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Theories of learning styles suggest that individuals think and learn best in different ways. These are not differences of ability but rather preferences for processing certain types of information or for processing information in certain types of way. If accurate, learning styles theories could have important implications for instruction because student achievement would be a product of the interaction of instruction and the student’s style. There is reason to think that people view learning styles theories as broadly accurate, but, in fact, scientific support for these theories is lacking. We suggest that educators’ time and energy are better spent on other theories that might aid instruction.
The Generalist’s Corner
The Scientific Status of Learning
Styles Theories
Daniel T. Willingham
, Elizabeth M. Hughes
, and David G. Dobolyi
Theories of learning styles suggest that individuals think and learn best in different ways. These are not differences of ability but
rather preferences for processing certain types of information or for processing information in certain types of way. If accurate,
learning styles theories could have important implications for instruction because student achievement would be a product of the
interaction of instruction and the student’s style. There is reason to think that people view learning styles theories as broadly
accurate, but, in fact, scientific support for these theories is lacking. We suggest that educators’ time and energy are better spent
on other theories that might aid instruction.
learning styles, academic achievement, cognitive style, individual differences, teaching methods
Learning styles theories are varied, but each of these theories
holds that people learn in different ways and that learning can
be optimized for an individual by tailoring instruction to his or
her style. For example, one theory has it that some people learn
best by watching (visual learners), some by listening (auditory
learners), and some by moving (kinesthetic learners). Thus, a
first grader learning to add numbers might benefit from an
introduction that respects her learning style: the visual learner
might view sets of objects, the auditory learner might listen to
rhythms, and the kinesthetic learner might manipulate beads on
an abacus. How marvelous it would be if this theory (or a sim-
ilar theory) was true. Ideas that students had found elusive
would suddenly click, all due to a modest change in teaching
practice. But is the theory true?
Certainly, belief in learning styles theories is widespread. A
recent review (Howard-Jones, 2014) showed that over 90%of
teachers in five countries (the United Kingdom, the Nether-
lands, Turkey, Greece, and China) agreed that individuals learn
better when they receive information tailored to their preferred
learning styles. Although data on U.S. teachers are limited
(Ballone & Czerniak, 2001), our experience has been that
belief in the accuracy of such theories is widespread among the
broader public. To test this impression, we conducted a brief
survey using Amazon Mechanical Turk. Participants (N¼
313, 53.4%female, mean age ¼35.2 years) rated on a 7-
point Likert-type scale (1 ¼strongly disagree and 7 ¼strongly
agree) their agreement with this statement: ‘‘There are consis-
tent differences among people in how they learn from different
experiences: specifically, some people generally learn best by
seeing, some generally learn best by listening, and some gener-
ally learn best by doing.’’ The mean rating was 6.35 (SD ¼
We observed this strong belief even though literature
reviews over the last 30 years have concluded that most evi-
dence does not support any of the learning styles theories. The
purpose of this article is to (a) clarify what learning styles the-
ories claim and distinguish them from theories of ability, (b)
summarize empirical research pertaining to learning styles, and
(c) provide suggestions for practice and implications supported
by empirical research.
What Are Learning Styles Theories?
Researchers have defined ‘‘learning styles’ in several ways
(Messick, 1984; Peterson, Rayner, & Armstrong, 2009), but
because we are interested primarily in applications to education
(and not, e.g., in how personality dimensions impact learning),
we focus on learning styles as (a) differential preferences for
processing certain types of information or (b) for processing
information in certain ways. The former definition would
include learning styles theories that differentiate between
visual, auditory, and kinesthetic learners (Dunn, Dunn, & Price,
1984) or between visual and verbal learners (Riding & Rayner,
1998). Learning styles theories based on preferences for certain
types of cognitive processing would include distinctions
between intuitive and analytic thinkers (Allinson & Hayes,
1996) or between activist, reflecting, or pragmatic thinkers
Department of Psychology, University of Virginia, Charlottesville, VA, USA
Duquense University, Pittsburgh, PA, USA
Corresponding Author:
Daniel T. Willingham, Department of Psychology, University of Virginia, Box
400400, Charlottesville, VA 22904, USA.
Teaching of Psychology
2015, Vol. 42(3) 266-271
ªThe Author(s) 2015
Reprints and permission:
DOI: 10.1177/0098628315589505
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(Honey & Mumford, 1992). Numerous theoretical distinctions
like these have been around since the 1950s (Cassidy, 2004).
