Is it really a neuromyth? A meta-analysis of the learning styles matching hypothesis

Abstract

Learning styles have been a contentious topic in education for years. The purpose of this study was to conduct a meta-analysis of the effects of matching instruction to modality learning styles compared to unmatched instruction on learning outcomes. A systematic search of the research findings yielded 21 eligible studies with 101 effect sizes and 1,712 participants for the meta-analysis. Based on robust variance estimation, there was an overall benefit of matching instruction to learning styles, g = 0.31, SE = 0.12, 95% CI = [0.05, 0.57], p = 0.02. However, only 26% of learning outcome measures indicated matched instruction benefits for at least two styles, indicating a crossover interaction supportive of the matching hypothesis. In total, 12 studies without sufficient statistical details for the meta-analysis were also examined for an indication of a crossover effect; 25% of these studies had findings indicative of a crossover interaction. Given the time and financial expenses of implementation coupled with low study quality, the benefits of matching instruction to learning styles are interpreted as too small and too infrequent to warrant widespread adoption.

Full text

Frontiers in Psychology 01 frontiersin.org
Is it really a neuromyth? A
meta-analysis of the learning
styles matching hypothesis
VirginiaClinton-Lisell * and ChristineLitzinger
Department of Education, Health, and Behavior Studies, University of North Dakota, Grand Forks, ND,
United States
Learning styles have been a contentious topic in education for years. The
purpose of this study was to conduct a meta-analysis of the eects of matching
instruction to modality learning styles compared to unmatched instruction
on learning outcomes. A systematic search of the research findings yielded
21 eligible studies with 101 eect sizes and 1,712 participants for the meta-
analysis. Based on robust variance estimation, there was an overall benefit of
matching instruction to learning styles, g = 0.31, SE = 0.12, 95% CI = [0.05, 0.57],
p = 0.02. However, only 26% of learning outcome measures indicated matched
instruction benefits for at least two styles, indicating a crossover interaction
supportive of the matching hypothesis. In total, 12 studies without sucient
statistical details for the meta-analysis were also examined for an indication of
a crossover eect; 25% of these studies had findings indicative of a crossover
interaction. Given the time and financial expenses of implementation coupled
with low study quality, the benefits of matching instruction to learning styles are
interpreted as too small and too infrequent to warrant widespread adoption.
KEYWORDS
learning styles, meta-analysis, modality, systematic review, crossover interaction
Introduction
Learning styles have been the topic of ongoing debate in education. Teacher education
textbooks oen state matching instruction to students’ preferred style will optimize learning
outcomes (i.e., the matching hypothesis; Cuevas, 2015; Wininger etal., 2019). In contrast,
cognitive scientists have argued there is a lack of empirical evidence to support the claims of
the matching hypothesis (Kirschner, 2017; Willingham, 2018). Because of the lack of known
empirical evidence supporting the matching hypothesis, there is understandable concern that
perpetuating the concept of learning styles could lead to wasting resources (namely, educator
time and eort) to match instruction as well as stereotyping students into restrictive categories
(Newton and Miah, 2017). However, a meta-analysis aggregating ndings compiled from an
exhaustive search for studies on matching instruction to learning styles has not been
conducted. Such a meta-analysis could bevery helpful in informing this ongoing debate
between educational practitioners and researchers. e purpose of this study was to conduct
a meta-analysis of learning outcomes comparing conditions in which instruction is matched
to students’ preferred learning styles to when instruction is unmatched to students’ preferred
learning styles.
OPEN ACCESS
EDITED BY
Alessandro Antonietti,
Catholic University of the Sacred Heart, Italy
REVIEWED BY
Mojtaba Tadayonifar,
Victoria University of Wellington,
NewZealand
Stephen B. R. E. Brown,
Red Deer Polytechnic, Canada
*CORRESPONDENCE
Virginia Clinton-Lisell
virginia.clinton@und.edu
RECEIVED 06 May 2024
ACCEPTED 17 June 2024
PUBLISHED 10 July 2024
CITATION
Clinton-Lisell V and Litzinger C (2024) Is it
really a neuromyth? A meta-analysis of the
learning styles matching hypothesis.
Front. Psychol. 15:1428732.
doi: 10.3389/fpsyg.2024.1428732
COPYRIGHT
© 2024 Clinton-Lisell and Litzinger. This is an
open-access article distributed under the
terms of the Creative Commons Attribution
License (CC BY). The use, distribution or
reproduction in other forums is permitted,
provided the original author(s) and the
copyright owner(s) are credited and that the
original publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or reproduction
is permitted which does not comply with
these terms.
TYPE Systematic Review
PUBLISHED 10 July 2024
DOI 10.3389/fpsyg.2024.1428732

