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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).
268 Teaching of Psychology 42(3)
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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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failing to understand the reverse wording. In the standard version,
very few subjects (2.1%) indicated that they thought the learning
styles theory is incorrect (as noted by choosing 1 or 2 for their
response). In the reverse-code condition, 20.4%of participants
chose a rating indicating disagreement. We suspect these subjects
wanted to agree with learning styles theory but got confused by the
wording (i.e., disagreeing with a negative statement).
Allinson, C., & Hayes, J. (1996). The cognitive style index. Journal of
Management Studies,33, 119–135.
Armstrong, T. (2000). Multiple intelligences in the classroom (2nd ed.).
Alexandria, VA: ASCD.
Arter, J. A., & Jenkins, J. A. (1979). Differential diagnosis-
prescriptive teaching: A critical appraisal. Review of Educational
Research,49, 517–555.
Ballone, L. M., & Czerniak, C. M. (2001). Teachers’ beliefs about
accommodating students’ learning styles in science classes. Elec-
tronic Journal of Science Education,6, 1–43.
Cassidy, S. (2004). Learning styles: An overview of theories, models,
and measures. Educational Psychology,24, 419–444.
Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Should
we be using learning styles? What research has to say to practice.
London, England: Learning and Skills Research Center.
Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional
methods: A handbook for research on interactions. Oxford, Eng-
land: Irvington.
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willing-
ham, D. T. (2013). Improving students’ learning and comprehen-
sion by using effective learning techniques: Promising directions
from cognitive and educational psychology. Psychological Science
in the Public Interest,14, 4–58.
Dunn, R., Dunn, K., & Price, G. E. (1984). Learning style inventory.
Lawrence, KS: Price Systems.
Gardner, H. (1983). Frames of mind: The theory of multiple intelli-
gences. New York, NY: Basic Books.
Gardner, H. (2013, October 16). Multiple intelligences are not learn-
ing styles. Retrieved from
Honey, P., & Mumford, A. (1992). The manual of learning styles.
Maidenhead, England: Peter Honey Publications.
Howard-Jones, P. A. (2014). Neuroscience and education: Myths and
messages. Nature Reviews Neuroscience,15, 817–824. doi:10.
Kampwirth, T. J., & Bates, M. (1980). Modality preference and teach-
ing method: A review of research. Academic Therapy,15,
Kavale, K. A., & Forness, S. R. (1987). Substance over style: Asses-
sing the efficacy of modality testing and teaching. Exceptional
Children,54, 228–239.
Kavale, K. A., Hirshoren, A., & Forness, S. R. (1998). Meta-analytic
validation of the Dunn and Dunn model of learning-style prefer-
ences: A critique of what was Dunn. Learning Disabilities
Research & Practice,13, 75–80.
Kozhevnikov, M., Evans, C., & Kosslyn, S. M. (2014). Cognitive style
as environmentally sensitive individual differences in cognition: A
modern synthesis and applications in education, business, and
management. Psychological Science in the Public Interest,15,
Kraemer, D. J. M., Rosenberg, L. M., & Thompson-Schill, S. L.
(2009). The neural correlates of visual and verbal cognitive styles.
The Journal of Neuroscience,29, 3729–3798.
Messick, S. (1984). The nature of cognitive styles: Problems and
promise in educational practice. Educational Psychologist,19,
National Mathematics Advisory Panel. (2008). Foundations for suc-
cess: The final report of the National mathematics advisory panel.
Washington, DC: U.S. Department of Education.
Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenom-
enon in many guises. Review of General Psychology,2, 175–220.
Osher, D., Bear, G. G., Sprague, J. R., & Doyle, W. (2010). How can
we improve school discipline? Educational Researcher,39, 48–58.
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2009). Learning
styles concepts and evidence. Psychological Science in the Public
Interest,9, 105–119.
Peterson, E. R., Rayner, S. G., & Armstrong, S. J. (2009). Researching
the psychology of cognitive style and learning style: Is there really
a future? Learning and Individual Differences,19, 518–523.
Reynolds, M., Wheldall, K., & Madelaine, A. (2011). What recent
reviews tell us about the efficacy of reading interventions for strug-
gling readers in the early years of schooling. International Journal
of Disability, Development, and Education,58, 257–286.
Riding, R., & Rayner, S. (1998). Cognitive styles and learning strate-
gies: Understanding style differences in learning behaviour. London,
England: David Fulton Publishers Ltd.
