest in this area. [21][22][23] However, the study of the role played by personality structural characteristics in the teaching-learning process, such as personality traits, has aroused less interest. In this case, existing studies have mainly focused on the relationship of these variables with academic performance, [24][25][26] motivation, [27] studying strategies, [28] or even with the mentioned learning styles [29,30] as well as with the presence of differential personality traits associated with students of different degrees. ...
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The European Higher Education Area was implemented more than a decade ago with the aim of improving internationally the competitiveness of European university education putting the spotlight on skills and competence development (and not only on knowledge acquisition). This work intends to analyze the impact of competence-based teaching methodologies on university students, as well as to contribute to the study of the individual personality traits differences regarding this impact. For this, a descriptive, quantitative, cross-sectional study was conducted with a non-randomised sample of university students. The sample was composed of a total of 499 students of the University of Huelva (350 from the Health Sciences degree, and 149 form other degrees), who completed a questionnaire on professional skills and teaching methods developed ad hoc for this research, as well as the brief version of the Spanish adaptation of the NEO Five-Factor Inventory. The results show that Health Sciences students feel more satisfied with the most participative and active methodologies, and they consider these better contribute to their future professional competence development. On the other hand, in relation to the big 5 personality traits studied, links have been found between competence development perception and personal preferences and the dimensions of extraversion, agreeableness, conscientiousness and openness to experience. This last factor, openness to experience, appears when analyzing the main differences among both groups, being Health Sciences students more intellectually curious, showing more openness and diversity of interests, in addition to being more creative, innovative, and flexible.
... Furthermore, surveys have indicated that a substantial proportion of both primary and secondary school teachers, as well as K-12 teachers, respond in favor to statements aligned with modality-specific learning styles (Dekker et al., 2012;Howard-Jones, 2014;Gleichgerrcht et al., 2015;Ferrero et al., 2016). The same positive attitudes have been observed among two-thirds of United States higher education faculty, and many reports that learning style theory is a part of teacher education and curriculum as a textbook principle (Dandy and Bendersky, 2014;Meyer and Murrell, 2014;Cuevas, 2015;Newton and Miah, 2017). ...
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A well-known hypothesis amongst educators and the general public is that matching instructional method with an individual’s modality-specific learning style improves learning. Several critical reports in the past decade, however, have shown that the psyc顺心彩票tric properties of the inventories applied to establish modality-specific learning styles have been poorly validated. Furthermore, theoretical development has challenged the theoretical basis for the modality-specific learning style model. Thus, the aim of the current study was to examine the psyc顺心彩票tric properties and relationship between, two modality-specific learning style inventories: the Barsch Learning Style Inventory (BLSI) and the Learning Style Survey (LSS). University students (n = 242) completed the two inventories, and their responses were subjected to confirmatory and exploratory factor analysis, as well as analysis of inter-item agreement (internal consistency). The results failed to support the expected three-factor measurement model and thus indicated questionable reliability and factorial validity of the two inventories. Analysis of inter-correlations between factors from the two inventories resulted in a one-factor solution explaining up to 40% of the variance, which is discussed as emerging through an overall multimodal learning style.
... In simple analogy, schools that teach in different ways would increase students' success. Audiovisual and kinesthetic learning theory opined that uniform instruction such as lecture does not have general effects on all learners but works for a portion of the population in the classroom (Cuevas, 2015). In spite of the efficacy of modality based instruction in the teaching and learning of biological concepts in other geographical context, it still remains unclear in Ghanaian context, the extent to which the use of audiovisual and/or kinesthetic teaching methods effect biology students' academic performance in ecology concept. ...
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This article investigated the effects of audiovisual and kinesthetic instructional methods on students' performance in ecology among Adu Gyamfi Senior High School Biology students, Ashanti Region. Quasi experimental design was adopted for the study. A sample of 100 students was purposively selected for the study. Ecology achievement test was constructed and used to collect data for the study and reliability coefficient of the instrument was calculated to be 0.81 using Kudder-Richardson formula 20 (KR-20). Data obtained from the study were subjected to both descriptive and inferential analysis such as mean, standard deviation, box plot, mean plot, eta squared effect size, one-way ANOVA, two-way ANOVA and Post-Hoc Tukey HSD test with the support of SPSS version 20. It was established that both kinesthetic (p = > .001) and audiovisual (p = > .001) instructional methods were superior to conventional lecture method in the teaching and learning of Ecology. However, the paired comparison of kinesthetic instructional method and audiovisual instructional method showed a mean difference of 1.29, which indicates no statistical significant difference between these two methods (p = .641 > .05), hence there was no superiority of one on the other. One of the reasons to this result might be because the students in the kinesthetic and audiovisual instructional groups were taught according to their learning styles. Finally, the study revealed that gender had no significant effect on students' performance in teaching and learning of ecological concepts. Again, there was no interaction effect of the teaching methods and gender on the students' academic performance in the concept of ecology. Based on this, it was concluded that SHS Biology students learn ecological concepts better, when instructional method matches students' learning styles. It is therefore recommended that students' learning styles should be considered when teaching ecological concepts, and teachers should be trained and retrained on the use of audiovisual and kinesthetic learning styles in teaching ecological concepts.