Clinton-Lisell and Litzinger 10.3389/fpsyg.2024.1428732
Frontiers in Psychology 02 frontiersin.org
Literature review
It is not controversial that there are substantial individual
dierences in student learning—teacher education and cognitive
science scholars agree on this concept. ere is substantial empirical
evidence that students’ academic performance and learning vary due
to background knowledge, motivation, and study strategies (Fong
etal., 2021; Smith etal., 2021), just to name a few examples. However,
the concept in learning styles theories that is controversial is the
meshing or matching hypothesis in which students learn better when
their instruction matches their preferred learning style (Pashler etal.,
2008; Cuevas, 2015; Lyle etal., 2023). A key aspect of the matching
hypothesis is that there is a crossover interaction (Kirschner, 2017),
also known as a qualitative interaction, in which a particular treatment
(in the case of learning styles, a particular modality of instruction) is
eective for at least one subgroup but a dierent treatment is eective
for another subgroup (Qiu and Wang, 2019). Generally speaking,
these crossover treatment interactions are rare (Petticrew etal., 2012;
Preacher and Sterba, 2019), but important to determining optimal
treatments for individuals (Olsen etal., 2019; Qiu and Wang, 2019).
ere are numerous learning styles (Dunn, 1990) as well as
cognitive styles in which the preferred order of processing information
varies (Calcaterra etal., 2005; Fiorina etal., 2007). e most prevalent
are preferred modalities for learning information (Dekker etal., 2012;
Brown, 2023). Learners are generally categorized through self-reports
of preferred modalities (An and Carr, 2017), such as the VAK typology
(visual, auditory, and kinesthetic; Fallace, 2023a,b). An example of
accommodating these styles would beto provide information for
learners categorized as “visual” in pictures, learners categorized as
“auditory” would process the same information best aurally, and
learners categorized as kinesthetic would have a hands-on activity
(Dunn and Dunn, 1975). en, a read/write category was added
making it the VARK typology for learners who were thought to best
process information through reading verbal information (as opposed
to visual learners who better processed pictures; Fleming and
Mills, 1992).
A typology similar to the VARK for categorizing learning styles is
the verbalizer/visualizer approach (Riding and Rayner, 1998).
According to this framework, verbalizers tend to mentally represent
information in words whereas visualizers (also called imagers) tend to
mentally represent information in mental pictures or diagrams
(Riding and Sadler-Smith, 1992; Knoll etal., 2017). Subsequently, the
developers of this framework argue that verbalizers better learn the
material presented in text and images better learn the material
presented in images (Riding and Sadler-Smith, 1992). is is
analogous to the visual and read/write learners in the VARK model.
Importantly, both the VARK and the verbalizer/visualizer approach
advocate matching the modality of the instruction to the students’
learning style.
Adapting instruction based on modality learning styles may
beconated with multimodal instruction. Multimodal instruction is
providing information to students in more than one modality, such as
a text with relevant pictures or diagrams (Bouchey etal., 2021). e
rationale for providing students with multiple modalities is grounded
in dual coding in which visual and verbal information are processed
in separate channels or pathways in the architecture of human
cognition (Paivio, 1991; Reed, 2006). Having information presented
in two modalities (and subsequently two channels) allows for more
information to beprocessed at a given time (Mayer and Anderson,
1992; Mayer, 2011). Multimodal instruction has been found to benet
learning for students (Mayer, 2017; Noetel etal., 2022). However, it
should be noted there are individual dierences in the degree of
benet, such as students with lower levels of background knowledge
tend to have more benet from adding visuals to verbal information
compared to their peers with higher levels of background knowledge
(Mayer, 2017). is is distinct from learning styles in that certain
students learn better than others in multimodal instruction, but there
is not a crossover in which students receive harm or benet from
multimodal instruction. Individuals who support learning styles have
been found to also support multimodal instruction (Nancekivell etal.,
2021). However, matching instruction to learning styles is more time-
consuming as it involves assessing for styles and purposefully
assigning modalities, rather than providing multiple options available
for all students.
ere are concerns that matching instruction to learning styles
relates to psychological essentialism, which is the belief that categories
of people are innate and biologically based (Gelman, 2003; Nancekivell
etal., 2020). An essentialist view of learning styles would bethat, for
example, visual learners are born with a predisposition to learning
visually and that this limits what they can learn through other
modalities. Indeed, essentialist and non-essentialist believers in
learning styles have been identied (Nancekivell et al., 2020).
Essentialist belief in learning styles may explain why visual learners
are perceived as more intelligent and better performing academically
than kinesthetic, “hands on,” learners (Sun etal., 2023). Relatedly,
learners who are told they have a particular style may have a self-
fullling prophecy in which they believe they can only learn in a
particular modality and subsequently do not develop necessary skills
in modalities outside of their style (Vasquez, 2009).
Given the resources involved and potential consequences relevant
to psychological essentialism, learning styles would logically need to
demonstrate remarkable ecacy to justify their use in education. In a
review of learning styles ecacy, a team of cognitive scientists focused
on student learning explained the criteria for validating the matching
or meshing hypothesis (Pashler etal., 2008). One is to categorize
learners based on a measure of learning style into at least two groups.
A second is that participants need to berandomly assigned to receive
instruction in a minimum of two methods (e.g., visual compared to
auditory information). Learners need to beassessed in the same
manner across styles and conditions. Finally, there needs to be a
crossover in which there is an interaction between the learning style
group and instruction in which matched instruction has higher
learning gains than unmatched instruction for each of the learning
style groups. is avoids the possibility that the instruction intended
to bematched for a particular style is simply better across style groups.
For example, college students who were prompted to visualize
statements (visual matching) remembered more statements than their
peers who were prompted to consider the sounds in the statements
(auditory matching) across learning style categories (Cuevas and
Dawson, 2018).
e review by Pashler etal. (2008) concluded that there was a lack
of empirical evidence to support matching instruction to student’s
learning styles that met their criteria for validating the matching
hypothesis. Since this time, there have been other reviews similarly
concluding that there is a lack of empirical support for matching
instruction to students’ learning styles (Cuevas, 2015; Klitmøller, 2015;

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Frontiers in Psychology 03 frontiersin.org
Aslaksen and Lorås, 2018). However, there has not been a meta-
analysis aggregating eects across studies to provide an estimate of
magnitude. Such an approach provides more precision that can
bededuced from individual studies and more power to detect eects
that may beprovided by a single study sample (Deeks etal., 2023).
Moreover, meta-analyses may help resolve controversies based on
conicting study ndings (Deeks etal., 2023).
Potential moderators
e modality of instruction for matching to learning styles should
beconsidered when considering eects. For example, verbalizer or
read/write learners may have their matched instruction involve
reading and auditory learners would receive the same information
aurally (e.g., Rogowsky etal., 2015, 2020; Lehmann and Seufert, 2020).
However, reading comprehension is somewhat better than listening
comprehension for inferential understanding in which readers need
to connect ideas from the text (Clinton-Lisell, 2022). However,
listening may bemore eective than reading when accompanied by
relevant visual representations, such as pictures or diagrams (Noetel
et al., 2022). In addition, non-verbal images (pictures) tend to
beremembered better than the same information presented in words
(Paivio and Csapo, 1973).
e modality of the assessment should be considered as a
potential moderator. Pashler et al.’s (2008) criteria understandably
require the learning assessment to bethe same modality in order to
make comparisons between matched and unmatched instructions
based on learning styles. However, this typically involves one method
of instruction being in the same modality as the assessment and the
comparison method of instruction being in a modality dierent than
the assessment. For example, a listening task would beconsidered
matched for auditory learners and a reading task would beconsidered
matched for read/write or verbal learners and the assessment would
bein writing, which is the same modality as the matched instruction
for read/write or verbal learners. It is possible that there is a modality-
match eect in which having the same modality at learning and
assessment would aect results (Mulligan and Osborn, 2009).
Encoding and producing information in the same modality may
beeasier than in dierent modalities (Staudigl and Hanslmayr, 2019),
and subsequently, whether the instruction modality and assessment
modality were the same should beconsidered.
Experimental studies comparing instructions matching and
unmatched to learning styles have been conducted with between-
subjects and within-subjects designs. With a between-subjects design,
participants are in separate groups and only experience one condition.
In the case of learning styles, participants would beplaced in a group
to either receive instruction matched or unmatched to their
categorized learning style. An advantage to between-subject designs
is that participants are unaware of conditions they were not assigned
to thereby preventing carryover eects from other conditions as well
as practice eects (Charness et al., 2012). However, dierent
individuals are compared by condition, and subsequently, prior group
dierences could confound eects thought to bedue to condition
(Gray etal., 2003; Adesope etal., 2017). In contrast, a within-subjects
design involves participants experiencing both instruction matched
and instruction unmatched to their learning style with dierent
materials and counterbalanced to prevent order eects. With a
within-subjects design, each participant serves as their own control,
which prevents prior group dierences at baseline to confound results
(Charness et al., 2012). Because these research designs are
comparable, but not identical, it is recommended that the study
design be tested as a moderator in meta-analyses (Borenstein
etal., 2009).
Study quality is an important consideration in meta-analyses as it
is possible for treatment eects to vary as a function of study quality
(Sterne etal., 2001; Feeley, 2020). However, removing low-quality
studies from analyses may lead to missing valuable data and meta-
analyses should strive to beas inclusive as possible to have an accurate
understanding of the accumulated evidence (Weaver, 2011; Feeley,
2020). Narrow inclusion criteria themselves may bias meta-analytic
ndings. Moreover, studies in social sciences and education (such as
the ones for the current meta-analysis) tend to receive low-quality
ratings due to methodological details (particularly internal
consistency) not being reported (Singer and Alexander, 2017; Feeley,
2020). However, the potential inuence of study quality should
beconsidered by coding the quality of each study using predetermined
quality criteria and including study quality as a moderator to assess its
potential contribution to varying eects (Pigott and Polanin, 2020; see
Austin etal., 2019; Zhu etal., 2021; Lam and Zhou, 2022).
The current study
e purpose of this study was to conduct a meta-analysis of
matching instruction to modality learning styles. In doing so, the
criteria from Pashler et al. (2008) are generally followed. One
exception is that non-randomized quasi-experiments are included
given the valuable information they provide due to their external
validity in education research (Waddington et al., 2022). ree
research questions guide this inquiry:
1 What is the aggregated eect of matching instruction to
learning styles compared to unmatched instruction on
learning outcomes?
2 How frequent is the crossover of matching instruction by style?
In other words, is there an interaction indicating benets to
matched instruction over unmatched instruction for at least
two of the styles examined?
3 Does the study design (between or within subjects), type of
styles, modality of instruction, or study quality moderate the
eects of matching instruction to learning styles?
Methods
e data extracted from the included studies and R code used for
analyses are available on the Open Science Framework (Clinton-
Lisell, 2023b).
Author positionality
Following guidance from Castillo and Babb (2024), information
about the authors’ backgrounds and identities is shared in this section.