Snider, V. E. (1992). Learning styles and learning to read: A critique.
Remedial and Special Education,54, 228–239.
Stahl, S. A. (1999). Different strokes for different folks? A critique of
learning styles. American Educator,23, 1–5.
Tarver, S., & Dawson, M. M. (1978). Modality preference and the
teaching of reading. Journal of Learning Disabilities,11, 17–29.
Thomas, P. R., & McKay, J. B. (2010). Cognitive styles and instruc-
tional design in university learning. Learning and Individual Dif-
ferences,20, 197–202.
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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... Most preservice teachers seem to have a favorable opinion of Gardner's MI theory (Rousseau, 2021). This is despite the longstanding critical debate on the theory's efficacy and validity from the technical perspectives of educational psychologists (Schulte et al., 2004;Bordelon and Banbury, 2005;Visser et al., 2006;Waterhouse, 2006;McGreal, 2013;Rogowsky et al., 2015;Willingham et al., 2015;Rousseau, 2021). Additionally, a study by Luo and Huang (2019) of English as a second language (ESL) teachers' self-perception of MI theory and the uses of the defined multiple intelligences found either ambiguity or no significant correlation between MI theory and its instructional strategies, further supporting the critics of MI theory based on it not having statistical validity. ...
... While most of the critique of MI theory is in the technical use of the word "intelligences" and the testing of MI theory's validity in correlating the intelligences to teaching and learning that have found no correlation between MI theory and its instructional strategies (e.g., Schulte et al., 2004;Visser et al., 2006;Waterhouse, 2006;Rogowsky et al., 2015;Willingham et al., 2015;Luo and Huang, 2019), there are a number of studies that demonstrated favorable findings that partially validate MI theory, though there are qualifying limitations (e.g., Mokhtar et al., 2008;Furnham, 2009;Wu and Alrabah, 2009;Dolati and Tahriri, 2017;Prast et al., 2018;Yidana et al., 2022). However, Pashler et al. (2008) suggested that some studies that suggest favorable findings of learning styles theories there might be ambiguity in the study design leading to inconclusive findings. ...
... Such a finding might offer further basis for why MI theory has been found to be popular with preservice teachers when considering the findings of Rousseau (2021). It also might underscore some of the concerns of critics of MI theory who suggest that MI theory lacks sufficient scientific basis for its claims regarding multiple intelligences (Waterhouse, 2006;Willingham et al., 2015). The critique of validity in strategies such as MI theory is important and should be emphasized in educational psychology, but the qualitative and anecdotal observations of teachers who are daily in their classrooms seeing favorable results from differentiated practice is at least as important and should likewise have a place in educational psychology curriculum. ...
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Gardner’s theory of multiple intelligences (MI) has been at the center of a long-running debate in educational psychology in terms of its generalizable validity. In this article, MI theory is discussed for a review of why and how MI theory may be contextually discussed for preservice teachers to learn about in their teacher education program. The semantic conceptual basis of intelligence in MI theory is discussed in comparison to learning styles theory with implications for the importance of the teaching of Universal Design for Learning and related frameworks in teacher education curriculum.
... Deep knowledge involves causal, logical reasoning, solving complex problems, and dealing with ambiguity; for that reason, the elaboration of deep knowledge is essentially more challenging (Bennet and Bennet, 2008;Chi, 2009;. Learning style theories have been developed in an attempt to adapt the learning setting to individual preferences (Felder and Silverman, 1988) and are to this day still very popular amongst practitioners even though discussed controversially (Willingham et al., 2015). 36 publications refer to literature on different learning styles, e.g., to the Felder and Silverman (1988) model that differentiates between four learning preferences cited in Aljameel et al. (2019), or the concept of Kolb (1984) who describes a four-stage cycle of learning styes, mentioned by Rajkumar and Ganapathy (2020). ...
Conference Paper
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Conversational Agents (CAs) are widely spread in a variety of domains, such as health and customer service. There is a recent trend of increasing publications and implementations of CAs in education. We conduct a systematic literature review to identify common methodologies, pedagogical CA roles, addressed target groups, the technologies and theories behind, as well as human-like design aspects. The initially found 3329 records were systematically reduced to 252 fully coded articles. Based on the analysis of the codings, we derive further research streams. Our results reveal a research gap for long-term studies on the use of CAs in education, and there is insufficient holistic design knowledge for pedagogical CAs. Moreover, target groups other than academic students are rarely considered. We condense our findings in a morphological box and conclude that pedagogical CAs have not yet reached their full potential of long-term practical application in education.