... Since Pashler et al. 's (2009) review, a number of reviews have investigated learning styles and educator perceptions of their application. These have ranged from reviews of empirical studies on the effect of learning styles-based instruction on learning (Arbuthnott and Kratzig, 2015;Cuevas, 2015;Kirschner, 2017;Aslaksen and Loras, 2018), to studies on the persistence, prevalence, and disservice learning styles based instruction has had on education (Howard-Jones, 2015;Willingham et al., 2015;Kirschner, 2017). Like Pashler et al., these reviews found no support for learning styles, but no study was conducted with school-aged children. ...
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Teachers commonly categorize students as visual or auditory learners. Despite a lack of empirical evidence, teaching to a student’s perceived learning style remains common practice in education (Pashler et al., 2009). Having conducted an extensive review of the literature, Pashler et al. (2009) noted, “...very few studies have even used an experimental methodology capable of testing the validity of learning styles applied to education” (p. 105). Rogowsky et al. (2015) published the first study following the experimental design prescribed by Pashler et al. Focusing specifically on the visual/auditory dichotomy, Rogowsky et al. (2015) examined the extent to which learning style predicts comprehension and retention based on mode of instruction. Their study has been noted as “The only study located through the systematic literature search across six different databases and the screening of more than 1000 records that was totally aligned with Pashler’s criteria” (Aslaksen and Loras, 2018, p. 3). The caveat to the 2015 study is that it was conducted with adult learners. The current study uses the same design and methodology as its predecessor, but on a school-aged population, making it the first of its kind. Consistent with earlier findings with adults, results failed to find a significant relationship between auditory or visual learning style preference and comprehension. Fifth graders with a visual learning style scored higher than those with an auditory learning style on listening and reading comprehension measures. As such, and counter to current educational beliefs and practices, teachers may actually be doing a disservice to students by using resources to determine their learning style and then tailoring the curriculum to match that learning style.
The purpose of this study is to find the students preferred learning style for the desired academic performance. The study will be beneficial for all the faculty if it can identify the preferred learning styles of student so that the faculty can use appropriate ICT technology in their teaching methodologies. The study is focused on basically 5 learning styles of students. Visual style, the teaching methodology that may be used for this style is either a power point presentation or a video lecture. The second one is auditory style which is the traditional lecture method/instruction method where the teacher details about the subject orally. The third style is kinaesthetic style in which the faculty will teach the student through various activities like role plays, simulations, activities, etc. The fourth style that is considered is student individual learning style, in this the faculty instructs the students individually the subject. The fifth style that is measured in this article is group learning, in this method the faculty explains the subject by dividing the class into groups. The research design used is exploratory. A standard structured VAK questionnaire is administered to students to gather data. The findings were inconclusive about the VAK styles, but have been detrimental with individual vs group learning styles.
In recent years, educators have started to use innovative pedagogies in response to the changing trends of language learning towards developing great proficiency, as the conventional approaches could no longer improve proficiency due to the interactive nature of language skills. Therefore, it is believed that the Flipped Learning (FL) approach, as one of these new pedagogies, can be appropriately used to enhance language skills due to its hybrid nature. On the other hand, the existing literature on the efficacy of the FL approach has mostly ignored psychological factors like motivation, personality traits, and learning styles. Thus, the current study aimed to see which learning styles fitted better in the FL approach. Further, it intended to explore which language skill and in which learning style the amount of improvement could be significant. Forty Iranian EFL learners took the PET pre-test and were divided into their preferred learning styles. Then, they underwent the FL approach for a semester. The results of the paired samples T-Test indicated a significant positive improvement in the students’ performance in the post-test (t (39) = ?7.698, p = .000). The results of the One-way ANOVA showed that there were significant differences among learning style groups in favor of the visual style, (F (4, 35) = 2.299, p = .034), and Mixed ANOVA results indicated that the most significant difference among skills was found between speaking and writing skills, (F (3, 105) = 8.018, p = .000).
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Drawing on the foundational theories of John Dewey and Kurt Lewin, we examine recent developments in theory and research on experiential learning and explore how this work can enhance experiential learning in higher education. We introduce the concept of learning space as a framework for understanding the interface between student learning styles and the institutional learning environment. We illustrate the use of the learning space framework in three case studies of longitudinal institutional development. Finally, we present principles for the enhancement of experiential learning in higher education and suggest how experiential learning can be applied throughout the educational environment by institutional development programs, including longitudinal outcome assessment, curriculum development, student development, and faculty development.