Note that the definitions provided earlier distinguish learn-
ing styles from abilities. The two are often confused, but the
distinction is important. It is relatively uncontroversial that
cognitive ability is multifaceted (e.g., verbal ability and facility
with space have distinct cognitive bases), and it is uncontrover-
sial that individuals vary in these abilities. For ‘‘styles’ to add
any value to an account of human cognition and learning, it
must mean something other than what ability means. While
styles refer to how one does things, abilities concern how well
one does them. The analogous distinction is made in sports:
Two basketball players may have equivalent ability but differ-
ent styles on the court. One may take risks, whereas the other
plays a conservative game.
Predictions and Data
Learning styles theories make two straightforward predic-
tions. First, a learning style is proposed to be a consistent attri-
bute of an individual, thus, a person’s learning style should be
constant across situations. Consequently, someone considered
an auditory learner would learn best through auditory pro-
cesses regardless of the subject matter (e.g., science, litera-
ture, or mathematics) or setting (e.g., school, sports practice,
or work). Second, cognitive function should be more effective
when it is consistent with a person’s preferred style; thus, the
visual learner should remember better (or problem-solve bet-
ter, or attend better) with visual materials than with other
Consider the first prediction. Simply enough, it means that if
you’re a visual learner today, you shouldn’t be an auditory lear-
ner tomorrow, or if you’re a visual learner on task X, you
shouldn’t be an auditory learner on task Y. This bar—consis-
tency—seems fairly low for a theoretical prediction, but most
learning styles theories have failed to vault it. Although there
are a multitude of inventories and models for assessing learning
styles, most are not reliable (Coffield, Moseley, Hall, & Eccles-
tone, 2004). And researchers are well aware of this problem. A
recent survey of 92 learning styles researchers showed that
problems of reliability were among their chief concerns with
progress in their field (Peterson et al., 2009).
Regarding the second prediction—cognitive performance—
one must draw a distinction between evidence that might sup-
port a learning styles theory and evidence that would prompt a
change in educational practice (Pashler, McDaniel, Rohrer, &
Bjork, 2009). To support the theory, one needs to observe a sta-
tistical interaction between the learning styles of individuals
and the method of instruction. For example, suppose we exam-
ined ‘visual learners’’ and ‘auditory learners.’’ Members in
each group would be randomly assigned to an instructional
condition, where material would be presented either visually
(e.g., a silent film) or auditorily (e.g., an audiotaped story). Par-
ticipants should learn better when they experience the material
in their preferred modality. Figure 1 shows a graph with a
hypothetical outcome on a test of participants’ memory for the
We see the predicted effect in Figure 1: Visual learners
remember more than auditory learners when the film is shown,
and the opposite pattern appears when participants listen to the
audiotape. But everyone learns best with a visual presentation.
Practical classroom implications require a particular pattern of
data that not only supports the theory but also shows that
instruction matched to learning styles optimizes achievement
for each group. In other words, the two lines in the graph would
have to cross, indicating (in this example) that the visual lear-
ners learned best when watching the film, whereas the auditory
learners learned best when listening to the story.
Is there support for either prediction—for educational prac-
tice, or barring that, at least that the theory might be correct
(even if it’s not helpful)? No. Several reviews that span decades
have evaluated the literature on learning styles (e.g., Arter &
Jenkins, 1979; Kampwirth & Bates, 1980; Kavale & Forness,
1987; Kavale, Hirshoren, & Forness, 1998; Pashler et al.,
2009; Snider, 1992; Stahl, 1999; Tarver & Dawson, 1978), and
each has drawn the conclusion that there is no viable evidence
to support the theory. Even a recent review intended to be
friendly to theories of learning styles (Kozhevnikov, Evans,
& Kosslyn, 2014) failed to claim that this prediction of the the-
ory has empirical support. The lack of supporting evidence is
especially unsurprising in light of the unreliability of most
instruments used to identify learners’ styles (for a review, see
Coffield et al., 2004).
There is an underlying challenge to conducting research on
learning styles: It is impossible to prove that something does
not exist. However unpromising the data today, a new experi-
mental paradigm may eventually reveal that the theory was
right all along. Still, given our focus on educational application,
we set a different standard. We don’t insist that the theory be
proven definitively wrong. We are interested in classroom
practice, and before a theory is permitted to influence class-
room practice, there should be an evidence that the theory is
correct. In fact, we need more. We not only need to know that
learning styles exist but also need to know that teaching to
learning styles benefits students in some way.
Figure 1. This pattern of data would support learning styles theories
but would indicate that differences in learning styles should not be
accommodated in instruction.
Willingham et al. 267
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Why Do People Believe Learning
Styles Theories?
There are probably multiple reasons why people believe learn-
ing styles theories are correct, and two of these reasons strike us
as especially relevant. First, people often take things to be sci-
entific fact when they have not seen any of the evidence that
they suppose must exist. For example, most educated people
believe in the atomic theory of matter, but their knowledge
of the supporting evidence is scant. It is just something that
‘they’’ (i.e., scientists) have figured out. People’s belief is fur-
ther bolstered by social proof: So many other people believe the
atomic theory of matter that it would seem oddly perverse to
challenge it. Furthermore, teachers are exposed to a plethora
of materials that purportedly respect students’ learning styles,
materials that often claim a scientific basis for their design.