... Diese individuellen Unterschiede in den Lernvoraussetzungen aufzugreifen, ist sinnvoll, um erfolgreiches Lernen zu ermöglichen. Im Gegensatz dazu kategorisieren "Lerntypen" Personen danach, wie sie Wissen aufnehmen und nicht wie gut sie dies tun (Willingham, Hughes & Dobolyi, 2015). Die Annahme des Konzepts der Lerntypen ist dabei, dass die individuellen Präferenzen der Lernenden für bestimmte Sinneskanäle unabhängig von ihren kognitiven Fähigkeiten und unabhängig von den Lerninhalten sind und bedeutsame Implikationen für die Gestaltung von Lernprozessen haben. ...
Es ist ein verbreiteter Mythos, dass für optimales Lernen individuelle Lerntypen (z.B. visuell) identifiziert und gezielt unterstützt werden sollten. Die wissenschaftlichen Befunde zeigen klar, dass eine Ausrichtung von Lernumgebungen an „Lerntypen“ keine förderlichen Effekte hat. Wieso hält sich der Mythos dennoch so hartnäckig und was können wir dagegen tun?
... We understand and acknowledge that many researchers believe that there is little evidence that identifying students' learning styles has a positive impact. Willingham et al. (2015) provided a thorough review and critique of the work on learning styles, ultimately concluding that students do not fit neatly into categories such as "visual learners" or "verbal learners" and that attempting to design instructional practices that focus solely on such categories would be a misguided venture. Instead, they suggested that teachers focus on instructional practices known to be effective (of which there are plenty) and that the notion of learning styles should be laid to rest both in instructional professional development programs and in teacher training programs. ...
Academic motivation is an essential predictor of school success in K 12 education. Accordingly, many meta-analyses have examined variables associated with academic motivation. However, a central question remains unanswered: What is the relative strength of the relations of both student variables (achievement, socioemotional variables, and background variables) and instructional variables (teacher variables, interventions, and technology) to academic motivation? To address this question, we conducted a systematic review of meta-analyses of constructs that focus on the question “Do I want to do this activity and why?” We included 125 first-order meta-analyses published before January 2021, with 487 first-order effect sizes, that investigated variables associated with academic motivation in K 12 education and were based on more than 8,839 primary studies and comprised almost 25 million students. We computed second-order standardized mean differences (SMD) using a two-level meta-analysis with robust variance estimation, considering moderators and including the methodological qualities and publication status of the meta-analyses. Our results showed that student variables (SMD = 0.39) and instructional variables (SMD = 0.43) had medium and similar second-order effect sizes. Of the student variables, socioemotional variables (SMD = 0.52) and achievement (SMD = 0.46) were more important than background variables (SMD = 0.19). Of the instructional variables, teacher variables (SMD = 0.61) were more important than interventions (SMD = 0.36) and technology (SMD = 0.35). Overall, the results provide the field with a clearer depiction of which student and instructional variables relate most closely to students’ academic motivation and thus have implications for the design of future interventions to foster students’ academic motivation in school.
... To date, well-known and prominent researchers in the fields of developmental and educational psychology, neuroscience, and learning have examined the existing literature on learning styles extensively and have found no credible evidence or quantifiable data to support claims that teaching to one specific learning style improves a child's learning outcomes (Husmann & O'Loughlin, 2018;Kirschner, 2017;Nancekivell et al., 2020;Pashler et al., 2008;Riener & Willingham, 2010;Tardif et al., 2015;Willingham et al., 2015;Yale Poorvu Center for Teaching and Learning, 2021). Additional findings reveal inconsistencies in learning styles research, as well as a lack of statistically significant relationships between the preference of a specific learning style and a child's learning. ...
... Those categorizations are known as 'learning styles.' Up to day, numerous theories about learning styles can be found, but in essence, they all refer to "differential preferences for processing certain types of information or for processing information in certain ways" (Willingham, Hughes, and Dobolyi 2015). In this manner, some theories distinguish preferences in the type of cognitive data processing and define intuitive and sensing learning styles. ...