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The present study intended to determine the learning style preference (LSP) of Iranian non-academic EFL students and examined if there was any relationship between their LSP and the three variables of gender, English proficiency level and final achievement scores. To this end, the Kolb Learning Style Inventory version 3.1, Oxford Placement Test 2 and a final achievement test were administered to 327 Iranian students in English language institutes. As for participants’ LSP, descriptive statistics showed that 39% of the participants were characterized as Assimilators and 40% Divergers. Besides, results of the computed Pearson Contingency Coefficient and Cramer’s V indicated that there was a low association between participants’ learning style preference (LSP) and their gender, proficiency level and achievement scores. The results of the study, therefore, supported the need for further exploration of the teaching and learning situation and related factors to identify how best to take account of students’ learning styles and what factors may have a stronger association with learners’ LSP.
Differentiated instruction is becoming critical in higher education due to student diversity and background knowledge. Differentiated instruction does not mean matching teaching styles with learning styles as suggested by the learning styles theory. Findings in recent research studies have proved the lack of credible evidence for the utility of the learning styles theory. As not scientifically proven, the theory serves to perpetuate the learning styles mythology in the educational psychology world. This paper will emphasize students readiness levels as a critical part of differentiated instruction that teachers should refer to rather than sticking to student preferences and/or learning styles. The paper also suggests strategies to differentiate instruction effectively. These strategies include, but are not limited to, identifying student readiness; making modifications of the instructional content, process, and product; and enhancing collaboration and autonomy in learning. The last part of the paper places an emphasis on the integration of teaching and practice. Differentiated instruction, at its best, should reflect a new pedagogy that can promote practical integration and knowledge transformation. If implemented thoroughly, differentiated instruction can demonstrate institutional effectiveness and equip students with diverse learning experiences to highly respond to increased challenges in the global society.
The present study was carried out in order to review learning styles and critical thinking disposition of pre-service science teachers in terms of sex, grade and age, and to address the relationship between their learning styles and critical thinking disposition. It used Kolb's Inventory of Learning Styles and California Scale of Critical Thinking Disposition. The study found that total scores of learning styles and critical thinking disposition of pre-service science teachers were not statistically significantly different in terms of their sex, grade and age groups. It was also found that the pre-service teachers mostly preferred divergent learning style (43.3%) followed by the assimilator learning style (33.0%) and that they least preferred the accommodative (13.0%) and convergent (10.6%) learning styles. Furthermore, it was determined that there was a low level of positive relationship between learning styles and critical thinking disposition for the pre-service science teachers, a low level of negative relationship between learning styles and critical thinking disposition for those with divergent learning style, and a low level of statistically significant relationship between learning styles and critical thinking disposition for those with accommodative learning style.
This paper considers the learning and assessment process of a mechanical engineering undergraduate student and applies it to designing a set of laboratory activities in the field of mechanics of materials. An informal survey was carried out among third-year mechanical engineering students on a four-year bachelor course at the University of Malta in order to find out about their preferred learning style. Thirty-one students were surveyed, which represented 12.5% of students following the BEng (Hons) course in mechanical engineering. The survey indicated that 59.7% of engineering students prefer learning through ‘feeling or concrete experience’, 9.7% prefer learning through ‘watching or reflective observation’, 12.9% prefer learning through ‘thinking or abstract conceptualisation’ and 17.7% prefer learning through ‘doing or active experimentation’. The laboratory activities were designed in such a way as to entice students to use hands-on learning to complement the theory explained during lectures. The four-stage Kolb learning cycle was used as a model on which to design the set of laboratory activities. An example of a topic in mechanics of materials is used in this study to assess the students' response in terms of Kolb's proposal for effective learning. The topic selected (combined bending and torsion) is part of the mechanical engineering degree curriculum.
This study aimed to identify VAK learning styles and problem solving styles of students, to check the relationship between these and to investigate the differences in the above-mentioned styles between male and female students and their fields of study. To this end, 102 students were selected through convenient sampling from Boushehr Islamic Azad University (Iran). Reid's learning style and Cassidy and Long's problem solving style questionnaires were administered to the sample. The data gathered were subjected to the statistical procedure of Pearson Product Moment correlation, two way repeated measures ANOVA, and Independent sample t-test. The results indicated that there is a positive relationship between VAK learning styles and problem solving styles. The results also showed that fields of study did not have an effect on VAK learning styles and problem solving styles. Further, it was found that gender has no effect on VAK learning styles, but it has an effect on problem solving styles.
The instructional value of web-based education systems has been an important area of research in information systems education. This study investigates the effect of various teaching methods on program design learning for students with specific learning styles in web-based education systems. The study takes first-year Computer Science and Information Engineering majors as the sample population. In an earlier study (Hung & Huang, 2008), Bayesian Network analysis of experimental data showed that students with "Input-oriented" and "Perception-oriented" learning styles performed less well on program design tests. Following on these results, this study further analyzes student learning performance in the program design course with a focus on the relationship between teaching methods and learning styles. Students are divided into experimental groups and a control group. The subjects' learning performances are analyzed with the bi-factor variable analysis in SPSS. The results indicate that (a) in learning program design, input-oriented students taught with the diagram method exhibit significantly better learning performance than those using other teaching methods; and (b) in learning program design, perception-oriented students taught with the analogy method exhibit significantly better learning performance than those using other teaching methods.