Once exposed to all these seemingly reliable (or at least not
overtly unreliable) sources, the confirmation bias (Nickerson,
1998) could easily support and maintain the belief. For exam-
ple, suppose a teacher was helping a student struggling with a
concept. The teacher tries a few different ways of explaining it
but to no avail. Finally, she draws a diagram, and the idea
clicks. It is natural for the teacher to conclude, ‘Ah, this stu-
dent must be a visual learner.’’ But perhaps any student would
have benefited from the diagram because it was an effective
way to communicate that particular idea. Or perhaps the stu-
dent needed to hear just one more explanation. Many accounts
of the sudden insight are possible, but the confirmation bias
would lead to an interpretation that supports one’s existing
A second possible reason for widespread belief is the confu-
sion between ability and style. As noted earlier, most research-
ers agree that ability is multifaceted and that people vary in
these abilities. From there, it is a short step to the idea that
weakness in one ability can be supplemented with strength in
another—for example, that a student having difficulty in math
might benefit from a lesson plan that played to his strength in
music. This ‘alternate route’ idea certainly looks like a style.
Gardner’s (1983) theory of multiple intelligences—which is an
abilities theory—has been interpreted this way for many years
(e.g., Armstrong, 2000), although Gardner (2013) has said that
this interpretation is inaccurate. The substitution idea is inaccu-
rate, Gardner maintains, because recoding simply cannot hap-
pen, and that is part of what makes different abilities (or, in
Gardner’s theories, intelligences) different. To do math, you
have to think mathematically. To use musical cognition to
think mathematically would be like trying to use a .wmv file
in Microsoft Excel. They are simply incompatible.
We agree with Gardner, but note that it is at least theoreti-
cally possible that there may be occasional exceptions. If one
could learn material equally well in two different ways, and
if those different ways match differences in human ability, then
recoding for individual students would not only be possible but
also be effective. Indeed, there are some limited data indicating
that people who believe they are better with mental images (or
better with words) do such recoding on their own (e.g.,
Kraemer, Rosenberg, & Thompson-Schill, 2009) and that this
recoding can benefit performance (e.g., Thomas & McKay,
2010). This is not an instance of learning styles, rather, it is
an instance of ability appearing as a style.
Why All the Fuss?
So the weight of evidence fails to support learning styles. So
what? Lots of theories are poorly supported and most do not
merit an article in Teaching of Psychology. The difference here
is that the idea has seeped into popular culture, and many peo-
ple believe it, perpetuating its (ungrounded) influence in educa-
tional settings and products. Happily, it seems only rarely to
influence how students study. Less happily, learning styles the-
ories, when invoked, are most often offered as an explanation
for poor classroom performance. Most of us have had a student
protest, ‘Your teaching is not compatible with my learning
style,’ with the expectation that the teacher will make individ-
ual accommodations that go beyond quality instruction.
Learning styles theories ought to be debunked, and a great
place for this to happen is in our psychology classrooms. One
could simply tackle it head on, of course, telling students about
the theory and the lack of evidence. But it strikes us as an excel-
lent opportunity to have students think through the problem
themselves. If they believe it, why do they believe it? What
does evidence look like in psychological science? What would
evidence for this particular theory look like? Could students
collect relevant evidence in the classroom? Indeed, evaluating
learning styles theories might serve as an excellent classroom
research project. Take, for example, the following two class-
room scenarios.
Class Activity Scenario 1
With the intent to explore challenges around research intended
to assess the impact of styles on learning, the teacher can mod-
erate a classroom experiment. To do this, the teacher might cre-
ate a learning activity that requires students to identify their
own best learning styles and then attempt to learn new material
(e.g., new vocabulary) via (a) their primary learning style or (b)
a different learning style. For example, visual learners and
auditory learners in the class might be presented with new
vocabulary. Students in each learning style group would be ran-
domly assigned to a learning condition, resulting in some visual
learners and auditory learners accessing the new vocabulary
visually (e.g., reading it in text) and some visual learners and
auditory learners accessing the new vocabulary auditorily
(e.g., listening to a recording). All students would be assessed
on the new vocabulary they learned, and class data would be
graphed and analyzed. Class discussions might focus on
expected results (e.g., higher performance on vocabulary
learned via a primary style), actual results, factors that may
have impacted results (e.g., preference or prior knowledge),
limitations to the research, and how the results may or may not
translate into classroom practice (see previous discussion about
Figure 1).