Clinton-Lisell and Litzinger 10.3389/fpsyg.2024.1428732
Frontiers in Psychology 04 frontiersin.org
e rst author learned a cognitive approach to educational
psychology during her doctoral and postdoctoral studies. During
these times, she was taught that there was a lack of empirical evidence
to support the concept of learning styles and that it was a common
myth of education. Furthermore, she is aware that learning styles have
origins rooted in ethnocentrism in which white scholars developed
the concept based on condescending attitudes toward children of
color (Fallace, 2019). As a white woman, this is a history she works to
bemindful of not repeating.
e second author has a master’s degree in school counseling and
was inuenced by behavioral and school counseling theories. e
career aspects of school counseling education supported the use of
learning style inventories during the time she received her training.
As a researcher, the second author became aware of learning styles
research that did not support the career education practices being
utilized in the educational setting. e second author shied practices
in her work away from using learning style inventories as part of
career education because of the current research on the topic. As a
white woman and rst-generation college student, she works to
bemindful of the social/cultural underpinnings that could inuence
the understanding of research in learning styles.
Inclusion criteria
Following Pashler etal.’ (2008) guidelines, studies for the learning
styles meta-analysis were included if they met the following criteria:
(1) participants were categorized in at least two types of learning styles
(e.g., visual and auditory), (2) there was at least one condition with
instruction and/or learning materials matching to the participants’
learning styles and at least one condition with instruction and/or
learning materials not matching to the participants’ learning styles, (3)
the unmatched condition for one type of learning style was considered
a matched condition for another learning style (so that a crossover
interaction could beexamined), (4) there was a measurement of
learning that was identical across conditions and styles, (5) the study
was disseminated in English because of the linguistic skills of the
research team, and (6) descriptive statistics were reported to calculate
eect sizes or the author of the study provided these upon request.
Systematic search
e rst step in the systematic search for relevant articles included
a broad search of the databases Web of Science, Scopus, PsycInfo/
EBSCOhost, ERIC, and ProQuest Dissertations and eses using the
search terms such as “learning style*” and “learning preference*.”
Dissertations and theses were important to include in the search as
they are less likely to beinuenced by publication bias in which
journal articles are more likely to get published when reporting
signicant results (Paez, 2017). A total of 6,299 citations were found
(see Figure1 for a ow chart of the systematic search process). Aer
duplicates were removed, 1,810 remained. ese citations were
screened based on titles and abstracts by at least two researchers
working independently using Abstrackr (Wallace etal., 2012). Based
on this screening, 40 reports were selected for full-text screening. Of
these studies, 12 were selected for inclusion (see Figure1 for reasons
for exclusion). A backwards search of the citations in these 12 reports
was conducted but did not yield additional studies. A forwards search
of the 12 reports yielded an additional 8 reports. e citations of
previous reviews were examined (Pashler etal., 2008; Cuevas, 2015;
Aslaksen and Lorås, 2018; Dinsmore etal., 2022), which yielded one
more report. is led to a total of 21 reports of 21 independent studies
in this meta-analysis.
Coding
To prepare the studies for analyses, two researchers coded the
methodological and bibliographic information about each study (see
Table1; κ = 0.89). Specically, the sample, study design, learning styles
examined, measures of learning, content of instruction/materials, and
assessment were recorded to describe studies (see Appendix A for
codebook). Study quality was determined based on What Works
Clearinghouse (2022) criteria and categorized as meeting standards,
meeting standards with reservations, or not meeting standards (see
Appendix A for details). Based on these standards, a study must bea
randomized experiment to meet standards (although not all
randomized experiments meet standards). In randomized
experiments, the chance of students being the control or intervention
should be equal. In contrast, quasi-experiments involve naturally
occurring groups, typically classes in educational research, or controls
matched through propensity score matching or regression
discontinuity design. Whether a study had randomization (for
between subjects) or counterbalancing (for within subjects) is noted
in the summary of studies in Table1. Other study quality criteria such
as the face validity for each outcome measure, reliability standards for
each outcome measure, and whether there was consistent data
collection across conditions are reported in Appendix Table B1. As
can be seenin Appendix Table B2, ve studies were determined to
WWC standards and the remainder did not meet WWC standards.
Statistical procedures
e eect sizes for each learning outcome comparing matched
and unmatched instruction were calculated. Hedges’ g was used as an
eect size calculated using Meta-Essential tools (Suurmond etal.,
2017). A positive Hedges’ g indicates better learning outcomes for
matched than unmatched instruction. To account for multiple eect
sizes within each study, a robust variance estimation (RVE) was used.
An RVE is a statistical technique that accounts for dependencies
within studies while still allowing for the unique contribution of each
eect size to beconsidered (Tanner-Smith etal., 2016). Each of the
study eect sizes is shown in Table2, and a forest plot is in Figure2.
Learning outcomes indicating a crossover interaction as articulated in
Pashler etal. (2008) in which at least two styles had higher learning
outcomes with matched instruction are bolded in Table2.
Results
e overall main eect of matching instruction to learning styles
on learning outcomes was estimated using RVE based on 21 studies
and 101 eect sizes and assumed dependency (intercorrelation of
dependent eects within studies) of ρ = 0.8. e ndings indicated an

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Frontiers in Psychology 05 frontiersin.org
overall positive eect on learning outcomes for matching instruction
to learning styles compared to unmatched instruction, g = 0.32,
SE = 0.12, 95% CI = [0.07, 0.57], p = 0.01. ere was substantial
variability with a τ
2
of 0.77 and I
2
of 91.17. A sensitivity analysis was
conducted with a range of dependent eect size correlations. As can
be seen in Table 3, the eect was consistent across
assumed dependencies.
Publication bias
Publication bias was examined to see whether there was
overreporting of positive eects. A funnel plot was generated using
the “metafor” package in R (Viechtbauer, 2010; see Figure3). Based
on a visual inspection of the funnel plot, the distribution of eect sizes
was approximately symmetrical with smaller and larger studies having
similar distances away from the mean (indicated by the vertical line;
Lin and Chu, 2018). Egger’s test of the intercept was not signicant,
b = −0.058, 95% CI [−0.52, 0.41], p = 0.11. Taken together, the funnel
plot and Egger’s test indicate that publication bias does not appear to
be the reason for the positive eect of matching instruction to
learning styles.
Crossover interactions
e number of crossover interactions (at least two styles beneted
from matched instruction within a learning outcome measure) was
calculated. Based on the tally of the bolded eect sizes in Table2, there
are 11 learning outcome measures in which matched instruction
beneted at least 2 learning styles as indicated by Hedges’ g greater
than 0. is was out of a total of 42 learning outcome measures that
were compared indicating that 26.19% had the type of crossover
interaction necessary to support the meshing hypothesis as articulated
by Pashler etal. (2008).
As indicated in Figure1, ve that had their full texts screened did
not have sucient statistics to calculate the eect sizes reported. In
addition, seven reports identied through other searches did not have
sucient statistics to calculate eect sizes but otherwise met inclusion
criteria. Based on the descriptions of the ndings of these 12 studies
in Tables 3, 4 of these studies indicated a crossover interaction
(25.00%). erefore, the ndings from the studies without sucient
statistics reported appear to besimilar to the studies included in the
meta-analysis in terms of crossover interactions.
Moderator analysis
To estimate whether these potential moderators varied the eect
of matching instruction to learning style, the package “robumeta” in
R was used (Fisher and Tipton, 2015). e study design (between or
within subjects), modality of instruction/materials (visual, verbal, or
auditory), whether the assessment was in the same modality as the
instruction, and study quality (does not meet WWC standards or
meets WWC standards) were all coecients estimated in the meta-
regression model. For consistency across studies, “visual” matched
instruction that was text-based was coded as “verbal or read/write.”
Based on the output of the meta-regression model, none of the
moderators were signicant (see Table5). erefore, it is unclear what
the source of variability in the aggregate ndings is.
FIGURE1
PRISMA flow chart of the systematic review process.