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This dissertation addresses decreased academic participation, low engagement and poor experience as issues often related to students’ retention in online learning courses. The issues were identified at the Department of Computer Science at RWTH Aachen University, Germany, although high dropout rates are a growing problem in Computer Science studies worldwide. A solving approach often used in addressing the before mentioned problems includes gamification and personalization techniques: Gamification is a process of applying game design principles in serious contexts (i.e., learning), while personalization refers to tailoring the context to users’ needs and characteristics. In this work, the two techniques are used in combination in the Personalized Gamification Model (PeGaM), created for designing an online course for learning programming languages. PeGaM is theoretically grounded in the principles of the Gamified Learning Theory and the theory of learning tendencies. Learning tendencies define learners’ preferences for a particular form of behavior, and those behaviors are seen as possible moderators of gamification success. Moderators are a concept explained in the Gamified Learning Theory, and refer to variables that can influence the impact of gamification on the targeted outcomes. Gamification success is a measure of the extent to which students behave in a manner that leads to successful learning. The conceptual model of PeGaM is an iterative process in which learning tendencies are used to identify students who are believed to be prone to avoid certain activities. Gamification is then incorporated in activities that are recognized as ‘likely to be avoided’ to produce a specific learning-related behavior responsible for a particular learning outcome. PeGaM model includes five conceptual steps and 19 design principles required for gamification of learning environments that facilitate student engagement, participation and experience. In practice, PeGaM was applied in an introductory JavaScript course with Bachelor students of Computer Science at RWTH Aachen University. The investigation was guided by the principles of the Design-Based Research approach. Through this approach, PeGaM was created, evaluated and revised, over three iterative cycles. The first cycle had an explorative character, included one control and one treatment group, and gathered 124 participants. The second and third cycle were experimental studies, in which 69 and 171 participants were randomly distributed along one control and two treatment groups. Through the three interventions, mixed methods were used to capture students’ academic participation (a measure of students’ online behavior in the course collected through activity logs), engagement (evaluated quantitatively through a questionnaire compiled to measure behavioral, emotional, and cognitive engagement), and gameful experience (quantitative measure of students’ experience with the gamified system). In addition, supporting data was collected through semi-structured interviews and open-ended survey questions. The empirical findings revealed that gamification with PeGaM contributes to learning outcomes and that the success of gamification is conditioned by the applicability of game elements with learners’ preferences and learning activities. Cross case comparisons supported the application of PeGaM design principles and demonstrated its potential. Even though limited support was found to confirm the moderating role of learners’ learning tendencies, the study demonstrated that the gamification of learning activities that students are likely to avoid can increase their participation - but must be carefully designed. Most importantly, it has been shown that educational gamification can support and enhance learning-related behavior but require relevant and meaningful learning activities in combination with carefully considered reward, collaborative and feedback mechanisms. The study provides practical and theoretical insights but also highlights challenges and limitations associated with personalized gamification thus offers suggestions for further investigation.
... Supporters (e.g., Barbe et al., 1979;Lovelace, 2005) advocate the matching of teaching and learning styles for effective learning. Other researchers (e.g., Pashler et al., 2009;Willingham et al., 2015) are against this idea because no conclusive evidence has been found. We agree with the latter viewpoint in that matching both styles will not ensure successful learning. ...
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This study investigated the productive vocabulary of EFL learners divided into two groups: multimodal (preference for two or three perceptual learning styles) and unimodal (preference for one perceptual learning style). The objectives of this research were twofold: (1) to identify the productive vocabulary of multimodal and unimodal EFL learners; and (2) to ascertain whether there were statistically significant differences between productive vocabulary and the preferences for learning (multimodality or unimodality). The sample consisted of 60 Spanish EFL learners (24 multimodal and 36 unimodal) in the 12th grade. The data collection instruments were the Learning Style Survey (Cohen et al., 2009) to divide the informants into multimodal and unimodal learners, and the 2,000-word version of the Productive Vocabulary Levels Test (Laufer & Nation, 1995, 1999) to measure their productive vocabulary. Then, data were coded and subjected to quantitative analyses. The findings indicated that multimodal learners had more productive vocabulary (1,186 words) than their unimodal peers (948 words). However, there were not statistically significant differences between multimodal and unimodal learners in their productive vocabulary. However , both the effect size and the strength of association were large. Therefore, the results suggested that EFL learners employed different sensory modalities to learn vocabulary. Key words: multimodality, unimodality, productive vocabulary, perceptual learning style preferences, English as a Foreign Language.