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Conducting this classroom experiment would take some
preplanning. Prior to starting this activity, students would need
to complete a learning styles assessment (e.g., http://¼questionnaire), and
the teacher would need to prepare a learning opportunity where
information is available via each learning style (e.g., text to
read and audio of text). The teacher would also have to mini-
mize influencing factors such as prior knowledge or time spent
on learning. Replicating this activity for learning other infor-
mation (e.g., mathematics, application of theory, summary of
story, and memorization of dates and events) would allow stu-
dents to explore if learning styles are consistent across content
and, if not, why.
Class Activity Scenario 2
Another activity might explore the reliability of the assess-
ment of learning styles. For example, do external factors,
such as experiences prior to taking the assessment, influence
the outcome? If students have recently completed activities
or had experiences that positively impacted outcomes, would
they be more or less likely to select an answer based on that
experience or memory? For example, if someone recently lis-
tened to an audible Global Positioning System (GPS) to find
a location, would that person be more likely to select an
audible method of delivery for directions over using a map,
even if they consider themselves to be a visual learner?
Would a bad experience with an auditory GPS, but a good
experience reading a map, prompt a self-identified auditory
learner to select a more visual method for directions? If
recent experiences matter, does that change the reliability
of the measure? Class exploration and discussion can address
these elements.
Differences and Commonalities in
Educational Practice
The hope underlying learning styles theories is that an under-
standing of student differences will improve instruction. But
then, too, we expect that there are some aspects of the mind that
do not differ, that are common across students, and that honor-
ing these basic features will improve instruction. There is a ten-
sion in applying these two types of knowledge in the classroom.
On one hand, obsession with student individuality will lead to
paralysis: If every student is unique, how can teachers draw on
their experiences with other students to improve the instruction
of this particular student? If each student is unique, there is no
reason to think that what worked before will work now. On the
other hand, if teachers focus solely on what they believe is true
of all students, then teachers are likely to identify one set of
‘best practices’’ and stubbornly apply those practices to all
To many, learning styles offer a middle ground—a middle
ground between treating every student the same way and treat-
ing every student uniquely. The proposed solution has been to
create categories of learners based on their unique learning
styles. Categorization means using a few, easily observed fea-
tures to infer that other features are present. For example, by
observing some perceptual features of an object—it is round,
red, and shiny—we categorize it as an apple and thus can safely
infer other nonobservable properties: It has seeds inside, it is
edible, and so on. Similarly, learning styles also categorize.
By gaining knowledge of a few properties (e.g., answers on a
questionnaire), teachers hope to infer other characteristics
(e.g., how the student will respond to different types of instruc-
tion) that can be used to improve the educational process. The
point of this article, however, is that such categorization ulti-
mately fails.
More broadly, the history of psychology shows very limited
success in finding any useful categorization scheme for stu-
dents. By far, the most successful type of categorization is one
that is already painfully obvious to educators: Differences in
prior knowledge and ability ought to be respected (Cronbach
& Snow, 1977).
Psychology has had much greater success describing com-
monalities among students than it has had in describing cate-
gorization schemes for differences. Researchers have
compiled a fairly impressive list of properties of the mind
that students share. And although going from lab to class-
room is not straightforward, there is evidence that students
benefit when educators deploy classroom methods that capi-
talize on those commonalities. For example, we know that
spacing learning over time and quizzing (among other meth-
ods) improve memory (Dunlosky, Rawson, Marsh, Nathan,
& Willingham, 2013). We know that teachers can modify the
classroom environment to decrease problem behaviors
(Osher, Bear, Sprague, & Doyle, 2010). In mathematics,
there is a particular developmental progression by which
teachers can best teach numbers and operations (National
Mathematics Advisory Panel, 2008). In reading, phonics
instruction benefits most children (Reynolds, Wheldall, &
Madelaine, 2011).
Thus, psychologists have made some impressive contribu-
tions to education. When it comes to learning styles, however,
the most we deserve is credit for effort and for persistence.
Learning styles theories have not panned out, and it is our
responsibility to ensure that students know that.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
The authors received no financial support for the research, authorship,
and/or publication of this article.
1. Half of the subjects saw a reverse-coded version (There are not
consistent differences among people in how they learn ...). The
reverse-coded mean was 5.22 (SD ¼2.19), which was significantly
lower than the rating for the standard question, t(311) ¼5.65,
p< .001. We suspect that this difference was due to some participants
Willingham et al. 269
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Author Biographies
Daniel T. Willingham is a pro-
fessor of psychology at the Uni-
versity of Virginia, where he has
taught since 1992. He trained as
a cognitive psychologist and today
focuses on the application of cog-
nitive psychology to K-16 educa-
tion. He writes the ‘‘Ask the
Cognitive Scientist’’ column for
American Educator magazine and
is the author of Why Don t Stu-
dents Like School?, When Can You
Trust the Experts?, and Raising
Kids Who Read.