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Frontiers in Psychology 06 frontiersin.org
TABLE1 Summary of studies.
Author (date),
dissemination type
Participants Design Learning styles Neutral/mixed
groups
Learning activity
(instruction/
material) and
assessment
Aslaksen and Lorås (2019),
journal article
22 college students
(average age 22.1 years)
Between subjects (randomly
assigned); laboratory study
Visual and auditory
(identied and recruited
to participate in an earlier
prior study based on the
Learning Style Survey
[LSS], Cohen etal.,
2019), completed prior to
learning activity
Only students clearly
identied as visual or
auditory learners on the
LSS were eligible to
participate
History lesson in audio or
text form, assessed using a
multiple-choice recall test
(written)
Burns (n.d.), undergraduate
capstone thesis
37 undergraduates
(average age 21.8 years)
Within-subjects
(counterbalanced); laboratory
study
Auditory and visual
(based on cuto scores
from the Styles of
Processing Scale,
Childers etal., 1985),
completed prior to
learning activity
None History lectures with audio
only and video, assessed
using verbatim ll-in-the-
blank items (written)
Chen (2020), journal article 75 university students
(between 19 and 25 years
old)
Between subjects (randomly
assigned); participation was
outside of coursework
Read/write and auditory
styles (highest score on
the visual, auditory, read/
write, kinesthetic, VARK
questionnaire; Fleming,
2001), completed prior to
learning activity
Not mentioned Web-based instructions on
how to use an electronic
pen tool with audio
narration or onscreen text,
assessed on pen skills
(kinesthetic)
Chen and Sun (2012), journal
article
139 h-grade students Between subjects (randomly
assigned); study participation
was during the school day
Verbal and visual (based
on the Styles of
Processing Scale,
Childers etal., 1985),
completed aer learning
activity
Not mentioned Multimedia material on
energy education with text
(matched to verbal), video,
or animation (both
matched to visual)
conditions, assessed using
multiple-choice questions
(written)
Chui etal. (2021), journal
article
18 trainee pilots (average
age 21.89 years)
Within-subjects
(counterbalanced); recruited
from ight training, but
participated outside of course
requirements
Visual and auditory styles
(based on whether their
visual or auditory score
on the VARK
questionnaire was
higher), completed
measure prior to learning
activity
Participants with equal
scores on visual and
auditory preferred
learning styles were
excluded (exact number
not stated)
Visual or auditory feedback
on ight simulator
performance, assessed
through follow-up ight
performance (kinesthetic)
Cuevas and Dawson (2018),
journal article
183 undergraduate and
graduate students
(between 19 and 50 years
old)
Between subjects (randomly
assigned); laboratory study
Visual and auditory
(highest score on the
VARK questionnaire,
Fleming and Mills, 1992),
completed prior to
learning activity
Participants with
equivalent visual and
auditory scores were
excluded (n= 21)
20 statements with
instructions to either
visualize statements
(visually matched) or
consider pronouncing
statements (auditory
matched) answering
questions about the
statements from memory
(written)
(Continued)

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TABLE1 (Continued)
Author (date),
dissemination type
Participants Design Learning styles Neutral/mixed
groups
Learning activity
(instruction/
material) and
assessment
Ge (2021), journal article 140 college students Between subjects (randomly
assigned); part of course
instruction across multiple
units
Visual and auditory
(Perceptual Learning
Style Preference
Questionnaire, Reid,
1987), completed prior to
learning activity
Participants who were
not categorized as a
visual or auditory style
based on questionnaire
scores were excluded
from the analyses
Web-based modules on
grammar with narration or
on-screen text, assessed
using multiple-choice
questions (written)
Hazra etal. (2013),
conference proceedings
139 graduate students Between subjects (randomly
assigned); laboratory study
Visual and verbal (based
on the Index of Learning
Styles scores, Felder and
Soloman, 1997), it is
unclear whether this was
completed prior to or
aer the learning activity
Some participants were
categorized as neutral
and analyzed separately
History and engineering
modules with either visual
or verbal modes, assessed
using gain scores
subtracting pretest from
posttest scores on recall,
recognition,
comprehension, and
transfer (written)
Kam etal. (2020), journal
article
60 English as a foreign
language college students
(average age 21.5)
Between subjects (randomly
assigned); laboratory study
Visual and auditory
(based on higher scores
on the Caption Reliance
Test, Leveridge and Yang,
2014), completed prior to
learning activity
Not described Video lecture on leadership
with and without captions,
assessed using multiple-
choice questions (listening)
Kassaian (2007), journal
article
66 university students Within-subjects
(counterbalanced); part of
instruction
Visual and auditory
(based on scores of both
the VAK, Chislet and
Chapman, 2005, and a
researcher-made self-
report), it is unclear
whether this was
completed prior to or
aer the learning activity
Participants who did not
have consistent results
on the two measures
were not included
(n= 3)
New vocabulary words
either listened to or viewed,
assessed through multiple-
choice recognition
questions and recall of
words (both written)
Lehmann and Seufert (2020),
journal article
42 university students
(average age 22.55 years)
Between subjects (randomly
assigned); laboratory study
Auditory and visual
modality preferences
(only those who had one
modality scoring in the
top third and the other
modality in the bottom
third of scores were
included), completed
prior to the learning
activity
A pool of 223 students
was used to select
students who had high
scores in one modality
and low scores in
another modality, those
not selected were
excluded from the study
A scientic text on
volcanos either read or
listened to, the recall was
assessed by literal multiple-
choice questions, and
comprehension was
assessed by open-response
questions (written)
Moser and Zumbach (2018),
journal article
124 or 113 (depending on
analyses) university
students (average age
25.17 years)
Between subjects (randomly
assigned); laboratory study
Verbal and visual (based
on Verbal-Visual
Learning Styles
Questionnaire and Santa
Barbara Learning Styles
Questionnaire, Mayer
and Massa, 2003
subscales scores, analyzed
separately), completed
prior to learning activities
Participants who were
not categorized as visual
or verbal style based on
questionnaire scores
were excluded from
analyses
Multimedia material on
plate tectonics with mostly
pictures (visual) and
mostly text (verbal)
conditions, assessed by
multiple choice and open-
response questions
(written, no mention of
visuals)
(Continued)