... Learning style debunkers have mostly probed whether the techniques used to measure learning style actually measure what they claim to measure (Kirschner 2017;Newton & Miah 2017;Riener & Willingham 2010). Other scholars like Willingham (2005) and Willingham, Hughes, and Dobolyi (2015) have disputed the value of learning styles in educational practice, claiming that adapting instruction to learners' individual learning styles does not lead to better learner outcomes. Hence, Husmann and O'Loughlin (2019) provide strong evidence that teachers and learners should not be promoting the concept of learning styles for studying and for teaching interventions. ...
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Recent studies have shown that learners receive and process information differently. As learners learn in various ways, it appears impossible to change each learner's learning style in the classroom. Instead, teachers might modify their teaching styles to be more consistent with their learners' learning styles. This study takes into account the following objectives: first to determine the learning styles of learners; secondly, to determine how much variance in academic performance in electricity and magnetism can be explained by the variation in learning styles when they receive learning style-based instructions; and lastly, to determine learners' experiences with learning style-based instructions. The study employs a theoretical framework to survey pertinent literature to review relevant literature and present various viewpoints on learning style-based instructions. A mixed-method sequential explanatory strategy was employed to achieve the intended objectives and to answer the research questions "what is the change in learners' achievement after experiencing learning style-based instruction?". In addition, a purposeful convenience sampling procedure was employed to select two schools from the target population. A total of 205 physical science learners took part in the study. Physical Science Achievement Test (PSAT), interviews and Index of Learning Style Questionnaire (ILSQ) were the primary instruments utilised to collect data. Data analysis was primarily conducted using descriptive and inferential statistics and framework analysis. The results indicate that the predominant learning styles preferences were active, sequential, visual, and sensing. In addition, the study found a significant difference between learners' achievement when they received learning style-based instruction in the physical sciences classroom. In light of the findings of this research, this study recommends that teachers assess the learning styles and modalities of their learners to create a teaching-learning plan that best suits their needs. Teachers must also consider that learning acquisition varies; instructions, activities, and learning materials must be altered to facilitate smooth delivery and effective instructional objectives. In addition, the study findings could help teachers become more sensitive to the differences learners bring to the classroom. Keywords: Electricity and magnetism, learner achievement, learning styles, learning-style based instructional strategies, physical sciences
Pressure to perform academically, financial stress, and accessibility of entering higher education institutions are common factors that impact the mental health of college students. Findings have suggested the mental health needs of college students worsened due to the COVID-19 pandemic. The purpose of this chapter is to provide a conceptualized mental health counseling perspective for promoting campus wellness with a growth-oriented philosophy that emphasizes how to support college students through SAMHSA's wellness model. The SAMHSA wellness model addresses eight domains: emotional, environmental, intellectual, occupational, physical, social, financial, and spiritual. Each domain will be explored with practical strategies for faculty and higher education leaders to implement across a campus setting.
Background Psychomotor skill instruction is a critical component of nursing education. For now, the optimal teaching method to help students acquire psychomotor skills remains elusive. A few studies have explored the effects of flipped classroom on skill instruction, but yield a mixed conclusion. Furthermore, little knowledge was eligible if flipped classroom was beneficial to all learners with different learning styles. Purpose This study aimed to investigate the effects of flipped classroom on nursing psychomotor skill instruction for students with different learning styles. Methods The sequential explanatory mixed-methods design was used. In the quantitative study, students in the control group and the intervention group were instructed by traditional laboratory class and flipped classroom respectively. Self-report questionnaires evaluated students' satisfaction and perceived stress before and after the project. Students' skill performance was videotaped and graded by a nursing teacher. Independent sample t-test and paired sample t-test were used to study differences within and between two groups. In the qualitative component, a multiple case study was implemented to investigate the learning experience of flipped classroom among students with different learning styles. Results The quantitative study results showed that flipped classroom was more effective in improving student performance than traditional laboratory class, for both active and passive learner. However, after flipped classroom intervention, the learning satisfaction of passive learners decreased significantly (P < 0.05), while the learning satisfaction of active learners showed an opposite increasing trend (P = 0.07). Furthermore, compared with traditional laboratory class, the stress perception of passive learners in flipped classroom also increased significantly (p < 0.001). The qualitative study analysis obtained similar results, although active students hold a more positive attitude toward flipped classroom, passive learners expressed more dissatisfaction and stress perception than their peers. Conclusions In terms of skill performance, learning satisfaction and stress perception of students, flipped classroom maybe more suitable for active learners rather than passive learners. Therefore, evaluating the learning styles of students is necessary before the implementation of flipped classroom.
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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.