270 Teaching of Psychology 42(3)
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Elizabeth M. Hughes is an assistant
professor of special education at
Duquesne University, in Pittsburgh,
PA. She is a former elementary
school teacher and received her doc-
toral degree from Clemson Univer-
sity. Her research focuses on
effective instructional approaches,
strategies, and assessments for stu-
dents who are low achievers or who
have disabilities in reading or mathe-
matics. Recent projects include
exploring the use of young adult lit-
erature featuring characters with disabilities to increase empathy and
content knowledge of students enrolled in teacher education pro-
grams and using video modeling to teach academic skills to adoles-
cents with autism spectrum disorder.
David G. Dobolyi is a PhD candi-
date in cognitive psychology at
the University of Virginia and a
student of Dr. Chad Dodson and
Dr. Michael Kubovy. He possesses
a background in computer pro-
gramming and software develop-
ment, and his research focuses on
using the best statistical modeling
techniques to answer a variety of
theoretical and applied questions.
Domains of interest include eye-
witness confidence, Parkinson’s
disease, and the evaluation of life
episodes through the use of ‘‘big
data.’’ His dissertation work exam-
ines the role of featural and familiarity justifications on the interpreta-
tion of eyewitness confidence and accuracy.
Willingham et al. 271
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... The kernel of truth for the LS neuromyth is that people have preferred modalities-typically the visual, the auditory, or the kinesthetic (VAK) modality. Expecting, however, that fitting the task to their preference will have a positive effect on performance in where the problem lies (Willingham, 2018) because preference is different from ability (Willingham, Hughes, & Dobolyi, 2015). ...
... Evidence on learners' differences in educational needs (Warnock & Norwich, 2010), which teachers should accommodate (Jordan, Schwartz, & McGhie-Richmond, 2009), and the importance in developing learner's metacognitive and self-regulation skills to enhance learning (Dinsmore, Alexander, & Loughlin, 2008) further add to the appeal-and need-of individualized instruction. However, anything between this truth and teachers' effort to cater instruction to match learners' preferred modalities to enhance learning is the LS myth (Willingham et al., 2015). Of note, this practice of matching instruction to preferred modality is commonly encountered in the literature as the meshing or matching hypothesis, but also the LS hypothesis or the LS notion (see, e.g., Cuevas, 2015;Kirschner & van Merriënboer, 2013;Newton, 2015;Pashler et al., 2008;Rogowsky, Calhoun, & Tallal, 2015). ...
... The present study adds to the limited body of systematic, empirical work that has explored whether matching instruction to an individual's LS results in better learning. As Willingham et al. (2015) suggest, to provide evidence for the LS hypothesis, one should be able to show that (a) there is a positive effect of the modality of teaching on achievement (main effect) and that (b) matching the LS of the participants "optimizes achievement for each group" (p. 267). ...
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The term learning styles (LS) describes the notion that individuals have a preferred modality of learning (i.e., vision, audition, or kinesthesis) and that matching instruction to this modality results in optimal learning. During the last decades, LS has received extensive criticism, yet they remain a virtual truism within education. One of the major strands of criticism is the fact that only a handful of studies have systematically put the LS assumptions to the test. In this study, we aimed to explore whether learners who are visual types will be better at learning sign‐words (i.e., ecologically valid stimuli) compared to auditory and kinesthetic types. Ninety‐nine volunteers (67 females, mean age = 28.66 years) naive to Greek Sign Language (GSL) were instructed to learn 20 GSL sign‐words. The volunteers further completed two LS questionnaires (i.e., the Barsch Learning Styles Inventory and the Learning Channels Inventory) and they also reported what their LS they believed was. No evidence of a difference in learning sign‐words among individuals with different LS (as identified by either of the LS questionnaires or by direct self‐report) was found, neither using a frequentist nor using a Bayesian approach to data analysis. Moreover, inconsistencies between the way participants were classified based on the different measures and direct self‐report were detected. These findings add further support to the criticism of the LS theory and its use in educational settings. We suggest that research and practice resources should be allocated to evidence‐based approaches.
... Different authors may have different definition for it, like for instance Parshler et al. (2009) defines learning style as the concept that individuals differ in regard to what mode of instruction or study is most effective for them. According to Willingham, Hughes & Dobolyi (2015), every individual differs in how they learn. As a learner, it is very much important that understand and recognize our own learning style that best suits us, so that it helps in improving the speed and quality of ones' learning. ...