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TABLE1 (Continued)
Author (date),
dissemination type
Participants Design Learning styles Neutral/mixed
groups
Learning activity
(instruction/
material) and
assessment
Moussa-Inaty etal. (2019),
journal article
61 undergraduate students
(between 19 and 30 years
old)
Between subjects (quasi-
experiment with groups
based on styles); laboratory
study
Visual and auditory
(based on VAK learning
style inventory scores),
completed prior to
learning activity
Students who scored
highest as kinesthetic
learners were not
included
Lessons on lightning with
auditory or written
conditions, assessed
through short answer
comprehension questions
(written)
Mujtaba etal. (2022), journal
article
80 English as a Second
Language students
(average age 18 years)
Between subjects (quasi-
experiment, group
assignment decision not
stated); part of classroom
instruction across multiple
sessions
Auditory and read/write
(highest score on the
VARK questionnaire,
Fleming and Mills, 1992),
completed prior to
learning activity
Some participants were
excluded because their
auditory or read/write
learning style scores
were not very high
(n= 30)
Audio and text-based
instruction on grammar,
assessed through oral
production (aural) and
writing tasks (written)
Papanagnou etal. (2016),
journal article
162 medical students Between subjects (randomly
assigned); part of medical
training
Auditory, visual, and
kinesthetic (based on
self-report of perceived
learning style), completed
prior to learning activity
Participants who
reported multiple
perceived learning style
modalities were
analyzed separately
Students were trained
individually on intravenous
(IV) needle placement by
instructors using verbal
instructions, guiding the
hands of the students, or
visually demonstrating,
assessed through successful
IV placement on the rst
attempt (kinesthetic)
Rassaei (2018), journal article 62 English as a Foreign
Language students
(between 21 and 37 years
old)
Between subjects (randomly
assigned); part of classroom
instruction in a single session
Auditory and visual
(based on scores above
the mid-cuto point on a
learning styles
questionnaire, Slack and
Norwich, 2007),
completed prior to
learning activity
Participants who scored
above the cuto point
for both auditory and
visual styles were
excluded from the study
(exact number not
stated)
Reading passages with
glosses for new vocabulary
(denitions appeared when
cursors were hovered over
the words) that were in
either text or audio,
assessed through
vocabulary production and
recognition (written)
Rassaei (2019), journal article 61 English as a Foreign
Language students
(between 18 and 37 years
old)
Between subjects (randomly
assigned); part of classroom
instruction in a single session
Auditory and read/write
based on the VARK
questionnaire (Fleming
and Mills, 1992)
Participants who could
not beassigned as
auditory or read/write
styles were excluded
from the study (n= 65)
Corrective feedback on
English article usage that is
audio (for auditory style)
or text (for read/write
style), assessed through an
oral test of article usage.
Riding and Douglas (1993),
journal article
40 15–16 year old high
school students
Between-subjects (randomly
assigned); participation was
part of the school day
Verbalizers and imagers
(based on categorization
from the Cognitive Styles
Analysis, Riding, 1991),
completed aer the
learning activity
ere was an
intermediate group
analyzed separately
(n= 19)
Tutorial on car brake
systems either only text
(matched with verbalizers)
or text plus pictures
(matched to visualizers),
assessed through short
recall questions, an
explanation question, a
problem-solving question,
and labeling questions
(written).
(Continued)

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Sensitivity analysis
A sensitivity analysis was conducted to examine whether altering the
inclusion criteria changed the results. ere was only one study in which
instruction was adopted to a kinesthetic learning style (Papanagnou
etal., 2016). Removing this study from the RVE analyses indicated an
overall aggregated benet of matched instructions to learning styles
compared to unmatched, g = 0.34, SE = 0.13, 95% CI = [0.08, 0.60],
p = 0.01 with 20 studies and 98 eect sizes. is nding is similar to the
ndings when the kinesthetic learning intervention was included. ere
were two studies that were quasi-experiments without random
assignment (Moussa-Inaty etal., 2019; Mujtaba etal., 2022). is was a
concern given that they did not demonstrate baseline equivalence in the
study quality coding (see Appendix Table B1). erefore, an RVE was
conducted with the two quasi-experiments removed from the analyses.
e results of the RVE were similar to the quasi-experiments removed
in that there was an overall aggregated benet of matched instruction to
learning styles compared to unmatched, g = 0.33, SE = 0.13, 95%
CI = [0.05, 0.61], p = 0.02 with 19 studies and 91 eect sizes.
Discussion
Educational researchers consider the concept of better learning
through matching instruction to learning styles to bea neuromyth
that completely lacks empirical evidence (Brown, 2023). However, the
ndings from this meta-analysis indicated a small, but statistically
reliable benet of matching instruction based on learning styles. is
aligns with the majority of educators’ perspectives (Dekker etal., 2012;
Nancekivell etal., 2020; Eitel etal., 2021) but conicts with the
conclusions of previous reviews by educational researchers (Cuevas,
2015; An and Carr, 2017; Aslaksen and Lorås, 2018; Dinsmore etal.,
2022; Yan and Fralick, 2022). What distinguishes this meta-analysis
from previous reviews is (1) its singular focus on studies comparing
instruction matched and unmatched to modality learning styles and
(2) its systematic approach to gathering relevant studies and
aggregating ndings. e lack of evidence noted in previous reviews
may bedue to a lack of power in individual studies. e cumulative
evidence of aggregated eects appeared to have sucient power to
detect an eect. However, the majority of learning outcomes did not
indicate a crossover interaction that would validate accommodation
to learning styles. However, a non-trivial minority of learning
outcomes did indicate the crossover interaction indicative of
supporting the matching hypothesis based on Pashler etal. (2008). An
important caveat is that most of the studies indicating a crossover
interaction did not meet quality standards as determined by the What
Works Clearinghouse (2022). Taken together, these ndings may
beinterpreted that it is too much of an overreach to insist learning
styles should beincorporated into instructional practices.
Given the time and resources required for matching instruction
to learning styles coupled with the potential for harm through
psychological essentialism (Vasquez, 2009; Fallace, 2019, 2023a,b;
Nancekivell etal., 2020; Sun etal., 2023), westated in the literature
review that accommodating instruction to learning styles would need
to have substantial benets to mitigate their potential for harm. To
consider this issue, it may behelpful to compare the eect size noted
TABLE1 (Continued)
Author (date),
dissemination type
Participants Design Learning styles Neutral/mixed
groups
Learning activity
(instruction/
material) and
assessment
Rogowsky etal. (2015),
journal article
41 adults (between 25 and
40 years old)
Between subjects; laboratory
study
Visual and auditory
learning styles (based on
categorization by the
Building Excellence
Online Learning Styles
Assessment Inventory;
Rundle and Dunn, 2010)
Participants with similar
preferences for visual
and auditory modality
were excluded (n= 53)
Reading or listening to
passages from a history
book, assessed using
written multiple-choice
questions (written).
Rogowsky etal. (2020),
journal article
34 h-grade students Within subjects; students
participated during the
school day
Visual and auditory styles
(categorized by the
Learning Styles: e Clue
to You! measure),
completed aer learning
activity
Participants with similar
preferences for visual
and auditory modality
were excluded (n= 73)
Reading or listening to
texts from a standardized
comprehension test,
assessed based on written
test items.
Tadayonifar etal. (2021),
journal article
13 English as a Foreign
Language students (ages
19–20)
Within-subjects (orders
randomly assigned); students
participated as part of class
activities
Read/write and auditory
styles (based on the
highest scores on the
VARK learning style
inventory, Fleming,
2001), completed prior to
learning activity
Participants with similar
scores were identied as
mixed styles
Reading passages with
glosses for new vocabulary
(denitions appeared when
cursors were hovered over
the words) that were either
in text or audio, assessed
through vocabulary test
e number of participants in this table is the number of participants in the analytic sample used to calculate eect sizes in the meta-analysis. Several studies excluded participants because of
their learning style scores or had conditions unrelated to the research questions of this meta-analysis and subsequently had larger samples than are reported in this table. Some studies did not
cite the authors of the learning styles measure they used and only provided the name of the measure.