... Among many, especially in the field of teaching and learning, it is of outmost important for both the faculty and students to understand about the learning styles. Every individual will have different way to learning style in order to acquire knowledge (Willingham, Hughes & Dobolyi, 2015). For some, they learn more through images, maps, and many more, and for some through hearing the spoken words, some through reading and writing and some through doing practically. ...
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The first year students are facing difficulties in learning C programming language when the module is being offered in their first year tenure. Ultimately, this leads to an increased failure rates in this module. On the other hand, even the faculties are having strenuous time teaching this module. The main aim of the case study is to assess and determine the different learning styles of first year students from Bachelors of engineering in Information Technology (BE1IT). And then, know the majority of the students' preferred learning style so that the faculties teaching C programming language are made aware about the findings. And also, recommend the faculty to redesign their pedagogical material to cater students as per their various learning styles. The VARK learning style inventory was adopted for the study which consists of 17 questionnaires. A class of BE1IT with 40 students comprising of 16 female and 24 male students were the participants. All the students were asked to take into consideration of themselves in how they learn best with the module-C programming language and then accordingly attempt those questionnaires and the data was collected. The study showed that majority of the students are kinaesthetic learner irrespective of the genders. The faculty teaching the first year students need to understand that this method may be helpful in helping the students learn better. The pedagogical materials used to teach them may be one of the factor which is not helping them to learn better as it is not correlating with their learning styles. After having known the different type of learners, the faculty were made aware of the findings and were recommended to redesign their pedagogy and andragogy materials accordingly to cater all the students' learning.
... However, the authors acknowledged that there is still a need for further research to explore the individual differences in learning and identify effective instructional practices. Willingham et al. (2015) examined the relationship between learning styles and academic performance. Their findings revealed that students who believed in matching instruction to their learning style did not perform better academically compared to those who did not hold this belief. ...
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The aim of the present investigation was to examine the relationship between various learning styles and study habits and their impact on academic success.A sample of 200 students was selected randomly from the two govt. and two private Schools of Delhi. Study Habit Inventory developed by Mukhopadhyay and Dr. D.N. Sansanwal (1985) was administered. This inventory includes nine components namely -comprehension, concentration, task orientation, study sets, interaction, drilling, supports, recording and language. Learning Styles Inventory prepared by Rita Dunn, Keneth Dunn and Gary E (1989) was also applied. This is a self-reported tool consisting of 90 statements. This inventory has four major aspects-. i) Their immediate environment (sound, light, temperature, seating design). ii) Their own emotionality (motivation, persistence, responsibility). iii) Their sociological performance (learning alone or in different sized groups). iv) Their physiological characteristics (perceptual strengths represented by auditory, verbal, tactile, kinesthetic and sequenced characteristics.It was found that coefficient of correlation between learning style and study habit of adolescent was significant and positive.
... The concept of Learning Styles, despite its popularity, has been recently questioned due to the mismatch between didactic practices and academic outcomes (Gleichgerrcht et al., 2015;Aslaksen and Lorås, 2018;Nancekivell et al., 2020). Although not universally accepted and considered partially inaccurate (Furey, 2020), this concept, when carefully interpreted, still provides valuable tools to close the gaps between interaction among learners, teachers, and educational content (Willingham et al., 2015). Rather than defining different ways to learn (Felder, 2020). . . . ...
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Higher education is a multivariable system by nature; thus, it is a complex task to maintain consistent academic success for students. This is a key factor to understand the positive and negative effects generated by the COVID-19 lockdown, particularly during the current stage of the "New Normal" period. The research presented herein considers a set of variables corresponding to students and faculty as causal factors to track, analyze, and assess the impact on the academic performance of engineering students in an urban Mexican university in both periods: online teaching during lockdown, and returning to face-to-face learning during the "New Normal".Through a hybrid survey, looking for representative learning styles, academic personality traits, and technology competencies, academic performance in both periods has been recorded along with each student's learning preference. The suggested analysis model sought correlations in the stated causal factors to find valuable behavioural patterns. The outcomes show that good students in both models have attained a high level of adaptation and feel competitive in them.On the contrary, students with lower adaptation have shown poor academic performance in both models, but they perceived the online model as the less effective learning environment.Particularly, personality traits appoint on a notable impact on performance. In addition, learning styles are not significant. Still, it has been suggested this situation could be due to a greater diversity of teaching approaches established by the faculty to take care of student performance.
... From the learning styles, movement classroom practice began to endorse more student-centered classroom techniques. For example, showing films to support visual learners, incorporating reflection activities for reflective thinkers, and group work for those learners who learn through interpersonal relationships [54]. Current literature suggests that the foundations of learning style theory are thin and asks us to question their adoption in the classroom [15]. ...
Conference Paper
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... Likewise, Knowledge itself isn't excessively significant, how to retain information through information securing, processing, retention and change into development is the wellspring of advancement execution [5]. In [6] contend that absorptive limit is the precondition that an association can get the information in a relatively low cost. The information absorptive limit incorporates possible absorptive limit (PACAP) and acknowledged absorptive limit (RACAP). ...
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Intelligent Autonomous Agents (IAAs) is basic to the endurance and improvement of the firm, making new items as a wellspring of supportable upper hand. Based on Information-based hypothesis, this paper centers around the four measurements that are obtaining, processing, change double-dealing and investigated the connection between absorptive limit and item advancement execution from the possible absorptive limit and acknowledged absorptive limit. This paper is useful to clarify the irregularity between absorptive limit and item advancement execution in surviving exploration, and further uncover the system between absorptive limit and new item development execution, enhance, extend the absorptive limit hypothesis and item advancement hypothesis by joining the two speculations. This paper can assist firms with explaining the connection between potential absorptive limit acknowledged absorptive limit and development execution, and give reference to firm dynamic, and can likewise give direction viably to firms to dispense assets.
... From the learning styles, movement classroom practice began to endorse more student-centered classroom techniques. For example, showing films to support visual learners, incorporating reflection activities for reflective thinkers, and group work for those learners who learn through interpersonal relationships [54]. Current literature suggests that the foundations of learning style theory are thin and asks us to question their adoption in the classroom [15]. ...
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This study aims to explore how the job shadowing program offered by the Sharjah government media bureau was experienced by communication and media students and professionals, and how it develops students’ skills and experience. The study also explores the impacts of the program during the Covid-19 period and the expectations from the program after the pandemic. The study works to help in creating effective training programs for media students with collaboration between universities and media organizations. A group discussion was conducted with 12 trainers and an online questionnaire was distributed to a sample of program attendees to monitor their viewpoints. 100 students responded to the survey. Results revealed that the job shadowing program has an effective role in developing students’ skills by enabling them to interact with professionals and benefiting from their experience. Students considered the program as an integrated way with the academic courses to support their practical skills. Results assured the need to design programs concerned with new topics, skills, and perspectives in media production to meet the needs of the media profession and academic requirements. Results revealed that coexistence and integration between academic programs and media institutions will be effective methods to develop the program. KeywordsCommunication and media studentsJob shadowing programExperiential learningTraining
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Confirmation bias, as the term is typically used in the psychological literature, connotes the seeking or interpreting of evidence in ways that are partial to existing beliefs, expectations, or a hypothesis in hand. The author reviews evidence of such a bias in a variety of guises and gives examples of its operation in several practical contexts. Possible explanations are considered, and the question of its utility or disutility is discussed.
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The key aims of this article are to relate the construct of cognitive style to current theories in cognitive psychology and neuroscience and to outline a framework that integrates the findings on individual differences in cognition across different disciplines. First, we characterize cognitive style as patterns of adaptation to the external world that develop on the basis of innate predispositions, the interactions among which are shaped by changing environmental demands. Second, we show that research on cognitive style in psychology and cross-cultural neuroscience, on learning styles in education, and on decision-making styles in business and management all address the same phenomena. Third, we review cognitive-psychology and neuroscience research that supports the validity of the concept of cognitive style. Fourth, we show that various styles from disparate disciplines can be organized into a single taxonomy. This taxonomy allows us to integrate all the well-documented cognitive, learning, and decision-making styles; all of these style types correspond to adaptive systems that draw on different levels of information processing. Finally, we discuss how the proposed approach might promote greater coherence in research and application in education, in business and management, and in other disciplines. © The Author(s) 2014.
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For several decades, myths about the brain - neuromyths - have persisted in schools and colleges, often being used to justify ineffective approaches to teaching. Many of these myths are biased distortions of scientific fact. Cultural conditions, such as differences in terminology and language, have contributed to a 'gap' between neuroscience and education that has shielded these distortions from scrutiny. In recent years, scientific communications across this gap have increased, although the messages are often distorted by the same conditions and biases as those responsible for neuromyths. In the future, the establishment of a new field of inquiry that is dedicated to bridging neuroscience and education may help to inform and to improve these communications.