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Frontiers in Psychology 10 frontiersin.org
TABLE2 Eect sizes with variances and number of participants.
Study, learning style group, condition (if more
than one), measure (if more than one),
subgroups (if any)
Number of
participants
Hedges’ gVariance of Hedges’ g
Aslaksen and Lorås (2019), auditory 9−1.25 0.44
Aslaksen and Lorås (2019), visual 13 0.85 0.30
Burns (n.d.), auditory 7−0.10 0.04
Burns (n.d.), visual 30 0.44 0.01
Chen (2020), auditory 41 −0.08 0.09
Chen (2020), read/write 34 0.26 0.11
Chui etal. (2021), auditory 9 1.56 0.09
Chui etal. (2021), visual 9 0.61 0.04
Chen and Sun (2012), verbal, interaction comparison 73 0.11 0.06
Chen and Sun (2012), visualizer, interactive treatment 66 0.08 0.07
Cuevas and Dawson (2018), auditory 118 −2.87 0.07
Cuevas and Dawson (2018), visual 65 2.13 0.10
Ge (2021), auditory 76 −0.74 0.06
Ge (2021), visual 64 1.20 0.07
Hazra etal. (2013), verbal, engineering comprehension 15 −0.41 0.24
Hazra etal. (2013), verbal, history comprehension 0.06 0.24
Hazra etal. (2013), verbal, engineering recall −0.21 0.24
Hazra etal. (2013), verbal, history recall 0.13 0.24
Hazra etal. (2013), verbal, engineering recognition −0.44 0.24
Hazra etal. (2013), verbal, history recognition 0.46 0.24
Hazra etal. (2013), verbal, engineering transfer 0.10 0.24
Hazra etal. (2013), verbal, history transfer 0.04 0.24
Hazra etal. (2013), visual, engineering comprehension 124 −0.10 0.03
Hazra etal. (2013), visual, history comprehension 0.43 0.03
Hazra etal. (2013), visual, engineering recall 0.00 0.03
Hazra etal. (2013), visual, history recall 0.25 0.03
Hazra etal. (2013), visual, engineering recognition 0.19 0.03
Hazra etal. (2013), visual, history recognition 0.30 0.03
Hazra etal. (2013), visual, engineering transfer −0.04 0.03
Hazra etal. (2013), visual, history transfer 0.16 0.03
Kam etal. (2020), auditory 29 0.76 0.14
Kam etal. (2020), visual 31 0.79 0.13
Kassaian (2007), auditory, week 1 29 0.78 0.02
Kassaian (2007), auditory, week 2 0.77 0.02
Kassaian (2007), visual, week 1 37 0.80 0.01
Kassaian (2007), visual, week 2 0.64 0.02
Lehmann and Seufert (2020), auditory comprehension 21 0.26 0.18
Lehmann and Seufert (2020), auditory recall −0.11 0.18
Lehmann and Seufert (2020), visual comprehension 21 1.04 0.20
Lehmann and Seufert (2020), visual recall 0.86 0.19
Moser and Zumbach (2018), verbalizer VVQ 42 0.45 0.09
Moser and Zumbach (2018), verbalizer SBLSQ 40 0.61 0.10
(Continued)

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TABLE2 (Continued)
Study, learning style group, condition (if more
than one), measure (if more than one),
subgroups (if any)
Number of
participants
Hedges’ gVariance of Hedges’ g
Moser and Zumbach (2018), visualizer VVQ 82 −0.03 0.05
Moser and Zumbach (2018), visualizer SBLSQ 73 −0.04 0.05
Moussa-Inaty etal. (2019), auditory 31 −0.25 0.12
Moussa-Inaty etal. (2019), visual 30 0.39 0.13
Mujtaba etal. (2022), auditory OPT delayed 40 1.40 0.12
Mujtaba etal. (2022), auditory OPT post 1.35 0.12
Mujtaba etal. (2022), auditory WT delayed 1.73 0.13
Mujtaba etal. (2022), auditory WT post 1.36 0.12
Mujtaba etal. (2022), visual OPT delayed 40 −0.53 0.10
Mujtaba etal. (2022), visual OPT post −0.56 0.10
Mujtaba etal. (2022), visual WT delayed −0.59 0.10
Mujtaba etal. (2022), visual WT post −0.63 0.10
Papanagnou etal. (2016), auditory 52 −0.07 0.34
Papanagnou etal. (2016), kinesthetic 62 0.52 0.07
Papanagnou etal. (2016), visual 48 −0.27 0.08
Rassaei (2018), auditory delayed production 32 2.58 0.22
Rassaei (2018), auditory delayed recognition 2.14 0.19
Rassaei (2018), auditory post production 2.02 0.18
Rassaei (2018), auditory post recognition 1.88 0.17
Rassaei (2018), visual delayed production 30 −1.47 0.16
Rassaei (2018), visual delayed recognition −1.23 0.15
Rassaei (2018), visual post production −1.48 0.16
Rassaei (2018), visual post recognition −1.35 0.16
Rassaei (2019), auditory delayed OPT 31 1.59 0.16
Rassaei (2019), auditory post OPT 1.72 0.17
Rassaei (2019), auditory delayed WT 1.69 0.17
Rassaei (2019), auditory post WT 1.77 0.17
Rassaei (2019), read/write delayed OPT 30 −0.34 0.13
Rassaei (2019), read/write post OPT −0.04 0.13
Rassaei (2019), read/write delayed WT −0.04 0.13
Rassaei (2019), read/write post WT −0.28 0.13
Riding and Douglas (1993), verbalizer, analytic subgroup explanation 10 −0.70 0.35
Riding and Douglas (1993), verbalizer, analytic subgroup labelling 0.12 0.33
Riding and Douglas (1993), verbalizer, analytic subgroup problem
solving
0.08 0.33
Riding and Douglas (1993), verbalizer, analytic subgroup short recall −1.20 0.40
Riding and Douglas (1993), verbalizer, wholist subgroup, explanation 10 −0.15 0.33
Riding and Douglas (1993), verbalizer, wholist subgroup, labeling −0.11 0.33
Riding and Douglas (1993), verbalizer, wholist subgroup, problem
solving
−0.37 0.33
Riding and Douglas (1993), verbalizer, wholist subgroup, short recall −0.17 0.33
Riding and Douglas (1993), visualizer, analytic subgroup explanation 10 1.51 0.44
(Continued)

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Frontiers in Psychology 12 frontiersin.org
in this meta-analysis (Hedges’ g = 0.32) with those from other methods
of adapting instruction. For example, the modality eect, in which
listening to verbal information while viewing visual representations,
rather than reading the same verbal information alongside visuals has
Hedges’ g of 0.70 (Noetel etal., 2022); that is, the benet of listening,
rather than reading verbal information that accompanies visual
representations across all students, appears to have twice the eect
than was noted in this meta-analysis matching instruction to learning
styles and would beless time-consuming and expensive to implement.
Removing interesting or irrelevant information included with the
lesson has Hedges’ g of 0.33 (Sundararajan and Adesope, 2020).
Segmenting instruction into meaningful learner-paced units has a
benet of Hedges’ g of 0.32 compared to continuous information (Rey
etal., 2019). Finally, an overall application of multimedia principles to
learning has Hedge’s g of 0.28 (Noetel etal., 2022).
When examining an overview of meta-analysis on multimedia
design for learning, accommodating instruction based on learning
styles in the current meta-analysis is generally about the same size or
smaller than various multimedia designs (e.g., signaling important
information, animation, and pleasant colors/anthropomorphic; Noetel
etal., 2022). However, all of the multimedia design principles reviewed
involved having students each receive the same instructional changes,
whereas accommodating instruction based on learning styles by
denition involves at least two types of instruction (Noetel etal.,
2022). In addition, 85% of the studies in the current meta-analysis did
not include all participants in the nal sample because their learning
styles scores did not allow for condent categorization and matching/
unmatching to instructional modality. erefore, we, the authors,
deeply question whether the found benets of learning styles in this
meta-analysis warrant accommodating instruction, especially for
widespread use. Based on previous studies, well-structured
instructional design may bemore eective across all students and
would involve less time than accommodating to learning styles.
Participant expectations may berelevant to interpreting the
ndings from the studies in this meta-analysis (Vasquez, 2009; Sun
etal., 2023). Generally, participants were asked about their modality
preferences and then engaged in a learning activity shortly
thereaer (only three studies specically stated participants were
asked to complete learning style measures aer the learning activity;
see Table1). If participants were aware of their learning styles prior
to engaging in a task that matched or unmatched their style, they
may have had dierent expectations for success and engagement
that aected their learning (Vasquez, 2009). For example, one study
categorized students based on fake/induced learning styles (Moser
and Zumbach, 2015). In other words, students took a learning styles
assessment and were told (incorrectly) that they scored as
visualizers or verbalizers. Students scored higher when their
instructional materials “matched” their fake/induced learning style
compared to the unmatched conditions, but there were no benets
to matching based on their actual categorizations based on the
learning styles assessment (Moser and Zumbach, 2015). is is
described in the learning styles genesis model in which appraisal and
decision processes based on external feedback about learning styles
along with previous experiences with modalities shape learning
outcomes (Moser and Zumbach, 2018). Moreover, participants may
have had a situational interest in the content triggered by
immediately receiving instruction in their stated preferences
(Bernacki and Walkington, 2018). is likely would not continue
long term as maintained interest requires a personal connection
(Høgheim and Reber, 2017).
TABLE2 (Continued)
Study, learning style group, condition (if more
than one), measure (if more than one),
subgroups (if any)
Number of
participants
Hedges’ gVariance of Hedges’ g
Riding and Douglas (1993), visualizer, analytic subgroup labeling 1.33 0.41
Riding and Douglas (1993), visualizer, analytic subgroup problem
solving
1.08 0.38
Riding and Douglas (1993), visualizer, analytic subgroup short recall 0.50 0.34
Riding and Douglas (1993), visualizer, wholist subgroup, explanation 10 1.11 0.39
Riding and Douglas (1993), visualizer, wholist subgroup, labeling 1.05 0.38
Riding and Douglas (1993), visualizer, wholist subgroup, problem
solving
1.30 0.41
Riding and Douglas (1993), visualizer, wholist subgroup, short recall 1.35 0.42
Rogowsky etal. (2015), auditory time one 21 −0.25 0.18
Rogowsky etal. (2015), auditory time two −0.24 0.18
Rogowsky etal. (2015), visual time one 20 −0.11 0.18
Rogowsky etal. (2015), visual time two −0.20 0.18
Rogowsky etal. (2020) auditory 12 0.17 0.03
Rogowsky etal. (2020) visual 22 −0.12 0.02
Tadayonifar etal. (2021), auditory 7 2.03 0.16
Tadayonifar etal. (2021), read/write 6 2.63 0.27
Learning outcomes indicating a crossover eect as articulated in Pashler etal. (2008) in which at least two styles had higher learning outcomes with matched instruction are bolded.

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Learning styles may beconated with modality-specic skills
(An and Carr, 2017). In other words, participants may simply
bebetter at reading if they indicate a read/write style or listening if
they indicate an auditory style. is results in a jangle fallacy in
which two similar constructs (e.g., modality skill and learning styles)
are considered dierent because they have dierent terminology
(Kelley, 1927; Beisley, 2023). It should benoted, however, that if this
is the case, skills in a modality may have been developed because of
preferences in that modality, which would, in turn, lead to more
practice and more skill in a particular modality. It would
be extremely dicult to disentangle the initiating factor in this
(hypothetical) perpetual cycle of skill and preference. However, more
inquiry into fake/induced learning styles such as that by Moser and
Zumbach (2015) would bea useful means of testing whether the skill
is confounded with style given that fake/induced styles would
be randomly assigned and subsequently modality skills should
besimilar across “styles.”
A key feature of the matching hypothesis is a crossover
interaction in which matched instruction benets learning for only
the group for which it is matched. is matched instruction diers
depending on the learning style of the student. In the current meta-
analysis, approximately one-fourth of the learning measures
indicated a crossover interaction in which there were positive eect
sizes for matched instruction for two dierent learning styles
(Kassaian, 2007; Chen and Sun, 2012; Hazra etal., 2013; Kam etal.,
2020; Lehmann and Seufert, 2020; Chui etal., 2021; Tadayonifar
etal., 2021). is raises the question of what characteristics of these
studies and learning measures may beresponsible for the crossover
interaction. However, these studies are quite heterogeneous.
Samples include young adult college students, elementary school
students, and aircra pilot trainees. e learning styles and their
inventories varied and included the Styles of Processing Scale
(Childers etal., 1985), VARK questionnaire (Fleming, 2001), Index
of Learning Styles Scores (Felder and Soloman, 1997), and Caption
Reliance Test (Leveridge and Yang, 2014). Content and learning
measures were also varied such as memory and recall of history
(Hazra et al., 2013), multiple-choice questions about energy
education (Chen and Sun, 2012), and ight simulator performance
(Chui et al., 2021). erefore, there does not seem to be any
consistent feature across these studies based on the information
coded for this meta-analysis that would elucidate the mechanism
behind the crossover interaction. Subsequently, the lack of
understanding of what circumstances could foster a crossover
interaction is an additional reason for caution in implementing the
matched instruction for learning styles. Without knowing what
features are conducive to eective matched instruction, it is
extremely dicult to have eectively matched instruction across
identied learning styles.
Implications
We advise extreme caution if using the ndings from this meta-
analysis to justify matching instruction to learning styles. If
choosing to incorporate learning styles, then learning styles should
never beascribed as a feature of a cultural group, especially by
FIGURE2
Forest plot of eect sizes.

Clinton-Lisell and Litzinger 10.3389/fpsyg.2024.1428732
Frontiers in Psychology 14 frontiersin.org
individuals outside of that group, as this leads to unwarranted and
potentially harmful expectations based on group membership
(Gutiérrez and Rogo, 2003; Fallace, 2023a,b). Moreover, learning
style interventions are costly in terms of both time and money
(Pashler etal., 2008). By denition, matching instruction based on
learning styles requires multiple versions of instruction or materials
to bedeveloped.
If learning styles are incorporated into education, westrongly
recommend that they beimplemented in the context of multimodality
for learning. By providing information in more than one modality,
such as text with visuals, the same materials could arguably appeal to
both verbal and visual learning styles while grounded in theories of
human cognition such as dual coding (Noetel etal., 2022). Engaging
multiple senses is generally benecial for learning (Nguyen etal.,
2022). In addition, providing students with audio-assisted text may
also be benecial, particularly for learning beyond one’s native
language (Clinton-Lisell, 2023a), and logically appeal to auditory and
verbal preferences. Not only is multimodality known to beeective
for learning but even individuals with strong essentialist beliefs about
learning styles support multimodal learning as eective (Nancekivell
et al., 2021). Moreover, oering multiple modalities for learning
provides an inclusive education for students with perceptual
disabilities to have access to the content (omas etal., 2015; Griful-
Freixenet etal., 2017).
Limitations and future directions
Limitations to the studies were included in the meta-analysis.
As indicated in the study quality coding, the majority of the
outcome measures did not have reliability metrics reported. e
lack of information about reliability, as noted in the study quality
scoring, leads to challenges in determining the validity of the
ndings. Indeed, the primary issue with study quality is due to an
inability to assess reliability due to a lack of reporting across
multiple studies. Unfortunately, a lack of reporting reliability
statistics is a common issue across multiple social science and
education disciplines (Barry etal., 2014; Lovejoy etal., 2014; Han,
2016; Parsons etal., 2019; Flake, 2021). is illustrates the need to
ensure that reliability is reported throughout the peer review and
publication process. Indeed, publication reporting standards in
psychology, through the American Psychological Association
(Appelbaum etal., 2018), state that the reliability of measures
should bereported.
TABLE3 Sensitivity analyses for the assumed dependency of eect sizes.
Rho = 0 Rho = 0.2 Rho = 0.4 Rho = 0.6 Rho = 0.8 Rho = 1
Hedges’ g0.33 0.32 0.32 0.32 0.32 0.32
Std. error 0.12 0.12 0.12 0.12 0.12 0.12
Tau .s q 0.77 0.77 0.77 0.77 0.77 0.78
FIGURE3
Funnel plot of eect sizes.