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Many students are being left behind by an educational system that some people believe is in crisis. Improving educational outcomes will require efforts on many fronts, but a central premise of this monograph is that one part of a solution involves helping students to better regulate their learning through the use of effective learning techniques. Fortunately, cognitive and educational psychologists have been developing and evaluating easy-to-use learning techniques that could help students achieve their learning goals. In this monograph, we discuss 10 learning techniques in detail and offer recommendations about their relative utility. We selected techniques that were expected to be relatively easy to use and hence could be adopted by many students. Also, some techniques (e.g., highlighting and rereading) were selected because students report relying heavily on them, which makes it especially important to examine how well they work. The techniques include elaborative interrogation, self-explanation, summarization, highlighting (or underlining), the keyword mnemonic, imagery use for text learning, rereading, practice testing, distributed practice, and interleaved practice. To offer recommendations about the relative utility of these techniques, we evaluated whether their benefits generalize across four categories of variables: learning conditions, student characteristics, materials, and criterion tasks. Learning conditions include aspects of the learning environment in which the technique is implemented, such as whether a student studies alone or with a group. Student characteristics include variables such as age, ability, and level of prior knowledge. Materials vary from simple concepts to mathematical problems to complicated science texts. Criterion tasks include different outcome measures that are relevant to student achievement, such as those tapping memory, problem solving, and comprehension. We attempted to provide thorough reviews for each technique, so this monograph is rather lengthy. However, we also wrote the monograph in a modular fashion, so it is easy to use. In particular, each review is divided into the following sections: General description of the technique and why it should work How general are the effects of this technique? 2a. Learning conditions 2b. Student characteristics 2c. Materials 2d. Criterion tasks Effects in representative educational contexts Issues for implementation Overall assessment The review for each technique can be read independently of the others, and particular variables of interest can be easily compared across techniques. To foreshadow our final recommendations, the techniques vary widely with respect to their generalizability and promise for improving student learning. Practice testing and distributed practice received high utility assessments because they benefit learners of different ages and abilities and have been shown to boost students’ performance across many criterion tasks and even in educational contexts. Elaborative interrogation, self-explanation, and interleaved practice received moderate utility assessments. The benefits of these techniques do generalize across some variables, yet despite their promise, they fell short of a high utility assessment because the evidence for their efficacy is limited. For instance, elaborative interrogation and self-explanation have not been adequately evaluated in educational contexts, and the benefits of interleaving have just begun to be systematically explored, so the ultimate effectiveness of these techniques is currently unknown. Nevertheless, the techniques that received moderate-utility ratings show enough promise for us to recommend their use in appropriate situations, which we describe in detail within the review of each technique. Five techniques received a low utility assessment: summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading. These techniques were rated as low utility for numerous reasons. Summarization and imagery use for text learning have been shown to help some students on some criterion tasks, yet the conditions under which these techniques produce benefits are limited, and much research is still needed to fully explore their overall effectiveness. The keyword mnemonic is difficult to implement in some contexts, and it appears to benefit students for a limited number of materials and for short retention intervals. Most students report rereading and highlighting, yet these techniques do not consistently boost students’ performance, so other techniques should be used in their place (e.g., practice testing instead of rereading). Our hope is that this monograph will foster improvements in student learning, not only by showcasing which learning techniques are likely to have the most generalizable effects but also by encouraging researchers to continue investigating the most promising techniques. Accordingly, in our closing remarks, we discuss some issues for how these techniques could be implemented by teachers and students, and we highlight directions for future research.
For several decades, myths about the brain - neuromyths - have persisted in schools and colleges, often being used to justify ineffective approaches to teaching. Many of these myths are biased distortions of scientific fact. Cultural conditions, such as differences in terminology and language, have contributed to a 'gap' between neuroscience and education that has shielded these distortions from scrutiny. In recent years, scientific communications across this gap have increased, although the messages are often distorted by the same conditions and biases as those responsible for neuromyths. In the future, the establishment of a new field of inquiry that is dedicated to bridging neuroscience and education may help to inform and to improve these communications.
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This paper examines the application of learning styles to the teaching of reading. The learning styles approach is based on the premise that learning styles can be assessed and the results can be used to determine instructional methods. Viewed in a historical context, learning style is not a new educational trend, but an extension of the well-worn process approaches that have been largely discredited. The application of learning styles to the teaching of reading is critiqued in light of four factors: (a) inability to adequately assess learning styles, (I$ failure to acknowledge the necessity of phonics instruction for beginning readers, (c) failure to consider the nature of reading disabilities, and (d) lack of convincing research. This critique suggests that the use of learning styles to prescribe methods of reading instruction must be viewed with skebticism.
An analysis of large and influential published reviews of research pertaining to the reading acquisition of young struggling readers in the early years of schooling was undertaken. The reviews were selected on the basis that they either had been commissioned by federal governments or had been conducted by reputable research institutions and had been released in the past 10 years. A search of published literature pertaining to the topic found three federal reviews (from the United States, the United Kingdom and Australia), a What Works Clearinghouse Report into beginning reading programmes, a review of reading interventions by Slavin et al. and a synthesis of meta-analyses by Hattie. Analysis of these reviews indicated that there are key commonalities in findings about how to teach reading to young students. Reviews of interventions revealed some flaws and therefore provide limited information useful to programme implementation and development for young struggling readers.