Clinton-Lisell and Litzinger 10.3389/fpsyg.2024.1428732
Frontiers in Psychology 15 frontiersin.org
e studies were all single sessions in duration and subsequently
claims about long-term eects cannot bedetermined from the
meta-analysis. Moreover, there was substantial variation in the
ndings across outcomes that was not explained in the meta-
regression. is could be due to insucient power to identify
moderators in the meta-regression analyses (Schmidt, 2017).
Furthermore, the studies were limited to those disseminated in
English due to the linguistic limitations of the research team. It is
possible the inclusion of more languages would have led to dierent
outcomes. In addition, all but two of the reviewed studies were from
journal articles. Although the publication analyses did not indicate
publication bias, it is still an issue to consider given that only two
studies were from the gray literature in which non-signicant
ndings are more likely to bereported (Cairo etal., 2020). ere is
also possible bias when considering studies as several authors were
contacted with requests for data to calculate eect sizes, but only
some of the authors provided this information. ere may
beresponse bias regarding the ndings that were calculated based
on author-provided data. However, it should benoted that authors
frequently do not respond to requests for data (Tedersoo
etal., 2021).
e studies in this meta-analysis all categorized their participants
based on learning styles, but the methods of categorization varied.
ere were a range of measures used and the cuto for categorization
of learning styles diered by study as well. is makes the
generalizability of the ndings challenging. Moreover, there was
substantial variability in the outcome measures. Only 21 studies were
identied that met the criteria for testing the matching hypothesis and
reported sucient statistics to conduct eect sizes. In particular, there
were not enough studies to examine whether having the learning
styles assessment before or aer the learning activity varied the
ndings. is is unfortunate given the concerns about self-fullling
TABLE4 Studies without sucient descriptive statistics reported to calculate eect sizes.
Author (date),
dissemination type
Description of findings
Bareither etal. (2013), journal article e read/write group scored higher when learning through modules (matched) than making clay models (unmatched). ere
were no dierences for the matched (clay models) and unmatched (modules) kinesthetic groups.
Fajari etal. (2020), journal article ere was a signicant interaction indicating a crossover eect in which both style groups beneted from instruction
matched to their styles.
Höer and Schwartz (2011), journal
article
ere was a signicant interaction between styles and conditions, which resulted in a crossover as indicated in the gure
indicating a benet of matching instruction to style.
Huang (2019), journal article e matched conditions had higher scores on learning outcomes than the unmatched conditions, but this was not
signicant and the interaction between styles and condition was not reported.
Koć-Januchta etal. (2019), journal article No signicant interaction between style and condition. Correlations with style scores, not learning style groups, were examined.
Koć-Januchta etal. (2020), journal article ere appeared to bea benet of matching instruction for visualizer, but not for verbalizers. Correlations with style scores, not
learning style groups, were examined.
Kollöel (2012), journal article Interactions between styles and conditions were not reported.
Kraemer etal. (2017), journal article e interaction between conditions and style was not signicant. e direction of the eect of matched compared to unmatched
instruction within style group could not bedetermined from the article.
Massa and Mayer (2006), Exp1 and
Exp2, journal article
No signicant interactions between style and condition for either experiment. e direction of the eect of matched compared to
unmatched instruction within style group could not bedetermined from the article.
Riding and Ashmore (1980), journal
article
e interaction between styles and condition was signicant. e matched conditions had higher scores on learning
outcomes than the unmatched conditions (crossover).
Sankey etal. (2011) Performance on learning outcomes by style groups and conditions was not reported.
omas and McKay (2010) Signicant interactions between styles and conditions were reported. Styles were not grouped, but the regression coecients
indicated a crossover eect in which style scores positively predicted learning outcomes in matched conditions, but not
unmatched conditions.
Bold indicates that the ndings of the study indicated a crossover eect.
TABLE5 Meta-regression model.
Beta SE t-value Dfs p95% CI Lower 95 CI Upper
Intercept 0.55 0.35 1.57 15.81 0.14 −0.20 1.30
Within or between 0.49 0.41 1.18 6.68 0.28 −0.50 1.47
Style −0.25 0.26 −0.97 12.31 0.35 −0.82 0.31
Assessment −0.18 0.24 −0.76 18.57 0.46 −0.69 0.32
Study quality 0.22 0.36 0.61 8.88 0.56 −0.60 1.04
Within-subjects studies were coded 0, and between-subjects studies were 1. Style was coded 0 for visual, 1 for verbal, and 2 for auditory. Assessment was coded 0 if the instruction and
assessment were in the same modality and 1 if they were in dierent modalities. Study quality was coded 0 for not meeting What Works Clearinghouse Standards and 1 for meeting standards.
SE, standard error; DFs, degrees of freedom.

Clinton-Lisell and Litzinger 10.3389/fpsyg.2024.1428732
Frontiers in Psychology 16 frontiersin.org
prophecies and ndings from Moser and Zumbach (2015) with fake,
induced learning styles.
Conclusion
Learning styles are a controversial topic in education. In this
meta-analysis, wesought to inform the controversy with aggregated
ndings based on a comprehensive search for studies. An overall
small, positive eect was noted. However, this aggregated eect should
beinterpreted with caution given that most studies did not indicate a
crossover interaction. Such a crossover interaction would have been
necessary to support the claim that matching instruction to learning
styles benets students from dierent learning styles. Given the high
amount of variability in the ndings and infrequent crossover
interactions, it is far from conclusive that there is actually a benet to
matching the modality of instruction to students’ learning styles.
Teaching with multiple modalities may bepreferable to the costly and
labor-intensive practice of matching instruction to learning styles
given the empirical evidence for benets across students for
multimodal instruction.
Data availability statement
e datasets presented in this study can befound in online
repositories. e names of the repository/repositories and accession
number(s) can befound at: https://osf.io/5heum/.
Author contributions
VC-L: Conceptualization, Data curation, Funding acquisition,
Supervision, Writing – original dra, Writing – review & editing. CL:
Conceptualization, Data curation, Writing – original dra, Writing
– review & editing.
Funding
e author(s) declare that nancial support was received for the
research, authorship, and/or publication of this article. Wethank
the University of North Dakota Alumni Foundation for supporting
this research by VC-L through the Rose Isabella Kelly
Fischer Professorship.
Acknowledgments
We thank Maylynn Riding In for her assistance screening titles
and abstracts.
Conflict of interest
e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could
beconstrued as a potential conict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their aliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may beevaluated in this article, or
claim that may bemade by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
e Supplementary material for this article can befound online
at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1428732/
full#supplementary-material
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